{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MF reloaded 1\n",
      "EMS reloaded\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from statsmodels.api import Logit\n",
    "import patsy\n",
    "\n",
    "from joblib import load, dump\n",
    "\n",
    "import model_functions as mf\n",
    "import eval_measures as ems\n",
    "\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 56) (41618369, 56)\n",
      "CPU times: user 1min 12s, sys: 38.9 s, total: 1min 51s\n",
      "Wall time: 1min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with pd.HDFStore('out/Training_2002_2005.h5') as cstore:\n",
    "    df_first = cstore['first_author']\n",
    "    df_last = cstore['last_author']\n",
    "    \n",
    "print df_first.shape, df_last.shape\n",
    "df_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load author years data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.82 s, sys: 1.15 s, total: 8.97 s\n",
      "Wall time: 8.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_authors = pd.read_csv(\"data/AuthorityFirstLastYears.txt\", sep=\"\\t\").rename(\n",
    "    columns={\"au_id\": \"auid\"})\n",
    "df_authors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auid</th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9731334_2</td>\n",
       "      <td>1997</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2155715_1</td>\n",
       "      <td>1990</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7867892_1</td>\n",
       "      <td>1994</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14609221_2</td>\n",
       "      <td>2003</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8101337_1</td>\n",
       "      <td>1993</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         auid  first_year  last_year\n",
       "0   9731334_2        1997       2009\n",
       "1   2155715_1        1990       2009\n",
       "2   7867892_1        1994       2009\n",
       "3  14609221_2        2003       2009\n",
       "4   8101337_1        1993       2007"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "auid          object\n",
       "first_year     int64\n",
       "last_year      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9300182</td>\n",
       "      <td>9300182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1989</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1865</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1980</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1994</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2003</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9999</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_year  last_year\n",
       "count     9300182    9300182\n",
       "mean         1989       1994\n",
       "std            16         15\n",
       "min          1865          0\n",
       "25%          1980       1986\n",
       "50%          1994       2000\n",
       "75%          2003       2007\n",
       "max          9999       2099"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 3), (3858, 3))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors[df_authors.first_year == 9999].shape, df_authors[df_authors.first_year <= 1900].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load author expertise data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.8 s, sys: 5.68 s, total: 1min 5s\n",
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_expertise = pd.read_csv(\"data/AuthorExpertise.txt\", sep=\"\\t\")\n",
    "df_expertise.columns, df_expertise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index([u'PMID', u'auid', u'match_len', u'match_prop', u'overall_coverage_len',\n",
       "        u'overall_coverage_prop'],\n",
       "       dtype='object'), (58761322, 6))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_expertise.columns, df_expertise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PMID                       int64\n",
       "auid                      object\n",
       "match_len                  int64\n",
       "match_prop               float64\n",
       "overall_coverage_len       int64\n",
       "overall_coverage_prop    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_expertise.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 56)\n",
      "(41618369, 58)\n",
      "CPU times: user 1min 6s, sys: 34.9 s, total: 1min 41s\n",
      "Wall time: 1min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_first.shape\n",
    "df_first = df_first.merge(df_authors, how=\"left\", on=\"auid\")\n",
    "print df_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_first[\"au_age\"] = df_first[\"source_year\"] - df_first[\"first_year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite',\n",
       "       u'first_year', u'last_year', u'au_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 59)\n",
      "(41619240, 64)\n",
      "CPU times: user 1min 1s, sys: 19.4 s, total: 1min 20s\n",
      "Wall time: 1min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_first.shape\n",
    "df_first = df_first.merge(df_expertise, how=\"left\",\n",
    "                          left_on=[\"source_id\",\"auid\"],\n",
    "                          right_on=[\"PMID\",\"auid\"],)\n",
    "print df_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite',\n",
       "       u'first_year', u'last_year', u'au_age', u'PMID', u'match_len',\n",
       "       u'match_prop', u'overall_coverage_len', u'overall_coverage_prop'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41619240, 63)\n",
      "CPU times: user 31.9 s, sys: 33.1 s, total: 1min 5s\n",
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_first = df_first.drop(\"PMID\", axis=1)\n",
    "print df_first.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 56)\n",
      "(41618369, 58)\n",
      "CPU times: user 1min 7s, sys: 36.8 s, total: 1min 44s\n",
      "Wall time: 1min 44s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_last.shape\n",
    "df_last = df_last.merge(df_authors, how=\"left\", on=\"auid\")\n",
    "print df_last.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_last[\"au_age\"] = df_last[\"source_year\"] - df_last[\"first_year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 59)\n",
      "(41619267, 64)\n",
      "(41619267, 63)\n",
      "CPU times: user 1min 33s, sys: 53.2 s, total: 2min 26s\n",
      "Wall time: 2min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_last.shape\n",
    "df_last = df_last.merge(df_expertise, how=\"left\",\n",
    "                          left_on=[\"source_id\",\"auid\"],\n",
    "                          right_on=[\"PMID\",\"auid\"],)\n",
    "print df_last.shape\n",
    "df_last = df_last.drop(\"PMID\", axis=1)\n",
    "print df_last.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Modelling considerations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TOP_15_COUNTRIES = [\"USA\", \"UNKNOWN\", \"UK\", \"JAPAN\", \"GERMANY\", \"FRANCE\", \"ITALY\",\n",
    "                    \"CANADA\", \"CHINA\", \"AUSTRALIA\", \"SPAIN\", \"NETHERLANDS\",\n",
    "                    \"SWEDEN\", \"INDIA\", \"OTHER\"]\n",
    "TOP_15_ETHNICITIES = [\"ENGLISH\", \"GERMAN\", \"HISPANIC\", \"CHINESE\",\n",
    "                      \"JAPANESE\", \"SLAV\", \"FRENCH\", \"ITALIAN\", \"INDIAN\",\n",
    "                      \"NORDIC\", \"ARAB\", \"DUTCH\", \"KOREAN\", \"UNKNOWN\", \"OTHER\"]\n",
    "GENDERS = [\"-\", \"F\", \"M\"]\n",
    "\n",
    "def prepare_data(df):\n",
    "    df[\"eth_weight\"] = 0.5 # Partial weight to multi ethnicity\n",
    "    df.ix[df.eth2 == \"UNKNOWN\", \"eth_weight\"] = 1 # Full weight to single ethnicity\n",
    "    df.ix[df.source_country == \"-\", \"source_country\"] = \"UNKNOWN\" # Set - to unknown\n",
    "    df.source_country = df.source_country.astype(\"category\", categories=TOP_15_COUNTRIES, ordered=False).fillna(\"OTHER\")\n",
    "    df.ix[df.eth1.isin(\n",
    "        [\"UNKNOWN\", \"TOOSHORT\", \"ERROR\"]),\n",
    "      \"eth1\"] = \"UNKNOWN\" # Set unknown ethnicities\n",
    "    df.ix[df.eth2.isin(\n",
    "            [\"UNKNOWN\", \"TOOSHORT\", \"ERROR\"]),\n",
    "          \"eth2\"] = \"UNKNOWN\" # Set unknown ethnicities\n",
    "    df.eth1 = df.eth1.astype(\"category\", categories=TOP_15_ETHNICITIES, ordered=False).fillna(\"OTHER\")\n",
    "    df.eth2 = df.eth2.astype(\"category\", categories=TOP_15_ETHNICITIES, ordered=False).fillna(\"OTHER\")\n",
    "    df.gender = df.gender.astype(\"category\", categories=GENDERS, ordered=False).fillna(\"-\")\n",
    "    df[[u'source_is_eng', u'source_is_journal', u'source_is_review',\n",
    "    u'source_is_case_rep', u'source_is_let_ed_com',\n",
    "    u'sink_is_eng', u'sink_is_journal', u'sink_is_review', u'sink_is_case_rep',\n",
    "    u'sink_is_let_ed_com', u'journal_same', u'affiliation_sim']] = df[[u'source_is_eng', u'source_is_journal', u'source_is_review',\n",
    "    u'source_is_case_rep', u'source_is_let_ed_com',\n",
    "    u'sink_is_eng', u'sink_is_journal', u'sink_is_review', u'sink_is_case_rep',\n",
    "    u'sink_is_let_ed_com', u'journal_same', u'affiliation_sim']].astype(\"bool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prepare_data(df_first)\n",
    "prepare_data(df_last)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall data statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 3s, sys: 41.4 s, total: 1min 45s\n",
      "Wall time: 1min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_t_first = df_first.pivot_table(index=\"gender\", columns=\"is_self_cite\",\n",
    "               values=[\"source_year\", u'source_n_mesh', u'source_is_eng', \n",
    "                       u'source_is_journal', u'source_is_review',\n",
    "                       u'source_is_case_rep', u'source_is_let_ed_com',\n",
    "                       u'source_T_novelty', u'source_V_novelty', u'source_PT_novelty',\n",
    "                       u'source_PV_novelty', u'source_ncites', u'source_n_authors',\n",
    "                       u'sink_year', u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng',\n",
    "                       u'sink_is_journal', u'sink_is_review', u'sink_is_case_rep',\n",
    "                       u'sink_is_let_ed_com', u'sink_T_novelty', u'sink_V_novelty',\n",
    "                       u'sink_PT_novelty', u'sink_PV_novelty', u'sink_n_authors',\n",
    "                       u'year_span', u'journal_same',\n",
    "                       u'mesh_sim', u'title_sim', u'lang_sim',\n",
    "                       u'affiliation_sim', u'pubtype_sim', u'cite_sim', u'author_sim',\n",
    "                       u'gender_sim', u'eth_sim', u'n_common_authors',\n",
    "                       u'sink_last_ncites', u'sink_prev_ncites',\n",
    "                       u'auth_last_npapers', u'auth_prev_papers',\n",
    "                       u'au_age',\n",
    "                       u'jj_sim', \n",
    "                       u'match_len', u'match_prop',\n",
    "                       u'overall_coverage_len', u'overall_coverage_prop'\n",
    "                      ],\n",
    "                        aggfunc=[np.mean, np.std])\n",
    "df_t_first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_t_first.T.unstack(level=0).to_csv(\"First_Author_gender_self_cites_means.txt\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th colspan=\"2\" halign=\"left\">-</th>\n",
       "      <th colspan=\"2\" halign=\"left\">F</th>\n",
       "      <th colspan=\"2\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>is_self_cite</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_year</th>\n",
       "      <th>0</th>\n",
       "      <td>2003.663</td>\n",
       "      <td>1.111</td>\n",
       "      <td>2003.631</td>\n",
       "      <td>1.112</td>\n",
       "      <td>2003.588</td>\n",
       "      <td>1.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003.635</td>\n",
       "      <td>1.114</td>\n",
       "      <td>2003.603</td>\n",
       "      <td>1.114</td>\n",
       "      <td>2003.563</td>\n",
       "      <td>1.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_n_mesh</th>\n",
       "      <th>0</th>\n",
       "      <td>13.492</td>\n",
       "      <td>5.859</td>\n",
       "      <td>13.340</td>\n",
       "      <td>5.463</td>\n",
       "      <td>12.566</td>\n",
       "      <td>5.427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.879</td>\n",
       "      <td>5.999</td>\n",
       "      <td>13.121</td>\n",
       "      <td>5.392</td>\n",
       "      <td>11.993</td>\n",
       "      <td>5.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_eng</th>\n",
       "      <th>0</th>\n",
       "      <td>0.984</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.995</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_journal</th>\n",
       "      <th>0</th>\n",
       "      <td>0.990</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.986</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_review</th>\n",
       "      <th>0</th>\n",
       "      <td>0.137</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_case_rep</th>\n",
       "      <th>0</th>\n",
       "      <td>0.036</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_let_ed_com</th>\n",
       "      <th>0</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.015</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_T_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>19.846</td>\n",
       "      <td>12.504</td>\n",
       "      <td>20.965</td>\n",
       "      <td>12.666</td>\n",
       "      <td>22.183</td>\n",
       "      <td>13.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.304</td>\n",
       "      <td>12.522</td>\n",
       "      <td>21.361</td>\n",
       "      <td>12.524</td>\n",
       "      <td>23.031</td>\n",
       "      <td>13.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_V_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>3254.876</td>\n",
       "      <td>10964.931</td>\n",
       "      <td>3607.520</td>\n",
       "      <td>12431.366</td>\n",
       "      <td>3947.314</td>\n",
       "      <td>8873.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3298.214</td>\n",
       "      <td>11458.840</td>\n",
       "      <td>3632.428</td>\n",
       "      <td>24886.111</td>\n",
       "      <td>4038.486</td>\n",
       "      <td>9871.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_PT_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>2.287</td>\n",
       "      <td>4.948</td>\n",
       "      <td>2.531</td>\n",
       "      <td>5.164</td>\n",
       "      <td>2.964</td>\n",
       "      <td>5.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.333</td>\n",
       "      <td>4.960</td>\n",
       "      <td>2.540</td>\n",
       "      <td>5.089</td>\n",
       "      <td>3.177</td>\n",
       "      <td>5.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_PV_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>12.492</td>\n",
       "      <td>678.392</td>\n",
       "      <td>24.137</td>\n",
       "      <td>1390.502</td>\n",
       "      <td>16.884</td>\n",
       "      <td>488.653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.853</td>\n",
       "      <td>1199.187</td>\n",
       "      <td>10.357</td>\n",
       "      <td>564.516</td>\n",
       "      <td>17.537</td>\n",
       "      <td>472.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_ncites</th>\n",
       "      <th>0</th>\n",
       "      <td>45.780</td>\n",
       "      <td>66.675</td>\n",
       "      <td>53.320</td>\n",
       "      <td>94.552</td>\n",
       "      <td>55.949</td>\n",
       "      <td>81.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.349</td>\n",
       "      <td>33.979</td>\n",
       "      <td>42.054</td>\n",
       "      <td>49.816</td>\n",
       "      <td>45.410</td>\n",
       "      <td>59.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_n_authors</th>\n",
       "      <th>0</th>\n",
       "      <td>5.130</td>\n",
       "      <td>3.021</td>\n",
       "      <td>5.094</td>\n",
       "      <td>3.145</td>\n",
       "      <td>5.102</td>\n",
       "      <td>3.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.176</td>\n",
       "      <td>4.281</td>\n",
       "      <td>5.183</td>\n",
       "      <td>3.538</td>\n",
       "      <td>5.068</td>\n",
       "      <td>3.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_year</th>\n",
       "      <th>0</th>\n",
       "      <td>1995.610</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1995.628</td>\n",
       "      <td>7.401</td>\n",
       "      <td>1995.435</td>\n",
       "      <td>7.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000.468</td>\n",
       "      <td>3.569</td>\n",
       "      <td>2000.044</td>\n",
       "      <td>3.687</td>\n",
       "      <td>1999.378</td>\n",
       "      <td>4.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_n_mesh</th>\n",
       "      <th>0</th>\n",
       "      <td>12.898</td>\n",
       "      <td>5.661</td>\n",
       "      <td>12.999</td>\n",
       "      <td>5.390</td>\n",
       "      <td>12.626</td>\n",
       "      <td>5.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.219</td>\n",
       "      <td>6.051</td>\n",
       "      <td>13.518</td>\n",
       "      <td>5.356</td>\n",
       "      <td>12.724</td>\n",
       "      <td>5.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_n_mesh_ex</th>\n",
       "      <th>0</th>\n",
       "      <td>58.737</td>\n",
       "      <td>24.327</td>\n",
       "      <td>59.668</td>\n",
       "      <td>23.138</td>\n",
       "      <td>58.163</td>\n",
       "      <td>23.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.834</td>\n",
       "      <td>26.808</td>\n",
       "      <td>62.792</td>\n",
       "      <td>23.686</td>\n",
       "      <td>59.273</td>\n",
       "      <td>24.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_eng</th>\n",
       "      <th>0</th>\n",
       "      <td>0.996</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.995</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_journal</th>\n",
       "      <th>0</th>\n",
       "      <td>0.985</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.989</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_review</th>\n",
       "      <th>0</th>\n",
       "      <td>0.146</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.051</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_case_rep</th>\n",
       "      <th>0</th>\n",
       "      <td>0.030</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.015</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_let_ed_com</th>\n",
       "      <th>0</th>\n",
       "      <td>0.016</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_T_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>16.561</td>\n",
       "      <td>11.402</td>\n",
       "      <td>17.127</td>\n",
       "      <td>11.490</td>\n",
       "      <td>17.856</td>\n",
       "      <td>11.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.326</td>\n",
       "      <td>11.820</td>\n",
       "      <td>19.033</td>\n",
       "      <td>11.811</td>\n",
       "      <td>19.854</td>\n",
       "      <td>12.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_V_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>2445.306</td>\n",
       "      <td>9045.168</td>\n",
       "      <td>2521.256</td>\n",
       "      <td>7979.934</td>\n",
       "      <td>2650.913</td>\n",
       "      <td>8870.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2514.894</td>\n",
       "      <td>6996.478</td>\n",
       "      <td>2669.978</td>\n",
       "      <td>12369.446</td>\n",
       "      <td>2852.831</td>\n",
       "      <td>7750.489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_PT_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>1.786</td>\n",
       "      <td>4.393</td>\n",
       "      <td>1.853</td>\n",
       "      <td>4.443</td>\n",
       "      <td>2.023</td>\n",
       "      <td>4.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.722</td>\n",
       "      <td>4.228</td>\n",
       "      <td>1.820</td>\n",
       "      <td>4.268</td>\n",
       "      <td>2.125</td>\n",
       "      <td>4.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_PV_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>14.636</td>\n",
       "      <td>550.987</td>\n",
       "      <td>14.576</td>\n",
       "      <td>766.610</td>\n",
       "      <td>17.488</td>\n",
       "      <td>794.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.081</td>\n",
       "      <td>450.898</td>\n",
       "      <td>6.804</td>\n",
       "      <td>199.527</td>\n",
       "      <td>12.890</td>\n",
       "      <td>467.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_n_authors</th>\n",
       "      <th>0</th>\n",
       "      <td>4.770</td>\n",
       "      <td>5.124</td>\n",
       "      <td>4.790</td>\n",
       "      <td>4.669</td>\n",
       "      <td>4.840</td>\n",
       "      <td>5.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.549</td>\n",
       "      <td>5.748</td>\n",
       "      <td>5.635</td>\n",
       "      <td>4.423</td>\n",
       "      <td>5.537</td>\n",
       "      <td>4.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">year_span</th>\n",
       "      <th>0</th>\n",
       "      <td>8.053</td>\n",
       "      <td>7.410</td>\n",
       "      <td>8.003</td>\n",
       "      <td>7.325</td>\n",
       "      <td>8.154</td>\n",
       "      <td>7.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.167</td>\n",
       "      <td>3.357</td>\n",
       "      <td>3.559</td>\n",
       "      <td>3.508</td>\n",
       "      <td>4.185</td>\n",
       "      <td>4.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">journal_same</th>\n",
       "      <th>0</th>\n",
       "      <td>0.077</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.218</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mesh_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.147</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.215</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">title_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.167</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.268</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">lang_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.982</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.996</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">affiliation_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.319</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.903</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pubtype_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.529</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.683</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">cite_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.031</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.111</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">author_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.018</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.425</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gender_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.890</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">eth_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.676</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.944</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">n_common_authors</th>\n",
       "      <th>0</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.782</td>\n",
       "      <td>2.913</td>\n",
       "      <td>2.738</td>\n",
       "      <td>2.023</td>\n",
       "      <td>2.441</td>\n",
       "      <td>1.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_last_ncites</th>\n",
       "      <th>0</th>\n",
       "      <td>25.266</td>\n",
       "      <td>165.534</td>\n",
       "      <td>24.339</td>\n",
       "      <td>165.747</td>\n",
       "      <td>22.232</td>\n",
       "      <td>142.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.577</td>\n",
       "      <td>14.424</td>\n",
       "      <td>6.685</td>\n",
       "      <td>13.854</td>\n",
       "      <td>7.326</td>\n",
       "      <td>15.854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_prev_ncites</th>\n",
       "      <th>0</th>\n",
       "      <td>354.507</td>\n",
       "      <td>6089.993</td>\n",
       "      <td>348.425</td>\n",
       "      <td>6061.923</td>\n",
       "      <td>264.824</td>\n",
       "      <td>4931.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.655</td>\n",
       "      <td>62.932</td>\n",
       "      <td>15.559</td>\n",
       "      <td>53.677</td>\n",
       "      <td>20.557</td>\n",
       "      <td>69.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">auth_last_npapers</th>\n",
       "      <th>0</th>\n",
       "      <td>3.199</td>\n",
       "      <td>3.842</td>\n",
       "      <td>2.853</td>\n",
       "      <td>2.792</td>\n",
       "      <td>4.398</td>\n",
       "      <td>4.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.112</td>\n",
       "      <td>6.052</td>\n",
       "      <td>4.311</td>\n",
       "      <td>4.535</td>\n",
       "      <td>6.748</td>\n",
       "      <td>6.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">auth_prev_papers</th>\n",
       "      <th>0</th>\n",
       "      <td>11.535</td>\n",
       "      <td>29.212</td>\n",
       "      <td>12.628</td>\n",
       "      <td>25.234</td>\n",
       "      <td>29.963</td>\n",
       "      <td>60.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.219</td>\n",
       "      <td>73.219</td>\n",
       "      <td>31.048</td>\n",
       "      <td>45.844</td>\n",
       "      <td>68.929</td>\n",
       "      <td>101.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">au_age</th>\n",
       "      <th>0</th>\n",
       "      <td>5.899</td>\n",
       "      <td>7.413</td>\n",
       "      <td>7.503</td>\n",
       "      <td>24.831</td>\n",
       "      <td>10.707</td>\n",
       "      <td>10.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.791</td>\n",
       "      <td>9.839</td>\n",
       "      <td>12.695</td>\n",
       "      <td>9.955</td>\n",
       "      <td>17.030</td>\n",
       "      <td>11.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">jj_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>26.353</td>\n",
       "      <td>88.874</td>\n",
       "      <td>27.633</td>\n",
       "      <td>99.535</td>\n",
       "      <td>29.691</td>\n",
       "      <td>93.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.845</td>\n",
       "      <td>232.484</td>\n",
       "      <td>63.721</td>\n",
       "      <td>246.925</td>\n",
       "      <td>63.500</td>\n",
       "      <td>190.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">match_len</th>\n",
       "      <th>0</th>\n",
       "      <td>33.007</td>\n",
       "      <td>25.003</td>\n",
       "      <td>35.544</td>\n",
       "      <td>23.991</td>\n",
       "      <td>39.156</td>\n",
       "      <td>22.873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.413</td>\n",
       "      <td>21.554</td>\n",
       "      <td>50.321</td>\n",
       "      <td>20.394</td>\n",
       "      <td>49.798</td>\n",
       "      <td>20.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">match_prop</th>\n",
       "      <th>0</th>\n",
       "      <td>0.516</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.795</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">overall_coverage_len</th>\n",
       "      <th>0</th>\n",
       "      <td>57.854</td>\n",
       "      <td>23.877</td>\n",
       "      <td>58.374</td>\n",
       "      <td>22.610</td>\n",
       "      <td>55.554</td>\n",
       "      <td>22.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.676</td>\n",
       "      <td>23.367</td>\n",
       "      <td>59.194</td>\n",
       "      <td>22.102</td>\n",
       "      <td>55.257</td>\n",
       "      <td>21.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">overall_coverage_prop</th>\n",
       "      <th>0</th>\n",
       "      <td>0.893</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.933</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                     -                    F             \\\n",
       "                                        mean        std      mean        std   \n",
       "                      is_self_cite                                             \n",
       "source_year           0             2003.663      1.111  2003.631      1.112   \n",
       "                      1             2003.635      1.114  2003.603      1.114   \n",
       "source_n_mesh         0               13.492      5.859    13.340      5.463   \n",
       "                      1               12.879      5.999    13.121      5.392   \n",
       "source_is_eng         0                0.984      0.124     0.987      0.114   \n",
       "                      1                0.995      0.072     0.992      0.091   \n",
       "source_is_journal     0                0.990      0.099     0.990      0.100   \n",
       "                      1                0.986      0.119     0.984      0.124   \n",
       "source_is_review      0                0.137      0.344     0.192      0.394   \n",
       "                      1                0.140      0.347     0.173      0.378   \n",
       "source_is_case_rep    0                0.036      0.186     0.030      0.169   \n",
       "                      1                0.011      0.107     0.012      0.107   \n",
       "source_is_let_ed_com  0                0.010      0.100     0.010      0.101   \n",
       "                      1                0.015      0.120     0.016      0.125   \n",
       "source_T_novelty      0               19.846     12.504    20.965     12.666   \n",
       "                      1               20.304     12.522    21.361     12.524   \n",
       "source_V_novelty      0             3254.876  10964.931  3607.520  12431.366   \n",
       "                      1             3298.214  11458.840  3632.428  24886.111   \n",
       "source_PT_novelty     0                2.287      4.948     2.531      5.164   \n",
       "                      1                2.333      4.960     2.540      5.089   \n",
       "source_PV_novelty     0               12.492    678.392    24.137   1390.502   \n",
       "                      1               19.853   1199.187    10.357    564.516   \n",
       "source_ncites         0               45.780     66.675    53.320     94.552   \n",
       "                      1               37.349     33.979    42.054     49.816   \n",
       "source_n_authors      0                5.130      3.021     5.094      3.145   \n",
       "                      1                5.176      4.281     5.183      3.538   \n",
       "sink_year             0             1995.610      7.494  1995.628      7.401   \n",
       "                      1             2000.468      3.569  2000.044      3.687   \n",
       "sink_n_mesh           0               12.898      5.661    12.999      5.390   \n",
       "                      1               13.219      6.051    13.518      5.356   \n",
       "sink_n_mesh_ex        0               58.737     24.327    59.668     23.138   \n",
       "                      1               60.834     26.808    62.792     23.686   \n",
       "sink_is_eng           0                0.996      0.065     0.996      0.063   \n",
       "                      1                0.995      0.068     0.995      0.073   \n",
       "sink_is_journal       0                0.985      0.123     0.983      0.129   \n",
       "                      1                0.989      0.105     0.988      0.108   \n",
       "sink_is_review        0                0.146      0.353     0.148      0.355   \n",
       "                      1                0.051      0.220     0.065      0.246   \n",
       "sink_is_case_rep      0                0.030      0.171     0.028      0.166   \n",
       "                      1                0.015      0.120     0.015      0.121   \n",
       "sink_is_let_ed_com    0                0.016      0.124     0.017      0.130   \n",
       "                      1                0.011      0.106     0.012      0.107   \n",
       "sink_T_novelty        0               16.561     11.402    17.127     11.490   \n",
       "                      1               18.326     11.820    19.033     11.811   \n",
       "sink_V_novelty        0             2445.306   9045.168  2521.256   7979.934   \n",
       "                      1             2514.894   6996.478  2669.978  12369.446   \n",
       "sink_PT_novelty       0                1.786      4.393     1.853      4.443   \n",
       "                      1                1.722      4.228     1.820      4.268   \n",
       "sink_PV_novelty       0               14.636    550.987    14.576    766.610   \n",
       "                      1               10.081    450.898     6.804    199.527   \n",
       "sink_n_authors        0                4.770      5.124     4.790      4.669   \n",
       "                      1                5.549      5.748     5.635      4.423   \n",
       "year_span             0                8.053      7.410     8.003      7.325   \n",
       "                      1                3.167      3.357     3.559      3.508   \n",
       "journal_same          0                0.077      0.267     0.071      0.257   \n",
       "                      1                0.218      0.413     0.184      0.387   \n",
       "mesh_sim              0                0.147      0.106     0.152      0.103   \n",
       "                      1                0.215      0.148     0.225      0.142   \n",
       "title_sim             0                0.167      0.155     0.162      0.153   \n",
       "                      1                0.268      0.200     0.258      0.195   \n",
       "lang_sim              0                0.982      0.131     0.984      0.124   \n",
       "                      1                0.996      0.064     0.991      0.094   \n",
       "affiliation_sim       0                0.319      0.466     0.299      0.458   \n",
       "                      1                0.903      0.296     0.899      0.301   \n",
       "pubtype_sim           0                0.529      0.265     0.519      0.263   \n",
       "                      1                0.683      0.282     0.660      0.283   \n",
       "cite_sim              0                0.031      0.046     0.029      0.043   \n",
       "                      1                0.111      0.132     0.103      0.122   \n",
       "author_sim            0                0.018      0.067     0.019      0.067   \n",
       "                      1                0.425      0.254     0.410      0.248   \n",
       "gender_sim            0                0.667      0.286     0.724      0.263   \n",
       "                      1                0.890      0.142     0.900      0.133   \n",
       "eth_sim               0                0.676      0.199     0.690      0.208   \n",
       "                      1                0.944      0.079     0.944      0.078   \n",
       "n_common_authors      0                0.140      0.566     0.150      0.578   \n",
       "                      1                2.782      2.913     2.738      2.023   \n",
       "sink_last_ncites      0               25.266    165.534    24.339    165.747   \n",
       "                      1                6.577     14.424     6.685     13.854   \n",
       "sink_prev_ncites      0              354.507   6089.993   348.425   6061.923   \n",
       "                      1               13.655     62.932    15.559     53.677   \n",
       "auth_last_npapers     0                3.199      3.842     2.853      2.792   \n",
       "                      1                5.112      6.052     4.311      4.535   \n",
       "auth_prev_papers      0               11.535     29.212    12.628     25.234   \n",
       "                      1               35.219     73.219    31.048     45.844   \n",
       "au_age                0                5.899      7.413     7.503     24.831   \n",
       "                      1               10.791      9.839    12.695      9.955   \n",
       "jj_sim                0               26.353     88.874    27.633     99.535   \n",
       "                      1               65.845    232.484    63.721    246.925   \n",
       "match_len             0               33.007     25.003    35.544     23.991   \n",
       "                      1               49.413     21.554    50.321     20.394   \n",
       "match_prop            0                0.516      0.339     0.569      0.326   \n",
       "                      1                0.795      0.204     0.814      0.187   \n",
       "overall_coverage_len  0               57.854     23.877    58.374     22.610   \n",
       "                      1               58.676     23.367    59.194     22.102   \n",
       "overall_coverage_prop 0                0.893      0.149     0.919      0.117   \n",
       "                      1                0.933      0.103     0.946      0.081   \n",
       "\n",
       "gender                                     M            \n",
       "                                        mean       std  \n",
