{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from lib import price_utils\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "reload(price_utils)\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "\n",
    "month = '2016-09-01'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Top ccg-presentation savings\n",
    "\n",
    "The SQL for this is abstracted into the `price_utils` library, q.v. for details.\n",
    "\n",
    "We normalise prescriptions to their generic equivalent.  See [the README](https://github.com/ebmdatalab/price-per-dose/blob/master/README.md) for details.\n",
    "\n",
    "The following is sorted by possible savings at the top decile. It tells us that CCG 13T  had the single biggest saving opportunity: to save £33,600 on its purchase of 8258 flu vaccines. It paid £9.92 per dose, when the lowest decile paid £5.85 per dose.\n",
    "\n",
    "It is limited to the top 1000 CCG-presentation tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "top_1000_ccg = price_utils.get_savings(group_by='pct', month=month, limit=1000, sql_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bnf_presentation</th>\n",
       "      <th>bnf_chemical</th>\n",
       "      <th>pct</th>\n",
       "      <th>category</th>\n",
       "      <th>avg_brand_count_per_practice</th>\n",
       "      <th>lowest_decile</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price_per_dose</th>\n",
       "      <th>possible_savings</th>\n",
       "      <th>formulation_swap</th>\n",
       "      <th>num_pack_sizes</th>\n",
       "      <th>flag_imported</th>\n",
       "      <th>flag_broken_bulk</th>\n",
       "      <th>flag_non_bioequivalence</th>\n",
       "      <th>flag_special_containers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bnf_code</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>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>13T</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1.14</td>\n",
       "      <td>5.92</td>\n",
       "      <td>25993</td>\n",
       "      <td>7.57</td>\n",
       "      <td>47,185.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>01H</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1.10</td>\n",
       "      <td>5.92</td>\n",
       "      <td>35744</td>\n",
       "      <td>6.58</td>\n",
       "      <td>26,395.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0403040W0AAAEAE</th>\n",
       "      <td>Venlafaxine_Cap 150mg M/R</td>\n",
       "      <td>Venlafaxine</td>\n",
       "      <td>11J</td>\n",
       "      <td>C</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>43145</td>\n",
       "      <td>0.98</td>\n",
       "      <td>24,669.05</td>\n",
       "      <td>Cap  / Tab</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>06N</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1.08</td>\n",
       "      <td>5.92</td>\n",
       "      <td>21795</td>\n",
       "      <td>6.82</td>\n",
       "      <td>23,602.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0601060D0AAA0A0</th>\n",
       "      <td>Glucose Blood Testing Reagents</td>\n",
       "      <td>Glucose Blood Testing Reagents</td>\n",
       "      <td>11X</td>\n",
       "      <td>NP8</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.25</td>\n",
       "      <td>594981</td>\n",
       "      <td>0.29</td>\n",
       "      <td>23,364.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bnf_presentation  \\\n",
       "bnf_code                                          \n",
       "1404000H0AAAFAF   Influenza_Vac Inact 0.5ml Pfs   \n",
       "1404000H0AAAFAF   Influenza_Vac Inact 0.5ml Pfs   \n",
       "0403040W0AAAEAE       Venlafaxine_Cap 150mg M/R   \n",
       "1404000H0AAAFAF   Influenza_Vac Inact 0.5ml Pfs   \n",
       "0601060D0AAA0A0  Glucose Blood Testing Reagents   \n",
       "\n",
       "                                   bnf_chemical  pct category  \\\n",
       "bnf_code                                                        \n",
       "1404000H0AAAFAF                       Influenza  13T      NP8   \n",
       "1404000H0AAAFAF                       Influenza  01H      NP8   \n",
       "0403040W0AAAEAE                     Venlafaxine  11J        C   \n",
       "1404000H0AAAFAF                       Influenza  06N      NP8   \n",
       "0601060D0AAA0A0  Glucose Blood Testing Reagents  11X      NP8   \n",
       "\n",
       "                 avg_brand_count_per_practice  lowest_decile  quantity  \\\n",
       "bnf_code                                                                 \n",
       "1404000H0AAAFAF                          1.14           5.92     25993   \n",
       "1404000H0AAAFAF                          1.10           5.92     35744   \n",
       "0403040W0AAAEAE                          1.21           0.38     43145   \n",
       "1404000H0AAAFAF                          1.08           5.92     21795   \n",
       "0601060D0AAA0A0                         14.05           0.25    594981   \n",
       "\n",
       "                 price_per_dose  possible_savings formulation_swap  \\\n",
       "bnf_code                                                             \n",
       "1404000H0AAAFAF            7.57         47,185.65              NaN   \n",
       "1404000H0AAAFAF            6.58         26,395.80              NaN   \n",
       "0403040W0AAAEAE            0.98         24,669.05      Cap  / Tab    \n",
       "1404000H0AAAFAF            6.82         23,602.99              NaN   \n",
       "0601060D0AAA0A0            0.29         23,364.66              NaN   \n",
       "\n",
       "                 num_pack_sizes  flag_imported flag_broken_bulk  \\\n",
       "bnf_code                                                          \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "0403040W0AAAEAE            3.00            nan             None   \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "0601060D0AAA0A0             nan            nan             None   \n",
       "\n",
       "                 flag_non_bioequivalence  flag_special_containers  \n",
       "bnf_code                                                           \n",
       "1404000H0AAAFAF                      nan                     1.00  \n",
       "1404000H0AAAFAF                      nan                     1.00  \n",
       "0403040W0AAAEAE                      nan                      nan  \n",
       "1404000H0AAAFAF                      nan                     1.00  \n",
       "0601060D0AAA0A0                      nan                      nan  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_1000_ccg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "How do the savings break down by Tariff Part VIII?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category\n",
      "A      £202,028.43 \n",
      "C     £2,629,889.43\n",
      "M      £787,242.56 \n",
      "NP8   £2,923,970.01\n"
     ]
    }
   ],
   "source": [
    "top_1000_ccg.to_csv(\"ccgs-%s.csv\" % month)\n",
    "print top_1000_ccg.groupby('category')['possible_savings'].sum().to_string(float_format='£{:,.2f}'.format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the top chemicals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bnf_chemical\n",
