{
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  {
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"
    },
    "image-7.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Updated 13 June, 2023 to exclude points from the analysis where `standard_relation` is not `=`. Thanks to Christian Feldmann for pointing out the mistake! There were a very small number of these and the changes don't affect any of the conclusions.*\n",
    "\n",
    "I periodically see people working with ChEMBL data and combining results from different xC50 (I'll use that abbreviation for IC50, AC50, XC50, and EC50 here) assays on the same target into a single data set. This generally leads to me starting off on a rant about how you really shouldn't do that. Well, unless you don't care about the quality of the data set that you're assembling.\n",
    "\n",
    "Since I've been doing a bunch of data set curation this week, I figured that it would be worthwhile to collect some data from ChEMBL in order to have some numbers and plots to back up my rant. This ended up being a bit of a rabbit hole (a fact which should surprise exactly no one... particularly not me). Here's a brief high-level description of what I did, the actual Python and SQL code is below. I'm working with ChEMBL_32 here.\n",
    "\n",
    "1. Grab all pChEMBL values corresponding to xC50 data from assays which have a target type of \"SINGLE PROTEIN\" (at the bottom of the post I remove that last restriction) and which are associated with a document with a publication year (I'm trying to avoid as many screening assays as possible for this exercise). \n",
    "2. I also ignore any data from assays which have \"mutant\" or \"recombinant\" in the title. ChEMBL often (always?) has the same target ID for proteins which are mutants and doing this helps to reduce the noise.\n",
    "2. Only keep assays which include between 20 and 100 unique compounds. Again, this is to try and avoid screening assays, review papers, etc.\n",
    "3. Find all pairs of assays which have the same target assigned and which include at least 5 duplicate compounds\n",
    "4. Grab the activity values for those duplicate compounds and compare them.\n",
    "\n",
    "Here's the TL;DR summary of the results.\n",
    "\n",
    "If I am careful about which xC50 assays I combine, making sure as much available metadata as possible matches, and then plot the pchembl values for duplicate compounds, I get the following:\n",
    "\n",
    "![image-6.png](attachment:image-6.png)\n",
    "\n",
    "The X axis here is the pChEMBL value in one assay, the Y axis is the pChEMBL value in the other assay.\n",
    "\n",
    "For these ~13K data points R$^2$ is 0.60 and Spearman's R is 0.82. 47% of the points differ by more than 0.3 log units in the two assays and 16% differ by more than one log unit. That's *a lot* of noise in the data and that is basically the best case scenario for combining xC50 assays.\n",
    "\n",
    "If I'm not careful at all, and just combine using the target ID and the type of the result (so that I'm not combining IC50 and EC50), I get the following (this time it's a hexbin plot, because otherwise you can't see anything):\n",
    "\n",
    "![image-7.png](attachment:image-7.png)\n",
    "\n",
    "For the ~100K data points R$^2$ is 0.53 and Spearman's R is 0.79. 55% of the points differ by more than 0.3 log units in the two assays and 21% differ by more than one log unit. That is, unsurprisingly, even worse than before.\n",
    "\n",
    "Maybe at some point I'll come back and include a patient explanation of the scientific reasons why it's bad idea to combine data from different xC50 assays, including some specific examples from ChEMBL, but it's hot in my office and I've been in this rabbit hole *MUCH TOO LONG* already, so I'm not doing that now.\n",
    "\n",
    "Thanks to Pat Walters for his feedback on an early draft of this.\n",
    "\n",
    "As always, I'm happy for feedback and comments!\n",
    "\n",
    "The code, along with some more results, follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-28T13:03:16.839970Z",
     "start_time": "2022-11-28T13:03:16.301427Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "from sklearn.metrics import r2_score \n",
    "\n",
    "%load_ext sql\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('tableau-colorblind10')\n",
    "plt.rcParams['font.size'] = '16'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "········\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "pw = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First run, reasonably restrictive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>standard_type</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Potency</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>EC50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>% inhibition</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>log(43000/Ki)</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IC50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AC50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>pIC50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>XC50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ki</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Inhibition</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kd</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Potency',),\n",
       " ('EC50',),\n",
       " ('% inhibition',),\n",
       " ('log(43000/Ki)',),\n",
       " ('IC50',),\n",
       " ('AC50',),\n",
       " ('pIC50',),\n",
       " ('XC50',),\n",
       " ('Ki',),\n",
       " ('Inhibition',),\n",
       " ('Kd',)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connection_string = f\"postgresql://<some details>/chembl_32\"\n",
    "%sql $connection_string \\\n",
    "  select distinct(standard_type) from activities where pchembl_value is not null;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an aside: I find it very strange that there are pChEMBL values for things like % inhibition. But I'm not going to dig into that here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step one: collect the counts of the amount of activity data we're interested in on a per assay basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-28T13:03:19.725682Z",
     "start_time": "2022-11-28T13:03:17.179502Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "79266 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connection_string = f\"postgresql://<some details>/chembl_32\"\n",
    "\n",
    "%sql $connection_string \\\n",
    "     drop table if exists IC50_assays\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select assay_id,assays.chembl_id assay_chembl_id,description,tid,targets.chembl_id target_chembl_id,\\\n",
    "    count(distinct(molregno)) cnt,pref_name into temporary table IC50_assays  \\\n",
    "          from activities  \\\n",
    "          join docs using (doc_id)  \\\n",
    "          join assays using(assay_id)  \\\n",
    "          join target_dictionary as targets using (tid) \\\n",
    "          where pchembl_value is not null   \\\n",
    "          and (standard_type='IC50' or standard_type='AC50' or \\\n",
    "               standard_type='pIC50' or standard_type='XC50' or standard_type='EC50') \\\n",
    "          and docs.year is not null  \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "          and target_type = 'SINGLE PROTEIN' \\\n",
    "          and lower(description) not like '%mutant%' and lower(description) not like '%recombinant%' \\\n",