       "                      is_self_cite                      \n",
       "source_year           0             2003.588     1.116  \n",
       "                      1             2003.563     1.118  \n",
       "source_n_mesh         0               12.566     5.427  \n",
       "                      1               11.993     5.363  \n",
       "source_is_eng         0                0.979     0.144  \n",
       "                      1                0.986     0.116  \n",
       "source_is_journal     0                0.984     0.125  \n",
       "                      1                0.975     0.157  \n",
       "source_is_review      0                0.249     0.432  \n",
       "                      1                0.260     0.439  \n",
       "source_is_case_rep    0                0.040     0.196  \n",
       "                      1                0.015     0.122  \n",
       "source_is_let_ed_com  0                0.017     0.127  \n",
       "                      1                0.027     0.162  \n",
       "source_T_novelty      0               22.183    13.109  \n",
       "                      1               23.031    13.119  \n",
       "source_V_novelty      0             3947.314  8873.425  \n",
       "                      1             4038.486  9871.289  \n",
       "source_PT_novelty     0                2.964     5.721  \n",
       "                      1                3.177     5.922  \n",
       "source_PV_novelty     0               16.884   488.653  \n",
       "                      1               17.537   472.326  \n",
       "source_ncites         0               55.949    81.095  \n",
       "                      1               45.410    59.791  \n",
       "source_n_authors      0                5.102     3.428  \n",
       "                      1                5.068     3.698  \n",
       "sink_year             0             1995.435     7.726  \n",
       "                      1             1999.378     4.569  \n",
       "sink_n_mesh           0               12.626     5.386  \n",
       "                      1               12.724     5.480  \n",
       "sink_n_mesh_ex        0               58.163    23.339  \n",
       "                      1               59.273    24.495  \n",
       "sink_is_eng           0                0.994     0.076  \n",
       "                      1                0.992     0.090  \n",
       "sink_is_journal       0                0.981     0.136  \n",
       "                      1                0.982     0.131  \n",
       "sink_is_review        0                0.144     0.351  \n",
       "                      1                0.085     0.279  \n",
       "sink_is_case_rep      0                0.037     0.189  \n",
       "                      1                0.020     0.141  \n",
       "sink_is_let_ed_com    0                0.019     0.137  \n",
       "                      1                0.018     0.132  \n",
       "sink_T_novelty        0               17.856    11.696  \n",
       "                      1               19.854    12.131  \n",
       "sink_V_novelty        0             2650.913  8870.405  \n",
       "                      1             2852.831  7750.489  \n",
       "sink_PT_novelty       0                2.023     4.664  \n",
       "                      1                2.125     4.792  \n",
       "sink_PV_novelty       0               17.488   794.826  \n",
       "                      1               12.890   467.356  \n",
       "sink_n_authors        0                4.840     5.046  \n",
       "                      1                5.537     4.668  \n",
       "year_span             0                8.154     7.650  \n",
       "                      1                4.185     4.428  \n",
       "journal_same          0                0.073     0.260  \n",
       "                      1                0.175     0.380  \n",
       "mesh_sim              0                0.154     0.109  \n",
       "                      1                0.210     0.143  \n",
       "title_sim             0                0.164     0.157  \n",
       "                      1                0.240     0.195  \n",
       "lang_sim              0                0.976     0.154  \n",
       "                      1                0.986     0.118  \n",
       "affiliation_sim       0                0.295     0.456  \n",
       "                      1                0.866     0.341  \n",
       "pubtype_sim           0                0.502     0.265  \n",
       "                      1                0.611     0.290  \n",
       "cite_sim              0                0.029     0.044  \n",
       "                      1                0.095     0.121  \n",
       "author_sim            0                0.015     0.060  \n",
       "                      1                0.365     0.241  \n",
       "gender_sim            0                0.826     0.215  \n",
       "                      1                0.915     0.120  \n",
       "eth_sim               0                0.685     0.207  \n",
       "                      1                0.933     0.089  \n",
       "n_common_authors      0                0.123     0.510  \n",
       "                      1                2.441     1.965  \n",
       "sink_last_ncites      0               22.232   142.185  \n",
       "                      1                7.326    15.854  \n",
       "sink_prev_ncites      0              264.824  4931.631  \n",
       "                      1               20.557    69.766  \n",
       "auth_last_npapers     0                4.398     4.851  \n",
       "                      1                6.748     6.964  \n",
       "auth_prev_papers      0               29.963    60.302  \n",
       "                      1               68.929   101.054  \n",
       "au_age                0               10.707    10.580  \n",
       "                      1               17.030    11.939  \n",
       "jj_sim                0               29.691    93.924  \n",
       "                      1               63.500   190.484  \n",
       "match_len             0               39.156    22.873  \n",
       "                      1               49.798    20.106  \n",
       "match_prop            0                0.662     0.307  \n",
       "                      1                0.865     0.164  \n",
       "overall_coverage_len  0               55.554    22.272  \n",
       "                      1               55.257    21.679  \n",
       "overall_coverage_prop 0                0.920     0.121  \n",
       "                      1                0.950     0.084  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', 100,\n",
    "                       'display.max_columns', 20,\n",
    "                       'display.precision', 3\n",
    "                      ):\n",
    "    display(df_t_first.T.unstack(level=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">mean</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">source_year</th>\n",
       "      <th colspan=\"2\" halign=\"left\">source_n_mesh</th>\n",
       "      <th colspan=\"2\" halign=\"left\">source_is_eng</th>\n",
       "      <th colspan=\"2\" halign=\"left\">source_is_journal</th>\n",
       "      <th colspan=\"2\" halign=\"left\">source_is_review</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">jj_sim</th>\n",
       "      <th colspan=\"2\" halign=\"left\">match_len</th>\n",
       "      <th colspan=\"2\" halign=\"left\">match_prop</th>\n",
       "      <th colspan=\"2\" halign=\"left\">overall_coverage_len</th>\n",
       "      <th colspan=\"2\" halign=\"left\">overall_coverage_prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_self_cite</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>...</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>2003.668525</td>\n",
       "      <td>2003.669454</td>\n",
       "      <td>12.921061</td>\n",
       "      <td>13.370146</td>\n",
       "      <td>0.978522</td>\n",
       "      <td>0.995681</td>\n",
       "      <td>0.986633</td>\n",
       "      <td>0.989529</td>\n",
       "      <td>0.171399</td>\n",
       "      <td>0.120635</td>\n",
       "      <td>...</td>\n",
       "      <td>107.418105</td>\n",
       "      <td>196.201352</td>\n",
       "      <td>26.964844</td>\n",
       "      <td>22.586342</td>\n",
       "      <td>0.321955</td>\n",
       "      <td>0.136271</td>\n",
       "      <td>23.582839</td>\n",
       "      <td>23.404009</td>\n",
       "      <td>0.171760</td>\n",
       "      <td>0.097271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>2003.634246</td>\n",
       "      <td>2003.616255</td>\n",
       "      <td>12.995463</td>\n",
       "      <td>13.705127</td>\n",
       "      <td>0.984779</td>\n",
       "      <td>0.993584</td>\n",
       "      <td>0.986530</td>\n",
       "      <td>0.988157</td>\n",
       "      <td>0.231312</td>\n",
       "      <td>0.149486</td>\n",
       "      <td>...</td>\n",
       "      <td>112.664784</td>\n",
       "      <td>169.739193</td>\n",
       "      <td>24.640737</td>\n",
       "      <td>21.337889</td>\n",
       "      <td>0.275971</td>\n",
       "      <td>0.119176</td>\n",
       "      <td>22.400058</td>\n",
       "      <td>22.330419</td>\n",
       "      <td>0.132850</td>\n",
       "      <td>0.079537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>2003.597499</td>\n",
       "      <td>2003.589226</td>\n",
       "      <td>12.822445</td>\n",
       "      <td>13.196785</td>\n",
       "      <td>0.980800</td>\n",
       "      <td>0.992040</td>\n",
       "      <td>0.985936</td>\n",
       "      <td>0.986839</td>\n",
       "      <td>0.227771</td>\n",
       "      <td>0.158791</td>\n",
       "      <td>...</td>\n",
       "      <td>91.100172</td>\n",
       "      <td>151.407318</td>\n",
       "      <td>22.974276</td>\n",
       "      <td>21.753687</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.104821</td>\n",
       "      <td>22.484419</td>\n",
       "      <td>22.627888</td>\n",
       "      <td>0.115065</td>\n",
       "      <td>0.075086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 192 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean                                                      \\\n",
       "              source_year              source_n_mesh            source_is_eng   \n",
       "is_self_cite            0            1             0          1             0   \n",
       "gender                                                                          \n",
       "-             2003.668525  2003.669454     12.921061  13.370146      0.978522   \n",
       "F             2003.634246  2003.616255     12.995463  13.705127      0.984779   \n",
       "M             2003.597499  2003.589226     12.822445  13.196785      0.980800   \n",
       "\n",
       "                                                                               \\\n",
       "                       source_is_journal           source_is_review             \n",
       "is_self_cite         1                 0         1                0         1   \n",
       "gender                                                                          \n",
       "-             0.995681          0.986633  0.989529         0.171399  0.120635   \n",
       "F             0.993584          0.986530  0.988157         0.231312  0.149486   \n",
       "M             0.992040          0.985936  0.986839         0.227771  0.158791   \n",
       "\n",
       "                ...            std                                    \\\n",
       "                ...         jj_sim              match_len              \n",
       "is_self_cite    ...              0           1          0          1   \n",
       "gender          ...                                                    \n",
       "-               ...     107.418105  196.201352  26.964844  22.586342   \n",
       "F               ...     112.664784  169.739193  24.640737  21.337889   \n",
       "M               ...      91.100172  151.407318  22.974276  21.753687   \n",
       "\n",
       "                                                                   \\\n",
       "             match_prop           overall_coverage_len              \n",
       "is_self_cite          0         1                    0          1   \n",
       "gender                                                              \n",
       "-              0.321955  0.136271            23.582839  23.404009   \n",
       "F              0.275971  0.119176            22.400058  22.330419   \n",
       "M              0.209302  0.104821            22.484419  22.627888   \n",
       "\n",
       "                                              \n",
       "             overall_coverage_prop            \n",
       "is_self_cite                     0         1  \n",
       "gender                                        \n",
       "-                         0.171760  0.097271  \n",
       "F                         0.132850  0.079537  \n",
       "M                         0.115065  0.075086  \n",
       "\n",
       "[3 rows x 192 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last = df_last.pivot_table(index=\"gender\", columns=\"is_self_cite\",\n",
    "               values=[\"source_year\", u'source_n_mesh', u'source_is_eng', \n",
    "                       u'source_is_journal', u'source_is_review',\n",
    "                       u'source_is_case_rep', u'source_is_let_ed_com',\n",
    "                       u'source_T_novelty', u'source_V_novelty', u'source_PT_novelty',\n",
    "                       u'source_PV_novelty', u'source_ncites', u'source_n_authors',\n",
    "                       u'sink_year', u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng',\n",
    "                       u'sink_is_journal', u'sink_is_review', u'sink_is_case_rep',\n",
    "                       u'sink_is_let_ed_com', u'sink_T_novelty', u'sink_V_novelty',\n",
    "                       u'sink_PT_novelty', u'sink_PV_novelty', u'sink_n_authors',\n",
    "                       u'year_span', u'journal_same',\n",
    "                       u'mesh_sim', u'title_sim', u'lang_sim',\n",
    "                       u'affiliation_sim', u'pubtype_sim', u'cite_sim', u'author_sim',\n",
    "                       u'gender_sim', u'eth_sim', u'n_common_authors',\n",
    "                       u'sink_last_ncites', u'sink_prev_ncites',\n",
    "                       u'auth_last_npapers', u'auth_prev_papers',\n",
    "                       u'au_age',\n",
    "                       u'jj_sim',\n",
    "                       u'match_len', u'match_prop',\n",
    "                       u'overall_coverage_len', u'overall_coverage_prop'\n",
    "                      ],\n",
    "                        aggfunc=[np.mean, np.std])\n",
    "df_t_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_t_last.T.unstack(level=0).to_csv(\"Last_Author_gender_self_cites_means.txt\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th colspan=\"2\" halign=\"left\">-</th>\n",
       "      <th colspan=\"2\" halign=\"left\">F</th>\n",
       "      <th colspan=\"2\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>is_self_cite</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_year</th>\n",
       "      <th>0</th>\n",
       "      <td>2003.669</td>\n",
       "      <td>1.110</td>\n",
       "      <td>2003.634</td>\n",
       "      <td>1.112</td>\n",
       "      <td>2003.597</td>\n",
       "      <td>1.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003.669</td>\n",
       "      <td>1.112</td>\n",
       "      <td>2003.616</td>\n",
       "      <td>1.114</td>\n",
       "      <td>2003.589</td>\n",
       "      <td>1.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_n_mesh</th>\n",
       "      <th>0</th>\n",
       "      <td>12.921</td>\n",
       "      <td>5.690</td>\n",
       "      <td>12.995</td>\n",
       "      <td>5.410</td>\n",
       "      <td>12.822</td>\n",
       "      <td>5.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.370</td>\n",
       "      <td>6.102</td>\n",
       "      <td>13.705</td>\n",
       "      <td>5.576</td>\n",
       "      <td>13.197</td>\n",
       "      <td>5.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_eng</th>\n",
       "      <th>0</th>\n",
       "      <td>0.979</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.996</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_journal</th>\n",
       "      <th>0</th>\n",
       "      <td>0.987</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.990</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_review</th>\n",
       "      <th>0</th>\n",
       "      <td>0.171</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.121</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_case_rep</th>\n",
       "      <th>0</th>\n",
       "      <td>0.046</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_is_let_ed_com</th>\n",
       "      <th>0</th>\n",
       "      <td>0.013</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_T_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>21.133</td>\n",
       "      <td>12.993</td>\n",
       "      <td>21.882</td>\n",
       "      <td>12.990</td>\n",
       "      <td>21.668</td>\n",
       "      <td>12.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.388</td>\n",
       "      <td>12.091</td>\n",
       "      <td>19.907</td>\n",
       "      <td>12.071</td>\n",
       "      <td>20.400</td>\n",
       "      <td>12.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_V_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>3534.550</td>\n",
       "      <td>11270.630</td>\n",
       "      <td>3773.758</td>\n",
       "      <td>8360.877</td>\n",
       "      <td>3841.652</td>\n",
       "      <td>11202.508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3097.273</td>\n",
       "      <td>13115.467</td>\n",
       "      <td>3109.591</td>\n",
       "      <td>6873.506</td>\n",
       "      <td>3328.334</td>\n",
       "      <td>9211.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_PT_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>2.593</td>\n",
       "      <td>5.319</td>\n",
       "      <td>2.810</td>\n",
       "      <td>5.526</td>\n",
       "      <td>2.807</td>\n",
       "      <td>5.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.096</td>\n",
       "      <td>4.570</td>\n",
       "      <td>2.236</td>\n",
       "      <td>4.731</td>\n",
       "      <td>2.392</td>\n",
       "      <td>4.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_PV_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>10.314</td>\n",
       "      <td>198.981</td>\n",
       "      <td>14.104</td>\n",
       "      <td>392.583</td>\n",
       "      <td>21.402</td>\n",
       "      <td>1042.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.777</td>\n",
       "      <td>329.167</td>\n",
       "      <td>9.857</td>\n",
       "      <td>358.987</td>\n",
       "      <td>11.951</td>\n",
       "      <td>446.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_ncites</th>\n",
       "      <th>0</th>\n",
       "      <td>46.306</td>\n",
       "      <td>59.037</td>\n",
       "      <td>56.770</td>\n",
       "      <td>95.505</td>\n",
       "      <td>54.664</td>\n",
       "      <td>85.583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.348</td>\n",
       "      <td>36.907</td>\n",
       "      <td>44.151</td>\n",
       "      <td>40.060</td>\n",
       "      <td>43.368</td>\n",
       "      <td>44.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">source_n_authors</th>\n",
       "      <th>0</th>\n",
       "      <td>5.176</td>\n",
       "      <td>3.610</td>\n",
       "      <td>4.827</td>\n",
       "      <td>3.125</td>\n",
       "      <td>5.150</td>\n",
       "      <td>3.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.300</td>\n",
       "      <td>5.136</td>\n",
       "      <td>5.035</td>\n",
       "      <td>3.475</td>\n",
       "      <td>5.218</td>\n",
       "      <td>3.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_year</th>\n",
       "      <th>0</th>\n",
       "      <td>1995.291</td>\n",
       "      <td>7.777</td>\n",
       "      <td>1995.531</td>\n",
       "      <td>7.548</td>\n",
       "      <td>1995.428</td>\n",
       "      <td>7.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999.435</td>\n",
       "      <td>4.195</td>\n",
       "      <td>1998.849</td>\n",
       "      <td>4.363</td>\n",
       "      <td>1998.634</td>\n",
       "      <td>4.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_n_mesh</th>\n",
       "      <th>0</th>\n",
       "      <td>12.621</td>\n",
       "      <td>5.579</td>\n",
       "      <td>12.843</td>\n",
       "      <td>5.349</td>\n",
       "      <td>12.720</td>\n",
       "      <td>5.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.382</td>\n",
       "      <td>5.947</td>\n",
       "      <td>13.759</td>\n",
       "      <td>5.327</td>\n",
       "      <td>13.264</td>\n",
       "      <td>5.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_n_mesh_ex</th>\n",
       "      <th>0</th>\n",
       "      <td>57.827</td>\n",
       "      <td>24.093</td>\n",
       "      <td>59.082</td>\n",
       "      <td>22.996</td>\n",
       "      <td>58.500</td>\n",
       "      <td>23.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.242</td>\n",
       "      <td>26.191</td>\n",
       "      <td>63.276</td>\n",
       "      <td>23.312</td>\n",
       "      <td>60.861</td>\n",
       "      <td>24.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_eng</th>\n",
       "      <th>0</th>\n",
       "      <td>0.995</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.997</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_journal</th>\n",
       "      <th>0</th>\n",
       "      <td>0.982</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.990</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_review</th>\n",
       "      <th>0</th>\n",
       "      <td>0.148</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.062</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_case_rep</th>\n",
       "      <th>0</th>\n",
       "      <td>0.038</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_is_let_ed_com</th>\n",
       "      <th>0</th>\n",
       "      <td>0.018</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_T_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>17.273</td>\n",
       "      <td>11.708</td>\n",
       "      <td>17.633</td>\n",
       "      <td>11.678</td>\n",
       "      <td>17.555</td>\n",
       "      <td>11.637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.166</td>\n",
       "      <td>11.370</td>\n",
       "      <td>17.394</td>\n",
       "      <td>11.234</td>\n",
       "      <td>17.810</td>\n",
       "      <td>11.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_V_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>2529.759</td>\n",
       "      <td>9435.899</td>\n",
       "      <td>2578.390</td>\n",
       "      <td>7800.220</td>\n",
       "      <td>2615.206</td>\n",
       "      <td>8625.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2404.246</td>\n",
       "      <td>5600.294</td>\n",
       "      <td>2391.179</td>\n",
       "      <td>12136.882</td>\n",
       "      <td>2542.507</td>\n",
       "      <td>9245.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_PT_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>1.927</td>\n",
       "      <td>4.580</td>\n",
       "      <td>1.961</td>\n",
       "      <td>4.590</td>\n",
       "      <td>1.971</td>\n",
       "      <td>4.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.558</td>\n",
       "      <td>3.983</td>\n",
       "      <td>1.573</td>\n",
       "      <td>3.966</td>\n",
       "      <td>1.734</td>\n",
       "      <td>4.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_PV_novelty</th>\n",
       "      <th>0</th>\n",
       "      <td>16.361</td>\n",
       "      <td>642.131</td>\n",
       "      <td>14.928</td>\n",
       "      <td>557.485</td>\n",
       "      <td>16.947</td>\n",
       "      <td>828.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.647</td>\n",
       "      <td>503.154</td>\n",
       "      <td>6.871</td>\n",
       "      <td>287.387</td>\n",
       "      <td>10.512</td>\n",
       "      <td>390.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_n_authors</th>\n",
       "      <th>0</th>\n",
       "      <td>4.731</td>\n",
       "      <td>5.115</td>\n",
       "      <td>4.708</td>\n",
       "      <td>4.688</td>\n",
       "      <td>4.821</td>\n",
       "      <td>5.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.520</td>\n",
       "      <td>5.983</td>\n",
       "      <td>5.386</td>\n",
       "      <td>4.548</td>\n",
       "      <td>5.491</td>\n",
       "      <td>4.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">year_span</th>\n",
       "      <th>0</th>\n",
       "      <td>8.377</td>\n",
       "      <td>7.692</td>\n",
       "      <td>8.103</td>\n",
       "      <td>7.475</td>\n",
       "      <td>8.169</td>\n",
       "      <td>7.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.234</td>\n",
       "      <td>4.030</td>\n",
       "      <td>4.768</td>\n",
       "      <td>4.227</td>\n",
       "      <td>4.955</td>\n",
       "      <td>4.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">journal_same</th>\n",
       "      <th>0</th>\n",
       "      <td>0.069</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mesh_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.151</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.196</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">title_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.243</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">lang_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.976</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.996</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">affiliation_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.242</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.870</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pubtype_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.521</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.681</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">cite_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.030</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.089</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">author_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.322</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gender_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.616</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.860</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">eth_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.640</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.931</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">n_common_authors</th>\n",
       "      <th>0</th>\n",
       "      <td>0.068</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.290</td>\n",
       "      <td>3.689</td>\n",
       "      <td>2.138</td>\n",
       "      <td>1.953</td>\n",
       "      <td>2.126</td>\n",
       "      <td>1.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_last_ncites</th>\n",
       "      <th>0</th>\n",
       "      <td>25.413</td>\n",
       "      <td>169.723</td>\n",
       "      <td>23.482</td>\n",
       "      <td>160.319</td>\n",
       "      <td>23.656</td>\n",
       "      <td>153.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.670</td>\n",
       "      <td>16.783</td>\n",
       "      <td>7.933</td>\n",
       "      <td>14.654</td>\n",
       "      <td>9.091</td>\n",
       "      <td>19.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sink_prev_ncites</th>\n",
       "      <th>0</th>\n",
       "      <td>385.753</td>\n",
       "      <td>6578.110</td>\n",
       "      <td>332.230</td>\n",
       "      <td>5930.452</td>\n",
       "      <td>298.336</td>\n",
       "      <td>5330.678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.078</td>\n",
       "      <td>98.974</td>\n",
       "      <td>28.786</td>\n",
       "      <td>72.432</td>\n",
       "      <td>33.947</td>\n",
       "      <td>104.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">auth_last_npapers</th>\n",
       "      <th>0</th>\n",
       "      <td>6.311</td>\n",
       "      <td>7.257</td>\n",
       "      <td>4.917</td>\n",
       "      <td>4.723</td>\n",
       "      <td>8.060</td>\n",
       "      <td>8.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.625</td>\n",
       "      <td>8.087</td>\n",
       "      <td>6.275</td>\n",
       "      <td>5.232</td>\n",
       "      <td>9.771</td>\n",
       "      <td>9.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">auth_prev_papers</th>\n",
       "      <th>0</th>\n",
       "      <td>49.145</td>\n",
       "      <td>80.955</td>\n",
       "      <td>43.300</td>\n",
       "      <td>55.122</td>\n",
       "      <td>89.871</td>\n",
       "      <td>108.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.821</td>\n",
       "      <td>106.007</td>\n",
       "      <td>67.926</td>\n",
       "      <td>66.621</td>\n",
       "      <td>121.084</td>\n",
       "      <td>127.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">au_age</th>\n",
       "      <th>0</th>\n",
       "      <td>13.199</td>\n",
       "      <td>54.156</td>\n",
       "      <td>15.811</td>\n",
       "      <td>10.592</td>\n",
       "      <td>20.591</td>\n",
       "      <td>11.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.764</td>\n",
       "      <td>24.810</td>\n",
       "      <td>20.439</td>\n",
       "      <td>9.624</td>\n",
       "      <td>23.681</td>\n",
       "      <td>10.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">jj_sim</th>\n",
       "      <th>0</th>\n",
       "      <td>27.960</td>\n",
       "      <td>107.418</td>\n",
       "      <td>29.395</td>\n",
       "      <td>112.665</td>\n",
       "      <td>28.511</td>\n",
       "      <td>91.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.157</td>\n",
       "      <td>196.201</td>\n",
       "      <td>48.006</td>\n",
       "      <td>169.739</td>\n",
       "      <td>47.886</td>\n",
       "      <td>151.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">match_len</th>\n",
       "      <th>0</th>\n",
       "      <td>43.598</td>\n",
       "      <td>26.965</td>\n",
       "      <td>46.601</td>\n",
       "      <td>24.641</td>\n",
       "      <td>51.027</td>\n",
       "      <td>22.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.995</td>\n",
       "      <td>22.586</td>\n",
       "      <td>57.283</td>\n",
       "      <td>21.338</td>\n",
       "      <td>57.233</td>\n",
       "      <td>21.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">match_prop</th>\n",
       "      <th>0</th>\n",
       "      <td>0.686</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.886</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">overall_coverage_len</th>\n",
       "      <th>0</th>\n",
       "      <td>54.771</td>\n",
       "      <td>23.583</td>\n",
       "      <td>56.158</td>\n",
       "      <td>22.400</td>\n",
       "      <td>56.709</td>\n",
       "      <td>22.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.226</td>\n",
       "      <td>23.404</td>\n",
       "      <td>60.749</td>\n",
       "      <td>22.330</td>\n",
       "      <td>59.634</td>\n",
       "      <td>22.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">overall_coverage_prop</th>\n",
       "      <th>0</th>\n",
       "      <td>0.872</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.933</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                     -                    F             \\\n",
       "                                        mean        std      mean        std   \n",
       "                      is_self_cite                                             \n",
       "source_year           0             2003.669      1.110  2003.634      1.112   \n",
       "                      1             2003.669      1.112  2003.616      1.114   \n",
       "source_n_mesh         0               12.921      5.690    12.995      5.410   \n",
       "                      1               13.370      6.102    13.705      5.576   \n",
       "source_is_eng         0                0.979      0.145     0.985      0.122   \n",
       "                      1                0.996      0.066     0.994      0.080   \n",
       "source_is_journal     0                0.987      0.115     0.987      0.115   \n",
       "                      1                0.990      0.102     0.988      0.108   \n",
       "source_is_review      0                0.171      0.377     0.231      0.422   \n",
       "                      1                0.121      0.326     0.149      0.357   \n",
       "source_is_case_rep    0                0.046      0.210     0.032      0.176   \n",
       "                      1                0.013      0.111     0.011      0.105   \n",
       "source_is_let_ed_com  0                0.013      0.115     0.014      0.115   \n",
       "                      1                0.011      0.104     0.012      0.110   \n",
       "source_T_novelty      0               21.133     12.993    21.882     12.990   \n",
       "                      1               19.388     12.091    19.907     12.071   \n",
       "source_V_novelty      0             3534.550  11270.630  3773.758   8360.877   \n",
       "                      1             3097.273  13115.467  3109.591   6873.506   \n",
       "source_PT_novelty     0                2.593      5.319     2.810      5.526   \n",
       "                      1                2.096      4.570     2.236      4.731   \n",
       "source_PV_novelty     0               10.314    198.981    14.104    392.583   \n",
       "                      1                9.777    329.167     9.857    358.987   \n",
       "source_ncites         0               46.306     59.037    56.770     95.505   \n",
       "                      1               39.348     36.907    44.151     40.060   \n",
       "source_n_authors      0                5.176      3.610     4.827      3.125   \n",
       "                      1                5.300      5.136     5.035      3.475   \n",
       "sink_year             0             1995.291      7.777  1995.531      7.548   \n",
       "                      1             1999.435      4.195  1998.849      4.363   \n",
       "sink_n_mesh           0               12.621      5.579    12.843      5.349   \n",
       "                      1               13.382      5.947    13.759      5.327   \n",
       "sink_n_mesh_ex        0               57.827     24.093    59.082     22.996   \n",
       "                      1               61.242     26.191    63.276     23.312   \n",
       "sink_is_eng           0                0.995      0.073     0.996      0.065   \n",
       "                      1                0.997      0.054     0.996      0.059   \n",
       "sink_is_journal       0                0.982      0.132     0.981      0.135   \n",
       "                      1                0.990      0.101     0.990      0.097   \n",
       "sink_is_review        0                0.148      0.355     0.151      0.358   \n",
       "                      1                0.062      0.242     0.076      0.265   \n",
       "sink_is_case_rep      0                0.038      0.191     0.031      0.174   \n",
       "                      1                0.013      0.114     0.012      0.111   \n",
       "sink_is_let_ed_com    0                0.018      0.133     0.019      0.136   \n",
       "                      1                0.010      0.101     0.010      0.098   \n",
       "sink_T_novelty        0               17.273     11.708    17.633     11.678   \n",
       "                      1               17.166     11.370    17.394     11.234   \n",
       "sink_V_novelty        0             2529.759   9435.899  2578.390   7800.220   \n",
       "                      1             2404.246   5600.294  2391.179  12136.882   \n",
       "sink_PT_novelty       0                1.927      4.580     1.961      4.590   \n",
       "                      1                1.558      3.983     1.573      3.966   \n",
       "sink_PV_novelty       0               16.361    642.131    14.928    557.485   \n",
       "                      1               11.647    503.154     6.871    287.387   \n",
       "sink_n_authors        0                4.731      5.115     4.708      4.688   \n",
       "                      1                5.520      5.983     5.386      4.548   \n",
       "year_span             0                8.377      7.692     8.103      7.475   \n",
       "                      1                4.234      4.030     4.768      4.227   \n",
       "journal_same          0                0.069      0.254     0.068      0.251   \n",
       "                      1                0.183      0.386     0.161      0.367   \n",
       "mesh_sim              0                0.151      0.110     0.152      0.104   \n",
       "                      1                0.196      0.136     0.203      0.127   \n",