      "Influenza                        £1,379,060.40\n",
      "Glucose Blood Testing Reagents    £853,190.71 \n",
      "Fluticasone Propionate (Inh)      £714,340.28 \n",
      "Venlafaxine                       £423,466.31 \n",
      "Pregabalin                        £400,508.84 \n"
     ]
    }
   ],
   "source": [
    "print top_1000_ccg.groupby('bnf_chemical')['possible_savings'].sum().sort_values(ascending=False).head().to_string(float_format='£{:,.2f}'.format)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Grouped by CCG\n",
    "\n",
    "The same data as above, but grouped by CCG rather than CCG-presentation pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Count  Total savings\n",
      "pct                      \n",
      "11J     36   £284,602.42\n",
      "13P     31   £229,372.15\n",
      "09G     28   £207,971.60\n",
      "11N     24   £179,535.34\n",
      "01H     21   £175,332.87\n"
     ]
    }
   ],
   "source": [
    "by_ccg = top_1000_ccg.groupby('pct')['possible_savings'] \\\n",
    "  .agg({'Count': 'count', 'Total savings': 'sum'}).sort_values('Total savings', ascending=False)\n",
    "print by_ccg.head().to_string(float_format='£{:,.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of £6543130.42 savings per month\n"
     ]
    }
   ],
   "source": [
    "print \"A total of £%.2f savings per month\" % by_ccg['Total savings'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Just focusing on Rich's CCG\n",
    "\n",
    "It could have saved £300k:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count                20.000000\n",
       "Total savings    161897.115308\n",
       "Name: 99P, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_ccg.loc('savings_presentations_pct')['99P']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 5 savings would have been:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "asd = price_utils.get_savings(for_entity='99P', month=month, limit=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bnf_presentation</th>\n",
       "      <th>bnf_chemical</th>\n",
       "      <th>pct</th>\n",
       "      <th>category</th>\n",
       "      <th>avg_brand_count_per_practice</th>\n",
       "      <th>lowest_decile</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price_per_dose</th>\n",
       "      <th>possible_savings</th>\n",
       "      <th>formulation_swap</th>\n",
       "      <th>num_pack_sizes</th>\n",
       "      <th>flag_imported</th>\n",
       "      <th>flag_broken_bulk</th>\n",
       "      <th>flag_non_bioequivalence</th>\n",
       "      <th>flag_special_containers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bnf_code</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>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>99P</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1.10</td>\n",
       "      <td>5.92</td>\n",
       "      <td>35736</td>\n",
       "      <td>6.48</td>\n",
       "      <td>20,338.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0302000K0AAAMAM</th>\n",
       "      <td>Budesonide/Formoterol Inh B/A 200/6(120D</td>\n",
       "      <td>Budesonide</td>\n",
       "      <td>99P</td>\n",
       "      <td>C</td>\n",
       "      <td>2.19</td>\n",
       "      <td>33.98</td>\n",
       "      <td>5042</td>\n",
       "      <td>36.58</td>\n",
       "      <td>13,145.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0601060D0AAA0A0</th>\n",
       "      <td>Glucose Blood Testing Reagents</td>\n",
       "      <td>Glucose Blood Testing Reagents</td>\n",
       "      <td>99P</td>\n",
       "      <td>NP8</td>\n",
       "      <td>11.61</td>\n",
       "      <td>0.24</td>\n",
       "      <td>931673</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12,942.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0408010AEAAAGAG</th>\n",
       "      <td>Pregabalin_Cap 300mg</td>\n",
       "      <td>Pregabalin</td>\n",
       "      <td>99P</td>\n",
       "      <td>C</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.99</td>\n",
       "      <td>93796</td>\n",
       "      <td>1.12</td>\n",
       "      <td>11,986.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0408010AEAAAEAE</th>\n",
       "      <td>Pregabalin_Cap 150mg</td>\n",
       "      <td>Pregabalin</td>\n",
       "      <td>99P</td>\n",
       "      <td>C</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.98</td>\n",
       "      <td>89637</td>\n",
       "      <td>1.11</td>\n",
       "      <td>11,896.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         bnf_presentation  \\\n",
       "bnf_code                                                    \n",
       "1404000H0AAAFAF             Influenza_Vac Inact 0.5ml Pfs   \n",
       "0302000K0AAAMAM  Budesonide/Formoterol Inh B/A 200/6(120D   \n",
       "0601060D0AAA0A0            Glucose Blood Testing Reagents   \n",
       "0408010AEAAAGAG                      Pregabalin_Cap 300mg   \n",
       "0408010AEAAAEAE                      Pregabalin_Cap 150mg   \n",
       "\n",
       "                                   bnf_chemical  pct category  \\\n",
       "bnf_code                                                        \n",
       "1404000H0AAAFAF                       Influenza  99P      NP8   \n",
       "0302000K0AAAMAM                      Budesonide  99P        C   \n",
       "0601060D0AAA0A0  Glucose Blood Testing Reagents  99P      NP8   \n",
       "0408010AEAAAGAG                      Pregabalin  99P        C   \n",
       "0408010AEAAAEAE                      Pregabalin  99P        C   \n",
       "\n",
       "                 avg_brand_count_per_practice  lowest_decile  quantity  \\\n",
       "bnf_code                                                                 \n",
       "1404000H0AAAFAF                          1.10           5.92     35736   \n",
       "0302000K0AAAMAM                          2.19          33.98      5042   \n",
       "0601060D0AAA0A0                         11.61           0.24    931673   \n",
       "0408010AEAAAGAG                          2.13           0.99     93796   \n",
       "0408010AEAAAEAE                          2.21           0.98     89637   \n",
       "\n",
       "                 price_per_dose  possible_savings formulation_swap  \\\n",
       "bnf_code                                                             \n",
       "1404000H0AAAFAF            6.48         20,338.19              NaN   \n",
       "0302000K0AAAMAM           36.58         13,145.11              NaN   \n",
       "0601060D0AAA0A0            0.25         12,942.32              NaN   \n",
       "0408010AEAAAGAG            1.12         11,986.01              NaN   \n",
       "0408010AEAAAEAE            1.11         11,896.73              NaN   \n",
       "\n",
       "                 num_pack_sizes  flag_imported flag_broken_bulk  \\\n",
       "bnf_code                                                          \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "0302000K0AAAMAM            3.00            nan             None   \n",