    "          group by (assay_id,assays.chembl_id,description,tid,targets.chembl_id,pref_name) order by cnt desc; \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick point here: there are ~79K assays with data meeting our criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab the activity values from the assays which have between 20 and 100 unique compounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "355240 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select assay_id,tid,molregno,pchembl_value into temporary table goldilocks from activities \\\n",
    "  join IC50_assays using (assay_id) \\\n",
    "  where pchembl_value is not null and cnt>=20 and cnt<=100; "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine the number of overlapping compounds between each pair of assays that have the same target. I really really appreciate how easy it is to do this in SQL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16631 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select c1.tid,c1.assay_id aid1,c2.assay_id aid2,count(distinct c1.molregno) ovl into temporary table goldilocks_ovl \\\n",
    " from goldilocks c1 cross join goldilocks c2 \\\n",
    "  join assays a1 on (c1.assay_id=a1.assay_id) \\\n",
    "  join assays a2 on (c2.assay_id=a2.assay_id) \\\n",
    " where c1.assay_id>c2.assay_id and c1.tid=c2.tid and c1.molregno=c2.molregno \\\n",
    " group by (c1.tid,c1.assay_id,c2.assay_id) order by ovl desc;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are ~16K pairs of assays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter that down to assay pairs which have at least 5 compounds in common. Also annotate the table with the overall compound counts in each assay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1390 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl2\n",
    "\n",
    "\n",
    "%sql \\\n",
    "  select ovl.*,count(distinct a1.molregno) a1cnt,count(distinct a2.molregno) a2cnt into temporary table goldilocks_ovl2 \\\n",
    "  from goldilocks_ovl ovl join activities a1 on (aid1=a1.assay_id) join activities a2 on (aid2=a2.assay_id)  \\\n",
    "  where ovl>5 group by (ovl.tid,ovl.aid1,ovl.aid2,ovl.ovl);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're down to 1390 pairs of assays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And further annotate it with ChEMBL IDs for the assays and targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1390 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl3\n",
    "\n",
    "\n",
    "%sql \\\n",
    "  select lu3.chembl_id target_chembl_id,lu1.chembl_id assay1_chembl_id,lu2.chembl_id assay2_chembl_id,ovl,a1cnt,a2cnt,aid1,aid2 into temporary table goldilocks_ovl3 from goldilocks_ovl2 \\\n",
    "  join chembl_id_lookup lu1 on (aid1=lu1.entity_id and lu1.entity_type='ASSAY') \\\n",
    "  join chembl_id_lookup lu2 on (aid2=lu2.entity_id and lu2.entity_type='ASSAY') \\\n",
    "  join chembl_id_lookup lu3 on (tid=lu3.entity_id and lu3.entity_type='TARGET');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start doing the analysis.\n",
    "\n",
    "The ChEMBL assay table has a number of fields describing the asssay. We'll start by only comparing pairs of assays where ALL of those fields match."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All assay parameters are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "516 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "82 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "160 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "104 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "where a1.assay_type = a2.assay_type \\\n",
    "and a1.assay_organism is not distinct from a2.assay_organism \\\n",
    "and a1.assay_category is not distinct from a2.assay_category \\\n",
    "and a1.assay_tax_id is not distinct from a2.assay_tax_id \\\n",
    "and a1.assay_strain is not distinct from a2.assay_strain \\\n",
    "and a1.assay_tissue is not distinct from a2.assay_tissue \\\n",
    "and a1.assay_cell_type is not distinct from a2.assay_cell_type \\\n",
    "and a1.assay_subcellular_fraction is not distinct from a2.assay_subcellular_fraction \\\n",
    "and a1.bao_format is not distinct from a2.bao_format \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "for row in d:\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and assay_id=:aid1 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a1 \\\n",
    "       join (select * from activities where pchembl_value is not null and assay_id=:aid2 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;\n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot that data and generate some stats. Descriptions of the plots and stats are below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_accum = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.60, Spearman R=0.82\n",
      "13117 points. Fraction > 0.3: 0.47, fraction > 1.0: 0.16\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.11\n",
      "\t bin=7: 0.14\n",
      "\t bin=8: 0.11\n",
      "\t bin=9: 0.04\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'no mismatches'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.2,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "delta_accum['all_match'] = delts\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first two plots are showing the same thing: the value in one assay plotted against the value in the other; on the left there's a scatter plot, the right has a hexbin plot which makes it a bit easier to see what's going on in the high density regions. The scatter plots include a solid line along y=x, dashed lines offset by $\\Delta$ pChEMBL = 1.0, and dot-dashed lines offset by $\\Delta$ pChEMBL = 0.3 (a common estimate of experimental error in these types of measurements). The histogram is of |$\\Delta$ pChEMBL|, there's no significance to the ordering, so the sign isn't important. There are 13K data points on this plot.\n",
    "\n",
    "There are some summary stats above the plots. These include R2, Spearman's R, the fraction of points where $\\Delta$ pChEMBL > 0.3 and the fraction of points where $\\Delta$ pChEMBL > 1.0. Finally, there are a series of values for the fraction of the points which would be assigned to different activity classes in the two assays for different pChEMBL activity thresholds. So here 14% of compounds would be classified differently if we set the threshold for activity at 100 nM.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think this is probably best case for IC50. And it's not very good. An R2 of 0.6 is pretty sad, and the differences observed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Only assay_type matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I repeat the analysis for any assay pairs which have the same `standard_type` (this can be things like \"functional\", or \"binding\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "1132 rows affected.\n",
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      "38 rows affected.\n",
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      "41 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "37 rows affected.\n",
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      "37 rows affected.\n",
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      "37 rows affected.\n",
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      "37 rows affected.\n",
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      "30 rows affected.\n",
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      "37 rows affected.\n",
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      "37 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "0 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "0 rows affected.\n",