       "title_sim             0                0.168      0.158     0.160      0.154   \n",
       "                      1                0.243      0.189     0.232      0.181   \n",
       "lang_sim              0                0.976      0.153     0.982      0.133   \n",
       "                      1                0.996      0.063     0.993      0.082   \n",
       "affiliation_sim       0                0.242      0.428     0.303      0.459   \n",
       "                      1                0.870      0.337     0.867      0.339   \n",
       "pubtype_sim           0                0.521      0.268     0.503      0.260   \n",
       "                      1                0.681      0.278     0.659      0.275   \n",
       "cite_sim              0                0.030      0.047     0.027      0.042   \n",
       "                      1                0.089      0.116     0.080      0.100   \n",
       "author_sim            0                0.009      0.047     0.010      0.050   \n",
       "                      1                0.322      0.228     0.315      0.222   \n",
       "gender_sim            0                0.616      0.307     0.671      0.290   \n",
       "                      1                0.860      0.168     0.864      0.162   \n",
       "eth_sim               0                0.640      0.199     0.686      0.208   \n",
       "                      1                0.931      0.093     0.921      0.094   \n",
       "n_common_authors      0                0.068      0.525     0.079      0.391   \n",
       "                      1                2.290      3.689     2.138      1.953   \n",
       "sink_last_ncites      0               25.413    169.723    23.482    160.319   \n",
       "                      1                7.670     16.783     7.933     14.654   \n",
       "sink_prev_ncites      0              385.753   6578.110   332.230   5930.452   \n",
       "                      1               25.078     98.974    28.786     72.432   \n",
       "auth_last_npapers     0                6.311      7.257     4.917      4.723   \n",
       "                      1                8.625      8.087     6.275      5.232   \n",
       "auth_prev_papers      0               49.145     80.955    43.300     55.122   \n",
       "                      1               84.821    106.007    67.926     66.621   \n",
       "au_age                0               13.199     54.156    15.811     10.592   \n",
       "                      1               18.764     24.810    20.439      9.624   \n",
       "jj_sim                0               27.960    107.418    29.395    112.665   \n",
       "                      1               53.157    196.201    48.006    169.739   \n",
       "match_len             0               43.598     26.965    46.601     24.641   \n",
       "                      1               56.995     22.586    57.283     21.338   \n",
       "match_prop            0                0.686      0.322     0.748      0.276   \n",
       "                      1                0.886      0.136     0.893      0.119   \n",
       "overall_coverage_len  0               54.771     23.583    56.158     22.400   \n",
       "                      1               60.226     23.404    60.749     22.330   \n",
       "overall_coverage_prop 0                0.872      0.172     0.905      0.133   \n",
       "                      1                0.933      0.097     0.944      0.080   \n",
       "\n",
       "gender                                     M             \n",
       "                                        mean        std  \n",
       "                      is_self_cite                       \n",
       "source_year           0             2003.597      1.115  \n",
       "                      1             2003.589      1.116  \n",
       "source_n_mesh         0               12.822      5.487  \n",
       "                      1               13.197      5.682  \n",
       "source_is_eng         0                0.981      0.137  \n",
       "                      1                0.992      0.089  \n",
       "source_is_journal     0                0.986      0.118  \n",
       "                      1                0.987      0.114  \n",
       "source_is_review      0                0.228      0.419  \n",
       "                      1                0.159      0.365  \n",
       "source_is_case_rep    0                0.038      0.190  \n",
       "                      1                0.012      0.110  \n",
       "source_is_let_ed_com  0                0.015      0.120  \n",
       "                      1                0.014      0.118  \n",
       "source_T_novelty      0               21.668     12.977  \n",
       "                      1               20.400     12.267  \n",
       "source_V_novelty      0             3841.652  11202.508  \n",
       "                      1             3328.334   9211.716  \n",
       "source_PT_novelty     0                2.807      5.545  \n",
       "                      1                2.392      4.958  \n",
       "source_PV_novelty     0               21.402   1042.992  \n",
       "                      1               11.951    446.572  \n",
       "source_ncites         0               54.664     85.583  \n",
       "                      1               43.368     44.693  \n",
       "source_n_authors      0                5.150      3.301  \n",
       "                      1                5.218      3.186  \n",
       "sink_year             0             1995.428      7.696  \n",
       "                      1             1998.634      4.777  \n",
       "sink_n_mesh           0               12.720      5.415  \n",
       "                      1               13.264      5.545  \n",
       "sink_n_mesh_ex        0               58.500     23.383  \n",
       "                      1               60.861     24.394  \n",
       "sink_is_eng           0                0.995      0.073  \n",
       "                      1                0.995      0.067  \n",
       "sink_is_journal       0                0.982      0.134  \n",
       "                      1                0.988      0.107  \n",
       "sink_is_review        0                0.147      0.354  \n",
       "                      1                0.085      0.279  \n",
       "sink_is_case_rep      0                0.035      0.184  \n",
       "                      1                0.014      0.116  \n",
       "sink_is_let_ed_com    0                0.019      0.135  \n",
       "                      1                0.012      0.108  \n",
       "sink_T_novelty        0               17.555     11.637  \n",
       "                      1               17.810     11.474  \n",
       "sink_V_novelty        0             2615.206   8625.002  \n",
       "                      1             2542.507   9245.305  \n",
       "sink_PT_novelty       0                1.971      4.600  \n",
       "                      1                1.734      4.280  \n",
       "sink_PV_novelty       0               16.947    828.530  \n",
       "                      1               10.512    390.248  \n",
       "sink_n_authors        0                4.821      5.011  \n",
       "                      1                5.491      4.461  \n",
       "year_span             0                8.169      7.621  \n",
       "                      1                4.955      4.648  \n",
       "journal_same          0                0.071      0.256  \n",
       "                      1                0.164      0.370  \n",
       "mesh_sim              0                0.152      0.107  \n",
       "                      1                0.194      0.128  \n",
       "title_sim             0                0.162      0.155  \n",
       "                      1                0.227      0.181  \n",
       "lang_sim              0                0.978      0.147  \n",
       "                      1                0.991      0.092  \n",
       "affiliation_sim       0                0.278      0.448  \n",
       "                      1                0.841      0.366  \n",
       "pubtype_sim           0                0.502      0.262  \n",
       "                      1                0.644      0.281  \n",
       "cite_sim              0                0.029      0.043  \n",
       "                      1                0.079      0.100  \n",
       "author_sim            0                0.008      0.044  \n",
       "                      1                0.301      0.216  \n",
       "gender_sim            0                0.815      0.215  \n",
       "                      1                0.896      0.133  \n",
       "eth_sim               0                0.679      0.204  \n",
       "                      1                0.917      0.097  \n",
       "n_common_authors      0                0.070      0.366  \n",
       "                      1                2.126      1.490  \n",
       "sink_last_ncites      0               23.656    153.000  \n",
       "                      1                9.091     19.574  \n",
       "sink_prev_ncites      0              298.336   5330.678  \n",
       "                      1               33.947    104.446  \n",
       "auth_last_npapers     0                8.060      8.278  \n",
       "                      1                9.771      9.401  \n",
       "auth_prev_papers      0               89.871    108.144  \n",
       "                      1              121.084    127.151  \n",
       "au_age                0               20.591     11.295  \n",
       "                      1               23.681     10.557  \n",
       "jj_sim                0               28.511     91.100  \n",
       "                      1               47.886    151.407  \n",
       "match_len             0               51.027     22.974  \n",
       "                      1               57.233     21.754  \n",
       "match_prop            0                0.830      0.209  \n",
       "                      1                0.916      0.105  \n",
       "overall_coverage_len  0               56.709     22.484  \n",
       "                      1               59.634     22.628  \n",
       "overall_coverage_prop 0                0.922      0.115  \n",
       "                      1                0.952      0.075  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', 100,\n",
    "                       'display.max_columns', 20,\n",
    "                       'display.precision', 3\n",
    "                      ):\n",
    "    display(df_t_last.T.unstack(level=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41619240, 64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.048513836389131565"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.is_self_cite.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41619267, 64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08648619400240759"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last.is_self_cite.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38019775\n",
       "1     3599492\n",
       "Name: is_self_cite, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last.is_self_cite.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39600131\n",
       "1     2019109\n",
       "Name: is_self_cite, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.is_self_cite.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "-    3.705023\n",
       "F    3.779226\n",
       "M    5.694023\n",
       "Name: is_self_cite, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first[[\"gender\", \"is_self_cite\"]].groupby(\"gender\").is_self_cite.mean()* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "-    6.557432\n",
       "F    7.179615\n",
       "M    9.276608\n",
       "Name: is_self_cite, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last[[\"gender\", \"is_self_cite\"]].groupby(\"gender\").is_self_cite.mean()* 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tables of gender self citation for age and prior papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filtered_data(df):\n",
    "    # Base version 1\n",
    "    df_filtered = df[(df.gender != \"-\")\n",
    "                & (df.source_ncites >= 10)\n",
    "                & (df.source_ncites <=60)]\n",
    "    print df_filtered.shape, df.shape\n",
    "    print \"Filter dataset is %.2f%% of the original data.\" % (df_filtered.shape[0] * 100./df.shape[0])\n",
    "    return df_filtered\n",
    "\n",
    "def aggregate_function(x, median_col=\"auth_prev_papers\", span=1):\n",
    "    median = x[median_col].median()\n",
    "    x = x[(x[median_col] >= (median - span))\n",
    "         & (x[median_col] <= (median + span))]\n",
    "    t = x.groupby(\"gender\")[\"is_self_cite\"].agg([np.mean, len])\n",
    "    t[\"median\"] = median\n",
    "    return t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26148675, 64) (41619240, 64)\n",
      "Filter dataset is 62.83% of the original data.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>0.045931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1075897.0</td>\n",
       "      <td>1850463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044391</td>\n",
       "      <td>0.053354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>797698.0</td>\n",
       "      <td>1502238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053800</td>\n",
       "      <td>0.063481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1037551.0</td>\n",
       "      <td>2141894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068604</td>\n",
       "      <td>0.080691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>881774.0</td>\n",
       "      <td>2023373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080206</td>\n",
       "      <td>0.096689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361556.0</td>\n",
       "      <td>927872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088466</td>\n",
       "      <td>0.109285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292644.0</td>\n",
       "      <td>863976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096430</td>\n",
       "      <td>0.125897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189672.0</td>\n",
       "      <td>682763.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                      \n",
       "gender      -         F         M   -          F          M\n",
       "au_age                                                     \n",
       "(4, 6]    NaN  0.039301  0.045931 NaN  1075897.0  1850463.0\n",
       "(6, 8]    NaN  0.044391  0.053354 NaN   797698.0  1502238.0\n",
       "(8, 12]   NaN  0.053800  0.063481 NaN  1037551.0  2141894.0\n",
       "(12, 18]  NaN  0.068604  0.080691 NaN   881774.0  2023373.0\n",
       "(18, 22]  NaN  0.080206  0.096689 NaN   361556.0   927872.0\n",
       "(22, 27]  NaN  0.088466  0.109285 NaN   292644.0   863976.0\n",
       "(27, 33]  NaN  0.096430  0.125897 NaN   189672.0   682763.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.9 s, sys: 6.72 s, total: 17.6 s\n",
      "Wall time: 17.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_t_first = filtered_data(df_first)\n",
    "display(df_t_first.pivot_table(index=pd.cut(df_t_first.au_age,\n",
    "                                    bins=[4,6,8,12,18,22,27,33]),\n",
    "                      columns=\"gender\", values=\"is_self_cite\", aggfunc=[np.mean, len]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "-         NaN\n",
       "F    3.962280\n",
       "M    5.787288\n",
       "Name: is_self_cite, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first[[\"gender\", \"is_self_cite\"]].groupby(\"gender\").is_self_cite.mean()* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">auth_prev_papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>2926360.0</td>\n",
       "      <td>8.735600</td>\n",
       "      <td>7.918158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>2299936.0</td>\n",
       "      <td>13.070437</td>\n",
       "      <td>12.257853</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>3179445.0</td>\n",
       "      <td>19.852068</td>\n",
       "      <td>18.575819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>2905147.0</td>\n",
       "      <td>33.785176</td>\n",
       "      <td>32.444887</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>1289428.0</td>\n",
       "      <td>50.750995</td>\n",
       "      <td>48.758790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>1156620.0</td>\n",
       "      <td>68.638081</td>\n",
       "      <td>70.166317</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>872435.0</td>\n",
       "      <td>91.798813</td>\n",
       "      <td>93.371988</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>996.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         auth_prev_papers                                                \\\n",
       "                    count       mean        std  min   25%   50%    75%   \n",
       "au_age                                                                    \n",
       "(4, 6]          2926360.0   8.735600   7.918158  1.0   4.0   7.0   11.0   \n",
       "(6, 8]          2299936.0  13.070437  12.257853  1.0   6.0  10.0   16.0   \n",
       "(8, 12]         3179445.0  19.852068  18.575819  1.0   8.0  15.0   25.0   \n",
       "(12, 18]        2905147.0  33.785176  32.444887  1.0  13.0  24.0   43.0   \n",
       "(18, 22]        1289428.0  50.750995  48.758790  1.0  19.0  37.0   66.0   \n",
       "(22, 27]        1156620.0  68.638081  70.166317  1.0  26.0  49.0   88.0   \n",
       "(27, 33]         872435.0  91.798813  93.371988  1.0  32.0  64.0  118.0   \n",
       "\n",
       "                  \n",
       "             max  \n",
       "au_age            \n",
       "(4, 6]     149.0  \n",
       "(6, 8]     169.0  \n",
       "(8, 12]    462.0  \n",
       "(12, 18]   640.0  \n",
       "(18, 22]   672.0  \n",
       "(22, 27]  1167.0  \n",
       "(27, 33]   996.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first.groupby(pd.cut(df_t_first.au_age,\n",
    "                                    bins=[4,6,8,12,18,22,27,33])).auth_prev_papers.describe().to_frame().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/pandas/indexes/category.py:118: RuntimeWarning: Values and categories have different dtypes. Did you mean to use\n",
      "'Categorical.from_codes(codes, categories)'?\n",
      "  data = Categorical(data, categories=categories, ordered=ordered)\n",
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/pandas/indexes/category.py:118: RuntimeWarning: None of the categories were found in values. Did you mean to use\n",
      "'Categorical.from_codes(codes, categories)'?\n",
      "  data = Categorical(data, categories=categories, ordered=ordered)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "         mean                     len                 median            \n",
       "gender      -         F         M   -       F       M      -     F     M\n",
       "au_age                                                                  \n",
       "(0, 1]    NaN  0.017071  0.018470   0  672893  883693    1.0   1.0   1.0\n",
       "(1, 2]    NaN  0.022868  0.024924   0  644699  838927    2.0   2.0   2.0\n",
       "(2, 3]    NaN  0.029854  0.031049   0  408996  553894    3.0   3.0   3.0\n",
       "(3, 4]    NaN  0.039952  0.040922   0  216412  328285    5.0   5.0   5.0\n",
       "(4, 6]    NaN  0.044158  0.044623   0  242514  397329    7.0   7.0   7.0\n",
       "(6, 8]    NaN  0.047341  0.050060   0  129466  222635   10.0  10.0  10.0\n",
       "(8, 12]   NaN  0.058824  0.059643   0  104073  206110   15.0  15.0  15.0\n",
       "(12, 18]  NaN  0.078402  0.074128   0   51874  115112   24.0  24.0  24.0\n",
       "(18, 22]  NaN  0.091831  0.094580   0   13808   33654   37.0  37.0  37.0\n",
       "(22, 27]  NaN  0.102627  0.099051   0    7347   22645   49.0  49.0  49.0\n",
       "(27, 33]  NaN  0.114641  0.118494   0    3777   13891   64.0  64.0  64.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first.groupby(pd.cut(df_t_first.au_age,\n",
    "                                    bins=[0,1,2,3,4,6,8,12,18,22,27,33])).apply(aggregate_function).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>19880</td>\n",
       "      <td>34822</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 13]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068207</td>\n",
       "      <td>0.069794</td>\n",
       "      <td>0</td>\n",
       "      <td>15541</td>\n",
       "      <td>30318</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(13, 14]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080898</td>\n",
       "      <td>0.071207</td>\n",
       "      <td>0</td>\n",
       "      <td>10025</td>\n",
       "      <td>24478</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(14, 15]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081695</td>\n",
       "      <td>0.076336</td>\n",
       "      <td>0</td>\n",
       "      <td>8801</td>\n",
       "      <td>22008</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(15, 16]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.078823</td>\n",
       "      <td>0.078074</td>\n",
       "      <td>0</td>\n",
       "      <td>7853</td>\n",
       "      <td>17112</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(16, 17]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082689</td>\n",
       "      <td>0.083403</td>\n",
       "      <td>0</td>\n",
       "      <td>6470</td>\n",
       "      <td>14436</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(17, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088354</td>\n",
       "      <td>0.084625</td>\n",
       "      <td>0</td>\n",
       "      <td>4199</td>\n",
       "      <td>12585</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 19]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099937</td>\n",
       "      <td>0.078418</td>\n",
       "      <td>0</td>\n",
       "      <td>4763</td>\n",
       "      <td>10648</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(19, 20]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.091979</td>\n",
       "      <td>0.096602</td>\n",
       "      <td>0</td>\n",
       "      <td>3740</td>\n",
       "      <td>8799</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 21]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.101410</td>\n",
       "      <td>0.096034</td>\n",
       "      <td>0</td>\n",
       "      <td>3688</td>\n",
       "      <td>8195</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(21, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.123810</td>\n",
       "      <td>0.093370</td>\n",
       "      <td>0</td>\n",
       "      <td>3150</td>\n",
       "      <td>6983</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 23]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089327</td>\n",
       "      <td>0.094742</td>\n",
       "      <td>0</td>\n",
       "      <td>2183</td>\n",
       "      <td>5668</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(23, 24]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090399</td>\n",
       "      <td>0.101774</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>5807</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                 median            \n",
       "gender      -         F         M   -       F       M      -     F     M\n",
       "au_age                                                                  \n",
       "(0, 1]    NaN  0.017071  0.018470   0  672893  883693    1.0   1.0   1.0\n",
       "(1, 2]    NaN  0.022868  0.024924   0  644699  838927    2.0   2.0   2.0\n",
       "(2, 3]    NaN  0.029854  0.031049   0  408996  553894    3.0   3.0   3.0\n",
       "(3, 4]    NaN  0.039952  0.040922   0  216412  328285    5.0   5.0   5.0\n",
       "(4, 5]    NaN  0.041940  0.041381   0  158153  244486    6.0   6.0   6.0\n",
       "(5, 6]    NaN  0.043040  0.043589   0  113965  182980    7.0   7.0   7.0\n",
       "(6, 7]    NaN  0.047336  0.049195   0   75376  131395    9.0   9.0   9.0\n",
       "(7, 8]    NaN  0.045909  0.052484   0   54804   98240   11.0  11.0  11.0\n",
       "(8, 9]    NaN  0.052262  0.054095   0   42383   81264   12.0  12.0  12.0\n",
       "(9, 10]   NaN  0.056391  0.055294   0   28267   58216   14.0  14.0  14.0\n",
       "(10, 11]  NaN  0.058894  0.058481   0   23500   44442   16.0  16.0  16.0\n",
       "(11, 12]  NaN  0.068008  0.064212   0   19880   34822   18.0  18.0  18.0\n",
       "(12, 13]  NaN  0.068207  0.069794   0   15541   30318   20.0  20.0  20.0\n",
       "(13, 14]  NaN  0.080898  0.071207   0   10025   24478   22.0  22.0  22.0\n",
       "(14, 15]  NaN  0.081695  0.076336   0    8801   22008   25.0  25.0  25.0\n",
       "(15, 16]  NaN  0.078823  0.078074   0    7853   17112   26.0  26.0  26.0\n",
       "(16, 17]  NaN  0.082689  0.083403   0    6470   14436   28.0  28.0  28.0\n",
       "(17, 18]  NaN  0.088354  0.084625   0    4199   12585   30.0  30.0  30.0\n",
       "(18, 19]  NaN  0.099937  0.078418   0    4763   10648   33.0  33.0  33.0\n",
       "(19, 20]  NaN  0.091979  0.096602   0    3740    8799   36.0  36.0  36.0\n",
       "(20, 21]  NaN  0.101410  0.096034   0    3688    8195   38.0  38.0  38.0\n",
       "(21, 22]  NaN  0.123810  0.093370   0    3150    6983   41.0  41.0  41.0\n",
       "(22, 23]  NaN  0.089327  0.094742   0    2183    5668   44.0  44.0  44.0\n",
       "(23, 24]  NaN  0.090399  0.101774   0    1604    5807   46.0  46.0  46.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first.groupby(pd.cut(df_t_first.au_age,\n",
    "                                    bins=range(25))).apply(aggregate_function).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002630</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>1508335.0</td>\n",
       "      <td>1698186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013839</td>\n",
       "      <td>0.014609</td>\n",
       "      <td>1102301.0</td>\n",
       "      <td>1358766.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.022141</td>\n",
       "      <td>0.022906</td>\n",
       "      <td>860697.0</td>\n",
       "      <td>1116372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.028143</td>\n",
       "      <td>0.029529</td>\n",
       "      <td>681661.0</td>\n",
       "      <td>936528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.032647</td>\n",
       "      <td>0.033089</td>\n",
       "      <td>551164.0</td>\n",
       "      <td>793562.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean                  len           \n",
       "gender                   F         M          F          M\n",
       "auth_prev_papers                                          \n",
       "0                 0.002630  0.002950  1508335.0  1698186.0\n",
       "1                 0.013839  0.014609  1102301.0  1358766.0\n",
       "2                 0.022141  0.022906   860697.0  1116372.0\n",
       "3                 0.028143  0.029529   681661.0   936528.0\n",
       "4                 0.032647  0.033089   551164.0   793562.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t = df_t_first.groupby([\"auth_prev_papers\", \"gender\"]).is_self_cite.agg([np.mean, len]).unstack()\n",
    "df_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
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       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.132075</td>\n",
       "      <td>0.172092</td>\n",
       "      <td>159.0</td>\n",
       "      <td>3533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.267647</td>\n",
       "      <td>0.226891</td>\n",
       "      <td>340.0</td>\n",
       "      <td>3927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.168317</td>\n",
       "      <td>0.163998</td>\n",
       "      <td>101.0</td>\n",
       "      <td>4122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.142045</td>\n",
       "      <td>0.147041</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.151181</td>\n",
       "      <td>0.187476</td>\n",
       "      <td>635.0</td>\n",
       "      <td>2587.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean              len        \n",
       "gender                   F         M      F       M\n",
       "auth_prev_papers                                   \n",
       "201               0.132075  0.172092  159.0  3533.0\n",
       "202               0.267647  0.226891  340.0  3927.0\n",
       "203               0.168317  0.163998  101.0  4122.0\n",
       "204               0.142045  0.147041  176.0  3278.0\n",
       "205               0.151181  0.187476  635.0  2587.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t[df_t.index > 200].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
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       "    <tr>\n",
       "      <th>match_prop</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.004069</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53329.0</td>\n",
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       "      <td>0.006989</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>316214.0</td>\n",
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       "    <tr>\n",
       "      <th>(0.3, 0.4]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010732</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>487158.0</td>\n",
       "      <td>700750.0</td>\n",
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       "    <tr>\n",
       "      <th>(0.4, 0.5]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016126</td>\n",
       "      <td>0.017434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>678922.0</td>\n",
       "      <td>1028072.0</td>\n",
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       "    <tr>\n",
       "      <th>(0.5, 0.6]</th>\n",
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       "      <td>0.022847</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>856422.0</td>\n",
       "      <td>1322024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.6, 0.7]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032174</td>\n",
       "      <td>0.034580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1085855.0</td>\n",
       "      <td>1796901.0</td>\n",
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       "    <tr>\n",
       "      <th>(0.7, 0.8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.045395</td>\n",
       "      <td>0.049450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1306052.0</td>\n",
       "      <td>2342643.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.8, 0.9]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.063754</td>\n",
       "      <td>0.072298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1342261.0</td>\n",
       "      <td>2872999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.9, 1]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102035</td>\n",
       "      <td>0.123153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1388735.0</td>\n",
       "      <td>4082615.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean                     len                      \n",
       "gender        -         F         M   -          F          M\n",
       "match_prop                                                   \n",
       "(0, 0.1]    NaN  0.004069  0.005368 NaN    53329.0    68362.0\n",
       "(0.1, 0.2]  NaN  0.004880  0.006203 NaN   170889.0   240850.0\n",
       "(0.2, 0.3]  NaN  0.006989  0.008101 NaN   316214.0   441031.0\n",
       "(0.3, 0.4]  NaN  0.010732  0.012003 NaN   487158.0   700750.0\n",
       "(0.4, 0.5]  NaN  0.016126  0.017434 NaN   678922.0  1028072.0\n",
       "(0.5, 0.6]  NaN  0.022847  0.024576 NaN   856422.0  1322024.0\n",
       "(0.6, 0.7]  NaN  0.032174  0.034580 NaN  1085855.0  1796901.0\n",
       "(0.7, 0.8]  NaN  0.045395  0.049450 NaN  1306052.0  2342643.0\n",
       "(0.8, 0.9]  NaN  0.063754  0.072298 NaN  1342261.0  2872999.0\n",
       "(0.9, 1]    NaN  0.102035  0.123153 NaN  1388735.0  4082615.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_t_first.pivot_table(index=pd.cut(df_t_first.match_prop,\n",
    "                                    bins=np.arange(0,1.1, 0.1)),\n",
    "                      columns=\"gender\", values=\"is_self_cite\", aggfunc=[np.mean, len]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.560099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.651669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_prop\n",
       "gender            \n",
       "F         0.560099\n",
       "M         0.651669"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first[[\"source_id\", \"gender\", \"match_prop\"]].groupby(\"source_id\").first().groupby(\"gender\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27526723, 64) (41619267, 64)\n",
      "Filter dataset is 66.14% of the original data.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043577</td>\n",
       "      <td>0.054559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245198.0</td>\n",
       "      <td>670691.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054254</td>\n",
       "      <td>0.068265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288087.0</td>\n",
       "      <td>913149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068714</td>\n",
       "      <td>0.080418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>713097.0</td>\n",
       "      <td>2403210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084920</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1148708.0</td>\n",
       "      <td>4332862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.097655</td>\n",
       "      <td>0.105987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>666520.0</td>\n",
       "      <td>2911318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.109483</td>\n",
       "      <td>0.113665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>646299.0</td>\n",
       "      <td>3328608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119224</td>\n",
       "      <td>0.123024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>474820.0</td>\n",
       "      <td>3166102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                      \n",
       "gender      -         F         M   -          F          M\n",
       "au_age                                                     \n",
       "(4, 6]    NaN  0.043577  0.054559 NaN   245198.0   670691.0\n",
       "(6, 8]    NaN  0.054254  0.068265 NaN   288087.0   913149.0\n",
       "(8, 12]   NaN  0.068714  0.080418 NaN   713097.0  2403210.0\n",
       "(12, 18]  NaN  0.084920  0.095047 NaN  1148708.0  4332862.0\n",
       "(18, 22]  NaN  0.097655  0.105987 NaN   666520.0  2911318.0\n",
       "(22, 27]  NaN  0.109483  0.113665 NaN   646299.0  3328608.0\n",
       "(27, 33]  NaN  0.119224  0.123024 NaN   474820.0  3166102.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.2 s, sys: 12.9 s, total: 29.1 s\n",
      "Wall time: 29.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_t_last = filtered_data(df_last)\n",
    "display(df_t_last.pivot_table(index=pd.cut(df_t_last.au_age,\n",
    "                                    bins=[4,6,8,12,18,22,27,33]),\n",
    "                      columns=\"gender\", values=\"is_self_cite\", aggfunc=[np.mean, len]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "-         NaN\n",
       "F    7.834364\n",
       "M    9.931546\n",
       "Name: is_self_cite, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last[[\"gender\", \"is_self_cite\"]].groupby(\"gender\").is_self_cite.mean()* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">auth_prev_papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>915889.0</td>\n",
       "      <td>12.139577</td>\n",
       "      <td>10.938851</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>1201236.0</td>\n",
       "      <td>19.737560</td>\n",
       "      <td>19.456762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>3116307.0</td>\n",
       "      <td>31.511303</td>\n",
       "      <td>30.970875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>5481570.0</td>\n",
       "      <td>52.363269</td>\n",
       "      <td>45.608768</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>3577838.0</td>\n",
       "      <td>78.718758</td>\n",
       "      <td>64.068521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>3974907.0</td>\n",
       "      <td>106.866907</td>\n",
       "      <td>95.144210</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>3640922.0</td>\n",
       "      <td>136.860927</td>\n",
       "      <td>112.021429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1359.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         auth_prev_papers                                                   \\\n",
       "                    count        mean         std  min   25%    50%    75%   \n",
       "au_age                                                                       \n",
       "(4, 6]           915889.0   12.139577   10.938851  1.0   5.0    9.0   16.0   \n",
       "(6, 8]          1201236.0   19.737560   19.456762  1.0   8.0   15.0   25.0   \n",
       "(8, 12]         3116307.0   31.511303   30.970875  1.0  14.0   23.0   39.0   \n",
       "(12, 18]        5481570.0   52.363269   45.608768  1.0  24.0   40.0   66.0   \n",
       "(18, 22]        3577838.0   78.718758   64.068521  1.0  36.0   61.0  102.0   \n",