       "0601060D0AAA0A0             nan            nan             None   \n",
       "0408010AEAAAGAG            3.00            nan             None   \n",
       "0408010AEAAAEAE            3.00            nan             None   \n",
       "\n",
       "                 flag_non_bioequivalence  flag_special_containers  \n",
       "bnf_code                                                           \n",
       "1404000H0AAAFAF                      nan                     1.00  \n",
       "0302000K0AAAMAM                      nan                     1.00  \n",
       "0601060D0AAA0A0                      nan                      nan  \n",
       "0408010AEAAAGAG                      nan                      nan  \n",
       "0408010AEAAAEAE                      nan                      nan  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asd.sort_values('possible_savings', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Save it to a cSV\n",
    "\n",
    "asd.sort_values('possible_savings', ascending=False).to_csv(\"99P-full-%s.csv\" % month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Same thing for practices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "top_1000_practice = price_utils.get_savings(group_by='practice',month=month, limit=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bnf_presentation</th>\n",
       "      <th>bnf_chemical</th>\n",
       "      <th>practice</th>\n",
       "      <th>pct</th>\n",
       "      <th>category</th>\n",
       "      <th>avg_brand_count_per_practice</th>\n",
       "      <th>lowest_decile</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price_per_dose</th>\n",
       "      <th>possible_savings</th>\n",
       "      <th>formulation_swap</th>\n",
       "      <th>num_pack_sizes</th>\n",
       "      <th>flag_imported</th>\n",
       "      <th>flag_broken_bulk</th>\n",
       "      <th>flag_non_bioequivalence</th>\n",
       "      <th>flag_special_containers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bnf_code</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>0208010L0AAAIAI</th>\n",
       "      <td>Dalteparin Sod_Inj 25,000u/ml 0.72ml Pfs</td>\n",
       "      <td>Dalteparin Sodium</td>\n",
       "      <td>K84055</td>\n",
       "      <td>10Q</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>10.16</td>\n",
       "      <td>110</td>\n",
       "      <td>137.41</td>\n",
       "      <td>13,996.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0908010P0AAAEAE</th>\n",
       "      <td>Sod Phenylbut_Liq Spec 1.25g/5ml</td>\n",
       "      <td>Sodium Phenylbutyrate</td>\n",
       "      <td>J82034</td>\n",
       "      <td>11A</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2500</td>\n",
       "      <td>5.56</td>\n",
       "      <td>11,187.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0908010S0AAACAC</th>\n",
       "      <td>Trientine Dihydroch_Cap 300mg</td>\n",
       "      <td>Trientine Dihydrochloride</td>\n",
       "      <td>E83638</td>\n",
       "      <td>07M</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>22.91</td>\n",
       "      <td>400</td>\n",
       "      <td>49.99</td>\n",
       "      <td>10,831.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0401010ADAABPBP</th>\n",
       "      <td>Melatonin_Tab 3mg</td>\n",
       "      <td>Melatonin</td>\n",
       "      <td>H82016</td>\n",
       "      <td>09G</td>\n",
       "      <td>NP8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>552</td>\n",
       "      <td>19.26</td>\n",
       "      <td>10,219.03</td>\n",
       "      <td>Tab  / Cap</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0908010S0AAACAC</th>\n",
       "      <td>Trientine Dihydroch_Cap 300mg</td>\n",
       "      <td>Trientine Dihydrochloride</td>\n",
       "      <td>F81069</td>\n",
       "      <td>06T</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>22.91</td>\n",
       "      <td>200</td>\n",
       "      <td>65.74</td>\n",
       "      <td>8,565.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0902012L0AABRBR</th>\n",
       "      <td>Sod Chlor_Liq Spec 292.5mg/5ml</td>\n",
       "      <td>Sodium Chloride</td>\n",
       "      <td>B83602</td>\n",
       "      <td>02N</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8000</td>\n",
       "      <td>1.10</td>\n",
       "      <td>8,309.15</td>\n",
       "      <td>Liq Spec  / Oral Soln</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>H82052</td>\n",
       "      <td>09H</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1771</td>\n",
       "      <td>9.94</td>\n",
       "      <td>7,119.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0407010Q0AAAAAA</th>\n",
       "      <td>Co-Proxamol_Tab 32.5mg/325mg</td>\n",
       "      <td>Co-Proxamol (Dextroprop HCl/Paracet)</td>\n",
       "      <td>M91011</td>\n",
       "      <td>05Y</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>3672</td>\n",
       "      <td>2.55</td>\n",
       "      <td>6,780.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0208010L0AAAHAH</th>\n",
       "      <td>Dalteparin Sod_Inj 25,000u/ml 0.6ml Pfs</td>\n",
       "      <td>Dalteparin Sodium</td>\n",
       "      <td>C82031</td>\n",
       "      <td>04C</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>8.47</td>\n",
       "      <td>60</td>\n",
       "      <td>115.16</td>\n",
       "      <td>6,401.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>G83006</td>\n",
       "      <td>07N</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1500</td>\n",
       "      <td>9.94</td>\n",
       "      <td>6,030.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         bnf_presentation  \\\n",
       "bnf_code                                                    \n",
       "0208010L0AAAIAI  Dalteparin Sod_Inj 25,000u/ml 0.72ml Pfs   \n",
       "0908010P0AAAEAE          Sod Phenylbut_Liq Spec 1.25g/5ml   \n",
       "0908010S0AAACAC             Trientine Dihydroch_Cap 300mg   \n",
       "0401010ADAABPBP                         Melatonin_Tab 3mg   \n",
       "0908010S0AAACAC             Trientine Dihydroch_Cap 300mg   \n",
       "0902012L0AABRBR            Sod Chlor_Liq Spec 292.5mg/5ml   \n",
       "1404000H0AAAFAF             Influenza_Vac Inact 0.5ml Pfs   \n",
       "0407010Q0AAAAAA              Co-Proxamol_Tab 32.5mg/325mg   \n",
       "0208010L0AAAHAH   Dalteparin Sod_Inj 25,000u/ml 0.6ml Pfs   \n",
       "1404000H0AAAFAF             Influenza_Vac Inact 0.5ml Pfs   \n",
       "\n",
       "                                         bnf_chemical practice  pct category  \\\n",
       "bnf_code                                                                       \n",
       "0208010L0AAAIAI                     Dalteparin Sodium   K84055  10Q      NP8   \n",
       "0908010P0AAAEAE                 Sodium Phenylbutyrate   J82034  11A      NP8   \n",
       "0908010S0AAACAC             Trientine Dihydrochloride   E83638  07M      NP8   \n",
       "0401010ADAABPBP                             Melatonin   H82016  09G      NP8   \n",