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      "36 rows affected.\n",
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      "36 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "35 rows affected.\n",
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      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
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      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
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      "40 rows affected.\n",
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      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
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      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "116 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "104 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "2 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "where a1.assay_type = a2.assay_type \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "aids = []\n",
    "for row in d:\n",
    "    cid1 = row[1]\n",
    "    cid2 = row[2]\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and assay_id=:aid1 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a1 \\\n",
    "       join (select * from activities where pchembl_value is not null and assay_id=:aid2 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;\n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n",
    "        aids.append((cid1,cid2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.55, Spearman R=0.77\n",
      "28098 points. Fraction > 0.3: 0.51, fraction > 1.0: 0.20\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.11\n",
      "\t bin=7: 0.17\n",
      "\t bin=8: 0.14\n",
      "\t bin=9: 0.04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'any assay field mismatch except assay type'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.1,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "delta_accum['only type'] = delts\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have more data, and even more deviation between the pairs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ignore assay type too\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally I'll even allow different `assay_types` to be compared. This is nuts, but I've seen people do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "1390 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "209 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "90 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "65 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "98 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "82 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "160 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "70 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "140 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "136 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "120 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "116 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "4 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "104 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "2 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "aids = []\n",
    "for row in d:\n",
    "    cid1 = row[1]\n",
    "    cid2 = row[2]\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and assay_id=:aid1 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a1 \\\n",
    "       join (select * from activities where pchembl_value is not null and assay_id=:aid2 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;\n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n",
    "        aids.append((cid1,cid2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.48, Spearman R=0.74\n",
      "33124 points. Fraction > 0.3: 0.54, fraction > 1.0: 0.21\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.12\n",
      "\t bin=7: 0.19\n",
      "\t bin=8: 0.15\n",
      "\t bin=9: 0.05\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'ignore assay_type too'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.1,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "delta_accum['ignore all'] = delts\n",
    "\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, things continue to devolve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Be less restrictive with the targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally I wanted to see what happens if I don't restrict this to situations where the target corresponds to a single protein... the rest of the analysis is what we saw above.\n",
    "\n",
    "The results actually end up not being hugely different. There's probably some statistics reason for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "187922 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connection_string = f\"postgresql://<some details>/chembl_32\"\n",
    "\n",
    "%sql $connection_string \\\n",
    "     drop table if exists IC50_assays\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select assay_id,assays.chembl_id assay_chembl_id,description,tid,targets.chembl_id target_chembl_id,\\\n",
    "    count(distinct(molregno)) cnt,pref_name into temporary table IC50_assays  \\\n",
    "          from activities  \\\n",
    "          join docs using (doc_id)  \\\n",
    "          join assays using(assay_id)  \\\n",
    "          join target_dictionary as targets using (tid) \\\n",
    "          where pchembl_value is not null   \\\n",
    "          and (standard_type='IC50' or standard_type='AC50' or \\\n",
    "               standard_type='pIC50' or standard_type='XC50' or standard_type='EC50') \\\n",
    "          and docs.year is not null  \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and standard_relation = '=' \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and target_type != 'UNCHECKED' and target_type != 'UNKNOWN' and target_type != 'NO TARGET' \\\n",
    "          group by (assay_id,assays.chembl_id,description,tid,targets.chembl_id,pref_name) order by cnt desc; \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "748687 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select assay_id,tid,molregno,pchembl_value into temporary table goldilocks from activities \\\n",
    "  join IC50_assays using (assay_id) \\\n",
    "  where pchembl_value is not null and cnt>=20 and cnt<=100; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "134857 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select c1.tid,c1.assay_id aid1,c2.assay_id aid2,count(distinct c1.molregno) ovl into temporary table goldilocks_ovl \\\n",
    " from goldilocks c1 cross join goldilocks c2 \\\n",
    "  join assays a1 on (c1.assay_id=a1.assay_id) \\\n",
    "  join assays a2 on (c2.assay_id=a2.assay_id) \\\n",
    " where c1.assay_id>c2.assay_id and c1.tid=c2.tid and c1.molregno=c2.molregno \\\n",
    " group by (c1.tid,c1.assay_id,c2.assay_id) order by ovl desc;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "4008 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl2\n",
    "\n",
    "\n",
    "%sql \\\n",
    "  select ovl.*,count(distinct a1.molregno) a1cnt,count(distinct a2.molregno) a2cnt into temporary table goldilocks_ovl2 \\\n",
    "  from goldilocks_ovl ovl join activities a1 on (aid1=a1.assay_id) join activities a2 on (aid2=a2.assay_id)  \\\n",
    "  where ovl>5 group by (ovl.tid,ovl.aid1,ovl.aid2,ovl.ovl);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "Done.\n",
      " * postgresql://<some details>/chembl_32\n",