       "(22, 27]        3974907.0  106.866907   95.144210  1.0  48.0   82.0  134.0   \n",
       "(27, 33]        3640922.0  136.860927  112.021429  1.0  63.0  107.0  176.0   \n",
       "\n",
       "                  \n",
       "             max  \n",
       "au_age            \n",
       "(4, 6]     103.0  \n",
       "(6, 8]     371.0  \n",
       "(8, 12]    545.0  \n",
       "(12, 18]   640.0  \n",
       "(18, 22]   672.0  \n",
       "(22, 27]  1167.0  \n",
       "(27, 33]  1359.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last.groupby(pd.cut(df_t_last.au_age,\n",
    "                                    bins=[4,6,8,12,18,22,27,33])).auth_prev_papers.describe().to_frame().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "      <th colspan=\"3\" halign=\"left\">median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012663</td>\n",
       "      <td>0.014134</td>\n",
       "      <td>0</td>\n",
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       "      <th>(1, 2]</th>\n",
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       "      <td>0</td>\n",
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       "      <td>142989</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>(2, 3]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031040</td>\n",
       "      <td>0.033840</td>\n",
       "      <td>0</td>\n",
       "      <td>38950</td>\n",
       "      <td>78191</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>(3, 4]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044148</td>\n",
       "      <td>0.046076</td>\n",
       "      <td>0</td>\n",
       "      <td>24463</td>\n",
       "      <td>58990</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049488</td>\n",
       "      <td>0.054344</td>\n",
       "      <td>0</td>\n",
       "      <td>41343</td>\n",
       "      <td>102200</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065949</td>\n",
       "      <td>0.070936</td>\n",
       "      <td>0</td>\n",
       "      <td>30827</td>\n",
       "      <td>88883</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.074164</td>\n",
       "      <td>0.081661</td>\n",
       "      <td>0</td>\n",
       "      <td>46613</td>\n",
       "      <td>158166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093752</td>\n",
       "      <td>0.096448</td>\n",
       "      <td>0</td>\n",
       "      <td>44479</td>\n",
       "      <td>170848</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103228</td>\n",
       "      <td>0.106392</td>\n",
       "      <td>0</td>\n",
       "      <td>18338</td>\n",
       "      <td>75316</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.118308</td>\n",
       "      <td>0.113358</td>\n",
       "      <td>0</td>\n",
       "      <td>11301</td>\n",
       "      <td>60737</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
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       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.159484</td>\n",
       "      <td>0.137148</td>\n",
       "      <td>0</td>\n",
       "      <td>5499</td>\n",
       "      <td>48065</td>\n",
       "      <td>107.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                median              \n",
       "gender      -         F         M   -      F       M      -      F      M\n",
       "au_age                                                                   \n",
       "(0, 1]    NaN  0.012663  0.014134   0  92394  154800    1.0    1.0    1.0\n",
       "(1, 2]    NaN  0.018248  0.019393   0  79132  142989    2.0    2.0    2.0\n",
       "(2, 3]    NaN  0.031040  0.033840   0  38950   78191    4.0    4.0    4.0\n",
       "(3, 4]    NaN  0.044148  0.046076   0  24463   58990    6.0    6.0    6.0\n",
       "(4, 6]    NaN  0.049488  0.054344   0  41343  102200    9.0    9.0    9.0\n",
       "(6, 8]    NaN  0.065949  0.070936   0  30827   88883   15.0   15.0   15.0\n",
       "(8, 12]   NaN  0.074164  0.081661   0  46613  158166   23.0   23.0   23.0\n",
       "(12, 18]  NaN  0.093752  0.096448   0  44479  170848   40.0   40.0   40.0\n",
       "(18, 22]  NaN  0.103228  0.106392   0  18338   75316   61.0   61.0   61.0\n",
       "(22, 27]  NaN  0.118308  0.113358   0  11301   60737   82.0   82.0   82.0\n",
       "(27, 33]  NaN  0.159484  0.137148   0   5499   48065  107.0  107.0  107.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last.groupby(pd.cut(df_t_last.au_age,\n",
    "                                    bins=[0,1,2,3,4,6,8,12,18,22,27,33])).apply(aggregate_function).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "      <th colspan=\"3\" halign=\"left\">median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
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       "      <th>M</th>\n",
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       "      <th>au_age</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>38950</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>(3, 4]</th>\n",
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       "      <td>0.044148</td>\n",
       "      <td>0.046076</td>\n",
       "      <td>0</td>\n",
       "      <td>24463</td>\n",
       "      <td>58990</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
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       "      <th>(9, 10]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065065</td>\n",
       "      <td>0.083231</td>\n",
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       "      <td>39168</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>(10, 11]</th>\n",
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       "      <td>0.080426</td>\n",
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       "      <td>0.086575</td>\n",
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       "      <td>31.0</td>\n",
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       "      <td>0.086962</td>\n",
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       "      <td>7992</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
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       "    <tr>\n",
       "      <th>(14, 15]</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.090820</td>\n",
       "      <td>0</td>\n",
       "      <td>9045</td>\n",
       "      <td>30654</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>(15, 16]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.097702</td>\n",
       "      <td>0.093341</td>\n",
       "      <td>0</td>\n",
       "      <td>7093</td>\n",
       "      <td>27705</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
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       "    <tr>\n",
       "      <th>(16, 17]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102696</td>\n",
       "      <td>0.104670</td>\n",
       "      <td>0</td>\n",
       "      <td>6602</td>\n",
       "      <td>24391</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>(18, 19]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.113051</td>\n",
       "      <td>0.105487</td>\n",
       "      <td>0</td>\n",
       "      <td>4352</td>\n",
       "      <td>22818</td>\n",
       "      <td>54.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>54.0</td>\n",
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       "    <tr>\n",
       "      <th>(19, 20]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.113076</td>\n",
       "      <td>0.104730</td>\n",
       "      <td>0</td>\n",
       "      <td>4864</td>\n",
       "      <td>18753</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
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       "    <tr>\n",
       "      <th>(20, 21]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102139</td>\n",
       "      <td>0.112132</td>\n",
       "      <td>0</td>\n",
       "      <td>3319</td>\n",
       "      <td>17301</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(21, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102107</td>\n",
       "      <td>0.103948</td>\n",
       "      <td>0</td>\n",
       "      <td>3369</td>\n",
       "      <td>16643</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 23]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.127448</td>\n",
       "      <td>0.128132</td>\n",
       "      <td>0</td>\n",
       "      <td>3217</td>\n",
       "      <td>15086</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(23, 24]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130902</td>\n",
       "      <td>0.117569</td>\n",
       "      <td>0</td>\n",
       "      <td>2605</td>\n",
       "      <td>12010</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                median            \n",
       "gender      -         F         M   -      F       M      -     F     M\n",
       "au_age                                                                 \n",
       "(0, 1]    NaN  0.012663  0.014134   0  92394  154800    1.0   1.0   1.0\n",
       "(1, 2]    NaN  0.018248  0.019393   0  79132  142989    2.0   2.0   2.0\n",
       "(2, 3]    NaN  0.031040  0.033840   0  38950   78191    4.0   4.0   4.0\n",
       "(3, 4]    NaN  0.044148  0.046076   0  24463   58990    6.0   6.0   6.0\n",
       "(4, 5]    NaN  0.048195  0.050784   0  22637   53383    8.0   8.0   8.0\n",
       "(5, 6]    NaN  0.050926  0.059258   0  19224   47926   10.0  10.0  10.0\n",
       "(6, 7]    NaN  0.059010  0.063816   0  16099   45866   13.0  13.0  13.0\n",
       "(7, 8]    NaN  0.065459  0.072830   0  14956   44844   16.0  16.0  16.0\n",
       "(8, 9]    NaN  0.075830  0.079277   0  14783   48173   19.0  19.0  19.0\n",
       "(9, 10]   NaN  0.065065  0.083231   0  12449   39168   22.0  22.0  22.0\n",
       "(10, 11]  NaN  0.083855  0.080426   0  11663   39129   25.0  25.0  25.0\n",
       "(11, 12]  NaN  0.074760  0.093647   0   9992   36584   28.0  28.0  28.0\n",
       "(12, 13]  NaN  0.086575  0.091422   0   8663   34948   31.0  31.0  31.0\n",
       "(13, 14]  NaN  0.086962  0.096403   0   7992   32385   34.0  34.0  34.0\n",
       "(14, 15]  NaN  0.099392  0.090820   0   9045   30654   38.0  38.0  38.0\n",
       "(15, 16]  NaN  0.097702  0.093341   0   7093   27705   42.0  42.0  42.0\n",
       "(16, 17]  NaN  0.102696  0.104670   0   6602   24391   46.0  46.0  46.0\n",
       "(17, 18]  NaN  0.099453  0.100758   0   5480   24266   51.0  51.0  51.0\n",
       "(18, 19]  NaN  0.113051  0.105487   0   4352   22818   54.0  54.0  54.0\n",
       "(19, 20]  NaN  0.113076  0.104730   0   4864   18753   59.0  59.0  59.0\n",
       "(20, 21]  NaN  0.102139  0.112132   0   3319   17301   64.0  64.0  64.0\n",
       "(21, 22]  NaN  0.102107  0.103948   0   3369   16643   68.0  68.0  68.0\n",
       "(22, 23]  NaN  0.127448  0.128132   0   3217   15086   72.0  72.0  72.0\n",
       "(23, 24]  NaN  0.130902  0.117569   0   2605   12010   78.0  78.0  78.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last.groupby(pd.cut(df_t_last.au_age,\n",
    "                                    bins=range(25))).apply(aggregate_function).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">len</th>\n",
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       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
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       "      <th>M</th>\n",
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       "    <tr>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>520850.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009851</td>\n",
       "      <td>0.010922</td>\n",
       "      <td>174400.0</td>\n",
       "      <td>293266.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.017412</td>\n",
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       "      <td>124628.0</td>\n",
       "      <td>231498.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021854</td>\n",
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       "      <td>211282.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027933</td>\n",
       "      <td>0.028851</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean                 len          \n",
       "gender                   F         M         F         M\n",
       "auth_prev_papers                                        \n",
       "0                 0.001768  0.002221  354618.0  520850.0\n",
       "1                 0.009851  0.010922  174400.0  293266.0\n",
       "2                 0.017412  0.018043  124628.0  231498.0\n",
       "3                 0.021854  0.022842  104605.0  211282.0\n",
       "4                 0.027933  0.028851   96515.0  203803.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t = df_t_last.groupby([\"auth_prev_papers\", \"gender\"]).is_self_cite.agg([np.mean, len]).unstack()\n",
    "df_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">len</th>\n",
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       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.169872</td>\n",
       "      <td>0.139676</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>24972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.208205</td>\n",
       "      <td>0.133783</td>\n",
       "      <td>975.0</td>\n",
       "      <td>23740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.120042</td>\n",
       "      <td>0.139193</td>\n",
       "      <td>958.0</td>\n",
       "      <td>21560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.169231</td>\n",
       "      <td>0.142836</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>19883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.141209</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>23582.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean               len         \n",
       "gender                   F         M       F        M\n",
       "auth_prev_papers                                     \n",
       "201               0.169872  0.139676  1248.0  24972.0\n",
       "202               0.208205  0.133783   975.0  23740.0\n",
       "203               0.120042  0.139193   958.0  21560.0\n",
       "204               0.169231  0.142836  1885.0  19883.0\n",
       "205               0.166667  0.141209  1686.0  23582.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t[df_t.index > 200].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_prop</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 0.1]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.007531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9925.0</td>\n",
       "      <td>17794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.1, 0.2]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005513</td>\n",
       "      <td>0.008563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28115.0</td>\n",
       "      <td>60377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.2, 0.3]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005923</td>\n",
       "      <td>0.009147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59427.0</td>\n",
       "      <td>130209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.3, 0.4]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008838</td>\n",
       "      <td>0.012541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97421.0</td>\n",
       "      <td>229974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.4, 0.5]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014203</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159619.0</td>\n",
       "      <td>400792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.5, 0.6]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023015</td>\n",
       "      <td>0.027089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242711.0</td>\n",
       "      <td>679087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.6, 0.7]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037999</td>\n",
       "      <td>0.039974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>409853.0</td>\n",
       "      <td>1277263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.7, 0.8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057539</td>\n",
       "      <td>0.059966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>731955.0</td>\n",
       "      <td>2607622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.8, 0.9]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083113</td>\n",
       "      <td>0.087707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1201356.0</td>\n",
       "      <td>5074137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.9, 1]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.125788</td>\n",
       "      <td>0.136504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930062.0</td>\n",
       "      <td>10960109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean                     len                       \n",
       "gender        -         F         M   -          F           M\n",
       "match_prop                                                    \n",
       "(0, 0.1]    NaN  0.004332  0.007531 NaN     9925.0     17794.0\n",
       "(0.1, 0.2]  NaN  0.005513  0.008563 NaN    28115.0     60377.0\n",
       "(0.2, 0.3]  NaN  0.005923  0.009147 NaN    59427.0    130209.0\n",
       "(0.3, 0.4]  NaN  0.008838  0.012541 NaN    97421.0    229974.0\n",
       "(0.4, 0.5]  NaN  0.014203  0.018079 NaN   159619.0    400792.0\n",
       "(0.5, 0.6]  NaN  0.023015  0.027089 NaN   242711.0    679087.0\n",
       "(0.6, 0.7]  NaN  0.037999  0.039974 NaN   409853.0   1277263.0\n",
       "(0.7, 0.8]  NaN  0.057539  0.059966 NaN   731955.0   2607622.0\n",
       "(0.8, 0.9]  NaN  0.083113  0.087707 NaN  1201356.0   5074137.0\n",
       "(0.9, 1]    NaN  0.125788  0.136504 NaN  1930062.0  10960109.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_t_last.pivot_table(index=pd.cut(df_t_last.match_prop,\n",
    "                                    bins=np.arange(0,1.1, 0.1)),\n",
    "                      columns=\"gender\", values=\"is_self_cite\", aggfunc=[np.mean, len]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.732513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.822449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_prop\n",
       "gender            \n",
       "F         0.732513\n",
       "M         0.822449"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last[[\"source_id\", \"gender\", \"match_prop\"]].groupby(\"source_id\").first().groupby(\"gender\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot author age and self-citation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_lower_quantile(x):\n",
    "    return pd.Series.quantile(x, q=0.05)\n",
    "    \n",
    "def get_upper_quantile(x):\n",
    "    return pd.Series.quantile(x, q=0.95)\n",
    "\n",
    "\n",
    "def mean_confidence_interval(data, confidence=0.95):\n",
    "    from scipy import stats\n",
    "    a = data*1.0\n",
    "    n = len(a)\n",
    "    m, se = a.mean(), stats.sem(a)\n",
    "    h = se * stats.t._ppf((1+confidence)/2., n-1)\n",
    "    return pd.Series([m, m-h, m+h], index=[\"mean\", \"ci_l\", \"ci_u\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean   -0.141473\n",
       "ci_l   -0.335985\n",
       "ci_u    0.053040\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_confidence_interval(np.random.randn(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">median</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "      <th colspan=\"2\" halign=\"left\">get_lower_quantile</th>\n",
       "      <th colspan=\"2\" halign=\"left\">get_upper_quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>73.0</td>\n",
       "      <td>36.0</td>\n",
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       "      <td>0.504951</td>\n",
       "      <td>0.549296</td>\n",
       "      <td>0.504951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.592105</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.592105</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0.592105</td>\n",
       "      <td>0.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.383721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.380282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.677419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.660870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.698276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.702128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.702128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.702128</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.774194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.578512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.578512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.743902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.918919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.983871</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.884615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    median           count        get_lower_quantile  \\\n",
       "gender                   F         M     F      M                  F   \n",
       "auth_prev_papers                                                       \n",
       "0                 0.000000  0.000000  58.0   62.0           0.000000   \n",
       "1                 0.549296  0.504951  73.0   36.0           0.296000   \n",
       "2                 0.592105  0.314286  35.0   30.0           0.592105   \n",
       "3                      NaN  0.383721   NaN  109.0                NaN   \n",
       "4                 0.677419       NaN  10.0    NaN           0.677419   \n",
       "5                      NaN  0.660870   NaN   69.0                NaN   \n",
       "6                 0.702128       NaN  17.0    NaN           0.702128   \n",
       "7                 0.774194       NaN  39.0    NaN           0.774194   \n",
       "11                     NaN  0.578512   NaN   62.0                NaN   \n",
       "12                     NaN  0.605263   NaN   47.0                NaN   \n",
       "14                0.918919       NaN  75.0    NaN           0.918919   \n",
       "16                0.884615       NaN  40.0    NaN           0.884615   \n",
       "20                     NaN  1.000000   NaN   25.0                NaN   \n",
       "26                     NaN  1.000000   NaN   43.0                NaN   \n",
       "75                     NaN  1.000000   NaN   20.0                NaN   \n",
       "126                    NaN  1.000000   NaN   50.0                NaN   \n",
       "129                    NaN  0.962963   NaN   18.0                NaN   \n",
       "146                    NaN  1.000000   NaN   18.0                NaN   \n",
       "156                    NaN  1.000000   NaN   32.0                NaN   \n",
       "178               1.000000       NaN  32.0    NaN           1.000000   \n",
       "\n",
       "                           get_upper_quantile            \n",
       "gender                   M                  F         M  \n",
       "auth_prev_papers                                         \n",
       "0                 0.000000           0.000000  0.000000  \n",
       "1                 0.504951           0.549296  0.504951  \n",
       "2                 0.314286           0.592105  0.314286  \n",
       "3                 0.380282                NaN  0.636364  \n",
       "4                      NaN           0.677419       NaN  \n",
       "5                 0.625000                NaN  0.698276  \n",
       "6                      NaN           0.702128       NaN  \n",
       "7                      NaN           0.774194       NaN  \n",
       "11                0.578512                NaN  0.743902  \n",
       "12                0.605263                NaN  0.605263  \n",
       "14                     NaN           0.983871       NaN  \n",
       "16                     NaN           0.884615       NaN  \n",
       "20                0.647059                NaN  1.000000  \n",
       "26                1.000000                NaN  1.000000  \n",
       "75                1.000000                NaN  1.000000  \n",
       "126               1.000000                NaN  1.000000  \n",
       "129               0.962963                NaN  0.962963  \n",
       "146               1.000000                NaN  1.000000  \n",
       "156               1.000000                NaN  1.000000  \n",
       "178                    NaN           1.000000       NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first.head(1000).groupby([\"auth_prev_papers\", \"gender\"]\n",
    "                             ).match_prop.agg([\n",
    "        pd.Series.median, pd.Series.count,\n",
    "        get_lower_quantile, get_upper_quantile\n",
    "    ]).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "markersize=3\n",
    "linestyle=\"-\"\n",
    "linewidth=0.5\n",
    "gender_params={\n",
    "    \"F\": dict(\n",
    "        label=\"Female\",\n",
    "        color=\"crimson\",\n",
    "        marker=\"o\",\n",
    "    ),\n",
    "    \"M\": dict(\n",
    "        label=\"Male\",\n",
    "        color=\"dodgerblue\",\n",
    "        marker=\"s\",\n",
    "    ),\n",
    "    \n",
    "}\n",
    "genders = [\"F\", \"M\"]\n",
    "\n",
    "plot_params=dict(\n",
    "    markersize=markersize,\n",
    "    capsize=0.5, elinewidth=0.01,\n",
    "    alpha=0.6,\n",
    "    linestyle=\"none\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TXwrtwPIz6cKdCxHyHHjwIATkBtRNV1EORKcEOcOHD2/yc3tdi1OnTuWpp57KlIG4++67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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee2dd8f950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,1, figsize=(8,4))\n",
    "for i, (df_t, title, axi) in enumerate(zip(\n",
    "        [df_t_first, df_t_last],\n",
    "        [\"First\", \"Last\"],\n",
    "        ax\n",
    "    )):\n",
    "    \n",
    "    df_t = df_t[~df_t.match_prop.isnull()].groupby([\"auth_prev_papers\", \"gender\"]).match_prop.agg([\n",
    "        pd.Series.median, pd.Series.count,\n",
    "        get_lower_quantile, get_upper_quantile\n",
    "    ]).unstack()\n",
    "    for j, gender in enumerate(genders):\n",
    "        axi.fill_between(df_t.index,\n",
    "                 df_t[\"get_lower_quantile\"][gender],\n",
    "                 df_t[\"get_upper_quantile\"][gender],\n",
    "                 color=gender_params[gender][\"color\"],\n",
    "                 alpha=0.1)\n",
    "\n",
    "        axi.plot(df_t.index, df_t[\"median\"][gender],\n",
    "                label=gender_params[gender][\"label\"],\n",
    "                color=gender_params[gender][\"color\"],\n",
    "                linewidth=linewidth,\n",
    "                linestyle=linestyle)\n",
    "    axi.set_ylim([0.3, 1.0])\n",
    "    axi.set_xlim([1, 100])\n",
    "    axi.set_title(title)\n",
    "    axi.set_xlabel(\"Author's prior papers\")\n",
    "    axi.set_ylabel(\"Median\\n($\\pm [0.25, 0.75]$ quantiles)\\nauthor expertise\")\n",
    "    axi.legend(title=\"Gender (Author position)\", loc=\"lower right\")\n",
    "sns.despine(offset=10)\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"Review_Figures/Author_papers_expertise_gender.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ci_l</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ci_u</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.410855</td>\n",
       "      <td>0.399922</td>\n",
       "      <td>0.410469</td>\n",
       "      <td>0.399567</td>\n",
       "      <td>0.411241</td>\n",
       "      <td>0.400277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.534227</td>\n",
       "      <td>0.521689</td>\n",
       "      <td>0.533804</td>\n",
       "      <td>0.521308</td>\n",
       "      <td>0.534650</td>\n",
       "      <td>0.522071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.597923</td>\n",
       "      <td>0.588261</td>\n",
       "      <td>0.597464</td>\n",
       "      <td>0.587861</td>\n",
       "      <td>0.598382</td>\n",
       "      <td>0.588662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.642608</td>\n",
       "      <td>0.626045</td>\n",
       "      <td>0.642119</td>\n",
       "      <td>0.625621</td>\n",
       "      <td>0.643098</td>\n",
       "      <td>0.626469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean                ci_l                ci_u          \n",
       "gender                   F         M         F         M         F         M\n",
       "auth_prev_papers                                                            \n",
       "0                 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
       "1                 0.410855  0.399922  0.410469  0.399567  0.411241  0.400277\n",
       "2                 0.534227  0.521689  0.533804  0.521308  0.534650  0.522071\n",
       "3                 0.597923  0.588261  0.597464  0.587861  0.598382  0.588662\n",
       "4                 0.642608  0.626045  0.642119  0.625621  0.643098  0.626469"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ci_l</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ci_u</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.408693</td>\n",
       "      <td>0.386917</td>\n",
       "      <td>0.407659</td>\n",
       "      <td>0.386120</td>\n",
       "      <td>0.409726</td>\n",
       "      <td>0.387713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.517016</td>\n",
       "      <td>0.492043</td>\n",
       "      <td>0.515838</td>\n",
       "      <td>0.491161</td>\n",
       "      <td>0.518195</td>\n",
       "      <td>0.492926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.567342</td>\n",
       "      <td>0.552091</td>\n",
       "      <td>0.566088</td>\n",
       "      <td>0.551190</td>\n",
       "      <td>0.568596</td>\n",
       "      <td>0.552992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.621439</td>\n",
       "      <td>0.596012</td>\n",
       "      <td>0.620200</td>\n",
       "      <td>0.595124</td>\n",
       "      <td>0.622678</td>\n",
       "      <td>0.596899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean                ci_l                ci_u          \n",
       "gender                   F         M         F         M         F         M\n",
       "auth_prev_papers                                                            \n",
       "0                 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
       "1                 0.408693  0.386917  0.407659  0.386120  0.409726  0.387713\n",
       "2                 0.517016  0.492043  0.515838  0.491161  0.518195  0.492926\n",
       "3                 0.567342  0.552091  0.566088  0.551190  0.568596  0.552992\n",
       "4                 0.621439  0.596012  0.620200  0.595124  0.622678  0.596899"
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41m3FuT0N5659uPak484vwmKzEdjsLEL+1o7o/+tLQGw0bsPC1kzPTKf1v8L+PM/YGfCs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\nCCGE8EnS5AghhBDCJ0mTI4QQQgif9AvHZC2g/GN5jQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff37f453f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(8,4))\n",
    "for i, (df_t, title, axi) in enumerate(zip(\n",
    "        [df_t_first, df_t_last],\n",
    "        [\"First\", \"Last\"],\n",
    "        ax\n",
    "    )):\n",
    "    \n",
    "    df_t = df_t[~df_t.match_prop.isnull()].groupby([\"auth_prev_papers\", \"gender\"]).match_prop.apply(\n",
    "        mean_confidence_interval\n",
    "    ).unstack().unstack()\n",
    "    display(df_t.head())\n",
    "    for j, gender in enumerate(genders):\n",
    "        axi.fill_between(df_t.index,\n",
    "                 df_t[\"ci_l\"][gender],\n",
    "                 df_t[\"ci_u\"][gender],\n",
    "                 color=gender_params[gender][\"color\"],\n",
    "                 alpha=0.5)\n",
    "\n",
    "        axi.plot(df_t.index, df_t[\"mean\"][gender],\n",
    "                label=gender_params[gender][\"label\"],\n",
    "                color=gender_params[gender][\"color\"],\n",
    "                linewidth=linewidth,\n",
    "                linestyle=linestyle)\n",
    "    axi.set_ylim([0.3, 1.0])\n",
    "    axi.set_xlim([1, 30])\n",
    "    axi.set_title(title)\n",
    "    axi.set_xlabel(\"Author's prior papers\")\n",
    "    axi.set_ylabel(\"Mean\\n($\\pm 95%$ CI)\\nauthor expertise\")\n",
    "    axi.legend(title=\"Gender\", loc=\"lower right\")\n",
    "sns.despine(offset=10)\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"Review_Figures/Author_papers_expertise_gender.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UObOysvjnP/9ZqfeWxVay2EoOsJ0stpIDbCeLreQA28pibtqkVERERGoMzfiI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\nhoKPiIiIJMYPS9390avD6c0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff37edcc810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,1, figsize=(8,4))\n",
    "for i, (df_t, title, axi) in enumerate(zip(\n",
    "        [df_t_first, df_t_last],\n",
    "        [\"First\", \"Last\"],\n",
    "        ax\n",
    "    )):\n",
    "    \n",
    "    df_t = df_t[~df_t.match_prop.isnull()].assign(\n",
    "        expertise=lambda x: pd.cut(x[\"match_prop\"],\n",
    "                                   bins=np.arange(0,1,0.1),\n",
    "                                   include_lowest=True,\n",
    "                                   right=True\n",
    "                                  )\n",
    "    ).groupby([\"expertise\", \"gender\"]).is_self_cite.agg([\n",
    "        pd.Series.mean, pd.Series.count,\n",
    "    ]).unstack()\n",
    "    width=0.4\n",
    "    for j, gender in enumerate(genders):\n",
    "        axi.bar(np.arange(df_t.index.shape[0]) + j*width, df_t[\"mean\"][gender],\n",
    "                label=gender_params[gender][\"label\"],\n",
    "                color=gender_params[gender][\"color\"],\n",
    "                width=width\n",
    "               )\n",
    "    #axi.set_ylim([0.3, 1.0])\n",
    "    #axi.set_xlim([1, 100])\n",
    "    axi.set_xticks(np.arange(df_t.index.shape[0]) + width / 2)\n",
    "    axi.set_xticklabels(df_t.index.values)\n",
    "    axi.set_title(title)\n",
    "    axi.set_xlabel(\"Author's expertise\")\n",
    "    axi.set_ylabel(\"Mean\\nself-citation\")\n",
    "    axi.legend(title=\"Gender\", loc=\"upper left\", ncol=2)\n",
    "sns.despine(offset=10)\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"Review_Figures/Self_citation_expertise_gender.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_predictions(df, formula, df_test, verbose=False, model_params=None):\n",
    "    if model_params is None:\n",
    "        model_params = dict()\n",
    "    if verbose:\n",
    "        print(df.shape, df_test.shape, formula)\n",
    "    y,X = patsy.dmatrices(formula, data=df, return_type=\"dataframe\")\n",
    "    if verbose:\n",
    "        print \"Created dataframes\"\n",
    "        print \"X.shape = %s, y.shape = %s\" % (X.shape, y.shape)\n",
    "    model = Logit(y,X).fit(disp=verbose,\n",
    "                           **model_params\n",
    "                           #method='lbfgs', maxiter=50\n",
    "                          )\n",
    "    if verbose:\n",
    "        display(model.summary2())\n",
    "    _, X_test = patsy.dmatrices(formula, data=df_test, return_type=\"dataframe\")\n",
    "    if verbose:\n",
    "        print X_test.shape\n",
    "    y_test = model.predict(X_test)\n",
    "    return y_test, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th>is_self_cite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   auth_prev_papers  is_self_cite\n",
       "0                 0             1\n",
       "1                 1             1\n",
       "2                 2             1\n",
       "3                 3             1\n",
       "4                 4             1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.DataFrame({\n",
    "            \"auth_prev_papers\": np.arange(0,150)\n",
    "        }).assign(is_self_cite=1)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((15801900, 64), (150, 2), 'is_self_cite ~ I(auth_prev_papers == 0)+ I(auth_prev_papers == 1)+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)')\n",
      "Created dataframes\n",
      "X.shape = (15801900, 5), y.shape = (15801900, 1)\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.187466\n",
      "         Iterations 10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>     <td>0.064</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>   <td>is_self_cite</td>         <td>AIC:</td>        <td>5924654.1657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2017-08-17 11:23</td>       <td>BIC:</td>        <td>5924727.0439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>      <td>15801900</td>      <td>Log-Likelihood:</td>   <td>-2.9623e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>4</td>            <td>LL-Null:</td>       <td>-3.1648e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>        <td>15801895</td>       <td>LLR p-value:</td>       <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>          <td>1.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>No. Iterations:</td>        <td>10.0000</td>             <td></td>                <td></td>      \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                     <td></td>                     <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                              <td>-4.3672</td>  <td>0.0105</td>  <td>-415.6943</td> <td>0.0000</td> <td>-4.3878</td> <td>-4.3466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(auth_prev_papers == 0)[T.True]</th>       <td>-1.4559</td>  <td>0.0176</td>  <td>-82.6117</td>  <td>0.0000</td> <td>-1.4905</td> <td>-1.4214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(auth_prev_papers == 1)[T.True]</th>       <td>-0.2761</td>  <td>0.0094</td>  <td>-29.5123</td>  <td>0.0000</td> <td>-0.2944</td> <td>-0.2577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(auth_prev_papers + 1)</th>         <td>1.4643</td>   <td>0.0175</td>   <td>83.5181</td>  <td>0.0000</td> <td>1.4299</td>  <td>1.4986</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(auth_prev_papers + 1) ** 2)</th> <td>-0.0989</td>  <td>0.0068</td>  <td>-14.4466</td>  <td>0.0000</td> <td>-0.1123</td> <td>-0.0855</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                                     Results: Logit\n",