       "0908010S0AAACAC             Trientine Dihydrochloride   F81069  06T      NP8   \n",
       "0902012L0AABRBR                       Sodium Chloride   B83602  02N      NP8   \n",
       "1404000H0AAAFAF                             Influenza   H82052  09H      NP8   \n",
       "0407010Q0AAAAAA  Co-Proxamol (Dextroprop HCl/Paracet)   M91011  05Y      NP8   \n",
       "0208010L0AAAHAH                     Dalteparin Sodium   C82031  04C      NP8   \n",
       "1404000H0AAAFAF                             Influenza   G83006  07N      NP8   \n",
       "\n",
       "                 avg_brand_count_per_practice  lowest_decile  quantity  \\\n",
       "bnf_code                                                                 \n",
       "0208010L0AAAIAI                             1          10.16       110   \n",
       "0908010P0AAAEAE                             1           1.09      2500   \n",
       "0908010S0AAACAC                             1          22.91       400   \n",
       "0401010ADAABPBP                             2           0.75       552   \n",
       "0908010S0AAACAC                             1          22.91       200   \n",
       "0902012L0AABRBR                             1           0.06      8000   \n",
       "1404000H0AAAFAF                             1           5.92      1771   \n",
       "0407010Q0AAAAAA                             1           0.70      3672   \n",
       "0208010L0AAAHAH                             1           8.47        60   \n",
       "1404000H0AAAFAF                             1           5.92      1500   \n",
       "\n",
       "                 price_per_dose  possible_savings        formulation_swap  \\\n",
       "bnf_code                                                                    \n",
       "0208010L0AAAIAI          137.41         13,996.85                     NaN   \n",
       "0908010P0AAAEAE            5.56         11,187.72                     NaN   \n",
       "0908010S0AAACAC           49.99         10,831.68                     NaN   \n",
       "0401010ADAABPBP           19.26         10,219.03             Tab  / Cap    \n",
       "0908010S0AAACAC           65.74          8,565.84                     NaN   \n",
       "0902012L0AABRBR            1.10          8,309.15  Liq Spec  / Oral Soln    \n",
       "1404000H0AAAFAF            9.94          7,119.42                     NaN   \n",
       "0407010Q0AAAAAA            2.55          6,780.45                     NaN   \n",
       "0208010L0AAAHAH          115.16          6,401.77                     NaN   \n",
       "1404000H0AAAFAF            9.94          6,030.00                     NaN   \n",
       "\n",
       "                 num_pack_sizes  flag_imported flag_broken_bulk  \\\n",
       "bnf_code                                                          \n",
       "0208010L0AAAIAI            3.00            nan             None   \n",
       "0908010P0AAAEAE            3.00            nan             None   \n",
       "0908010S0AAACAC             nan            nan             None   \n",
       "0401010ADAABPBP             nan            nan             None   \n",
       "0908010S0AAACAC             nan            nan             None   \n",
       "0902012L0AABRBR             nan            nan             None   \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "0407010Q0AAAAAA            3.00            nan             None   \n",
       "0208010L0AAAHAH            3.00            nan             None   \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "\n",
       "                 flag_non_bioequivalence  flag_special_containers  \n",
       "bnf_code                                                           \n",
       "0208010L0AAAIAI                      nan                      nan  \n",
       "0908010P0AAAEAE                      nan                      nan  \n",
       "0908010S0AAACAC                      nan                      nan  \n",
       "0401010ADAABPBP                      nan                      nan  \n",
       "0908010S0AAACAC                      nan                      nan  \n",
       "0902012L0AABRBR                      nan                      nan  \n",
       "1404000H0AAAFAF                      nan                     1.00  \n",
       "0407010Q0AAAAAA                      nan                      nan  \n",
       "0208010L0AAAHAH                      nan                      nan  \n",
       "1404000H0AAAFAF                      nan                     1.00  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_1000_practice.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What savings appear only once in this list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 113 singletons in the practice-presentation savings list, for example:\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bnf_presentation</th>\n",
       "      <th>bnf_chemical</th>\n",
       "      <th>practice</th>\n",
       "      <th>pct</th>\n",
       "      <th>category</th>\n",
       "      <th>avg_brand_count_per_practice</th>\n",
       "      <th>lowest_decile</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price_per_dose</th>\n",
       "      <th>possible_savings</th>\n",
       "      <th>formulation_swap</th>\n",
       "      <th>num_pack_sizes</th>\n",
       "      <th>flag_imported</th>\n",
       "      <th>flag_broken_bulk</th>\n",
       "      <th>flag_non_bioequivalence</th>\n",
       "      <th>flag_special_containers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bnf_code</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>0902012L0AABXBX</th>\n",
       "      <td>Sod Chlor_Liq Spec 585mg/5ml</td>\n",
       "      <td>Sodium Chloride</td>\n",
       "      <td>G82228</td>\n",
       "      <td>09E</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.63</td>\n",
       "      <td>600</td>\n",
       "      <td>7.57</td>\n",
       "      <td>4,164.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202020L0AACDCD</th>\n",
       "      <td>Sod Chlor_Neb Soln 7% 5ml Btl</td>\n",
       "      <td>Sodium Chloride</td>\n",
       "      <td>F84006</td>\n",
       "      <td>08M</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>6.09</td>\n",
       "      <td>30</td>\n",
       "      <td>137.77</td>\n",
       "      <td>3,950.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>091102000AABDBD</th>\n",
       "      <td>Ubidecarenone_Oral Dps 5mg/ml</td>\n",
       "      <td>Other Enzyme Preps 0911020</td>\n",
       "      <td>B86003</td>\n",
       "      <td>03C</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.93</td>\n",
       "      <td>3,756.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0908010N0AAAIAI</th>\n",
       "      <td>Sod Benz_Liq Spec 1g/5ml</td>\n",
       "      <td>Sodium Benzoate</td>\n",
       "      <td>C88048</td>\n",
       "      <td>03N</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2000</td>\n",
       "      <td>2.54</td>\n",
       "      <td>3,713.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0404000L0AAAYAY</th>\n",