      "4008 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl3\n",
    "\n",
    "\n",
    "%sql \\\n",
    "  select lu3.chembl_id target_chembl_id,lu1.chembl_id assay1_chembl_id,lu2.chembl_id assay2_chembl_id,ovl,a1cnt,a2cnt,aid1,aid2 into temporary table goldilocks_ovl3 from goldilocks_ovl2 \\\n",
    "  join chembl_id_lookup lu1 on (aid1=lu1.entity_id and lu1.entity_type='ASSAY') \\\n",
    "  join chembl_id_lookup lu2 on (aid2=lu2.entity_id and lu2.entity_type='ASSAY') \\\n",
    "  join chembl_id_lookup lu3 on (tid=lu3.entity_id and lu3.entity_type='TARGET');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All assay parameters are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "1531 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "94 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "69 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "82 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "112 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "160 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "134 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "72 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "104 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "where a1.assay_type = a2.assay_type \\\n",
    "and a1.assay_organism is not distinct from a2.assay_organism \\\n",
    "and a1.assay_category is not distinct from a2.assay_category \\\n",
    "and a1.assay_tax_id is not distinct from a2.assay_tax_id \\\n",
    "and a1.assay_strain is not distinct from a2.assay_strain \\\n",
    "and a1.assay_tissue is not distinct from a2.assay_tissue \\\n",
    "and a1.assay_cell_type is not distinct from a2.assay_cell_type \\\n",
    "and a1.assay_subcellular_fraction is not distinct from a2.assay_subcellular_fraction \\\n",
    "and a1.bao_format is not distinct from a2.bao_format \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "for row in d:\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and assay_id=:aid1 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a1 \\\n",
    "       join (select * from activities where pchembl_value is not null and assay_id=:aid2 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;\n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.53, Spearman R=0.79\n",
      "40326 points. Fraction > 0.3: 0.54, fraction > 1.0: 0.20\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.13\n",
      "\t bin=7: 0.16\n",
      "\t bin=8: 0.13\n",
      "\t bin=9: 0.04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'no mismatches'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.1,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "delta_accum['permissive all_match'] = delts\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Only assay_type matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "3634 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "94 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "89 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "209 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "86 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "90 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "81 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "80 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "80 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "80 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "79 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "74 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "69 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "69 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "65 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "118 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "98 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
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      "49 rows affected.\n",
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      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
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      "47 rows affected.\n",
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      "47 rows affected.\n",
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      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
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      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
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      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
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      "47 rows affected.\n",
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      "47 rows affected.\n",
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      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
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      "46 rows affected.\n",
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      "46 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
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      "45 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
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      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "82 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "84 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "112 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "160 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "134 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
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      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "70 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "136 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "72 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "116 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "116 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "90 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "96 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "91 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "96 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "104 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "2 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "where a1.assay_type = a2.assay_type \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "aids = []\n",
    "for row in d:\n",
    "    cid1 = row[1]\n",
    "    cid2 = row[2]\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and assay_id=:aid1 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a1 \\\n",
    "       join (select * from activities where pchembl_value is not null and assay_id=:aid2 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;\n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n",
    "        aids.append((cid1,cid2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.55, Spearman R=0.80\n",
      "95414 points. Fraction > 0.3: 0.54, fraction > 1.0: 0.20\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.12\n",
      "\t bin=7: 0.16\n",
      "\t bin=8: 0.14\n",
      "\t bin=9: 0.05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'any assay field mismatch except assay type'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.1,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "delta_accum['permissive only type'] = delts\n",
    "\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ignore assay type too\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://<some details>/chembl_32\n",