       "========================================================================================\n",
       "Model:                     Logit                   Pseudo R-squared:        0.064       \n",
       "Dependent Variable:        is_self_cite            AIC:                     5924654.1657\n",
       "Date:                      2017-08-17 11:23        BIC:                     5924727.0439\n",
       "No. Observations:          15801900                Log-Likelihood:          -2.9623e+06 \n",
       "Df Model:                  4                       LL-Null:                 -3.1648e+06 \n",
       "Df Residuals:              15801895                LLR p-value:             0.0000      \n",
       "Converged:                 1.0000                  Scale:                   1.0000      \n",
       "No. Iterations:            10.0000                                                      \n",
       "----------------------------------------------------------------------------------------\n",
       "                                        Coef.  Std.Err.     z     P>|z|   [0.025  0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept                              -4.3672   0.0105 -415.6943 0.0000 -4.3878 -4.3466\n",
       "I(auth_prev_papers == 0)[T.True]       -1.4559   0.0176  -82.6117 0.0000 -1.4905 -1.4214\n",
       "I(auth_prev_papers == 1)[T.True]       -0.2761   0.0094  -29.5123 0.0000 -0.2944 -0.2577\n",
       "np.log10(auth_prev_papers + 1)          1.4643   0.0175   83.5181 0.0000  1.4299  1.4986\n",
       "I(np.log10(auth_prev_papers + 1) ** 2) -0.0989   0.0068  -14.4466 0.0000 -0.1123 -0.0855\n",
       "========================================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "formula = (\"is_self_cite ~ \"\n",
    "               \"I(auth_prev_papers == 0)\"\n",
    "               \"+ I(auth_prev_papers == 1)\"\n",
    "               \"+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "              )\n",
    "y_test, model = model_predictions(\n",
    "    df_t_first[(df_t_first.gender == \"M\") \n",
    "               & (df_t_first.auth_prev_papers <= 100)],\n",
    "    formula,\n",
    "    df_test, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coef.      -1.455942\n",
       "Std.Err.    0.017624\n",
       "Name: I(auth_prev_papers == 0)[T.True], dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary2().tables[1].ix[\"I(auth_prev_papers == 0)[T.True]\", [\"Coef.\", \"Std.Err.\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">auth_prev_papers</th>\n",
       "      <th colspan=\"3\" halign=\"left\">is_self_cite</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>10.205991</td>\n",
       "      <td>15.322557</td>\n",
       "      <td>9172202</td>\n",
       "      <td>0.038016</td>\n",
       "      <td>0.191234</td>\n",
       "      <td>9172202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>16.654528</td>\n",
       "      <td>21.158397</td>\n",
       "      <td>15801900</td>\n",
       "      <td>0.050600</td>\n",
       "      <td>0.219179</td>\n",
       "      <td>15801900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       auth_prev_papers                      is_self_cite                    \n",
       "                   mean        std       len         mean       std       len\n",
       "gender                                                                       \n",
       "-                   NaN        NaN         0          NaN       NaN         0\n",
       "F             10.205991  15.322557   9172202     0.038016  0.191234   9172202\n",
       "M             16.654528  21.158397  15801900     0.050600  0.219179  15801900"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paper_filter = 100\n",
    "df_t_data = df_t_first\n",
    "df_t_data = df_t_data[(df_t_data.auth_prev_papers <= paper_filter)]\n",
    "df_t_full = df_t_data.groupby(\"gender\")[[\"auth_prev_papers\", \"is_self_cite\"]].agg([np.mean, np.std, len])\n",
    "df_t_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>10.205991</td>\n",
       "      <td>15.322557</td>\n",
       "      <td>9172202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>16.654528</td>\n",
       "      <td>21.158397</td>\n",
       "      <td>15801900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean        std       len\n",
       "gender                                \n",
       "-             NaN        NaN         0\n",
       "F       10.205991  15.322557   9172202\n",
       "M       16.654528  21.158397  15801900"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_full[\"auth_prev_papers\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">auth_prev_papers</th>\n",
       "      <th colspan=\"4\" halign=\"left\">is_self_cite</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>error</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>10.205991</td>\n",
       "      <td>15.322557</td>\n",
       "      <td>9172202</td>\n",
       "      <td>0.005059</td>\n",
       "      <td>0.038016</td>\n",
       "      <td>0.191234</td>\n",
       "      <td>9172202</td>\n",
       "      <td>0.000063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>16.654528</td>\n",
       "      <td>21.158397</td>\n",
       "      <td>15801900</td>\n",
       "      <td>0.005323</td>\n",
       "      <td>0.050600</td>\n",
       "      <td>0.219179</td>\n",
       "      <td>15801900</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       auth_prev_papers                                is_self_cite            \\\n",
       "                   mean        std       len     error         mean       std   \n",
       "gender                                                                          \n",
       "-                   NaN        NaN         0       NaN          NaN       NaN   \n",
       "F             10.205991  15.322557   9172202  0.005059     0.038016  0.191234   \n",
       "M             16.654528  21.158397  15801900  0.005323     0.050600  0.219179   \n",
       "\n",
       "                            \n",
       "             len     error  \n",
       "gender                      \n",
       "-              0       NaN  \n",
       "F        9172202  0.000063  \n",
       "M       15801900  0.000055  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_full[(\"auth_prev_papers\", \"error\")] = df_t_full[(\"auth_prev_papers\", \"std\")] / np.sqrt(df_t_full[(\"auth_prev_papers\", \"len\")])\n",
    "df_t_full[(\"is_self_cite\", \"error\")] = np.sqrt(\n",
    "    (df_t_full[(\"is_self_cite\", \"mean\")] * (1-df_t_full[(\"is_self_cite\", \"mean\")]))/df_t_full[(\"is_self_cite\", \"len\")])\n",
    "df_t_full = df_t_full.sort_index(level=0, axis=1)\n",
    "df_t_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 16.65452756])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_full.ix[gender, [(\"auth_prev_papers\", \"mean\")]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_age_self_cite_data(df_t_data):\n",
    "    df_t = df_t_data.groupby(\n",
    "        [\"auth_prev_papers\", \"gender\"]).is_self_cite.agg([np.mean, len]).unstack()\n",
    "    df_t_full = df_t_data.assign(\n",
    "        auth_prev_papers=np.log10(df_t_data[\"auth_prev_papers\"] + 1).copy()\n",
    "    ).groupby(\"gender\")[[\"auth_prev_papers\", \"is_self_cite\"]].agg([np.mean, np.std, len])\n",
    "    df_t_full[(\"auth_prev_papers\", \"error\")] = df_t_full[(\"auth_prev_papers\", \"std\")] / np.sqrt(df_t_full[(\"auth_prev_papers\", \"len\")])\n",
    "    df_t_full[(\"is_self_cite\", \"error\")] = np.sqrt(\n",
    "        ((df_t_full[(\"is_self_cite\", \"mean\")]\n",
    "          * (1-df_t_full[(\"is_self_cite\", \"mean\")])\n",
    "         )/df_t_full[(\"is_self_cite\", \"len\")]))\n",
    "    df_t_full[(\"auth_prev_papers\", \"l_error\")] = 10**(\n",
    "        df_t_full[(\"is_self_cite\", \"mean\")] - df_t_full[(\"is_self_cite\", \"error\")]) - 1\n",
    "    df_t_full[(\"auth_prev_papers\", \"u_error\")] = 10**(\n",
    "        df_t_full[(\"is_self_cite\", \"mean\")] + df_t_full[(\"is_self_cite\", \"error\")]) - 1\n",
    "    df_t_full = df_t_full.sort_index(level=0, axis=1)\n",
    "    \n",
    "    error = np.sqrt(df_t[\"mean\"].multiply(1 - df_t[\"mean\"]).divide(df_t[\"len\"]))\n",
    "    return df_t, df_t_full, error\n",
    "\n",
    "\n",
    "def plot_age_vs_self_cite(axi, df_t_data, df_t, df_t_full, error, gender,\n",
    "                          gender_params, plot_params, model_params,\n",
    "                          formula, df_test, error_plot=True,\n",
    "                          mean_line=False, verbose=False):\n",
    "    \"\"\"\n",
    "    TODO: Add beta of Male Female indicator to the plot. \n",
    "    \"\"\"\n",
    "    error_params = gender_params[gender]\n",
    "    if error_plot:\n",
    "        axi.errorbar(df_t.index.values, df_t[\"mean\"][gender].values,\n",
    "                     yerr=error[gender].values,\n",
    "                     color=gender_params[gender][\"color\"],\n",
    "                     #label=gender_params[gender][\"label\"],\n",
    "                     marker=gender_params[gender][\"marker\"],\n",
    "                     **plot_params\n",
    "                    )\n",
    "    else:\n",
    "        axi.plot(df_t.index.values, df_t[\"mean\"][gender].values,\n",
    "                     color=gender_params[gender][\"color\"],\n",
    "                     #label=gender_params[gender][\"label\"],\n",
    "                     marker=gender_params[gender][\"marker\"],\n",
    "                     **dict([(k,v)\n",
    "                         for k,v in plot_params.items()\n",
    "                         if k not in set([\"capsize\", \"elinewidth\"])\n",
    "                     ])\n",
    "                    )\n",
    "\n",
    "    x_mean = 10**(df_t_full.ix[gender, [(\"auth_prev_papers\", \"mean\")]].values)-1\n",
    "    y_mean = df_t_full.ix[gender, [(\"is_self_cite\", \"mean\")]].values\n",
    "    y_mean_err = df_t_full.ix[gender, [(\"is_self_cite\", \"error\")]].values\n",
    "    if mean_line:\n",
    "        axi.axhline(\n",
    "            y=y_mean[0],\n",
    "            color=gender_params[gender][\"color\"],\n",
    "            label=\"{} ({:.2f}% self-citations)\".format(\n",
    "                gender_params[gender][\"label\"], y_mean[0]*100),\n",
    "            linestyle=\"--\",\n",
    "            lw=linewidth+1\n",
    "        )\n",
    "    else:\n",
    "        axi.errorbar(\n",
    "            x_mean, y_mean,\n",
    "            #xerr=[df_t_full[(\"auth_prev_papers\", \"l_error\")].values,\n",
    "            #      df_t_full[(\"auth_prev_papers\", \"u_error\")].values],\n",
    "            yerr=y_mean_err,\n",
    "            color=gender_params[gender][\"color\"],\n",
    "            marker=gender_params[gender][\"marker\"],\n",
    "            label=\"{}(x={:.2f},y={:.2f})\".format(\n",
    "                gender_params[gender][\"label\"], x_mean[0], y_mean[0]*100),\n",
    "            ms=10,\n",
    "        )\n",
    "\n",
    "    print \"Fitting model for %s\" % gender\n",
    "    model = None\n",
    "    try:\n",
    "        y_test, model = model_predictions(\n",
    "                df_t_data,\n",
    "                formula, df_test, verbose=verbose, model_params=model_params)\n",
    "        axi.plot(df_test[\"auth_prev_papers\"], y_test,\n",
    "                 color=gender_params[gender][\"color\"],\n",
    "                 linestyle=\"-\",\n",
    "                 linewidth=linewidth\n",
    "                )\n",
    "    except:\n",
    "        print(\"Modeling failed.\")\n",
    "        print(df_t_data[df_t_data.auth_prev_papers < 2].auth_prev_papers.value_counts())\n",
    "        import traceback\n",
    "        traceback.print_exc()\n",
    "    return axi, model\n",
    "\n",
    "def plot_age_dist(axi_dist, df_t, gender, gender_params, cdf=False):\n",
    "    # Plot distribution\n",
    "    y = (df_t[\"len\"][gender] * 1./ df_t[\"len\"][gender].sum())\n",
    "    if cdf:\n",
    "        y = y.cumsum()\n",
    "    axi_dist.plot(df_t.index, y,\n",
    "                  color=gender_params[gender][\"color\"],\n",
    "                  label=gender_params[gender][\"label\"],\n",
    "                  marker=gender_params[gender][\"marker\"],\n",
    "                  linestyle=\"-\",\n",
    "                  alpha=0.5,\n",
    "                  markersize=markersize\n",
    "    )\n",
    "    return axi_dist\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.6,\n",
       " 'capsize': 0.5,\n",
       " 'elinewidth': 0.01,\n",
       " 'linestyle': 'none',\n",
       " 'markersize': 3}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting model for F\n",
      "Fitting model for M\n",
      "Fitting model for F\n",
      "Fitting model for M\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jqzfh/Zqumyo1R2Hkd7rNM3cEgH1uNin//Q4lW4PHpABsPNzK/KzaMHjWEtWW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ceMGcPcuXOpqqrCbO6dBdz8OWu6y+V0cuGTo5x/7W0CB4QRkjSYkGviPH55qtVpzEZcXm/Mb/OD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff37ecf3050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "formula = (\"is_self_cite ~ \"\n",
    "               \"I(auth_prev_papers == 0)\"\n",
    "               \"+ I(auth_prev_papers == 1)\"\n",
    "               \"+ C(gender, levels=['M', 'F'])\"\n",
    "               \"+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "              )\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=2, ncols=2,\n",
    "    sharex=True, sharey='row',\n",
    "    gridspec_kw={'height_ratios': [2, 1]},\n",
    "    figsize=(8,6))\n",
    "paper_filter = 100\n",
    "\n",
    "for i, (df_t_data, title) in enumerate(zip(\n",
    "        [df_t_first, df_t_last],\n",
    "        [\"First\", \"Last\"],\n",
    "    )):\n",
    "    axi, axi_dist = ax[:, i].flatten()\n",
    "    #df_t_data = df_t_data[(df_t_data.auth_prev_papers <= paper_filter)]\n",
    "        \n",
    "    df_t, df_t_full, error = get_age_self_cite_data(df_t_data)\n",
    "    for j, gender in enumerate(genders):\n",
    "        df_test = pd.DataFrame({\n",
    "                    \"auth_prev_papers\": np.arange(0,min([paper_filter, 150]))\n",
    "                }).assign(is_self_cite=1, gender=gender)\n",
    "        model_params = dict()\n",
    "        axi, model = plot_age_vs_self_cite(axi,\n",
    "                                    df_t_data, #[df_t_data.gender == gender],\n",
    "                                    df_t, df_t_full, error, gender,\n",
    "                                    gender_params, {\n",
    "                                        k: (1 if k == \"markersize\" else v)\n",
    "                                        for k,v in plot_params.items()\n",
    "                                    }, model_params,\n",
    "                                    formula, df_test, mean_line=True, verbose=False)\n",
    "        axi_dist = plot_age_dist(axi_dist, df_t, gender, gender_params, cdf=True)\n",
    "        legend_title = \"\"\n",
    "        if model:\n",
    "            model_stats = model.summary2().tables[1].ix[\n",
    "                \"C(gender, levels=['M', 'F'])[T.F]\",\n",
    "                [\"Coef.\", \"Std.Err.\"]].values\n",
    "            legend_title = \"$\\\\beta_{F-M}=%.3f\\ (%.3f)$\" % (model_stats[0], model_stats[1])\n",
    "    axi.set_ylim([0, 0.15])\n",
    "    axi.set_xlim([0, 100])\n",
    "    axi.set_title(title)\n",
    "    axi.set_xlabel(\"Age (pub-count)\")\n",
    "    axi.set_ylabel(\"Self-citation proportion\")\n",
    "    axi.legend(loc='lower right', title=legend_title)\n",
    "\n",
    "    axi_dist.legend(loc='lower right')\n",
    "    axi_dist.set_xlabel(\"Age (pub-count)\")\n",
    "    axi_dist.set_ylabel(\"Cumulative proportion\\nof citations\")\n",
    "    \n",
    "#ax.legend(title=\"Gender (Author position)\", loc=\"lower right\")\n",
    "sns.despine(offset=10)\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"Review_Figures/Author_papers_self_cite_gender.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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adpzWGGtUkyPiARkFkFn40+ODVjMZBS071ogRI9i6dStnz57l9OnTREVF0a1bNywWC23a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kV0kkNERETkDoeriIiIKCoxySEiIqKoxCSHiIiIohKTHCIiIopKTHKIiIgoKjHJISIioqgUE5s+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\nEFFJkhwhhBBCRCVJcoQQQggRlf4/6DoP5l6IkPUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff37aa61f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4,2, sharey=\"row\", figsize=(8,16))\n",
    "\n",
    "filters = [\n",
    "    (lambda x: x == 0, \"$%s = 0$\", 20),\n",
    "    (lambda x: (x > 0) & (x <= 0.5), \"$0 < %s \\leq 0.5$\", 50,),\n",
    "    (lambda x: (x > 0.5) & (x < 1), \"$0.5 < %s < 1$\", 100),\n",
    "    (lambda x: x == 1, \"$%s = 1$\", 100),\n",
    "          ]\n",
    "for i, (idx_filter, filter_title, paper_filter) in enumerate(filters):\n",
    "    for j, (df_t, title, axi) in enumerate(zip(\n",
    "            [df_t_first, df_t_last],\n",
    "            [\"First\", \"Last\"],\n",
    "            ax[i]\n",
    "        )):\n",
    "        df_t = df_t[idx_filter(df_t.match_prop) & (df_t.auth_prev_papers <= paper_filter)].groupby(\n",
    "            [\"auth_prev_papers\", \"gender\"]).is_self_cite.agg([np.mean, len]).unstack()\n",
    "        error = np.sqrt(df_t[\"mean\"].multiply(1 - df_t[\"mean\"]).divide(df_t[\"len\"]))\n",
    "        axi.errorbar(df_t.index, df_t[\"mean\"][\"F\"], yerr=error[\"F\"],\n",
    "                 label=\"Female\", color=\"crimson\", linestyle=\"none\", marker=\"o\", alpha=0.7)\n",
    "        axi.errorbar(df_t.index, df_t[\"mean\"][\"M\"], yerr=error[\"M\"],\n",
    "                     label=\"Male\", color=\"dodgerblue\", linestyle=\"none\", marker=\"^\", alpha=0.7)\n",
    "        axi.set_title(\"%s [%s]\" % (title, filter_title % \"exp\"))\n",
    "        axi.set_xlabel(\"Author's prior papers\")\n",
    "        axi.set_ylabel(\"Self-citation proportion\")\n",
    "        axi.legend(loc='best', title=\"Gender\")\n",
    "#ax.legend(title=\"Gender (Author position)\", loc=\"lower right\")\n",
    "sns.despine(offset=10)\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"Review_Figures/Author_papers_self_cite_gender_exp.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Journal names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 26s, sys: 6.56 s, total: 1min 32s\n",
      "Wall time: 1min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_journals = pd.read_csv(\"data/FullArticlesData.txt\", sep=\"\\t\", usecols=[\"PMID\", \"journal\"])\n",
    "df_journals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PMID</th>\n",
       "      <th>journal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26151966</td>\n",
       "      <td>J Hum Lact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26151965</td>\n",
       "      <td>J Hum Lact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26151955</td>\n",
       "      <td>EuroIntervention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26151954</td>\n",
       "      <td>EuroIntervention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26151953</td>\n",
       "      <td>EuroIntervention</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PMID           journal\n",
       "0  26151966        J Hum Lact\n",
       "1  26151965        J Hum Lact\n",
       "2  26151955  EuroIntervention\n",
       "3  26151954  EuroIntervention\n",
       "4  26151953  EuroIntervention"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "J Biol Chem                   171068\n",
       "Science                       167415\n",
       "PLoS One                      133591\n",
       "Lancet                        129945\n",
       "Proc Natl Acad Sci U S A      121705\n",
       "Nature                        104418\n",
       "Br Med J                       97226\n",
       "Biochim Biophys Acta           96039\n",
       "Biochem Biophys Res Commun     78341\n",
       "Phys Rev Lett                  76322\n",
       "N Engl J Med                   72020\n",
       "JAMA                           66849\n",
       "BMJ                            65858\n",
       "Biochemistry                   62430\n",
       "J Immunol                      62245\n",
       "Brain Res                      56834\n",
       "Am J Physiol                   54726\n",
       "Biochem J                      54355\n",
       "J Bacteriol                    51716\n",
       "J Am Chem Soc                  50057\n",
       "Cancer Res                     48966\n",
       "Ann N Y Acad Sci               47684\n",
       "J Urol                         47368\n",
       "Phys Rev B Condens Matter      46890\n",
       "FEBS Lett                      46770\n",
       "Appl Opt                       43386\n",
       "Blood                          43160\n",
       "J Virol                        42269\n",
       "Med J Aust                     41119\n",
       "Ugeskr Laeger                  40687\n",
       "Name: journal, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journals.journal.value_counts().head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Journal categories\n",
    "\n",
    "* MEDICINE - NEMJ, JAMA, LANCET\n",
    "* BIOLOGY - CELL, Journal of Bio Chem\n",
    "* Bioinformatics - PLoS Com Bio, BMC BioInfo\n",
    "* EPIDEMIOLOGY - MMWR. Morbidity and Mortality Weekly Report, Emerging Infectious Diseases, International Journal of Epidemiology\n",
    "* DENTISTRY - Journal of Endodontics, Journal of Clinical Periodontology, Journal of Dental Research\n",
    "* GENERIC - Proc Natl Acad Sci U S A, Nature, Science, PLoS One"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BIOLOGY': {'Adv Exp Med Biol',\n",
       "  'Biochem J',\n",
       "  'Biochemistry',\n",
       "  'Biochim Biophys Acta',\n",
       "  'Bioinformatics',\n",
       "  'Cell',\n",
       "  'FEBS Lett',\n",
       "  'J Bacteriol',\n",
       "  'J Biol Chem',\n",
       "  'J Virol',\n",
       "  'Mol Cell',\n",
       "  'Nucleic Acids Res'},\n",
       " 'GENERIC': {'Ann N Y Acad Sci',\n",
       "  'Nature',\n",
       "  'Proc Natl Acad Sci U S A',\n",
       "  'Science'},\n",
       " 'MEDICINE': {'BMJ',\n",
       "  'Blood',\n",
       "  'Brain Res',\n",
       "  'Cancer Res',\n",
       "  'Circulation',\n",
       "  'Clin Cancer Res',\n",
       "  'Gut',\n",
       "  'J Am Coll Cardiol',\n",
       "  'J Clin Oncol',\n",
       "  'J Immunol',\n",
       "  'J Urol',\n",
       "  'JAMA',\n",
       "  'Lancet',\n",
       "  'N Engl J Med'}}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JOURNAL_NAMES  = dict(\n",
    "    GENERIC=set(['Proc Natl Acad Sci U S A', 'Nature', 'Science',\n",
    "                 'Ann N Y Acad Sci',]), # General Science\n",
    "    MEDICINE = set(['JAMA', 'Lancet', 'N Engl J Med',\n",
    "                    'BMJ', 'Cancer Res', 'Clin Cancer Res', 'J Clin Oncol',\n",
    "                    'J Am Coll Cardiol', 'Gut', 'Circulation', 'Blood',\n",
    "                    'J Immunol', 'Brain Res', #'Am J Physiol',\n",
    "                    'J Urol', \n",
    "                    #'Med J Aust', 'Ugeskr Laeger'\n",
    "                   ]), # General Medicine\n",
    "    BIOLOGY = set(['J Biol Chem', 'Cell',  'Adv Exp Med Biol',\n",
    "                   'Mol Cell', 'Biochim Biophys Acta', 'Biochemistry',\n",
    "                   'Biochem J', 'FEBS Lett', 'J Bacteriol', 'J Virol',\n",
    "                   'Bioinformatics', 'Nucleic Acids Res',\n",
    "                  ]), # Biology\n",
    ")\n",
    "\n",
    "JOURNAL_NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lancet               129945\n",
       "N Engl J Med          72020\n",
       "JAMA                  66849\n",
       "BMJ                   65858\n",
       "J Immunol             62245\n",
       "Brain Res             56834\n",
       "Cancer Res            48966\n",
       "J Urol                47368\n",
       "Blood                 43160\n",
       "Circulation           40094\n",
       "J Am Coll Cardiol     21580\n",
       "J Clin Oncol          21075\n",
       "Gut                   15952\n",
       "Clin Cancer Res       14845\n",
       "Name: journal, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journals[df_journals.journal.isin(JOURNAL_NAMES[\"MEDICINE\"])].journal.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PMID</th>\n",
       "      <th>journal</th>\n",
       "      <th>JOURNAL_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26151898</td>\n",
       "      <td>Brain Res</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26151676</td>\n",
       "      <td>J Urol</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26151285</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26151284</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26151282</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PMID    journal JOURNAL_TYPE\n",
       "0  26151898  Brain Res     MEDICINE\n",
       "1  26151676     J Urol     MEDICINE\n",
       "2  26151285       JAMA     MEDICINE\n",
       "3  26151284       JAMA     MEDICINE\n",
       "4  26151282       JAMA     MEDICINE"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df_journals[df_journals.journal.isin(v)\n",
    "                      ][[\"PMID\", \"journal\"]].assign(JOURNAL_TYPE=k).reset_index(drop=True)\n",
    "          for k,v in JOURNAL_NAMES.items()]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auth_prev_papers</th>\n",
       "      <th>gender</th>\n",
       "      <th>is_self_cite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   auth_prev_papers gender  is_self_cite\n",
       "0                 0      M             1\n",
       "1                 1      M             1\n",
       "2                 2      M             1\n",
       "3                 3      M             1\n",
       "4                 4      M             1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.assign(gender=gender).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_journal_data(df_t_data, journal_name):\n",
    "    journal_ids = set(df_journals.ix[df_journals.journal == journal_name, \"PMID\"].values)\n",
    "    print(journal_name, len(journal_ids))\n",
    "    df_t_data = df_t_data[(df_t_data.source_id.isin(journal_ids))]\n",
    "    return df_t_data\n",
    "    \n",
    "\n",
    "def plot_journal_data(formula, selected_journals, filename,\n",
    "                      paper_filter = 10000, min_citations=20000, verbose=False):\n",
    "    nrows=len(selected_journals)\n",
    "    fig = plt.figure(figsize=(8,4*nrows))\n",
    "    grid_size=(nrows,2)\n",
    "    journal_group_stats = []\n",
    "    for k, journal_name in enumerate(selected_journals):\n",
    "        journal_ids = set(df_journals.ix[df_journals.journal == journal_name, \"PMID\"].values)\n",
    "        print(journal_name, len(journal_ids))\n",
    "        for i, (df_t_data, title) in enumerate(zip(\n",
    "                [df_t_first, df_t_last],\n",
    "                [\"First\", \"Last\"],\n",
    "            )):\n",
    "            axi = plt.subplot2grid(grid_size, (k, i))\n",
    "            df_t_data = df_t_data[\n",
    "                (df_t_data.auth_prev_papers <= paper_filter)\n",
    "                & (df_t_data.source_id.isin(journal_ids))\n",
    "            ]\n",
    "            df_t, df_t_full, error = get_age_self_cite_data(df_t_data)\n",
    "            print(title, df_t_data.shape)\n",
    "            if df_t_data.shape[0] < min_citations:\n",
    "                    continue\n",
    "            legend_title=\"\"\n",
    "            model_stats = [np.nan, np.nan, np.nan]\n",
    "            for j, gender in enumerate(genders):\n",
    "                df_test = pd.DataFrame({\n",
    "                    \"auth_prev_papers\": np.arange(0,min([paper_filter, 150]))\n",
    "                }).assign(is_self_cite=1, gender=gender)\n",
    "                model_params = dict(method='lbfgs', maxiter=50)\n",
    "                axi, model = plot_age_vs_self_cite(axi, df_t_data, df_t, df_t_full,\n",
    "                                            error, gender, gender_params, plot_params, model_params,\n",
    "                                            formula, df_test, mean_line=True, error_plot=False)\n",
    "                if model:\n",
    "                    model_stats = model.summary2().tables[1].ix[\n",
    "                        \"C(gender, levels=['M', 'F'])[T.F]\",\n",
    "                        [\"Coef.\", \"Std.Err.\", \"P>|z|\"]].values.tolist()\n",
    "                    legend_title = \"$\\\\beta_{F-M}=%.3f\\\\ (%.3f)$\" % (model_stats[0], model_stats[1])\n",
    "            journal_group_stats.append(\n",
    "                [journal_name, title, df_t_data.shape[0]]+model_stats)\n",
    "            axi.set_ylim([0, 0.24])\n",
    "            xlim_max = 150\n",
    "            if title == \"First\":\n",
    "                xlim_max = 50\n",
    "            axi.set_xlim([0, min([paper_filter, xlim_max])])\n",
    "            axi.set_title(\"{0} [n={1:,}]\\n({2})\".format(title, df_t_data.shape[0], journal_name))\n",
    "            axi.set_xlabel(\"Age (pub count)\")\n",
    "            axi.set_ylabel(\"Self-citation proportion\")\n",
    "            legend = axi.legend(loc='upper left', title=legend_title, ncol=1)\n",
    "            legend.get_frame().set_facecolor('#FFFFFF')\n",
    "        #ax.legend(title=\"Gender (Author position)\", loc=\"lower right\")\n",
    "    sns.despine(offset=10)\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(filename, bbox_inches=\"tight\")\n",
    "    return fig, journal_group_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_proportion(journal_name, paper_filter=100, verbose=False):\n",
    "    journal_ids = set(df_journals.ix[df_journals.journal == journal_name, \"PMID\"].values)\n",
    "    if verbose:\n",
    "        print(journal_name, len(journal_ids))\n",
    "    print(\"Paper cutoff: {}\".format(paper_filter))\n",
    "    for i, (df_t_data, title) in enumerate(zip(\n",
    "            [df_t_first, df_t_last],\n",
    "            [\"First\", \"Last\"],\n",
    "        )):\n",
    "        df_t = df_t_data[\n",
    "            (df_t_data.source_id.isin(journal_ids))\n",
    "        ]\n",
    "        print(df_t.shape)\n",
    "        display(df_t.gender.value_counts())\n",
    "        total_papers = df_t.shape[0]\n",
    "        df_t = df_t[(df_t.auth_prev_papers <= paper_filter)]\n",
    "        print(\"{}\\tProportion: {:.2f}%\".format(title, df_t.shape[0]* 100./total_papers))       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paper cutoff: 30\n",
      "(298356, 64)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "M    201791\n",
       "F     96565\n",
       "-         0\n",
       "Name: gender, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\tProportion: 89.78%\n",
      "(334968, 64)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "M    284467\n",
       "F     50501\n",
       "-         0\n",
       "Name: gender, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\tProportion: 26.02%\n"
     ]
    }
   ],
   "source": [
    "get_proportion('Proc Natl Acad Sci U S A', paper_filter=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Cell', 17515)\n",
      "('First', (32399, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (37042, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n"
     ]
    },
    {
     "data": {
      "image/png": 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m+T5f3bp1De3zt27dYsOGDTg4ONCwYUPDMY0bN2bTpk20bds2x/O7urry/fff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\n9+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff37330dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "journal_formula = (\"is_self_cite ~ \"\n",
    "               \"I(auth_prev_papers == 0)\"\n",
    "                \"+ I(auth_prev_papers == 1)\"\n",
    "                \"+ C(gender, levels=['M', 'F'])\"\n",
    "               \"+ np.log10(auth_prev_papers + 1)\"\n",
    "                \"+ I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "              )\n",
    "selected_journals = {'Cell',}\n",
    "filename = \"Review_Figures/Author_papers_self_cite_gender_journals.pdf\"\n",
    "fig, journal_group_stats = plot_journal_data(journal_formula, selected_journals, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Author Position</th>\n",
       "      <th colspan=\"4\" halign=\"left\">First</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <td>32399</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.075531</td>\n",
       "      <td>0.007951</td>\n",
       "      <td>37042</td>\n",
       "      <td>-0.08922</td>\n",
       "      <td>0.04975</td>\n",
       "      <td>0.072915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Author Position     First                                    Last           \\\n",
       "                citations      beta    stderr   p-value citations     beta   \n",
       "Journal                                                                      \n",
       "Cell                32399 -0.200469  0.075531  0.007951     37042 -0.08922   \n",
       "\n",
       "Author Position                     \n",
       "                  stderr   p-value  \n",
       "Journal                             \n",
       "Cell             0.04975  0.072915  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(journal_group_stats, columns=[\n",
    "    \"Journal\", \"Author Position\", \"citations\",\n",
    "    \"beta\", \"stderr\", \"p-value\"\n",
    "]).pivot(index=\"Journal\", columns=\"Author Position\").reorder_levels([1,0], axis=1).sortlevel(0, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot per journal category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEDICINE\n",
      "('Cancer Res', 48966)\n",
      "('First', (131329, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (149313, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Circulation', 40094)\n",
      "('First', (92220, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (98741, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('N Engl J Med', 72020)\n",
      "('First', (30970, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (31971, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('BMJ', 65858)\n",
      "('First', (23987, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (24438, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Am Coll Cardiol', 21580)\n",
      "('First', (50523, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (52579, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Clin Oncol', 21075)\n",
      "('First', (61722, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (62049, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Immunol', 62245)\n",
      "('First', (208354, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (228129, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('JAMA', 66849)\n",
      "('First', (33674, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (34651, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Gut', 15952)\n",
      "('First', (31027, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (32663, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Clin Cancer Res', 14845)\n",
      "('First', (91687, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (96551, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Brain Res', 56834)\n",
      "('First', (100389, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (108379, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Urol', 47368)\n",
      "('First', (49314, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (50379, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Lancet', 129945)\n",
      "('First', (26344, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (26301, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Blood', 43160)\n",
      "('First', (140887, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (152394, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "GENERIC\n",
      "('Science', 167415)\n",
      "('First', (41328, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (45043, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Proc Natl Acad Sci U S A', 121705)\n",
      "('First', (298356, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (334968, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Ann N Y Acad Sci', 47684)\n",