       "      <td>Dexamfet Sulf/Amfet_Cap 15mg/15mg M/R</td>\n",
       "      <td>Dexamfetamine Sulfate</td>\n",
       "      <td>F83678</td>\n",
       "      <td>08H</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>11.28</td>\n",
       "      <td>180</td>\n",
       "      <td>31.84</td>\n",
       "      <td>3,700.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0101012B0AABSBS</th>\n",
       "      <td>Sod Bicarb_Liq Spec 50mg/5ml</td>\n",
       "      <td>Sodium Bicarbonate</td>\n",
       "      <td>Y00243</td>\n",
       "      <td>06N</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2835</td>\n",
       "      <td>1.24</td>\n",
       "      <td>3,501.45</td>\n",
       "      <td>Liq Spec  / Oral Soln</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0309010L0AAAHAH</th>\n",
       "      <td>Dextrometh Hydrob_Oral Susp 30mg/5ml</td>\n",
       "      <td>Dextromethorphan Hydrobromide</td>\n",
       "      <td>G82085</td>\n",
       "      <td>99J</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1780</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3,483.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0107010S0AAABAB</th>\n",
       "      <td>Sucralfate_Enem 2g In 20ml</td>\n",
       "      <td>Sucralfate</td>\n",
       "      <td>E85681</td>\n",
       "      <td>07Y</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>11.71</td>\n",
       "      <td>20</td>\n",
       "      <td>180.00</td>\n",
       "      <td>3,365.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0902021S0AAAZAZ</th>\n",
       "      <td>Sod Chlor_I/V Inf 0.9% 1L</td>\n",
       "      <td>Sodium Chloride</td>\n",
       "      <td>A85005</td>\n",
       "      <td>13T</td>\n",
       "      <td>NP8</td>\n",
       "      <td>2</td>\n",
       "      <td>1.67</td>\n",
       "      <td>70</td>\n",
       "      <td>47.79</td>\n",
       "      <td>3,228.16</td>\n",
       "      <td>I/V Inf  / Blad Irrig</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0906040G0AABEBE</th>\n",
       "      <td>Colecal_Cap 2,200u (Old)</td>\n",
       "      <td>Colecalciferol</td>\n",
       "      <td>M85033</td>\n",
       "      <td>13P</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>200</td>\n",
       "      <td>16.14</td>\n",
       "      <td>3,177.93</td>\n",
       "      <td>Cap  / Tab</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      bnf_presentation  \\\n",
       "bnf_code                                                 \n",
       "0902012L0AABXBX           Sod Chlor_Liq Spec 585mg/5ml   \n",
       "1202020L0AACDCD          Sod Chlor_Neb Soln 7% 5ml Btl   \n",
       "091102000AABDBD          Ubidecarenone_Oral Dps 5mg/ml   \n",
       "0908010N0AAAIAI               Sod Benz_Liq Spec 1g/5ml   \n",
       "0404000L0AAAYAY  Dexamfet Sulf/Amfet_Cap 15mg/15mg M/R   \n",
       "0101012B0AABSBS           Sod Bicarb_Liq Spec 50mg/5ml   \n",
       "0309010L0AAAHAH   Dextrometh Hydrob_Oral Susp 30mg/5ml   \n",
       "0107010S0AAABAB             Sucralfate_Enem 2g In 20ml   \n",
       "0902021S0AAAZAZ              Sod Chlor_I/V Inf 0.9% 1L   \n",
       "0906040G0AABEBE               Colecal_Cap 2,200u (Old)   \n",
       "\n",
       "                                  bnf_chemical practice  pct category  \\\n",
       "bnf_code                                                                \n",
       "0902012L0AABXBX                Sodium Chloride   G82228  09E      NP8   \n",
       "1202020L0AACDCD                Sodium Chloride   F84006  08M      NP8   \n",
       "091102000AABDBD     Other Enzyme Preps 0911020   B86003  03C      NP8   \n",
       "0908010N0AAAIAI                Sodium Benzoate   C88048  03N      NP8   \n",
       "0404000L0AAAYAY          Dexamfetamine Sulfate   F83678  08H      NP8   \n",
       "0101012B0AABSBS             Sodium Bicarbonate   Y00243  06N      NP8   \n",
       "0309010L0AAAHAH  Dextromethorphan Hydrobromide   G82085  99J      NP8   \n",
       "0107010S0AAABAB                     Sucralfate   E85681  07Y      NP8   \n",
       "0902021S0AAAZAZ                Sodium Chloride   A85005  13T      NP8   \n",
       "0906040G0AABEBE                 Colecalciferol   M85033  13P      NP8   \n",
       "\n",
       "                 avg_brand_count_per_practice  lowest_decile  quantity  \\\n",
       "bnf_code                                                                 \n",
       "0902012L0AABXBX                             1           0.63       600   \n",
       "1202020L0AACDCD                             1           6.09        30   \n",
       "091102000AABDBD                             1           1.17      1000   \n",
       "0908010N0AAAIAI                             1           0.68      2000   \n",
       "0404000L0AAAYAY                             1          11.28       180   \n",
       "0101012B0AABSBS                             1           0.01      2835   \n",
       "0309010L0AAAHAH                             1           0.76      1780   \n",
       "0107010S0AAABAB                             1          11.71        20   \n",
       "0902021S0AAAZAZ                             2           1.67        70   \n",
       "0906040G0AABEBE                             1           0.25       200   \n",
       "\n",
       "                 price_per_dose  possible_savings        formulation_swap  \\\n",
       "bnf_code                                                                    \n",
       "0902012L0AABXBX            7.57          4,164.02                     NaN   \n",
       "1202020L0AACDCD          137.77          3,950.41                     NaN   \n",
       "091102000AABDBD            4.93          3,756.90                     NaN   \n",
       "0908010N0AAAIAI            2.54          3,713.29                     NaN   \n",
       "0404000L0AAAYAY           31.84          3,700.86                     NaN   \n",
       "0101012B0AABSBS            1.24          3,501.45  Liq Spec  / Oral Soln    \n",
       "0309010L0AAAHAH            2.72          3,483.61                     NaN   \n",
       "0107010S0AAABAB          180.00          3,365.81                     NaN   \n",
       "0902021S0AAAZAZ           47.79          3,228.16  I/V Inf  / Blad Irrig    \n",
       "0906040G0AABEBE           16.14          3,177.93             Cap  / Tab    \n",
       "\n",
       "                 num_pack_sizes  flag_imported flag_broken_bulk  \\\n",
       "bnf_code                                                          \n",
       "0902012L0AABXBX            3.00            nan             None   \n",
       "1202020L0AACDCD            3.00            nan             None   \n",
       "091102000AABDBD             nan            nan             None   \n",
       "0908010N0AAAIAI            3.00            nan             None   \n",
       "0404000L0AAAYAY            3.00           1.00             None   \n",
       "0101012B0AABSBS            3.00            nan             None   \n",
       "0309010L0AAAHAH            3.00            nan             None   \n",