      "4008 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "94 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "89 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "88 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "209 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "86 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "90 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "81 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "80 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "80 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "80 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "79 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "74 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "69 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "69 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "65 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "99 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "63 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "77 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "118 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "73 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "55 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "57 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "98 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "49 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
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      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
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      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "47 rows affected.\n",
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      "47 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "59 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "82 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "71 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "84 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "112 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "160 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "54 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "134 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
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      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "76 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
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      "53 rows affected.\n",
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      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
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      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
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      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
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      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "70 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "62 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "140 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "68 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "136 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "72 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "64 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "120 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "79 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "116 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "116 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "90 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "85 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "50 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "52 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "96 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "96 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "91 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "92 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "61 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "67 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "46 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "51 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
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      "22 rows affected.\n",
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      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
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      "22 rows affected.\n",
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      "22 rows affected.\n",
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      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
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      "22 rows affected.\n",
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      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "43 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
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      "22 rows affected.\n",
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      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
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      "21 rows affected.\n",
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      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "4 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "53 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "41 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "66 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "104 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "2 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "34 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "0 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://<some details>/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "aids = []\n",
    "for row in d:\n",
    "    cid1 = row[1]\n",
    "    cid2 = row[2]\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and assay_id=:aid1 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a1 \\\n",
    "       join (select * from activities where pchembl_value is not null and assay_id=:aid2 \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and standard_relation = '=' \\\n",
    "       ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;\n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n",
    "        aids.append((cid1,cid2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.53, Spearman R=0.79\n",
      "102986 points. Fraction > 0.3: 0.55, fraction > 1.0: 0.21\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.12\n",
      "\t bin=7: 0.17\n",
      "\t bin=8: 0.14\n",
      "\t bin=9: 0.05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'ignore assay_type too'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.1,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "delta_accum['permissive ignore all'] = delts\n",
    "\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare the various delta histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check to see if we can learn anything interesting by putting the histograms of delta values together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(delta_accum.values(),bins=20,density=True,label=list(delta_accum.keys()),log=False);\n",
    "plt.xlabel('|$\\Delta$ pChEMBL|')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot that on a log scale too so that we can see more info about the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(delta_accum.values(),bins=20,density=True,label=list(delta_accum.keys()),log=True);\n",
    "plt.xlabel('|$\\Delta$ pChEMBL|')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No super strong conclusion jumps out at me from those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}