      "('First', (52540, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (54224, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Nature', 104418)\n",
      "('First', (46138, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (50625, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "BIOLOGY\n",
      "('Mol Cell', 5804)\n",
      "('First', (33538, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (39524, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Biochim Biophys Acta', 96039)\n",
      "('First', (104434, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (110138, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Adv Exp Med Biol', 31938)\n",
      "('First', (21625, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (22072, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Biochem J', 54355)\n",
      "('First', (91576, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (97174, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Biochemistry', 62430)\n",
      "('First', (182433, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (204527, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Nucleic Acids Res', 39570)\n",
      "('First', (98322, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (104933, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('FEBS Lett', 46770)\n",
      "('First', (94021, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (99364, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Cell', 17515)\n",
      "('First', (32399, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (37042, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Bacteriol', 51716)\n",
      "('First', (102012, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (109737, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Bioinformatics', 10026)\n",
      "('First', (20756, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (23014, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Virol', 42269)\n",
      "('First', (155081, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (172265, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('J Biol Chem', 171068)\n",
      "('First', (676859, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n",
      "('Last', (758553, 64))\n",
      "Fitting model for F\n",
      "Fitting model for M\n"
     ]
    },
    {
     "data": {
      "image/png": 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W5PTp04wYMYJx48Yxbtw4AC5cuKCvqhZClC2JNcLYZMRjYZBly5bxzDPP8Mwzz5R1UYzq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\n/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ctfLLOQBgQEOKNcQmiOJ3b27SxHBlCJNaIjrrhgd3RMSSDsy1Gfp9Uk57HHHuPgwYOc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F26dAEgMDCQXr16YTabOXv2LNdeey3z58+v037Onj3LkCFDOHnyJIqiUFpaSs+ePatsU9+a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\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YdoWTPbSrb2hu8jfDeH01Gegk1ojy6DOzyfrpCJpzV7Bzd8NtQHfcH32wxOsoJgPCrsFf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f9+D+cfnM0vl64mOKJxbHkuO2LtjVBZ/XaWkZFBWFgYAP7+/uTl5REUFIROp+7K09MTg8FA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\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jJe5Ho9Fw+/Zt/P39K1yO0rZxc3PD3d2dp556isTERB599FEee+wx/XY5OTmMHDmS2NhY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Gp3eS6SRdtvxzh8WhWb2B2iO5puJQuK/JP6vppc+l9li1bHIrzPKULiVbkinkNpKvgQ92QkxA\nHTcfWI9h3yEC/5KKb/+LHR2azcjOvu0j03Gdm7XyzLGs0zy2SkNPZQV4KXjjub5WjFII0RxpybGS\n+oG8tXWQGAYY9NyX+z9U+w4QOHIYPuk3ODQ+e5CrjvaRJmnP8NHaPHrEREGxwrQBrxDCtqTIsZC5\nUe56Axw5AxqtgQm1Wwn+bSOBfx2Gb/owB0QrXIkUh+6vsrwaPQYWPZIAOFerc0fJLEXhzGR2lYVa\nG+W+7RRszdbgv2cPF+3dTEwARMx+nBnbQ3jq1V088488B0QshHAWX3yeza3XRjk6DJuQGUDCmUlL\njoWadiloVm8gb2MWn1x8E8lJIbz/6URUYSGokrrg/6dJAOgKikFvMP4XGWTYHLkKFJ4gq6CO0Q90\ndnQYNiHdrcKZSZFjoYZdCgYDfLxdS75PH0Z9tpDoG69A9ehdVG/b1+iHroqOQFdQbOp/lxP6hTxl\naXHhubK259EjUunoMGxGuluFM5Mip42Ol8LyHQb+pCjjhp9WEXzvbYQ8OAKAoDsbDy42ThE934Ij\nJ/QLyVWgcHdrfijgofuS5SLHBuQzFeZIkWOhOj2s3A0Vp8t55MeVhKZdif8Ly9v0GnJCv5BcBQp3\nptcb0GgVhEYEUCgXOVYnF47CHClyLJBXDku26rklO5OLqvMJmT4OLz+fNr+OnNCdm1wVCmvbvOEI\nl3Y35gq5yLE++UyFOVLkmPHNQdiTXcYjP62g04O3or54uKNDEjYiV4XC2jK3l/HMY5cA8ErgEEgz\nfq8yHBmUG3HHC0e52LIumULegspamPurgdofN/HQ0bUsHvY3XshLbLR7t3AvftcMAJVKrgqFVejr\n9Oj0Cnz9vB0dinAhMiXfuqQlpxlHz8DYD8vpcXQ3BV2iuP2pq1A0KW4aFjsZqfaNT9iGO14VCsfZ\n+ssx+nVVOzoM4WKkC866pMhp4ufj8NvGk6QcOYh3WDD6ympHhySEcEEbtpXy2JgU07/lYkhYQi62\nrEuKnHMMBli5y4D+l608Gl7IS336NVrjRhKUEKItqnQQGOrn6DCEh5ExPY1JkQPo9DD/x1oG/vQV\ng2/rhe+Ay3kNaG2VYil6hBAtOVOkIcxP4egwhAeSCRSNSZEDpH9cS3DOUcoG3sLwAW2fGi6EEA1l\nf/QzkfsKKHr+Fww1tXJVLexGxvQ05vFFTuFZ0OSX0rNfNxRqGSQohOi410q64xfThU0lp5mp2ipX\n1cJuZExPYx4/hfy9r/O5OEgrBY4QwmqqffwJUNahio2SZQmEcCCPbsn5/VgtXQ7s5Mm+3lSvW2Vc\nJwWpgIUQHVPr40fwwAS8FAqiUvs4OhwhPJbHtuTo9PCfr45w1//1ofq3HbL4khDCKjSrN3DVoY08\nf/aHZicoaFZvoHDyAjSrN9g/OCE8jMcWOR/+UMJI5RHU3eNlpVshhNVU/bodDIYWL5pkRVsh7Mcj\nu6ueXGcga3ctA/qlcQUyUEsIS8kaHOb5XTMA/qtp8aJJZr8IYT8eWeRk7T5N97JcdDk6WlsLRwjR\nmKzBYYHrryL4WBaBtw9o9m65qBLCfjyuu6q0Uo9BU4mPjwpdQbGjwxHCpUjXrnk5h4uJj/DI60ch\nnI7H/RJXfn6MRQFbiCg6JYlaiDaSVgjzjudo6BLn7+gwhBB4WJFTVWug+EQJF0+7y9GhCCHc1Hu5\nkXQhgK8yZfsXIRzNo4qcT1Yf4699vR0dhhDCjVXWgl+Qr+nfMlhbCMfxmDE5dXo4eOgMfW7q6+hQ\nhBBuzktxfnNOmTIuhON4TEvO2nXHGN5DgUIhOwMLIWxj75NvcmtwLx5PHW66TaaMC+E4HlPkbNpZ\nyuxn+zk6DCGEG9tWHUK/uoPA+SJHBmsL4Tge0111NCSB6T94zNsVQjjAYVUEfYcmOzoMIcQ5Vm/J\nyc7OZtmyZQQHB5OYmMjo0aMBWLt2LZs3b6a2tpY77riDXr16MWvWLCIjI6mqqmLmzJnWDqWRTiV5\n1B6rQRb/E8I9OGOuCeiRQOjI5hcBFELYn9WLnGXLljFx4kRiY2MZN24co0aNQq1W8/HHH7Ny5Uqq\nq6t58sknGT58OIMGDWLEiBEsWrSIrVu3MnDgQGuHY6IwcG7xPylyhHAHzphrii/pz7QGU8enZZ6/\nT6aTOy9PnQHnCe/b6kVOcXExMTExAISEhKDRaAgPD0elMh7K19cXrVZLUVER/fsbB+JFR0dTWFjY\n6uuuWrWKVatWNbpNq9VaHNf0iv/JwD8h3Igtck1H84yXl0xscEWeul2JJ7xvqxc5MTEx5OfnExsb\nS2lpKWFhYQB4eRnHw1RWVhIQEEBsbCz5+fkAnDx5kpSUlFZfNz09nfT09Ea36XQ68vPzTYmuNVFz\nJ7bn7QghnJQtck1H80zT1hppvXENUfMmOToEh/CE960wGAwGa77g4cOHee+99wgODqZnz57s2rWL\nV155he+++47ffvuN2tpa7rrrLpKTk5k1axbh4eFotVpmzJhhzTCEEG5Oco0QwhyrFzlCCCGEEM5A\n5lQLIYQQwi1JkSOEEEIItyRFjhBCCCHckhQ5QgghhHBLUuQIIYQQwi15xAad9etcCCFsJyYmxrQQ\nnyeSPCOE7bU1z3hERsrPzyc1VVblEsKWMjMzSUhIcHQYDiN5Rgjba2ue8YgiJyYmhp49e7JkyRJH\nh2KR8ePHS6w2ILHazvjx4y1aEdidOSrPOOK7Isd0j+O54jHbmmc8oshRqVSo1WqXucqUWG1DYrUd\ntVrt0V1V4Lg8I8d0n2N6wnu09zFl4LEQQggh3JIUOUIIIYRwS1LkCCGEEMItKWfNmjXL0UHYS58+\nfRwdgsUkVtuQWG3H1eK1FUd8DnJM9zmmJ7xHex5TdiEXQgghhFuS7iohhBBCuCUpcoQQQgjhlqTI\nEUIIIYRbkiJHCCGEEG7J7Zcozc7OZtmyZQQHB5OYmMjo0aMdHdIFKioqWLp0KXv27OH999/n9ddf\nR6fTUVxczNSpUwkPD3d0iCaHDx/m7bffJjw8HG9vb1QqldPGmpWVxZIlS4iMjMTPzw/AaWMFMBgM\nPP744/Tq1YuqqiqnjvXzzz9n7dq1dO/enZCQEGpqapw6XluzZ55x1G/Q3t/PsrIy3nrrLdRqNdHR\n0RQVFdn0mAcPHuTDDz8kLCwMvV6PwWCw2fHM5fyioiKrf5+aHvONN95Ao9FQWFjIhAkTUCgUNj8m\nwB9//MHDDz/M1q1b7fK7cfuWnGXLljFx4kRmzJjBhg0b0Gq1jg7pArW1tTzyyCMYDAZycnIoKSlh\nypQpjBw5ko8//tjR4V3gueeeY8aMGWRlZTl1rCqVipkzZzJ9+nR27Njh1LECvP/++/Tt2xe9Xu/0\nsQIEBASgUqmIjo52iXhtyd55xhG/QXt/Pz/99FNCQkLw9vYGsPkxf/31V/785z/z1FNPsX37dpse\nz1zOt8X3qeExAa666ipmzJjByJEj2bx5s12OWVJSwpo1a0zTx+3xu3H7Iqe4uNi0oVdISAgajcbB\nEV0oPDycwMBAAIqKioiOjgYgOjqawsJCR4Z2gR49ehAREcHy5cu57LLLnDrWpKQk8vPzmTBhAlde\neaVTx7pp0yZ8fX3p168fgFPHCjB06FBeeuklpkyZwpo1a0z7VjlrvLZmzzzjiN+gI76fOTk59OvX\nj4kTJ/Ljjz/a/JhpaWm88847TJs2zXQcWx3PXM63xfep4THBWOScOHGCb7/9lr/85S82P6Zer+eN\nN95g4sSJpvvt8btx+yInJiaG/Px8AEpLSwkLC3NwRK2LjY2loKAAgJMnTxIfH+/giBrTarW8+OKL\n9O3bl7/+9a9OHeuuXbvo1q0b7777Lr///junTp0CnDPW9evXU1xczBdffMHmzZvZunUr4JyxgvEE\nVFdXB9oYxeEAAAYVSURBVEB8fLzpCsxZ47U1e+YZR/wGHfH9jIyMNP3/uro6m7/PFStW8PLLL5OR\nkQFg+nva+jvdXM63x/dp48aNfPjhh7z44osEBQXZ/Jh79uxBr9ezYsUKTpw4wWeffWaX9+n2iwEe\nPnyY9957j+DgYHr27El6erqjQ7rAjh07WLduHd999x033nij6faSkhKmTp3qVIXZ3//+dzZv3kzP\nnj0BY/JRKpVOGevmzZv5z3/+g7+/P3V1dURERFBTU+OUsdbbvHkz27ZtQ6vVOnWse/bsYenSpcTH\nxxMQEIBOp3PqeG3NnnnGkb9Be34/CwoKyMjIIDo6mpiYGMrKymx6zM2bN/Pdd98RFhZGcXExYWFh\nNjueuZxfUlJi9e9Tw2MOHz6c9evXc8MNNwDQv39/kpKSbHrMG2+8kSeeeAI/Pz8eeOAB/vnPf9rl\nd+P2RY4QQgghPJPbd1cJIYQQwjNJkSOEEEIItyRFjhBCCCHckhQ5QgghhHBLUuQIIYQQwi1JkSNa\nZY3Jd5s2beLNN9+0+PH33nsv5eXlHT6upbZv3868efPsdjwhxIUk1whbkCJHtGrRokUsWrSo3c83\nGAy8/fbbjB8/3opRWccff/zBP//5TwYMGEBRURFHjx51dEhCeCzJNcIW3H6DTtF+R48eJTIykqKi\nIjQaDYGBgezdu5e5c+fSo0cPcnJyeOaZZ9Dr9bz99tuEhISQlJTE2LFjTa+xY8cOkpOT8fHx4a23\n3iI3N5eIiAhyc3OZN28es2bN4v777yclJYV7772Xt99+G4B3332XgoICoqKiTMusAxw/fpxXXnkF\nLy8vrrrqKh544AFeeOEFtFotZ86c4dlnn6WoqIj169czffp0Pv/8c8rLywkODubnn38mMTGRnTt3\nMnfuXJYtW0ZJSQnXXnstt956K1988QWTJk2y++cshKeTXCNsRVpyRIs+//xzbr75ZtLS0lizZg0A\nK1euZOrUqbzwwgucPn0agIULFzJr1iwyMjL4/fffKS0tNb3G3r17SUlJMf07OTmZyZMn06tXL375\n5ZcWj33DDTewYMEC9u3bx5kzZ0y3L126lKeeeoolS5bg7+/P9u3b8fLyIiMjg8cee8y0021z4uLi\neOKJJ7jyyivZsmULw4YN48YbbyQpKYnevXuze/fudn9WQoj2k1wjbEVackSzampq+PHHH8nNzQWM\nm8jdfffdnD592rShWlJSEmBcfn3BggWm5xUVFREaGgpARUUFUVFRpteNjY0FICIiwpS4mtOlSxcA\nOnXqRElJiWlJ9fz8fNNr3Hnnnaxdu5a4uDjAmFjq96dqTn0carWa6urqRvcFBQXZtW9eCGEkuUbY\nkhQ5olnffPMN48eP56abbgJg8eLF7Nq1i7CwMIqKiggPD+fw4cOAMZlMnTqV0NBQjh07ZkoaYPxB\nnz171vTvvLw8AE6dOkXv3r1Rq9WmzR0bJqKTJ08SHh5OUVERERERptvj4+PJyckhLCyMpUuXcuWV\nV7J582YATpw4QXx8fKPXLCwsxMfHp9n3qFAoTIMdKyoqCA4O7tiHJoRoM8k1wpakyBHN+vzzz1my\nZInp38OHD+eDDz7gnnvu4dVXXyUxMZHg4GAUCgWPP/44M2bMwMfHh4CAAF566SXT83r16sU333zD\nyJEjAdi/fz+zZs0iPz+fRx55BG9vb95991169epFQECA6Xnr169n5cqV9OrVy3SlBjBu3Dhmz56N\nl5cXl19+Of369WP16tVMnz6dsrIypkyZQmRkJDk5OSxcuJDTp0+TnJzc7Hvs0aMH06dPZ8CAAWg0\nGvr06WPtj1EIYYbkGmFLskGnaJOjR4+iVquJj49nzJgxzJkzh06dOrX4eL1ez/33388//vEP3nvv\nPVJSUhg2bJgdI7bM1KlTeeihh+jRo4ejQxFCILlGWIe05Ig2qampYdasWURFRZGcnNxq0gHw8vLi\n0UcfZenSpXaKsO127txJWFiYJB0hnIjkGmEN0pIjhBBCCLckU8iFEEII4ZakyBFCCCGEW5IiRwgh\nhBBuSYocIYQQQrglKXKEEEII4ZakyBFCCCGEW/p/dr7tQUA1or0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee4230e9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZslyPmZKSwtixY4tUjry2ya0carWa/v3706dPH3r16sV3331n2D45OZnx48fn86oIUfGY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff36d629f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rBSApKSlb+fK6/qc9mXkYwMzMTL+8Tp06rF+/nhs3bjBnzhy2bduGnZ0dERER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qIot9xyi/Ljjz82u9327duVXbt2KV999ZWyffv2JrepzUuffPKJUlRU1LoA6snJyWk2twnXknly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\nVgqRIxAI6kVZzkHUkqca2hSBHXCH3BxXorbfd54G9mXAkSzQXSm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GwqLImT17NsuWLaNT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74QonCKvCcnJiYGV1dXAOzt7dFoNKjVaszM9E1ZWVmh1WqJjo7mxRf1QyhdXFyIiorK97hb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\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0mGrMcRjWC8/V83TWKlT1uw5lf6fLilGF6Svm3Llzh3HjxjF48GD++c9/cu7cOaD82Kcr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v3XffpaCggICAALp3714fd90VrC2eNFxaiNawd65RFIWXX36ZPXv28M9//pMbb7wRo9HI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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff36cc7e2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "journal_formula = (\"is_self_cite ~ \"\n",
    "               \"I(auth_prev_papers == 0)\"\n",
    "                \"+ I(auth_prev_papers == 1)\"\n",
    "                \" + C(gender, levels=['M', 'F'])\"\n",
    "               \"+ np.log10(auth_prev_papers + 1)\"\n",
    "                \"+ I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "              )\n",
    "df_journal_stats = {}\n",
    "for journal_cat, selected_journals in JOURNAL_NAMES.items():\n",
    "    print(journal_cat)\n",
    "    filename = \"Review_Figures/Author_papers_self_cite_gender_journals_{}.pdf\".format(journal_cat)\n",
    "    fig, journal_group_stats = plot_journal_data(journal_formula, selected_journals, filename)\n",
    "    df_journal_stats[journal_cat] = journal_group_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Author Position</th>\n",
       "      <th colspan=\"4\" halign=\"left\">First</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Journal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Biology</th>\n",
       "      <th>J Biol Chem</th>\n",
       "      <td>676859</td>\n",
       "      <td>-0.095</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.000</td>\n",
       "      <td>758553</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochemistry</th>\n",
       "      <td>182433</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.143</td>\n",
       "      <td>204527</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Virol</th>\n",
       "      <td>155081</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.013</td>\n",
       "      <td>172265</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochim Biophys Acta</th>\n",
       "      <td>104434</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.344</td>\n",
       "      <td>110138</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Bacteriol</th>\n",
       "      <td>102012</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.535</td>\n",
       "      <td>109737</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleic Acids Res</th>\n",
       "      <td>98322</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.002</td>\n",
       "      <td>104933</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEBS Lett</th>\n",
       "      <td>94021</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.005</td>\n",
       "      <td>99364</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochem J</th>\n",
       "      <td>91576</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.000</td>\n",
       "      <td>97174</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mol Cell</th>\n",
       "      <td>33538</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.182</td>\n",
       "      <td>39524</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <td>32399</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.008</td>\n",
       "      <td>37042</td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adv Exp Med Biol</th>\n",
       "      <td>21625</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.124</td>\n",
       "      <td>22072</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bioinformatics</th>\n",
       "      <td>20756</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.437</td>\n",
       "      <td>23014</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">Medicine</th>\n",
       "      <th>J Immunol</th>\n",
       "      <td>208354</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.389</td>\n",
       "      <td>228129</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood</th>\n",
       "      <td>140887</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.140</td>\n",
       "      <td>152394</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cancer Res</th>\n",
       "      <td>131329</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.057</td>\n",
       "      <td>149313</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain Res</th>\n",
       "      <td>100389</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>108379</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Circulation</th>\n",
       "      <td>92220</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.575</td>\n",
       "      <td>98741</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clin Cancer Res</th>\n",
       "      <td>91687</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.101</td>\n",
       "      <td>96551</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Clin Oncol</th>\n",
       "      <td>61722</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.093</td>\n",
       "      <td>62049</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Am Coll Cardiol</th>\n",
       "      <td>50523</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.248</td>\n",
       "      <td>52579</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Urol</th>\n",
       "      <td>49314</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.000</td>\n",
       "      <td>50379</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAMA</th>\n",
       "      <td>33674</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.001</td>\n",
       "      <td>34651</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut</th>\n",
       "      <td>31027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.065</td>\n",
       "      <td>1.000</td>\n",
       "      <td>32663</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Engl J Med</th>\n",
       "      <td>30970</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.270</td>\n",
       "      <td>31971</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lancet</th>\n",
       "      <td>26344</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.209</td>\n",
       "      <td>26301</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMJ</th>\n",
       "      <td>23987</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.009</td>\n",
       "      <td>24438</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Science</th>\n",
       "      <th>Proc Natl Acad Sci U S A</th>\n",
       "      <td>298356</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.001</td>\n",
       "      <td>334968</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ann N Y Acad Sci</th>\n",
       "      <td>52540</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.288</td>\n",
       "      <td>54224</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nature</th>\n",
       "      <td>46138</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.003</td>\n",
       "      <td>50625</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>41328</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.004</td>\n",
       "      <td>45043</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Author Position                       First                            Last  \\\n",
       "                                  citations   beta stderr p-value citations   \n",
       "         Journal                                                              \n",
       "Biology  J Biol Chem                 676859 -0.095  0.013   0.000    758553   \n",
       "         Biochemistry                182433 -0.036  0.024   0.143    204527   \n",
       "         J Virol                     155081 -0.063  0.025   0.013    172265   \n",
       "         Biochim Biophys Acta        104434 -0.028  0.029   0.344    110138   \n",
       "         J Bacteriol                 102012 -0.020  0.032   0.535    109737   \n",
       "         Nucleic Acids Res            98322 -0.107  0.035   0.002    104933   \n",
       "         FEBS Lett                    94021 -0.086  0.031   0.005     99364   \n",
       "         Biochem J                    91576 -0.176  0.034   0.000     97174   \n",
       "         Mol Cell                     33538 -0.090  0.067   0.182     39524   \n",
       "         Cell                         32399 -0.200  0.076   0.008     37042   \n",
       "         Adv Exp Med Biol             21625 -0.081  0.053   0.124     22072   \n",
       "         Bioinformatics               20756 -0.080  0.103   0.437     23014   \n",
       "Medicine J Immunol                   208354 -0.021  0.024   0.389    228129   \n",
       "         Blood                       140887 -0.041  0.028   0.140    152394   \n",
       "         Cancer Res                  131329  0.056  0.029   0.057    149313   \n",
       "         Brain Res                   100389 -0.071  0.031   0.025    108379   \n",
       "         Circulation                  92220 -0.020  0.036   0.575     98741   \n",
       "         Clin Cancer Res              91687  0.057  0.035   0.101     96551   \n",
       "         J Clin Oncol                 61722  0.069  0.041   0.093     62049   \n",
       "         J Am Coll Cardiol            50523 -0.070  0.061   0.248     52579   \n",
       "         J Urol                       49314  0.274  0.063   0.000     50379   \n",
       "         JAMA                         33674  0.164  0.050   0.001     34651   \n",
       "         Gut                          31027  0.000  0.065   1.000     32663   \n",
       "         N Engl J Med                 30970  0.068  0.061   0.270     31971   \n",
       "         Lancet                       26344 -0.074  0.059   0.209     26301   \n",
       "         BMJ                          23987  0.170  0.065   0.009     24438   \n",
       "Science  Proc Natl Acad Sci U S A    298356 -0.067  0.020   0.001    334968   \n",
       "         Ann N Y Acad Sci             52540 -0.035  0.033   0.288     54224   \n",
       "         Nature                       46138 -0.162  0.054   0.003     50625   \n",
       "         Science                      41328 -0.154  0.053   0.004     45043   \n",
       "\n",
       "Author Position                                          \n",
       "                                    beta stderr p-value  \n",
       "         Journal                                         \n",
       "Biology  J Biol Chem               0.009  0.010   0.348  \n",
       "         Biochemistry              0.030  0.017   0.084  \n",
       "         J Virol                   0.024  0.018   0.185  \n",
       "         Biochim Biophys Acta      0.003  0.025   0.915  \n",
       "         J Bacteriol               0.082  0.022   0.000  \n",
       "         Nucleic Acids Res        -0.051  0.027   0.061  \n",
       "         FEBS Lett                 0.007  0.027   0.805  \n",
       "         Biochem J                -0.075  0.026   0.004  \n",
       "         Mol Cell                 -0.203  0.045   0.000  \n",
       "         Cell                     -0.089  0.050   0.073  \n",
       "         Adv Exp Med Biol          0.101  0.056   0.070  \n",
       "         Bioinformatics            0.009  0.082   0.912  \n",
       "Medicine J Immunol                -0.017  0.017   0.324  \n",
       "         Blood                     0.000  0.022   0.984  \n",
       "         Cancer Res                0.051  0.022   0.018  \n",
       "         Brain Res                 0.004  0.028   0.882  \n",
       "         Circulation              -0.051  0.035   0.143  \n",
       "         Clin Cancer Res           0.050  0.031   0.113  \n",
       "         J Clin Oncol              0.056  0.041   0.176  \n",
       "         J Am Coll Cardiol         0.124  0.057   0.030  \n",
       "         J Urol                    0.099  0.063   0.113  \n",
       "         JAMA                     -0.012  0.055   0.825  \n",
       "         Gut                       0.028  0.069   0.683  \n",
       "         N Engl J Med              0.030  0.065   0.637  \n",
       "         Lancet                   -0.061  0.065   0.348  \n",
       "         BMJ                       0.123  0.073   0.091  \n",
       "Science  Proc Natl Acad Sci U S A -0.016  0.015   0.297  \n",
       "         Ann N Y Acad Sci          0.214  0.034   0.000  \n",
       "         Nature                    0.028  0.044   0.531  \n",
       "         Science                   0.066  0.041   0.107  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "journal_table_cols = [\n",
    "    \"Journal\", \"Author Position\", \"citations\",\n",
    "    \"beta\", \"stderr\", \"p-value\"\n",
    "]\n",
    "JOURNAL_CAT_NAMINGS={\n",
    "    \"GENERIC\": \"Science\",\n",
    "    \"MEDICINE\": \"Medicine\",\n",
    "    \"BIOLOGY\": \"Biology\",\n",
    "}\n",
    "df_journal_stats_all = pd.concat(\n",
    "    {\n",
    "        JOURNAL_CAT_NAMINGS[k]: pd.DataFrame(journal_group_stats,\n",
    "                        columns=journal_table_cols).pivot(\n",
    "            index=\"Journal\", columns=\"Author Position\"\n",
    "        ).reorder_levels([1,0], axis=1).sortlevel(0, axis=1).sort_values(\n",
    "            (\"First\", \"citations\"), ascending=False)\n",
    "        \n",
    "        for k,journal_group_stats in df_journal_stats.items()\n",
    "    }\n",
    ")\n",
    "with pd.option_context(\"display.precision\",3, 'display.float_format', lambda x: '%.3f' % x):\n",
    "    display(df_journal_stats_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrrrrrrrr}\n",
      "\\toprule\n",
      "        &                  &     First &        &        &         &      Last &        &        &         \\\\\n",
      "        &                  & citations &   beta & stderr & p-value & citations &   beta & stderr & p-value \\\\\n",
      "{} & Journal &           &        &        &         &           &        &        &         \\\\\n",
      "\\midrule\n",
      "Biology & J Biol Chem &    676859 & -0.095 &  0.013 &   0.000 &    758553 &  0.009 &  0.010 &   0.348 \\\\\n",
      "        & Biochemistry &    182433 & -0.036 &  0.024 &   0.143 &    204527 &  0.030 &  0.017 &   0.084 \\\\\n",
      "        & J Virol &    155081 & -0.063 &  0.025 &   0.013 &    172265 &  0.024 &  0.018 &   0.185 \\\\\n",
      "        & Biochim Biophys Acta &    104434 & -0.028 &  0.029 &   0.344 &    110138 &  0.003 &  0.025 &   0.915 \\\\\n",
      "        & J Bacteriol &    102012 & -0.020 &  0.032 &   0.535 &    109737 &  0.082 &  0.022 &   0.000 \\\\\n",
      "        & Nucleic Acids Res &     98322 & -0.107 &  0.035 &   0.002 &    104933 & -0.051 &  0.027 &   0.061 \\\\\n",
      "        & FEBS Lett &     94021 & -0.086 &  0.031 &   0.005 &     99364 &  0.007 &  0.027 &   0.805 \\\\\n",
      "        & Biochem J &     91576 & -0.176 &  0.034 &   0.000 &     97174 & -0.075 &  0.026 &   0.004 \\\\\n",
      "        & Mol Cell &     33538 & -0.090 &  0.067 &   0.182 &     39524 & -0.203 &  0.045 &   0.000 \\\\\n",
      "        & Cell &     32399 & -0.200 &  0.076 &   0.008 &     37042 & -0.089 &  0.050 &   0.073 \\\\\n",
      "        & Adv Exp Med Biol &     21625 & -0.081 &  0.053 &   0.124 &     22072 &  0.101 &  0.056 &   0.070 \\\\\n",
      "        & Bioinformatics &     20756 & -0.080 &  0.103 &   0.437 &     23014 &  0.009 &  0.082 &   0.912 \\\\\n",
      "Medicine & J Immunol &    208354 & -0.021 &  0.024 &   0.389 &    228129 & -0.017 &  0.017 &   0.324 \\\\\n",
      "        & Blood &    140887 & -0.041 &  0.028 &   0.140 &    152394 &  0.000 &  0.022 &   0.984 \\\\\n",
      "        & Cancer Res &    131329 &  0.056 &  0.029 &   0.057 &    149313 &  0.051 &  0.022 &   0.018 \\\\\n",
      "        & Brain Res &    100389 & -0.071 &  0.031 &   0.025 &    108379 &  0.004 &  0.028 &   0.882 \\\\\n",
      "        & Circulation &     92220 & -0.020 &  0.036 &   0.575 &     98741 & -0.051 &  0.035 &   0.143 \\\\\n",
      "        & Clin Cancer Res &     91687 &  0.057 &  0.035 &   0.101 &     96551 &  0.050 &  0.031 &   0.113 \\\\\n",
      "        & J Clin Oncol &     61722 &  0.069 &  0.041 &   0.093 &     62049 &  0.056 &  0.041 &   0.176 \\\\\n",
      "        & J Am Coll Cardiol &     50523 & -0.070 &  0.061 &   0.248 &     52579 &  0.124 &  0.057 &   0.030 \\\\\n",
      "        & J Urol &     49314 &  0.274 &  0.063 &   0.000 &     50379 &  0.099 &  0.063 &   0.113 \\\\\n",
      "        & JAMA &     33674 &  0.164 &  0.050 &   0.001 &     34651 & -0.012 &  0.055 &   0.825 \\\\\n",
      "        & Gut &     31027 &  0.000 &  0.065 &   1.000 &     32663 &  0.028 &  0.069 &   0.683 \\\\\n",
      "        & N Engl J Med &     30970 &  0.068 &  0.061 &   0.270 &     31971 &  0.030 &  0.065 &   0.637 \\\\\n",
      "        & Lancet &     26344 & -0.074 &  0.059 &   0.209 &     26301 & -0.061 &  0.065 &   0.348 \\\\\n",
      "        & BMJ &     23987 &  0.170 &  0.065 &   0.009 &     24438 &  0.123 &  0.073 &   0.091 \\\\\n",
      "Science & Proc Natl Acad Sci U S A &    298356 & -0.067 &  0.020 &   0.001 &    334968 & -0.016 &  0.015 &   0.297 \\\\\n",
      "        & Ann N Y Acad Sci &     52540 & -0.035 &  0.033 &   0.288 &     54224 &  0.214 &  0.034 &   0.000 \\\\\n",
      "        & Nature &     46138 & -0.162 &  0.054 &   0.003 &     50625 &  0.028 &  0.044 &   0.531 \\\\\n",
      "        & Science &     41328 & -0.154 &  0.053 &   0.004 &     45043 &  0.066 &  0.041 &   0.107 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.precision\",3, 'display.float_format', lambda x: '%.3f' % x):\n",
    "    print(df_journal_stats_all.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 8)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journal_stats_all.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot full models for journals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite',\n",
       "       u'first_year', u'last_year', u'au_age', u'match_len', u'match_prop',\n",
       "       u'overall_coverage_len', u'overall_coverage_prop', u'eth_weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "journal_model_formula = (\"is_self_cite ~ \"\n",
    "            \"I(auth_prev_papers == 0)\"\n",
    "           \"+ I(auth_prev_papers == 1)\"\n",
    "           \"+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "           \"+ C(gender, levels=['M', 'F'])\"\n",
    "           #\"+ C(source_country, levels=TOP_15_COUNTRIES)\"\n",
    "           #\"+ mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)\"\n",
    "           #\"+ I(source_ncites == 1)\"\n",
    "           \"+ np.log10(source_ncites)\"\n",
    "           \"+ I(np.log10(source_ncites)**2) \"#\"+ I(np.log10(source_ncites)**3)\"\n",
    "           #\"+ I(source_n_authors > 20)\"\n",
    "           #              \" + np.log10(np.clip(source_n_authors, 0, 20))\"\n",
    "           #\"+ I(np.log10(np.clip(source_n_authors, 0, 20)) ** 2)\"\n",
    "           #\"+ np.log10(source_n_mesh_ex + 1) + \"#\"I(source_n_mesh_ex == 0)\" \n",
    "           #\"+ np.log10(sink_n_mesh_ex + 1) + I(sink_n_mesh_ex == 0)\"\n",
    "           \"+ I(year_span < 0) + I(year_span == 0)\"\n",
    "                         \" + mf.score_log_1(year_span) + I(mf.score_log_1(year_span)**2)\"\n",
    "           \"+ I(sink_prev_ncites == 0) \"\n",
    "                         \"+ np.log10(sink_prev_ncites + 1) + I(np.log10(sink_prev_ncites + 1)**2)\"\n",
    "           \"+ I(jj_sim == 0) + np.log10(jj_sim + 1) + I(np.log10(jj_sim + 1)**2) + journal_same\"\n",
    "           #\"+ source_is_eng + source_is_journal + source_is_review + source_is_case_rep + source_is_let_ed_com\"\n",
    "           #\"+ sink_is_eng + sink_is_journal + sink_is_review + sink_is_case_rep + sink_is_let_ed_com\"\n",
    "           #\"+ np.log10(np.nan_to_num(source_V_novelty) + 1)\"\n",
    "           #\"+ np.log10(np.nan_to_num(sink_V_novelty) + 1) + I(np.log10(np.nan_to_num(sink_V_novelty) + 1)**2)\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Cancer Res', 48966)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Cancer Res', 48966)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Circulation', 40094)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Circulation', 40094)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('N Engl J Med', 72020)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('N Engl J Med', 72020)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('BMJ', 65858)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('BMJ', 65858)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Am Coll Cardiol', 21580)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Am Coll Cardiol', 21580)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Clin Oncol', 21075)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Clin Oncol', 21075)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Immunol', 62245)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Immunol', 62245)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('JAMA', 66849)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('JAMA', 66849)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Gut', 15952)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Gut', 15952)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Clin Cancer Res', 14845)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Clin Cancer Res', 14845)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Brain Res', 56834)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Brain Res', 56834)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Urol', 47368)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Urol', 47368)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Lancet', 129945)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Lancet', 129945)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Blood', 43160)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Blood', 43160)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Science', 167415)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Science', 167415)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Proc Natl Acad Sci U S A', 121705)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Proc Natl Acad Sci U S A', 121705)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Ann N Y Acad Sci', 47684)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Ann N Y Acad Sci', 47684)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Nature', 104418)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Nature', 104418)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Mol Cell', 5804)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Mol Cell', 5804)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Biochim Biophys Acta', 96039)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Biochim Biophys Acta', 96039)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Adv Exp Med Biol', 31938)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Adv Exp Med Biol', 31938)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Biochem J', 54355)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Biochem J', 54355)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Biochemistry', 62430)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Biochemistry', 62430)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Nucleic Acids Res', 39570)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Nucleic Acids Res', 39570)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('FEBS Lett', 46770)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('FEBS Lett', 46770)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Cell', 17515)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Cell', 17515)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Bacteriol', 51716)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Bacteriol', 51716)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('Bioinformatics', 10026)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('Bioinformatics', 10026)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Virol', 42269)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Virol', 42269)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Biol Chem', 171068)\n",
      "Last\n",
      "('J Biol Chem', 171068)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "journal_full_model_stats = dict()\n",
    "for journal_cat, selected_journals in JOURNAL_NAMES.items():\n",
    "    journal_group_full_model_stats = []\n",
    "    for journal_name in selected_journals:\n",
    "        for i, (df_t_data, title) in enumerate(zip(\n",
    "                        [df_t_first, df_t_last],\n",
    "                        [\"First\", \"Last\"],\n",
    "                    )):\n",
    "            print(title)\n",
    "            model_stats = [np.nan, np.nan, np.nan]\n",
    "            df_t_data = get_journal_data(df_t_data, journal_name).copy()\n",
    "            #prepare_data(df_t_data)\n",
    "            try:\n",
    "                y_test, model = model_predictions(\n",
    "                    df_t_data, journal_model_formula,\n",
    "                    df_t_data.iloc[:3], verbose=False,\n",
    "                    model_params=dict(method='lbfgs', maxiter=100))\n",
    "                model_stats = model.summary2().tables[1].ix[\n",
    "                        \"C(gender, levels=['M', 'F'])[T.F]\",\n",
    "                        [\"Coef.\", \"Std.Err.\", \"P>|z|\"]].values.tolist()\n",
    "            except:\n",
    "                print(\"Failed to fit\")\n",
    "            journal_group_full_model_stats.append(\n",
    "                [journal_name, title, df_t_data.shape[0]]+model_stats\n",
    "            )\n",
    "    journal_full_model_stats[journal_cat] = journal_group_full_model_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Author Position</th>\n",
       "      <th colspan=\"4\" halign=\"left\">First</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Journal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Biology</th>\n",
       "      <th>J Biol Chem</th>\n",
       "      <td>676859</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.000</td>\n",
       "      <td>758553</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochemistry</th>\n",
       "      <td>182433</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.859</td>\n",
       "      <td>204527</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Virol</th>\n",
       "      <td>155081</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.018</td>\n",
       "      <td>172265</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochim Biophys Acta</th>\n",
       "      <td>104434</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.895</td>\n",
       "      <td>110138</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Bacteriol</th>\n",
       "      <td>102012</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.265</td>\n",
       "      <td>109737</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleic Acids Res</th>\n",
       "      <td>98322</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.315</td>\n",
       "      <td>104933</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEBS Lett</th>\n",
       "      <td>94021</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.248</td>\n",
       "      <td>99364</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochem J</th>\n",