       "0107010S0AAABAB            3.00            nan             None   \n",
       "0902021S0AAAZAZ            3.00            nan             None   \n",
       "0906040G0AABEBE             nan            nan             None   \n",
       "\n",
       "                 flag_non_bioequivalence  flag_special_containers  \n",
       "bnf_code                                                           \n",
       "0902012L0AABXBX                      nan                      nan  \n",
       "1202020L0AACDCD                      nan                      nan  \n",
       "091102000AABDBD                      nan                      nan  \n",
       "0908010N0AAAIAI                      nan                      nan  \n",
       "0404000L0AAAYAY                      nan                      nan  \n",
       "0101012B0AABSBS                      nan                      nan  \n",
       "0309010L0AAAHAH                      nan                      nan  \n",
       "0107010S0AAABAB                      nan                      nan  \n",
       "0902021S0AAAZAZ                      nan                     1.00  \n",
       "0906040G0AABEBE                      nan                      nan  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = top_1000_practice.groupby('bnf_presentation')\n",
    "singletons = grouped.filter(lambda x: len(x) == 1).sort_values('possible_savings', ascending=False)\n",
    "print \"There are %s singletons in the practice-presentation savings list, for example:\" % len(singletons)\n",
    "singletons.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "top_1000_practice.to_csv(\"practices-%s.csv\" % month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# What is the distribution of the easiest cost savings?\n",
    "\n",
    "For each practice, compute and sum its top three savings, then plot these in a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "top_10 = price_utils.top_savings_per_entity(top_n=10, entity='practice')\n",
    "top_10.to_csv(\"each_practice_top_10_savings-%s.csv\" % month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "top_1 = price_utils.top_savings_per_entity(top_n=1, entity='practice', summed=False)\n",
    "top_1.to_csv(\"top_saving_for_each_practice_%s.csv\" % month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>top_savings_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7637.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2002.218267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1363.336244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1079.671028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1722.737374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2600.779768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17809.622578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       top_savings_sum\n",
       "count      7637.000000\n",
       "mean       2002.218267\n",
       "std        1363.336244\n",
       "min           0.000000\n",
       "25%        1079.671028\n",
       "50%        1722.737374\n",
       "75%        2600.779768\n",
       "max       17809.622578"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>practice</th>\n",
       "      <th>top_savings_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4876</th>\n",
       "      <td>K84055</td>\n",
       "      <td>17809.622578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>B83602</td>\n",
       "      <td>17773.665735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4382</th>\n",
       "      <td>J82094</td>\n",
       "      <td>16966.668402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>J82034</td>\n",
       "      <td>13671.041531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5798</th>\n",
       "      <td>M85063</td>\n",
       "      <td>13549.065237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     practice  top_savings_sum\n",
       "4876   K84055     17809.622578\n",
       "714    B83602     17773.665735\n",
       "4382   J82094     16966.668402\n",
       "4334   J82034     13671.041531\n",
       "5798   M85063     13549.065237"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10.sort_values('top_savings_sum', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E1vj0G1T1Iv/PgkoJZIYal7rGAhCNhKiKhOhJDJU8b2PM/FFMH8vFwIMAqroN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935d7b7950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(top_10.top_savings_sum)\n",
    "plt.title(\"Distribution of sum of top 10 savings by practice\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JiFwAzFXVc3DWB/SfKns3zrS484BLRWTBMXRuB+5V1eU4Te7D0+zcpvisfmk/rKoXuT/m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\nGwsmhmEYRruxYGIYhmG0m/8HVh8sEHTxlmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935c1f3a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asd = top_10[top_10.top_savings_sum < top_10.top_savings_sum.quantile(.95)]\n",
    "g = sns.distplot(asd.top_savings_sum)\n",
    "plt.title(\"Distribution of sum of top 10 savings by practice, 95th percentile\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same thing, by CCG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "top_10_pct = price_utils.top_savings_per_entity(top_n=10, entity='pct')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>top_savings_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>213.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40,915.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22,779.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>42.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25,620.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36,276.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48,537.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>142,077.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       top_savings_sum\n",
       "count           213.00\n",
       "mean         40,915.60\n",
       "std          22,779.51\n",
       "min              42.77\n",
       "25%          25,620.72\n",
       "50%          36,276.98\n",
       "75%          48,537.48\n",
       "max         142,077.13"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_pct.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g80AUuEdV73XzWA/8Avikqj7mbnsG8AFdON+p/0ZVXxtfNZixKPL5jxv4720d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\ncERVG1Q1DjwKXDrMMQHgQmC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YtI8F1cVEyaWrZ4BQVx/NHd3Ut0Q4cPj4i2tL/PnMKS+i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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935c49b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(top_10_pct.top_savings_sum)\n",
    "plt.title(\"Distribution of sum of top 10 savings by CCG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "top_1000_product = price_utils.get_savings(group_by='product', month=month, limit=1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bnf_presentation</th>\n",
       "      <th>bnf_chemical</th>\n",
       "      <th>category</th>\n",
       "      <th>avg_brand_count_per_practice</th>\n",
       "      <th>lowest_decile</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price_per_dose</th>\n",
       "      <th>possible_savings</th>\n",
       "      <th>formulation_swap</th>\n",
       "      <th>num_pack_sizes</th>\n",
       "      <th>flag_imported</th>\n",
       "      <th>flag_broken_bulk</th>\n",
       "      <th>flag_non_bioequivalence</th>\n",
       "      <th>flag_special_containers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bnf_code</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>1404000H0AAAFAF</th>\n",
       "      <td>Influenza_Vac Inact 0.5ml Pfs</td>\n",
       "      <td>Influenza</td>\n",
       "      <td>NP8</td>\n",
       "      <td>1.10</td>\n",
       "      <td>5.92</td>\n",
       "      <td>2055110</td>\n",
       "      <td>6.62</td>\n",
       "      <td>1,522,419.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0601060D0AAA0A0</th>\n",
       "      <td>Glucose Blood Testing Reagents</td>\n",
       "      <td>Glucose Blood Testing Reagents</td>\n",
       "      <td>NP8</td>\n",
       "      <td>12.05</td>\n",
       "      <td>0.25</td>\n",
       "      <td>57333666</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1,043,779.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0302000N0AABGBG</th>\n",
       "      <td>Fluticasone/Salmeterol_Inh 250/25mcg120D</td>\n",
       "      <td>Fluticasone Propionate (Inh)</td>\n",
       "      <td>C</td>\n",
       "      <td>2.06</td>\n",
       "      <td>52.05</td>\n",
       "      <td>158950</td>\n",
       "      <td>56.59</td>\n",
       "      <td>809,566.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0403040W0AAAEAE</th>\n",
       "      <td>Venlafaxine_Cap 150mg M/R</td>\n",
       "      <td>Venlafaxine</td>\n",
       "      <td>C</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2668261</td>\n",
       "      <td>0.65</td>\n",
       "      <td>639,892.88</td>\n",
       "      <td>Cap  / Tab</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0302000K0AAAMAM</th>\n",
       "      <td>Budesonide/Formoterol Inh B/A 200/6(120D</td>\n",
       "      <td>Budesonide</td>\n",
       "      <td>C</td>\n",
       "      <td>2.20</td>\n",
       "      <td>33.98</td>\n",
       "      <td>225338</td>\n",
       "      <td>36.74</td>\n",
       "      <td>627,801.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         bnf_presentation  \\\n",
       "bnf_code                                                    \n",
       "1404000H0AAAFAF             Influenza_Vac Inact 0.5ml Pfs   \n",
       "0601060D0AAA0A0            Glucose Blood Testing Reagents   \n",
       "0302000N0AABGBG  Fluticasone/Salmeterol_Inh 250/25mcg120D   \n",
       "0403040W0AAAEAE                 Venlafaxine_Cap 150mg M/R   \n",
       "0302000K0AAAMAM  Budesonide/Formoterol Inh B/A 200/6(120D   \n",
       "\n",
       "                                   bnf_chemical category  \\\n",
       "bnf_code                                                   \n",
       "1404000H0AAAFAF                       Influenza      NP8   \n",
       "0601060D0AAA0A0  Glucose Blood Testing Reagents      NP8   \n",
       "0302000N0AABGBG    Fluticasone Propionate (Inh)        C   \n",
       "0403040W0AAAEAE                     Venlafaxine        C   \n",
       "0302000K0AAAMAM                      Budesonide        C   \n",
       "\n",
       "                 avg_brand_count_per_practice  lowest_decile  quantity  \\\n",
       "bnf_code                                                                 \n",
       "1404000H0AAAFAF                          1.10           5.92   2055110   \n",
       "0601060D0AAA0A0                         12.05           0.25  57333666   \n",
       "0302000N0AABGBG                          2.06          52.05    158950   \n",
       "0403040W0AAAEAE                          1.49           0.38   2668261   \n",
       "0302000K0AAAMAM                          2.20          33.98    225338   \n",
       "\n",
       "                 price_per_dose  possible_savings formulation_swap  \\\n",
       "bnf_code                                                             \n",
       "1404000H0AAAFAF            6.62      1,522,419.70              NaN   \n",
       "0601060D0AAA0A0            0.26      1,043,779.81              NaN   \n",
       "0302000N0AABGBG           56.59        809,566.81              NaN   \n",
       "0403040W0AAAEAE            0.65        639,892.88      Cap  / Tab    \n",
       "0302000K0AAAMAM           36.74        627,801.21              NaN   \n",
       "\n",
       "                 num_pack_sizes  flag_imported flag_broken_bulk  \\\n",
       "bnf_code                                                          \n",
       "1404000H0AAAFAF            3.00            nan             None   \n",
       "0601060D0AAA0A0             nan            nan             None   \n",
       "0302000N0AABGBG            3.00            nan             None   \n",
       "0403040W0AAAEAE            3.00            nan             None   \n",
       "0302000K0AAAMAM            3.00            nan             None   \n",
       "\n",
       "                 flag_non_bioequivalence  flag_special_containers  \n",
       "bnf_code                                                           \n",
       "1404000H0AAAFAF                      nan                     1.00  \n",
       "0601060D0AAA0A0                      nan                      nan  \n",
       "0302000N0AABGBG                      nan                     1.00  \n",
       "0403040W0AAAEAE                      nan                      nan  \n",
       "0302000K0AAAMAM                      nan                     1.00  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_1000_product.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What if every practice enacted all its top 3 savings?\n",
    "\n",
    "The following shows that there would be £11m of savings, including: £3.4m on blood test strips, £1.1m on vaccines, £1.2m on Fluticasone/Salmeterol inhalers, and £600k on Pregabalin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "# A list of all the generic presentations in the top three savings for each practice\n",
    "df = price_utils.all_presentations_in_per_entity_top_n(top_n=3, entity='practice', month='2016-09-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8005429.8248444144"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total savings if each practice enacted its top three savings\n",
    "df.top_savings_sum.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "presentation\n",
       "Influenza_Vac Inact 0.5ml Pfs              1,248,087.89\n",
       "Glucose Blood Testing Reagents               653,892.81\n",
       "Fluticasone/Salmeterol_Inh 250/25mcg120D     326,968.99\n",
       "Co-Proxamol_Tab 32.5mg/325mg                 218,023.28\n",
       "Lamotrigine_Tab 100mg                        204,093.73\n",
       "Name: top_savings_sum, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The top savings by presentation if each practice enacted its top three savings\n",
    "by_presentation = df.groupby('presentation').top_savings_sum.sum().sort_values(inplace=False, ascending=False)\n",