       "      <td>91576</td>\n",
       "      <td>-0.153</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.000</td>\n",
       "      <td>97174</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mol Cell</th>\n",
       "      <td>33538</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.124</td>\n",
       "      <td>39524</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <td>32399</td>\n",
       "      <td>-0.151</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.047</td>\n",
       "      <td>37042</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adv Exp Med Biol</th>\n",
       "      <td>21625</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.069</td>\n",
       "      <td>22072</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bioinformatics</th>\n",
       "      <td>20756</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.336</td>\n",
       "      <td>23014</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">Medicine</th>\n",
       "      <th>J Immunol</th>\n",
       "      <td>208354</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.254</td>\n",
       "      <td>228129</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood</th>\n",
       "      <td>140887</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.217</td>\n",
       "      <td>152394</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cancer Res</th>\n",
       "      <td>131329</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.100</td>\n",
       "      <td>149313</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain Res</th>\n",
       "      <td>100389</td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.003</td>\n",
       "      <td>108379</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Circulation</th>\n",
       "      <td>92220</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.155</td>\n",
       "      <td>98741</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clin Cancer Res</th>\n",
       "      <td>91687</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.066</td>\n",
       "      <td>96551</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Clin Oncol</th>\n",
       "      <td>61722</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.064</td>\n",
       "      <td>62049</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Am Coll Cardiol</th>\n",
       "      <td>50523</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.271</td>\n",
       "      <td>52579</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Urol</th>\n",
       "      <td>49314</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.000</td>\n",
       "      <td>50379</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAMA</th>\n",
       "      <td>33674</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.000</td>\n",
       "      <td>34651</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut</th>\n",
       "      <td>31027</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.605</td>\n",
       "      <td>32663</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Engl J Med</th>\n",
       "      <td>30970</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.780</td>\n",
       "      <td>31971</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lancet</th>\n",
       "      <td>26344</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.207</td>\n",
       "      <td>26301</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMJ</th>\n",
       "      <td>23987</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.396</td>\n",
       "      <td>24438</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Science</th>\n",
       "      <th>Proc Natl Acad Sci U S A</th>\n",
       "      <td>298356</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.688</td>\n",
       "      <td>334968</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ann N Y Acad Sci</th>\n",
       "      <td>52540</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.929</td>\n",
       "      <td>54224</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nature</th>\n",
       "      <td>46138</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.047</td>\n",
       "      <td>50625</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>41328</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.025</td>\n",
       "      <td>45043</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Author Position                       First                            Last  \\\n",
       "                                  citations   beta stderr p-value citations   \n",
       "         Journal                                                              \n",
       "Biology  J Biol Chem                 676859 -0.103  0.014   0.000    758553   \n",
       "         Biochemistry                182433 -0.005  0.026   0.859    204527   \n",
       "         J Virol                     155081 -0.063  0.027   0.018    172265   \n",
       "         Biochim Biophys Acta        104434 -0.004  0.031   0.895    110138   \n",
       "         J Bacteriol                 102012 -0.038  0.034   0.265    109737   \n",
       "         Nucleic Acids Res            98322 -0.037  0.037   0.315    104933   \n",
       "         FEBS Lett                    94021 -0.037  0.032   0.248     99364   \n",
       "         Biochem J                    91576 -0.153  0.036   0.000     97174   \n",
       "         Mol Cell                     33538 -0.105  0.068   0.124     39524   \n",
       "         Cell                         32399 -0.151  0.076   0.047     37042   \n",
       "         Adv Exp Med Biol             21625 -0.101  0.055   0.069     22072   \n",
       "         Bioinformatics               20756 -0.105  0.109   0.336     23014   \n",
       "Medicine J Immunol                   208354 -0.028  0.025   0.254    228129   \n",
       "         Blood                       140887 -0.035  0.028   0.217    152394   \n",
       "         Cancer Res                  131329  0.050  0.030   0.100    149313   \n",
       "         Brain Res                   100389 -0.099  0.034   0.003    108379   \n",
       "         Circulation                  92220  0.053  0.037   0.155     98741   \n",
       "         Clin Cancer Res              91687  0.066  0.036   0.066     96551   \n",
       "         J Clin Oncol                 61722  0.078  0.042   0.064     62049   \n",
       "         J Am Coll Cardiol            50523 -0.068  0.062   0.271     52579   \n",
       "         J Urol                       49314  0.364  0.066   0.000     50379   \n",
       "         JAMA                         33674  0.204  0.051   0.000     34651   \n",
       "         Gut                          31027  0.035  0.067   0.605     32663   \n",
       "         N Engl J Med                 30970  0.017  0.063   0.780     31971   \n",
       "         Lancet                       26344 -0.075  0.059   0.207     26301   \n",
       "         BMJ                          23987  0.057  0.067   0.396     24438   \n",
       "Science  Proc Natl Acad Sci U S A    298356 -0.008  0.020   0.688    334968   \n",
       "         Ann N Y Acad Sci             52540  0.003  0.034   0.929     54224   \n",
       "         Nature                       46138 -0.110  0.055   0.047     50625   \n",
       "         Science                      41328 -0.122  0.054   0.025     45043   \n",
       "\n",
       "Author Position                                          \n",
       "                                    beta stderr p-value  \n",
       "         Journal                                         \n",
       "Biology  J Biol Chem               0.015  0.010   0.122  \n",
       "         Biochemistry              0.024  0.018   0.186  \n",
       "         J Virol                   0.005  0.019   0.781  \n",
       "         Biochim Biophys Acta      0.050  0.026   0.057  \n",
       "         J Bacteriol               0.063  0.023   0.006  \n",
       "         Nucleic Acids Res        -0.031  0.028   0.271  \n",
       "         FEBS Lett                 0.005  0.028   0.857  \n",
       "         Biochem J                -0.080  0.027   0.003  \n",
       "         Mol Cell                 -0.176  0.045   0.000  \n",
       "         Cell                     -0.122  0.051   0.016  \n",
       "         Adv Exp Med Biol          0.091  0.058   0.114  \n",
       "         Bioinformatics           -0.006  0.085   0.941  \n",
       "Medicine J Immunol                -0.020  0.018   0.253  \n",
       "         Blood                     0.003  0.023   0.896  \n",
       "         Cancer Res                0.041  0.022   0.064  \n",
       "         Brain Res                 0.016  0.029   0.571  \n",
       "         Circulation              -0.034  0.036   0.341  \n",
       "         Clin Cancer Res           0.048  0.032   0.135  \n",
       "         J Clin Oncol              0.085  0.042   0.042  \n",
       "         J Am Coll Cardiol         0.202  0.058   0.000  \n",
       "         J Urol                    0.162  0.064   0.012  \n",
       "         JAMA                     -0.059  0.056   0.290  \n",
       "         Gut                       0.065  0.070   0.353  \n",
       "         N Engl J Med              0.007  0.066   0.920  \n",
       "         Lancet                   -0.011  0.065   0.872  \n",
       "         BMJ                       0.126  0.075   0.092  \n",
       "Science  Proc Natl Acad Sci U S A  0.021  0.015   0.161  \n",
       "         Ann N Y Acad Sci          0.216  0.035   0.000  \n",
       "         Nature                    0.017  0.044   0.709  \n",
       "         Science                   0.081  0.041   0.051  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "journal_table_cols = [\n",
    "    \"Journal\", \"Author Position\", \"citations\",\n",
    "    \"beta\", \"stderr\", \"p-value\"\n",
    "]\n",
    "JOURNAL_CAT_NAMINGS={\n",
    "    \"GENERIC\": \"Science\",\n",
    "    \"MEDICINE\": \"Medicine\",\n",
    "    \"BIOLOGY\": \"Biology\",\n",
    "}\n",
    "\n",
    "df_journal_full_model_stats = pd.concat(\n",
    "    {\n",
    "        JOURNAL_CAT_NAMINGS[k]: pd.DataFrame(journal_group_stats,\n",
    "                        columns=journal_table_cols).pivot(\n",
    "            index=\"Journal\", columns=\"Author Position\"\n",
    "        ).reorder_levels([1,0], axis=1).sortlevel(0, axis=1).sort_values(\n",
    "            (\"First\", \"citations\"), ascending=False)\n",
    "        \n",
    "        for k,journal_group_stats in journal_full_model_stats.items()\n",
    "    }\n",
    ")\n",
    "with pd.option_context(\"display.precision\",3, 'display.float_format', lambda x: '%.3f' % x):\n",
    "    display(df_journal_full_model_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medicine\n",
      "Science\n",
      "Biology\n"
     ]
    },
    {
     "data": {
      "image/png": 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uyu/3++1aLC8vTw8++KCuu+46jRo1SlOnTlXjxo2DeqzX61VaWpoKCgqUmpoa4kqB8EWO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\nAAAAG/1f2bNZnJ+ikaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff367a5f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3,4, sharex=True, sharey=\"row\", figsize=(12,9))\n",
    "bins=[-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3]\n",
    "for j, journal_cat in enumerate(set(df_journal_stats_all.index.get_level_values(0).tolist())):\n",
    "    print journal_cat\n",
    "    for i, title in enumerate([\"First\", \"Last\"]):\n",
    "        df_journal_stats_all.reset_index(level=1).ix[\n",
    "            journal_cat, (title, \"beta\")].plot(kind=\"hist\", bins=bins, ax=ax[j, 2*i])\n",
    "        df_journal_full_model_stats.reset_index(level=1).ix[\n",
    "            journal_cat, (title, \"beta\")].plot(kind=\"hist\", bins=bins, ax=ax[j, 2*i+1])\n",
    "        ax[j, 2*i].set_title(\"Small Model ({})\".format(title))\n",
    "        ax[j, 2*i].axvline(x=0, color=\"r\", linestyle=\"--\", lw=1)\n",
    "        ax[j, 2*i+1].set_title(\"Full Model ({})\".format(title))\n",
    "        ax[j, 2*i+1].axvline(x=0, color=\"r\", linestyle=\"--\", lw=1)\n",
    "    ax[j, 0].set_ylabel(journal_cat)\n",
    "sns.despine(offset=10)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.0, 0.0, 0.0), (0.0, 0.0, 0.0)]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette([\"#ff0000\", \"#000000\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SoX2uXbvGm2++aUR4IiJlXnR0NEOHDmXGjBn89a9/Zffu3Rw7doxvv/22WMeH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3c139eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette([\"#ff0000\", \"#000000\"]):\n",
    "    fig, ax = plt.subplots(2,3, sharex=True, figsize=(15, 8))\n",
    "    for i, title in enumerate([\"First\", \"Last\"]):\n",
    "        for j, journal_name in enumerate([\"Science\", \"Biology\", \"Medicine\"]):\n",
    "            pd.concat({\n",
    "                \"small\": df_journal_stats_all.ix[journal_name,(title, \"beta\")],\n",
    "                \"big\": df_journal_full_model_stats.ix[journal_name,(title, \"beta\")]\n",
    "            }, axis=1).plot.barh(ax=ax[i,j])\n",
    "            ax[i,j].set_ylabel(journal_name)\n",
    "            ax[i,j].axvline(x=0, linestyle=\"--\", color=\"0.5\", linewidth=0.5)\n",
    "        ax[i, 1].set_title(title)\n",
    "\n",
    "    sns.despine(offset=10)\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(\"Review_Figures/Journal_cat_betas_big_small.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indiv journal analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "journal_model_formula = (\"is_self_cite ~ \"\n",
    "            \"I(auth_prev_papers == 0)\"\n",
    "           \"+ I(auth_prev_papers == 1)\"\n",
    "           \"+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "           \"+ C(gender, levels=['M', 'F'])\"\n",
    "           \"+ C(source_country, levels=TOP_15_COUNTRIES)\"\n",
    "           \"+ mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)\"\n",
    "           #\"+ I(source_ncites == 1)\"\n",
    "           \"+ np.log10(source_ncites)\"\n",
    "           \"+ I(np.log10(source_ncites)**2) \"#\"+ I(np.log10(source_ncites)**3)\"\n",
    "           #\"+ I(source_n_authors > 20)\"\n",
    "           #              \" + np.log10(np.clip(source_n_authors, 0, 20))\"\n",
    "           #\"+ I(np.log10(np.clip(source_n_authors, 0, 20)) ** 2)\"\n",
    "           \"+ np.log10(source_n_mesh_ex + 1) + \"#\"I(source_n_mesh_ex == 0)\" \n",
    "           \"+ np.log10(sink_n_mesh_ex + 1) + I(sink_n_mesh_ex == 0)\"\n",
    "           \"+ I(year_span < 0) + I(year_span == 0)\"\n",
    "                         \" + mf.score_log_1(year_span) + I(mf.score_log_1(year_span)**2)\"\n",
    "           \"+ I(sink_prev_ncites == 0) \"\n",
    "                         \"+ np.log10(sink_prev_ncites + 1) + I(np.log10(sink_prev_ncites + 1)**2)\"\n",
    "           \"+ I(jj_sim == 0) + np.log10(jj_sim + 1) + I(np.log10(jj_sim + 1)**2) + journal_same\"\n",
    "           \"+ source_is_eng + source_is_journal + source_is_review + source_is_case_rep + source_is_let_ed_com\"\n",
    "           \"+ sink_is_eng + sink_is_journal + sink_is_review + sink_is_case_rep + sink_is_let_ed_com\"\n",
    "           \"+ np.log10(np.nan_to_num(source_V_novelty) + 1)\"\n",
    "           \"+ np.log10(np.nan_to_num(sink_V_novelty) + 1) + I(np.log10(np.nan_to_num(sink_V_novelty) + 1)**2)\"\n",
    "            \"+ I(match_prop == 0) + I(match_prop == 1) + match_prop + I(match_prop**2)\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "('J Biol Chem', 171068)\n",
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n",
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n",
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.234</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>   <td>is_self_cite</td>         <td>AIC:</td>        <td>179540.6798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2017-08-17 12:10</td>       <td>BIC:</td>        <td>180294.7442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>676859</td>       <td>Log-Likelihood:</td>    <td>-89704.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>             <td>65</td>            <td>LL-Null:</td>      <td>-1.1704e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>676793</td>        <td>LLR p-value:</td>      <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>0.0000</td>           <td>Scale:</td>         <td>1.0000</td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                    <td></td>                                    <th>Coef.</th>    <th>Std.Err.</th>       <th>z</th>     <th>P>|z|</th>    <th>[0.025</th>        <th>0.975]</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                            <td>-1.1151</td> <td>1014742.2815</td>  <td>-0.0000</td> <td>1.0000</td> <td>-1988859.4405</td> <td>1988857.2103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(auth_prev_papers == 0)[T.True]</th>                                     <td>-0.3155</td>    <td>0.3863</td>     <td>-0.8168</td> <td>0.4140</td>    <td>-1.0726</td>       <td>0.4416</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(auth_prev_papers == 1)[T.True]</th>                                     <td>-0.0406</td>    <td>0.0391</td>     <td>-1.0381</td> <td>0.2992</td>    <td>-0.1171</td>       <td>0.0360</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(gender, levels=['M', 'F'])[T.F]</th>                                    <td>-0.0968</td>    <td>0.0143</td>     <td>-6.7576</td> <td>0.0000</td>    <td>-0.1249</td>       <td>-0.0688</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.UNKNOWN]</th>                <td>-0.1516</td>    <td>0.2209</td>     <td>-0.6863</td> <td>0.4925</td>    <td>-0.5845</td>       <td>0.2813</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.UK]</th>                     <td>-0.1262</td>    <td>0.0301</td>     <td>-4.1859</td> <td>0.0000</td>    <td>-0.1853</td>       <td>-0.0671</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.JAPAN]</th>                  <td>0.2444</td>     <td>0.0374</td>     <td>6.5406</td>  <td>0.0000</td>    <td>0.1712</td>        <td>0.3177</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.GERMANY]</th>                <td>0.1017</td>     <td>0.0331</td>     <td>3.0733</td>  <td>0.0021</td>    <td>0.0368</td>        <td>0.1665</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.FRANCE]</th>                 <td>-0.0219</td>    <td>0.0387</td>     <td>-0.5650</td> <td>0.5721</td>    <td>-0.0977</td>       <td>0.0540</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.ITALY]</th>                  <td>0.1600</td>     <td>0.0554</td>     <td>2.8902</td>  <td>0.0038</td>    <td>0.0515</td>        <td>0.2685</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.CANADA]</th>                 <td>0.0903</td>     <td>0.0314</td>     <td>2.8797</td>  <td>0.0040</td>    <td>0.0288</td>        <td>0.1518</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.CHINA]</th>                  <td>-0.3567</td>    <td>0.2333</td>     <td>-1.5287</td> <td>0.1263</td>    <td>-0.8141</td>       <td>0.1006</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.AUSTRALIA]</th>              <td>-0.0291</td>    <td>0.0478</td>     <td>-0.6078</td> <td>0.5434</td>    <td>-0.1228</td>       <td>0.0647</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.SPAIN]</th>                  <td>0.1367</td>     <td>0.0545</td>     <td>2.5102</td>  <td>0.0121</td>    <td>0.0300</td>        <td>0.2434</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.NETHERLANDS]</th>            <td>0.1273</td>     <td>0.0658</td>     <td>1.9354</td>  <td>0.0529</td>    <td>-0.0016</td>       <td>0.2562</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.SWEDEN]</th>                 <td>0.0434</td>     <td>0.0543</td>     <td>0.7994</td>  <td>0.4241</td>    <td>-0.0630</td>       <td>0.1498</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.INDIA]</th>                  <td>0.2383</td>     <td>0.1171</td>     <td>2.0358</td>  <td>0.0418</td>    <td>0.0089</td>        <td>0.4677</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.OTHER]</th>                  <td>0.0448</td>     <td>0.0246</td>     <td>1.8203</td>  <td>0.0687</td>    <td>-0.0034</td>       <td>0.0930</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(sink_n_mesh_ex == 0)[T.True]</th>                                       <td>0.0896</td>     <td>0.1947</td>     <td>0.4603</td>  <td>0.6453</td>    <td>-0.2920</td>       <td>0.4712</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(year_span < 0)[T.True]</th>                                             <td>0.0594</td>     <td>1.0043</td>     <td>0.0591</td>  <td>0.9528</td>    <td>-1.9090</td>       <td>2.0278</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(year_span == 0)[T.True]</th>                                            <td>0.8778</td>     <td>0.0508</td>     <td>17.2810</td> <td>0.0000</td>    <td>0.7782</td>        <td>0.9773</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(sink_prev_ncites == 0)[T.True]</th>                                     <td>0.0694</td>     <td>0.0356</td>     <td>1.9506</td>  <td>0.0511</td>    <td>-0.0003</td>       <td>0.1392</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(jj_sim == 0)[T.True]</th>                                               <td>-0.4495</td>    <td>0.7676</td>     <td>-0.5856</td> <td>0.5582</td>    <td>-1.9539</td>       <td>1.0549</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>journal_same[T.True]</th>                                                 <td>0.5355</td>     <td>0.0515</td>     <td>10.3910</td> <td>0.0000</td>    <td>0.4345</td>        <td>0.6365</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_eng[T.True]</th>                                                <td>-1.1151</td> <td>1014742.2815</td>  <td>-0.0000</td> <td>1.0000</td> <td>-1988859.4405</td> <td>1988857.2103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_journal[T.True]</th>                                            <td>-1.1548</td>    <td>0.5198</td>     <td>-2.2217</td> <td>0.0263</td>    <td>-2.1735</td>       <td>-0.1360</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_review[T.True]</th>                                             <td>0.0277</td>     <td>0.0715</td>     <td>0.3872</td>  <td>0.6986</td>    <td>-0.1124</td>       <td>0.1677</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_case_rep[T.True]</th>                                           <td>-0.1183</td>    <td>0.3727</td>     <td>-0.3175</td> <td>0.7509</td>    <td>-0.8489</td>       <td>0.6122</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_let_ed_com[T.True]</th>                                         <td>0.1008</td>     <td>0.7345</td>     <td>0.1372</td>  <td>0.8909</td>    <td>-1.3389</td>       <td>1.5404</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_eng[T.True]</th>                                                  <td>-0.6509</td>    <td>0.3638</td>     <td>-1.7890</td> <td>0.0736</td>    <td>-1.3640</td>       <td>0.0622</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_journal[T.True]</th>                                              <td>0.4820</td>     <td>0.1462</td>     <td>3.2963</td>  <td>0.0010</td>    <td>0.1954</td>        <td>0.7686</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_review[T.True]</th>                                               <td>-1.0143</td>    <td>0.0296</td>    <td>-34.2150</td> <td>0.0000</td>    <td>-1.0724</td>       <td>-0.9562</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_case_rep[T.True]</th>                                             <td>-0.7960</td>    <td>0.1584</td>     <td>-5.0264</td> <td>0.0000</td>    <td>-1.1063</td>       <td>-0.4856</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_let_ed_com[T.True]</th>                                           <td>-0.7621</td>    <td>0.1487</td>     <td>-5.1242</td> <td>0.0000</td>    <td>-1.0536</td>       <td>-0.4706</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(match_prop == 0)[T.True]</th>                                           <td>-0.2242</td>    <td>0.3861</td>     <td>-0.5806</td> <td>0.5615</td>    <td>-0.9810</td>       <td>0.5326</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(match_prop == 1)[T.True]</th>                                           <td>0.0969</td>     <td>0.0389</td>     <td>2.4908</td>  <td>0.0127</td>    <td>0.0206</td>        <td>0.1731</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(auth_prev_papers + 1)</th>                                       <td>0.4224</td>     <td>0.0791</td>     <td>5.3403</td>  <td>0.0000</td>    <td>0.2674</td>        <td>0.5775</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(auth_prev_papers + 1) ** 2)</th>                               <td>0.1131</td>     <td>0.0303</td>     <td>3.7324</td>  <td>0.0002</td>    <td>0.0537</td>        <td>0.1725</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[0]</th>  <td>-0.0599</td>    <td>0.0298</td>     <td>-2.0092</td> <td>0.0445</td>    <td>-0.1183</td>       <td>-0.0015</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[1]</th>  <td>-0.1120</td>    <td>0.0350</td>     <td>-3.2019</td> <td>0.0014</td>    <td>-0.1805</td>       <td>-0.0434</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[2]</th>  <td>0.0933</td>     <td>0.0311</td>     <td>3.0022</td>  <td>0.0027</td>    <td>0.0324</td>        <td>0.1543</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[3]</th>  <td>-0.2355</td>    <td>0.0361</td>     <td>-6.5291</td> <td>0.0000</td>    <td>-0.3062</td>       <td>-0.1648</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[4]</th>  <td>-0.0574</td>    <td>0.0319</td>     <td>-1.8003</td> <td>0.0718</td>    <td>-0.1198</td>       <td>0.0051</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[5]</th>  <td>-0.1281</td>    <td>0.0330</td>     <td>-3.8871</td> <td>0.0001</td>    <td>-0.1928</td>       <td>-0.0635</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[6]</th>  <td>-0.1766</td>    <td>0.0488</td>     <td>-3.6194</td> <td>0.0003</td>    <td>-0.2722</td>       <td>-0.0810</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[7]</th>  <td>-0.0676</td>    <td>0.0363</td>     <td>-1.8632</td> <td>0.0624</td>    <td>-0.1387</td>       <td>0.0035</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[8]</th>  <td>-0.0178</td>    <td>0.0431</td>     <td>-0.4119</td> <td>0.6804</td>    <td>-0.1023</td>       <td>0.0668</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[9]</th>  <td>-0.2396</td>    <td>0.0552</td>     <td>-4.3411</td> <td>0.0000</td>    <td>-0.3478</td>       <td>-0.1314</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[10]</th> <td>-0.0564</td>    <td>0.0588</td>     <td>-0.9600</td> <td>0.3371</td>    <td>-0.1717</td>       <td>0.0588</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[11]</th> <td>-0.1512</td>    <td>0.0515</td>     <td>-2.9346</td> <td>0.0033</td>    <td>-0.2522</td>       <td>-0.0502</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[12]</th> <td>-0.0210</td>    <td>0.1207</td>     <td>-0.1742</td> <td>0.8617</td>    <td>-0.2576</td>       <td>0.2156</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[13]</th> <td>0.0319</td>     <td>0.0376</td>     <td>0.8497</td>  <td>0.3955</td>    <td>-0.0417</td>       <td>0.1056</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(source_ncites)</th>                                              <td>-1.0072</td>    <td>0.9217</td>     <td>-1.0927</td> <td>0.2745</td>    <td>-2.8137</td>       <td>0.7994</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(source_ncites) ** 2)</th>                                      <td>-0.2492</td>    <td>0.2990</td>     <td>-0.8333</td> <td>0.4047</td>    <td>-0.8352</td>       <td>0.3369</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(source_n_mesh_ex + 1)</th>                                       <td>0.7598</td>     <td>0.0475</td>     <td>15.9939</td> <td>0.0000</td>    <td>0.6667</td>        <td>0.8529</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(sink_n_mesh_ex + 1)</th>                                         <td>0.0052</td>     <td>0.0472</td>     <td>0.1100</td>  <td>0.9124</td>    <td>-0.0872</td>       <td>0.0976</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.score_log_1(year_span)</th>                                            <td>2.9238</td>     <td>0.1599</td>     <td>18.2805</td> <td>0.0000</td>    <td>2.6103</td>        <td>3.2372</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(mf.score_log_1(year_span) ** 2)</th>                                    <td>-2.7949</td>    <td>0.1094</td>    <td>-25.5502</td> <td>0.0000</td>    <td>-3.0093</td>       <td>-2.5805</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(sink_prev_ncites + 1)</th>                                       <td>-0.5054</td>    <td>0.0613</td>     <td>-8.2510</td> <td>0.0000</td>    <td>-0.6255</td>       <td>-0.3854</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(sink_prev_ncites + 1) ** 2)</th>                               <td>-0.3242</td>    <td>0.0237</td>    <td>-13.6848</td> <td>0.0000</td>    <td>-0.3707</td>       <td>-0.2778</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(jj_sim + 1)</th>                                                 <td>-0.4062</td>    <td>1.5699</td>     <td>-0.2587</td> <td>0.7958</td>    <td>-3.4832</td>       <td>2.6708</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(jj_sim + 1) ** 2)</th>                                         <td>0.0404</td>     <td>0.7995</td>     <td>0.0506</td>  <td>0.9597</td>    <td>-1.5265</td>       <td>1.6074</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(np.nan_to_num(source_V_novelty) + 1)</th>                        <td>-0.0181</td>    <td>0.0112</td>     <td>-1.6097</td> <td>0.1075</td>    <td>-0.0401</td>       <td>0.0039</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(np.nan_to_num(sink_V_novelty) + 1)</th>                          <td>0.0351</td>     <td>0.0578</td>     <td>0.6083</td>  <td>0.5430</td>    <td>-0.0781</td>       <td>0.1483</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(np.nan_to_num(sink_V_novelty) + 1) ** 2)</th>                  <td>-0.0056</td>    <td>0.0106</td>     <td>-0.5307</td> <td>0.5956</td>    <td>-0.0264</td>       <td>0.0151</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>match_prop</th>                                                           <td>2.0059</td>     <td>0.2477</td>     <td>8.0978</td>  <td>0.0000</td>    <td>1.5204</td>        <td>2.4914</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(match_prop ** 2)</th>                                                   <td>0.9533</td>     <td>0.1826</td>     <td>5.2216</td>  <td>0.0000</td>    <td>0.5955</td>        <td>1.3112</td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                                                           Results: Logit\n",
       "====================================================================================================================================\n",
       "Model:                                    Logit                                  Pseudo R-squared:                       0.234      \n",
       "Dependent Variable:                       is_self_cite                           AIC:                                    179540.6798\n",
       "Date:                                     2017-08-17 12:10                       BIC:                                    180294.7442\n",
       "No. Observations:                         676859                                 Log-Likelihood:                         -89704.    \n",
       "Df Model:                                 65                                     LL-Null:                                -1.1704e+05\n",
       "Df Residuals:                             676793                                 LLR p-value:                            0.0000     \n",
       "Converged:                                0.0000                                 Scale:                                  1.0000     \n",
       "------------------------------------------------------------------------------------------------------------------------------------\n",
       "                                                                      Coef.    Std.Err.      z     P>|z|      [0.025       0.975]   \n",
       "------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                            -1.1151 1014742.2815  -0.0000 1.0000 -1988859.4405 1988857.2103\n",
       "I(auth_prev_papers == 0)[T.True]                                     -0.3155       0.3863  -0.8168 0.4140       -1.0726       0.4416\n",
       "I(auth_prev_papers == 1)[T.True]                                     -0.0406       0.0391  -1.0381 0.2992       -0.1171       0.0360\n",
       "C(gender, levels=['M', 'F'])[T.F]                                    -0.0968       0.0143  -6.7576 0.0000       -0.1249      -0.0688\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.UNKNOWN]                -0.1516       0.2209  -0.6863 0.4925       -0.5845       0.2813\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.UK]                     -0.1262       0.0301  -4.1859 0.0000       -0.1853      -0.0671\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.JAPAN]                   0.2444       0.0374   6.5406 0.0000        0.1712       0.3177\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.GERMANY]                 0.1017       0.0331   3.0733 0.0021        0.0368       0.1665\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.FRANCE]                 -0.0219       0.0387  -0.5650 0.5721       -0.0977       0.0540\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.ITALY]                   0.1600       0.0554   2.8902 0.0038        0.0515       0.2685\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.CANADA]                  0.0903       0.0314   2.8797 0.0040        0.0288       0.1518\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.CHINA]                  -0.3567       0.2333  -1.5287 0.1263       -0.8141       0.1006\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.AUSTRALIA]              -0.0291       0.0478  -0.6078 0.5434       -0.1228       0.0647\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.SPAIN]                   0.1367       0.0545   2.5102 0.0121        0.0300       0.2434\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.NETHERLANDS]             0.1273       0.0658   1.9354 0.0529       -0.0016       0.2562\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.SWEDEN]                  0.0434       0.0543   0.7994 0.4241       -0.0630       0.1498\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.INDIA]                   0.2383       0.1171   2.0358 0.0418        0.0089       0.4677\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.OTHER]                   0.0448       0.0246   1.8203 0.0687       -0.0034       0.0930\n",
       "I(sink_n_mesh_ex == 0)[T.True]                                        0.0896       0.1947   0.4603 0.6453       -0.2920       0.4712\n",
       "I(year_span < 0)[T.True]                                              0.0594       1.0043   0.0591 0.9528       -1.9090       2.0278\n",
       "I(year_span == 0)[T.True]                                             0.8778       0.0508  17.2810 0.0000        0.7782       0.9773\n",
       "I(sink_prev_ncites == 0)[T.True]                                      0.0694       0.0356   1.9506 0.0511       -0.0003       0.1392\n",
       "I(jj_sim == 0)[T.True]                                               -0.4495       0.7676  -0.5856 0.5582       -1.9539       1.0549\n",
       "journal_same[T.True]                                                  0.5355       0.0515  10.3910 0.0000        0.4345       0.6365\n",
       "source_is_eng[T.True]                                                -1.1151 1014742.2815  -0.0000 1.0000 -1988859.4405 1988857.2103\n",
       "source_is_journal[T.True]                                            -1.1548       0.5198  -2.2217 0.0263       -2.1735      -0.1360\n",
       "source_is_review[T.True]                                              0.0277       0.0715   0.3872 0.6986       -0.1124       0.1677\n",
       "source_is_case_rep[T.True]                                           -0.1183       0.3727  -0.3175 0.7509       -0.8489       0.6122\n",
       "source_is_let_ed_com[T.True]                                          0.1008       0.7345   0.1372 0.8909       -1.3389       1.5404\n",
       "sink_is_eng[T.True]                                                  -0.6509       0.3638  -1.7890 0.0736       -1.3640       0.0622\n",
       "sink_is_journal[T.True]                                               0.4820       0.1462   3.2963 0.0010        0.1954       0.7686\n",
       "sink_is_review[T.True]                                               -1.0143       0.0296 -34.2150 0.0000       -1.0724      -0.9562\n",
       "sink_is_case_rep[T.True]                                             -0.7960       0.1584  -5.0264 0.0000       -1.1063      -0.4856\n",
       "sink_is_let_ed_com[T.True]                                           -0.7621       0.1487  -5.1242 0.0000       -1.0536      -0.4706\n",
       "I(match_prop == 0)[T.True]                                           -0.2242       0.3861  -0.5806 0.5615       -0.9810       0.5326\n",
       "I(match_prop == 1)[T.True]                                            0.0969       0.0389   2.4908 0.0127        0.0206       0.1731\n",
       "np.log10(auth_prev_papers + 1)                                        0.4224       0.0791   5.3403 0.0000        0.2674       0.5775\n",
       "I(np.log10(auth_prev_papers + 1) ** 2)                                0.1131       0.0303   3.7324 0.0002        0.0537       0.1725\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[0]  -0.0599       0.0298  -2.0092 0.0445       -0.1183      -0.0015\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[1]  -0.1120       0.0350  -3.2019 0.0014       -0.1805      -0.0434\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[2]   0.0933       0.0311   3.0022 0.0027        0.0324       0.1543\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[3]  -0.2355       0.0361  -6.5291 0.0000       -0.3062      -0.1648\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[4]  -0.0574       0.0319  -1.8003 0.0718       -0.1198       0.0051\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[5]  -0.1281       0.0330  -3.8871 0.0001       -0.1928      -0.0635\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[6]  -0.1766       0.0488  -3.6194 0.0003       -0.2722      -0.0810\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[7]  -0.0676       0.0363  -1.8632 0.0624       -0.1387       0.0035\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[8]  -0.0178       0.0431  -0.4119 0.6804       -0.1023       0.0668\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[9]  -0.2396       0.0552  -4.3411 0.0000       -0.3478      -0.1314\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[10] -0.0564       0.0588  -0.9600 0.3371       -0.1717       0.0588\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[11] -0.1512       0.0515  -2.9346 0.0033       -0.2522      -0.0502\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[12] -0.0210       0.1207  -0.1742 0.8617       -0.2576       0.2156\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[13]  0.0319       0.0376   0.8497 0.3955       -0.0417       0.1056\n",
       "np.log10(source_ncites)                                              -1.0072       0.9217  -1.0927 0.2745       -2.8137       0.7994\n",
       "I(np.log10(source_ncites) ** 2)                                      -0.2492       0.2990  -0.8333 0.4047       -0.8352       0.3369\n",
       "np.log10(source_n_mesh_ex + 1)                                        0.7598       0.0475  15.9939 0.0000        0.6667       0.8529\n",
       "np.log10(sink_n_mesh_ex + 1)                                          0.0052       0.0472   0.1100 0.9124       -0.0872       0.0976\n",
       "mf.score_log_1(year_span)                                             2.9238       0.1599  18.2805 0.0000        2.6103       3.2372\n",
       "I(mf.score_log_1(year_span) ** 2)                                    -2.7949       0.1094 -25.5502 0.0000       -3.0093      -2.5805\n",
       "np.log10(sink_prev_ncites + 1)                                       -0.5054       0.0613  -8.2510 0.0000       -0.6255      -0.3854\n",
       "I(np.log10(sink_prev_ncites + 1) ** 2)                               -0.3242       0.0237 -13.6848 0.0000       -0.3707      -0.2778\n",
       "np.log10(jj_sim + 1)                                                 -0.4062       1.5699  -0.2587 0.7958       -3.4832       2.6708\n",
       "I(np.log10(jj_sim + 1) ** 2)                                          0.0404       0.7995   0.0506 0.9597       -1.5265       1.6074\n",
       "np.log10(np.nan_to_num(source_V_novelty) + 1)                        -0.0181       0.0112  -1.6097 0.1075       -0.0401       0.0039\n",
       "np.log10(np.nan_to_num(sink_V_novelty) + 1)                           0.0351       0.0578   0.6083 0.5430       -0.0781       0.1483\n",
       "I(np.log10(np.nan_to_num(sink_V_novelty) + 1) ** 2)                  -0.0056       0.0106  -0.5307 0.5956       -0.0264       0.0151\n",
       "match_prop                                                            2.0059       0.2477   8.0978 0.0000        1.5204       2.4914\n",
       "I(match_prop ** 2)                                                    0.9533       0.1826   5.2216 0.0000        0.5955       1.3112\n",
       "====================================================================================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last\n",
      "('J Biol Chem', 171068)\n",
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n",
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n",
      "Using class based MultiVal\n",
      "using complimentary weights for 2 columns. w and 1-w\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.096</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>   <td>is_self_cite</td>         <td>AIC:</td>        <td>554395.4400</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2017-08-17 12:12</td>       <td>BIC:</td>        <td>555157.0251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>758553</td>       <td>Log-Likelihood:</td>  <td>-2.7713e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>             <td>65</td>            <td>LL-Null:</td>      <td>-3.0642e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>758487</td>        <td>LLR p-value:</td>      <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>0.0000</td>           <td>Scale:</td>         <td>1.0000</td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                    <td></td>                                    <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                            <td>-1.0288</td>    <td>nan</td>      <td>nan</td>     <td>nan</td>    <td>nan</td>     <td>nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(auth_prev_papers == 0)[T.True]</th>                                     <td>-0.2344</td>  <td>1.0262</td>   <td>-0.2284</td> <td>0.8193</td> <td>-2.2457</td> <td>1.7768</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(auth_prev_papers == 1)[T.True]</th>                                     <td>-0.1873</td>  <td>0.1806</td>   <td>-1.0368</td> <td>0.2998</td> <td>-0.5413</td> <td>0.1668</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(gender, levels=['M', 'F'])[T.F]</th>                                    <td>0.0079</td>   <td>0.0099</td>   <td>0.7957</td>  <td>0.4262</td> <td>-0.0115</td> <td>0.0273</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.UNKNOWN]</th>                <td>-0.0486</td>  <td>0.1286</td>   <td>-0.3780</td> <td>0.7054</td> <td>-0.3007</td> <td>0.2034</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.UK]</th>                     <td>-0.0829</td>  <td>0.0154</td>   <td>-5.3700</td> <td>0.0000</td> <td>-0.1132</td> <td>-0.0526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.JAPAN]</th>                  <td>-0.2183</td>  <td>0.0331</td>   <td>-6.5893</td> <td>0.0000</td> <td>-0.2832</td> <td>-0.1534</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.GERMANY]</th>                <td>-0.0776</td>  <td>0.0188</td>   <td>-4.1269</td> <td>0.0000</td> <td>-0.1145</td> <td>-0.0408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.FRANCE]</th>                 <td>-0.2002</td>  <td>0.0229</td>   <td>-8.7309</td> <td>0.0000</td> <td>-0.2452</td> <td>-0.1553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.ITALY]</th>                  <td>-0.2002</td>  <td>0.0314</td>   <td>-6.3652</td> <td>0.0000</td> <td>-0.2618</td> <td>-0.1385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.CANADA]</th>                 <td>0.0199</td>   <td>0.0161</td>   <td>1.2323</td>  <td>0.2178</td> <td>-0.0117</td> <td>0.0514</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.CHINA]</th>                  <td>-0.0940</td>  <td>0.0746</td>   <td>-1.2612</td> <td>0.2072</td> <td>-0.2402</td> <td>0.0521</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.AUSTRALIA]</th>              <td>-0.1661</td>  <td>0.0269</td>   <td>-6.1854</td> <td>0.0000</td> <td>-0.2188</td> <td>-0.1135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.SPAIN]</th>                  <td>-0.0066</td>  <td>0.0328</td>   <td>-0.2024</td> <td>0.8396</td> <td>-0.0709</td> <td>0.0576</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.NETHERLANDS]</th>            <td>-0.0523</td>  <td>0.0391</td>   <td>-1.3355</td> <td>0.1817</td> <td>-0.1290</td> <td>0.0244</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.SWEDEN]</th>                 <td>-0.1117</td>  <td>0.0309</td>   <td>-3.6108</td> <td>0.0003</td> <td>-0.1724</td> <td>-0.0511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.INDIA]</th>                  <td>-0.1204</td>  <td>0.0620</td>   <td>-1.9428</td> <td>0.0520</td> <td>-0.2419</td> <td>0.0011</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(source_country, levels=TOP_15_COUNTRIES)[T.OTHER]</th>                  <td>-0.0602</td>  <td>0.0140</td>   <td>-4.3050</td> <td>0.0000</td> <td>-0.0877</td> <td>-0.0328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(sink_n_mesh_ex == 0)[T.True]</th>                                       <td>-0.1592</td>  <td>0.1078</td>   <td>-1.4765</td> <td>0.1398</td> <td>-0.3704</td> <td>0.0521</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(year_span < 0)[T.True]</th>                                             <td>0.0014</td>   <td>0.6576</td>   <td>0.0022</td>  <td>0.9983</td> <td>-1.2875</td> <td>1.2903</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(year_span == 0)[T.True]</th>                                            <td>0.5991</td>   <td>0.0289</td>   <td>20.7070</td> <td>0.0000</td> <td>0.5424</td>  <td>0.6558</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(sink_prev_ncites == 0)[T.True]</th>                                     <td>0.3229</td>   <td>0.0218</td>   <td>14.8031</td> <td>0.0000</td> <td>0.2802</td>  <td>0.3657</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(jj_sim == 0)[T.True]</th>                                               <td>-0.2231</td>  <td>0.2987</td>   <td>-0.7471</td> <td>0.4550</td> <td>-0.8086</td> <td>0.3623</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>journal_same[T.True]</th>                                                 <td>0.3037</td>   <td>0.0199</td>   <td>15.2433</td> <td>0.0000</td> <td>0.2647</td>  <td>0.3428</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_eng[T.True]</th>                                                <td>-1.0288</td>    <td>nan</td>      <td>nan</td>     <td>nan</td>    <td>nan</td>     <td>nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_journal[T.True]</th>                                            <td>-0.9845</td>  <td>0.2395</td>   <td>-4.1107</td> <td>0.0000</td> <td>-1.4540</td> <td>-0.5151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_review[T.True]</th>                                             <td>-0.1588</td>  <td>0.0568</td>   <td>-2.7943</td> <td>0.0052</td> <td>-0.2703</td> <td>-0.0474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_case_rep[T.True]</th>                                           <td>-0.0570</td>  <td>0.1963</td>   <td>-0.2905</td> <td>0.7714</td> <td>-0.4418</td> <td>0.3278</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_is_let_ed_com[T.True]</th>                                         <td>0.0010</td>   <td>0.5815</td>   <td>0.0018</td>  <td>0.9986</td> <td>-1.1388</td> <td>1.1408</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_eng[T.True]</th>                                                  <td>-0.8783</td>  <td>0.1587</td>   <td>-5.5356</td> <td>0.0000</td> <td>-1.1892</td> <td>-0.5673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_journal[T.True]</th>                                              <td>-0.2457</td>  <td>0.0684</td>   <td>-3.5913</td> <td>0.0003</td> <td>-0.3798</td> <td>-0.1116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_review[T.True]</th>                                               <td>-0.5397</td>  <td>0.0136</td>  <td>-39.6901</td> <td>0.0000</td> <td>-0.5663</td> <td>-0.5130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_case_rep[T.True]</th>                                             <td>-0.2957</td>  <td>0.0696</td>   <td>-4.2515</td> <td>0.0000</td> <td>-0.4320</td> <td>-0.1594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sink_is_let_ed_com[T.True]</th>                                           <td>-0.6900</td>  <td>0.0731</td>   <td>-9.4360</td> <td>0.0000</td> <td>-0.8334</td> <td>-0.5467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(match_prop == 0)[T.True]</th>                                           <td>-0.2425</td>  <td>1.0063</td>   <td>-0.2409</td> <td>0.8096</td> <td>-2.2149</td> <td>1.7299</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(match_prop == 1)[T.True]</th>                                           <td>0.1231</td>   <td>0.0129</td>   <td>9.5287</td>  <td>0.0000</td> <td>0.0978</td>  <td>0.1485</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(auth_prev_papers + 1)</th>                                       <td>1.0005</td>   <td>0.0679</td>   <td>14.7432</td> <td>0.0000</td> <td>0.8675</td>  <td>1.1335</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(auth_prev_papers + 1) ** 2)</th>                               <td>-0.1519</td>  <td>0.0182</td>   <td>-8.3469</td> <td>0.0000</td> <td>-0.1876</td> <td>-0.1162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[0]</th>  <td>0.0106</td>   <td>0.0148</td>   <td>0.7171</td>  <td>0.4733</td> <td>-0.0184</td> <td>0.0396</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[1]</th>  <td>-0.0934</td>  <td>0.0218</td>   <td>-4.2762</td> <td>0.0000</td> <td>-0.1362</td> <td>-0.0506</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[2]</th>  <td>-0.0298</td>  <td>0.0243</td>   <td>-1.2243</td> <td>0.2208</td> <td>-0.0774</td> <td>0.0179</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[3]</th>  <td>-0.0604</td>  <td>0.0321</td>   <td>-1.8825</td> <td>0.0598</td> <td>-0.1234</td> <td>0.0025</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[4]</th>  <td>-0.1143</td>  <td>0.0264</td>   <td>-4.3291</td> <td>0.0000</td> <td>-0.1660</td> <td>-0.0625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[5]</th>  <td>-0.1421</td>  <td>0.0189</td>   <td>-7.5037</td> <td>0.0000</td> <td>-0.1792</td> <td>-0.1049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[6]</th>  <td>0.0717</td>   <td>0.0262</td>   <td>2.7336</td>  <td>0.0063</td> <td>0.0203</td>  <td>0.1232</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[7]</th>  <td>-0.0863</td>  <td>0.0251</td>   <td>-3.4424</td> <td>0.0006</td> <td>-0.1354</td> <td>-0.0372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[8]</th>  <td>0.0081</td>   <td>0.0224</td>   <td>0.3626</td>  <td>0.7169</td> <td>-0.0358</td> <td>0.0520</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[9]</th>  <td>-0.2584</td>  <td>0.0404</td>   <td>-6.3895</td> <td>0.0000</td> <td>-0.3377</td> <td>-0.1791</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[10]</th> <td>-0.0958</td>  <td>0.0313</td>   <td>-3.0580</td> <td>0.0022</td> <td>-0.1572</td> <td>-0.0344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[11]</th> <td>-0.1248</td>  <td>0.0425</td>   <td>-2.9376</td> <td>0.0033</td> <td>-0.2081</td> <td>-0.0415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[12]</th> <td>-0.1557</td>  <td>0.0886</td>   <td>-1.7569</td> <td>0.0789</td> <td>-0.3293</td> <td>0.0180</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[13]</th> <td>-0.0404</td>  <td>0.0211</td>   <td>-1.9096</td> <td>0.0562</td> <td>-0.0818</td> <td>0.0011</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(source_ncites)</th>                                              <td>-0.8598</td>  <td>0.5269</td>   <td>-1.6319</td> <td>0.1027</td> <td>-1.8925</td> <td>0.1728</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(source_ncites) ** 2)</th>                                      <td>-0.1725</td>  <td>0.1699</td>   <td>-1.0153</td> <td>0.3100</td> <td>-0.5054</td> <td>0.1605</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(source_n_mesh_ex + 1)</th>                                       <td>0.3641</td>   <td>0.0248</td>   <td>14.6846</td> <td>0.0000</td> <td>0.3155</td>  <td>0.4127</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(sink_n_mesh_ex + 1)</th>                                         <td>0.1979</td>   <td>0.0251</td>   <td>7.8960</td>  <td>0.0000</td> <td>0.1488</td>  <td>0.2470</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mf.score_log_1(year_span)</th>                                            <td>1.6899</td>   <td>0.0719</td>   <td>23.5173</td> <td>0.0000</td> <td>1.5490</td>  <td>1.8307</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(mf.score_log_1(year_span) ** 2)</th>                                    <td>-1.0177</td>  <td>0.0418</td>  <td>-24.3582</td> <td>0.0000</td> <td>-1.0995</td> <td>-0.9358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(sink_prev_ncites + 1)</th>                                       <td>0.1455</td>   <td>0.0294</td>   <td>4.9526</td>  <td>0.0000</td> <td>0.0879</td>  <td>0.2031</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(sink_prev_ncites + 1) ** 2)</th>                               <td>-0.3254</td>  <td>0.0094</td>  <td>-34.4898</td> <td>0.0000</td> <td>-0.3438</td> <td>-0.3069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(jj_sim + 1)</th>                                                 <td>-0.2896</td>  <td>0.5924</td>   <td>-0.4889</td> <td>0.6249</td> <td>-1.4506</td> <td>0.8714</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(jj_sim + 1) ** 2)</th>                                         <td>0.2417</td>   <td>0.2921</td>   <td>0.8274</td>  <td>0.4080</td> <td>-0.3308</td> <td>0.8142</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(np.nan_to_num(source_V_novelty) + 1)</th>                        <td>-0.0344</td>  <td>0.0060</td>   <td>-5.7730</td> <td>0.0000</td> <td>-0.0460</td> <td>-0.0227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log10(np.nan_to_num(sink_V_novelty) + 1)</th>                          <td>0.0829</td>   <td>0.0277</td>   <td>2.9870</td>  <td>0.0028</td> <td>0.0285</td>  <td>0.1373</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(np.log10(np.nan_to_num(sink_V_novelty) + 1) ** 2)</th>                  <td>-0.0073</td>  <td>0.0052</td>   <td>-1.4186</td> <td>0.1560</td> <td>-0.0175</td> <td>0.0028</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>match_prop</th>                                                           <td>0.7078</td>   <td>0.4721</td>   <td>1.4994</td>  <td>0.1338</td> <td>-0.2174</td> <td>1.6331</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(match_prop ** 2)</th>                                                   <td>1.5720</td>   <td>0.2839</td>   <td>5.5364</td>  <td>0.0000</td> <td>1.0155</td>  <td>2.1285</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                                                   Results: Logit\n",
       "=====================================================================================================================\n",
       "Model:                               Logit                             Pseudo R-squared:                  0.096      \n",
       "Dependent Variable:                  is_self_cite                      AIC:                               554395.4400\n",
       "Date:                                2017-08-17 12:12                  BIC:                               555157.0251\n",
       "No. Observations:                    758553                            Log-Likelihood:                    -2.7713e+05\n",
       "Df Model:                            65                                LL-Null:                           -3.0642e+05\n",
       "Df Residuals:                        758487                            LLR p-value:                       0.0000     \n",
       "Converged:                           0.0000                            Scale:                             1.0000     \n",
       "---------------------------------------------------------------------------------------------------------------------\n",
       "                                                                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
       "---------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                            -1.0288      nan      nan    nan     nan     nan\n",
       "I(auth_prev_papers == 0)[T.True]                                     -0.2344   1.0262  -0.2284 0.8193 -2.2457  1.7768\n",
       "I(auth_prev_papers == 1)[T.True]                                     -0.1873   0.1806  -1.0368 0.2998 -0.5413  0.1668\n",
       "C(gender, levels=['M', 'F'])[T.F]                                     0.0079   0.0099   0.7957 0.4262 -0.0115  0.0273\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.UNKNOWN]                -0.0486   0.1286  -0.3780 0.7054 -0.3007  0.2034\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.UK]                     -0.0829   0.0154  -5.3700 0.0000 -0.1132 -0.0526\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.JAPAN]                  -0.2183   0.0331  -6.5893 0.0000 -0.2832 -0.1534\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.GERMANY]                -0.0776   0.0188  -4.1269 0.0000 -0.1145 -0.0408\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.FRANCE]                 -0.2002   0.0229  -8.7309 0.0000 -0.2452 -0.1553\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.ITALY]                  -0.2002   0.0314  -6.3652 0.0000 -0.2618 -0.1385\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.CANADA]                  0.0199   0.0161   1.2323 0.2178 -0.0117  0.0514\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.CHINA]                  -0.0940   0.0746  -1.2612 0.2072 -0.2402  0.0521\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.AUSTRALIA]              -0.1661   0.0269  -6.1854 0.0000 -0.2188 -0.1135\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.SPAIN]                  -0.0066   0.0328  -0.2024 0.8396 -0.0709  0.0576\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.NETHERLANDS]            -0.0523   0.0391  -1.3355 0.1817 -0.1290  0.0244\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.SWEDEN]                 -0.1117   0.0309  -3.6108 0.0003 -0.1724 -0.0511\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.INDIA]                  -0.1204   0.0620  -1.9428 0.0520 -0.2419  0.0011\n",
       "C(source_country, levels=TOP_15_COUNTRIES)[T.OTHER]                  -0.0602   0.0140  -4.3050 0.0000 -0.0877 -0.0328\n",
       "I(sink_n_mesh_ex == 0)[T.True]                                       -0.1592   0.1078  -1.4765 0.1398 -0.3704  0.0521\n",
       "I(year_span < 0)[T.True]                                              0.0014   0.6576   0.0022 0.9983 -1.2875  1.2903\n",
       "I(year_span == 0)[T.True]                                             0.5991   0.0289  20.7070 0.0000  0.5424  0.6558\n",
       "I(sink_prev_ncites == 0)[T.True]                                      0.3229   0.0218  14.8031 0.0000  0.2802  0.3657\n",
       "I(jj_sim == 0)[T.True]                                               -0.2231   0.2987  -0.7471 0.4550 -0.8086  0.3623\n",
       "journal_same[T.True]                                                  0.3037   0.0199  15.2433 0.0000  0.2647  0.3428\n",
       "source_is_eng[T.True]                                                -1.0288      nan      nan    nan     nan     nan\n",
       "source_is_journal[T.True]                                            -0.9845   0.2395  -4.1107 0.0000 -1.4540 -0.5151\n",
       "source_is_review[T.True]                                             -0.1588   0.0568  -2.7943 0.0052 -0.2703 -0.0474\n",
       "source_is_case_rep[T.True]                                           -0.0570   0.1963  -0.2905 0.7714 -0.4418  0.3278\n",
       "source_is_let_ed_com[T.True]                                          0.0010   0.5815   0.0018 0.9986 -1.1388  1.1408\n",
       "sink_is_eng[T.True]                                                  -0.8783   0.1587  -5.5356 0.0000 -1.1892 -0.5673\n",
       "sink_is_journal[T.True]                                              -0.2457   0.0684  -3.5913 0.0003 -0.3798 -0.1116\n",
       "sink_is_review[T.True]                                               -0.5397   0.0136 -39.6901 0.0000 -0.5663 -0.5130\n",
       "sink_is_case_rep[T.True]                                             -0.2957   0.0696  -4.2515 0.0000 -0.4320 -0.1594\n",
       "sink_is_let_ed_com[T.True]                                           -0.6900   0.0731  -9.4360 0.0000 -0.8334 -0.5467\n",
       "I(match_prop == 0)[T.True]                                           -0.2425   1.0063  -0.2409 0.8096 -2.2149  1.7299\n",
       "I(match_prop == 1)[T.True]                                            0.1231   0.0129   9.5287 0.0000  0.0978  0.1485\n",
       "np.log10(auth_prev_papers + 1)                                        1.0005   0.0679  14.7432 0.0000  0.8675  1.1335\n",
       "I(np.log10(auth_prev_papers + 1) ** 2)                               -0.1519   0.0182  -8.3469 0.0000 -0.1876 -0.1162\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[0]   0.0106   0.0148   0.7171 0.4733 -0.0184  0.0396\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[1]  -0.0934   0.0218  -4.2762 0.0000 -0.1362 -0.0506\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[2]  -0.0298   0.0243  -1.2243 0.2208 -0.0774  0.0179\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[3]  -0.0604   0.0321  -1.8825 0.0598 -0.1234  0.0025\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[4]  -0.1143   0.0264  -4.3291 0.0000 -0.1660 -0.0625\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[5]  -0.1421   0.0189  -7.5037 0.0000 -0.1792 -0.1049\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[6]   0.0717   0.0262   2.7336 0.0063  0.0203  0.1232\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[7]  -0.0863   0.0251  -3.4424 0.0006 -0.1354 -0.0372\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[8]   0.0081   0.0224   0.3626 0.7169 -0.0358  0.0520\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[9]  -0.2584   0.0404  -6.3895 0.0000 -0.3377 -0.1791\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[10] -0.0958   0.0313  -3.0580 0.0022 -0.1572 -0.0344\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[11] -0.1248   0.0425  -2.9376 0.0033 -0.2081 -0.0415\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[12] -0.1557   0.0886  -1.7569 0.0789 -0.3293  0.0180\n",
       "mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)[13] -0.0404   0.0211  -1.9096 0.0562 -0.0818  0.0011\n",
       "np.log10(source_ncites)                                              -0.8598   0.5269  -1.6319 0.1027 -1.8925  0.1728\n",
       "I(np.log10(source_ncites) ** 2)                                      -0.1725   0.1699  -1.0153 0.3100 -0.5054  0.1605\n",
       "np.log10(source_n_mesh_ex + 1)                                        0.3641   0.0248  14.6846 0.0000  0.3155  0.4127\n",
       "np.log10(sink_n_mesh_ex + 1)                                          0.1979   0.0251   7.8960 0.0000  0.1488  0.2470\n",
       "mf.score_log_1(year_span)                                             1.6899   0.0719  23.5173 0.0000  1.5490  1.8307\n",
       "I(mf.score_log_1(year_span) ** 2)                                    -1.0177   0.0418 -24.3582 0.0000 -1.0995 -0.9358\n",
       "np.log10(sink_prev_ncites + 1)                                        0.1455   0.0294   4.9526 0.0000  0.0879  0.2031\n",
       "I(np.log10(sink_prev_ncites + 1) ** 2)                               -0.3254   0.0094 -34.4898 0.0000 -0.3438 -0.3069\n",
       "np.log10(jj_sim + 1)                                                 -0.2896   0.5924  -0.4889 0.6249 -1.4506  0.8714\n",
       "I(np.log10(jj_sim + 1) ** 2)                                          0.2417   0.2921   0.8274 0.4080 -0.3308  0.8142\n",
       "np.log10(np.nan_to_num(source_V_novelty) + 1)                        -0.0344   0.0060  -5.7730 0.0000 -0.0460 -0.0227\n",
       "np.log10(np.nan_to_num(sink_V_novelty) + 1)                           0.0829   0.0277   2.9870 0.0028  0.0285  0.1373\n",
       "I(np.log10(np.nan_to_num(sink_V_novelty) + 1) ** 2)                  -0.0073   0.0052  -1.4186 0.1560 -0.0175  0.0028\n",
       "match_prop                                                            0.7078   0.4721   1.4994 0.1338 -0.2174  1.6331\n",
       "I(match_prop ** 2)                                                    1.5720   0.2839   5.5364 0.0000  1.0155  2.1285\n",
       "=====================================================================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for journal_name in [\"J Biol Chem\"]:\n",
    "    for i, (df_t_data, title) in enumerate(zip(\n",
    "                    [df_t_first, df_t_last],\n",
    "                    [\"First\", \"Last\"],\n",
    "                )):\n",
    "        print(title)\n",
    "        model_stats = [np.nan, np.nan, np.nan]\n",
    "        df_t_data = get_journal_data(df_t_data, journal_name).copy()\n",
    "        #prepare_data(df_t_data)\n",
    "        try:\n",
    "            y_test, model = model_predictions(\n",
    "                df_t_data, journal_model_formula,\n",
    "                df_t_data.iloc[:3], verbose=False,\n",
    "                model_params=dict(method='lbfgs', maxiter=100))\n",
    "            display(model.summary2())\n",
    "        except:\n",
    "            print(\"Failed to fit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Author Position</th>\n",
       "      <th colspan=\"4\" halign=\"left\">First</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Journal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Biology</th>\n",
       "      <th>J Bacteriol</th>\n",
       "      <td>102012</td>\n",
       "      <td>-0.037663</td>\n",
       "      <td>0.033765</td>\n",
       "      <td>2.646655e-01</td>\n",
       "      <td>109737</td>\n",
       "      <td>0.063241</td>\n",
       "      <td>0.022894</td>\n",
       "      <td>5.739561e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biochem J</th>\n",
       "      <td>91576</td>\n",
       "      <td>-0.152946</td>\n",
       "      <td>0.036076</td>\n",
       "      <td>2.239402e-05</td>\n",
       "      <td>97174</td>\n",
       "      <td>-0.079645</td>\n",
       "      <td>0.027055</td>\n",
       "      <td>3.241796e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mol Cell</th>\n",
       "      <td>33538</td>\n",
       "      <td>-0.105273</td>\n",
       "      <td>0.068364</td>\n",
       "      <td>1.235868e-01</td>\n",
       "      <td>39524</td>\n",
       "      <td>-0.176283</td>\n",
       "      <td>0.045367</td>\n",
       "      <td>1.020336e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <td>32399</td>\n",
       "      <td>-0.151300</td>\n",
       "      <td>0.076033</td>\n",
       "      <td>4.659994e-02</td>\n",
       "      <td>37042</td>\n",
       "      <td>-0.122344</td>\n",
       "      <td>0.050564</td>\n",
       "      <td>1.553732e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Medicine</th>\n",
       "      <th>J Clin Oncol</th>\n",
       "      <td>61722</td>\n",
       "      <td>0.077715</td>\n",
       "      <td>0.041989</td>\n",
       "      <td>6.419382e-02</td>\n",
       "      <td>62049</td>\n",
       "      <td>0.085109</td>\n",
       "      <td>0.041894</td>\n",
       "      <td>4.219990e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Am Coll Cardiol</th>\n",
       "      <td>50523</td>\n",
       "      <td>-0.068152</td>\n",
       "      <td>0.061966</td>\n",
       "      <td>2.714106e-01</td>\n",
       "      <td>52579</td>\n",
       "      <td>0.202210</td>\n",
       "      <td>0.057868</td>\n",
       "      <td>4.752258e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Urol</th>\n",
       "      <td>49314</td>\n",
       "      <td>0.363535</td>\n",
       "      <td>0.065951</td>\n",
       "      <td>3.543983e-08</td>\n",
       "      <td>50379</td>\n",
       "      <td>0.162318</td>\n",
       "      <td>0.064427</td>\n",
       "      <td>1.175524e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <th>Ann N Y Acad Sci</th>\n",
       "      <td>52540</td>\n",
       "      <td>0.003086</td>\n",
       "      <td>0.034389</td>\n",
       "      <td>9.285022e-01</td>\n",
       "      <td>54224</td>\n",
       "      <td>0.216270</td>\n",
       "      <td>0.035162</td>\n",
       "      <td>7.713824e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Author Position                First                                    \\\n",
       "                           citations      beta    stderr       p-value   \n",
       "         Journal                                                         \n",
       "Biology  J Bacteriol          102012 -0.037663  0.033765  2.646655e-01   \n",
       "         Biochem J             91576 -0.152946  0.036076  2.239402e-05   \n",
       "         Mol Cell              33538 -0.105273  0.068364  1.235868e-01   \n",
       "         Cell                  32399 -0.151300  0.076033  4.659994e-02   \n",
       "Medicine J Clin Oncol          61722  0.077715  0.041989  6.419382e-02   \n",
       "         J Am Coll Cardiol     50523 -0.068152  0.061966  2.714106e-01   \n",
       "         J Urol                49314  0.363535  0.065951  3.543983e-08   \n",
       "Science  Ann N Y Acad Sci      52540  0.003086  0.034389  9.285022e-01   \n",
       "\n",
       "Author Position                 Last                                    \n",
       "                           citations      beta    stderr       p-value  \n",
       "         Journal                                                        \n",
       "Biology  J Bacteriol          109737  0.063241  0.022894  5.739561e-03  \n",
       "         Biochem J             97174 -0.079645  0.027055  3.241796e-03  \n",
       "         Mol Cell              39524 -0.176283  0.045367  1.020336e-04  \n",
       "         Cell                  37042 -0.122344  0.050564  1.553732e-02  \n",
       "Medicine J Clin Oncol          62049  0.085109  0.041894  4.219990e-02  \n",
       "         J Am Coll Cardiol     52579  0.202210  0.057868  4.752258e-04  \n",
       "         J Urol                50379  0.162318  0.064427  1.175524e-02  \n",
       "Science  Ann N Y Acad Sci      54224  0.216270  0.035162  7.713824e-10  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journal_full_model_stats[\n",
    "    (df_journal_full_model_stats[(\"Last\", \"p-value\")] < 0.05)\n",
    "    #& (df_journal_full_model_stats[(\"Last\", \"beta\")] > 0)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Journal with both first and last authorships having significant gender effect.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Author Position</th>\n",
       "      <th colspan=\"4\" halign=\"left\">First</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "      <th>citations</th>\n",
       "      <th>beta</th>\n",
       "      <th>stderr</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Journal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Biology</th>\n",
       "      <th>Biochem J</th>\n",
       "      <td>91576</td>\n",
       "      <td>-0.152946</td>\n",
       "      <td>0.036076</td>\n",
       "      <td>2.239402e-05</td>\n",
       "      <td>97174</td>\n",
       "      <td>-0.079645</td>\n",
       "      <td>0.027055</td>\n",
       "      <td>0.003242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <td>32399</td>\n",
       "      <td>-0.151300</td>\n",
       "      <td>0.076033</td>\n",
       "      <td>4.659994e-02</td>\n",
       "      <td>37042</td>\n",
       "      <td>-0.122344</td>\n",
       "      <td>0.050564</td>\n",
       "      <td>0.015537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medicine</th>\n",
       "      <th>J Urol</th>\n",
       "      <td>49314</td>\n",
       "      <td>0.363535</td>\n",
       "      <td>0.065951</td>\n",
       "      <td>3.543983e-08</td>\n",
       "      <td>50379</td>\n",
       "      <td>0.162318</td>\n",
       "      <td>0.064427</td>\n",
       "      <td>0.011755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Author Position        First                                        Last  \\\n",
       "                   citations      beta    stderr       p-value citations   \n",
       "         Journal                                                           \n",
       "Biology  Biochem J     91576 -0.152946  0.036076  2.239402e-05     97174   \n",
       "         Cell          32399 -0.151300  0.076033  4.659994e-02     37042   \n",
       "Medicine J Urol        49314  0.363535  0.065951  3.543983e-08     50379   \n",
       "\n",
       "Author Position                                   \n",
       "                        beta    stderr   p-value  \n",
       "         Journal                                  \n",
       "Biology  Biochem J -0.079645  0.027055  0.003242  \n",
       "         Cell      -0.122344  0.050564  0.015537  \n",
       "Medicine J Urol     0.162318  0.064427  0.011755  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Journal with both first and last authorships having significant gender effect.\")\n",
    "df_journal_full_model_stats[\n",
    "    (df_journal_full_model_stats[(\"Last\", \"p-value\")] < 0.05)\n",
    "    #& (df_journal_full_model_stats[(\"Last\", \"beta\")] > 0)\n",
    "    & (df_journal_full_model_stats[(\"First\", \"p-value\")] < 0.05)\n",
    "    #& (df_journal_full_model_stats[(\"First\", \"beta\")] > 0)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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