    "by_presentation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chemical\n",
       "Influenza                              1,248,087.89\n",
       "Glucose Blood Testing Reagents           653,892.81\n",
       "Levetiracetam                            467,345.08\n",
       "Lamotrigine                              400,015.67\n",
       "Fluticasone Propionate (Inh)             357,933.49\n",
       "Colecalciferol                           272,246.74\n",
       "Venlafaxine                              255,845.48\n",
       "Co-Proxamol (Dextroprop HCl/Paracet)     218,286.56\n",
       "Melatonin                                195,446.73\n",
       "Quetiapine                               179,358.28\n",
       "Name: top_savings_sum, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The top savings by chemical if each practice enacted its top three savings\n",
    "by_subpara = df.groupby('chemical').top_savings_sum.sum().sort_values(inplace=False, ascending=False)\n",
    "by_subpara.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And just its top 1 saving?\n",
    "£4 million of savings, mostly on blood testing strips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "reload(price_utils)\n",
    "# A list of all the generic presentations in the top one savings for each practice\n",
    "df2 = price_utils.all_presentations_in_per_entity_top_n(top_n=1, entity='practice', month='2016-09-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4053973.1634699339"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.top_savings_sum.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chemical\n",
       "Influenza                              872,999.58\n",
       "Glucose Blood Testing Reagents         286,237.36\n",
       "Levetiracetam                          167,526.91\n",
       "Colecalciferol                         161,614.67\n",
       "Co-Proxamol (Dextroprop HCl/Paracet)   154,986.86\n",
       "Lamotrigine                            151,070.72\n",
       "Dalteparin Sodium                      140,174.34\n",
       "Melatonin                              106,998.08\n",
       "Fluticasone Propionate (Inh)            99,458.51\n",
       "Venlafaxine                             83,735.83\n",
       "Name: top_savings_sum, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The top savings by chemical if each practice enacted its top saving\n",
    "by_chemical2 = df2.groupby('chemical').top_savings_sum.sum().sort_values(inplace=False, ascending=False)\n",
    "by_chemical2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are unusual top savings?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the actions for the top drugs?\n",
    "\n",
    "1. Switch from *Aviva testing strips (Roche Diabetes Care Ltd)* or *Mobile cassette (Roche Diabetes Care Ltd)* or *Contour Next testing strips (Bayer Diagnostics Manufacturing Ltd)* or *FreeStyle Optium testing strips (Abbott Laboratories Ltd)* to *GlucoRx Nexus testing strips (GlucoRx Ltd)* or *WaveSense JAZZ testing strips (AgaMatrix Europe Ltd)*\n",
    "2. Switch from *Seretide 250 Evohaler* to *Sirdupla 25micrograms/dose / 250micrograms/dose inhaler* \n",
    "3. Switch from *Fluarix Tetra vaccine suspension for injection 0.5ml pre-filled syringes* to *Influvac Sub-unit vaccine suspension for injection 0.5ml pre-filled syringes*\n",
    "4. Switch from *Lyrica 75mg capsules* to *Alzain 75mg capsules*\n",
    "5. Switch from *Symbicort 200/6 Turbohaler* to *DuoResp Spiromax 160micrograms/dose / 4.5micrograms/dose dry powder inhaler*\n",
    "\n",
    "## What reasons are there for not switching?\n",
    "\n",
    "There are a number of costs relating to switching, including:\n",
    "\n",
    "* With device-based drugs, patients require training in new devices\n",
    "* Patients are reluctant to accept change when they perceive a certain brand as working\n",
    "* GPs are reluctant to accept change when they see it as continuous background pressure (e.g. moving targets, ever-cheaper products becoming available)\n",
    "* (Possibly unfounded) views among HCPs that alternatives are not clinincally equivalent or at least comparable (e.g. tetravalent vs trivalent vaccines; different glucose meters)\n",
    "* Drugs in category M which are not yet licenced for all uses (e.g. Pregabalin)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What if practices only enacted cost savings that were at least £1000?\n",
    "\n",
    "There would still be £3m of savings. Plotting total savings against a minimum threshold shows something close to an exponential dropoff (the closest fit is a 4PL curve with the inflection point relatively close to zero: `y = 2694457 + 35665420*e^(-0.003723744*x)`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: 34660180.154420972,\n",
       " 110: 17036379.520410381,\n",
       " 210: 10099491.686985292,\n",
       " 310: 6805120.1687839311,\n",
       " 410: 4916021.642091901,\n",
       " 510: 3760646.821409048,\n",
       " 610: 2969136.2199055143,\n",
       " 710: 2398577.0791196171,\n",
       " 810: 2011993.3460341,\n",
       " 910: 1730405.6307992071,\n",
       " 1010: 1520966.8835880314}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {}\n",
    "for threshold in [10, 110, 210, 310, 410, 510, 610, 710, 810, 910, 1010]:\n",
    "    d[threshold] = price_utils.cost_savings_at_minimum_for_practice(threshold, month='2016-09-01').top_savings_sum.sum()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT\n",
      "  bnf.presentation,\n",
      "  bnf.chemical,\n",
      "  savings.presentations.pct AS pct,\n",
      "  savings.generic_presentation AS generic_presentation,\n",
      "  savings.category AS category,\n",
      "  savings.avg_brand_count_per_practice AS avg_brand_count_per_practice,\n",
      "  savings.deciles.lowest_decile AS lowest_decile,\n",
      "  savings.quantity AS quantity,\n",
      "  savings.price_per_dose AS price_per_dose,\n",
      "  savings.possible_savings AS possible_savings\n",
      "FROM (\n",
      "  SELECT\n",
      "      presentations.pct, \n",
      "      generic_presentation,\n",
      "      deciles.lowest_decile,\n",
      "      AVG(brand_count) AS avg_brand_count_per_practice,\n",
      "      MAX(category) AS category,\n",
      "      SUM(quantity) AS quantity,\n",
      "      AVG(price_per_dose) AS price_per_dose,\n",
      "      SUM(possible_savings) AS possible_savings\n",
      "    FROM (\n",
      "    -- Always select per-practice initially, so we can apply \"diode\"\n",
      "    -- to exclude negative cost savings\n",
      "    SELECT\n",
      "      presentations.practice,\n",
      "      presentations.pct,\n",
      "      presentations.generic_presentation AS generic_presentation,\n",
      "      COUNT(DISTINCT bnf_code) AS brand_count,\n",
      "      MAX(presentations.category) AS category,\n",
      "      deciles.lowest_decile,\n",
      "      SUM(presentations.quantity) AS quantity,\n",
      "      SUM(presentations.net_cost)/SUM(presentations.quantity) AS price_per_dose,\n",
      "      GREATEST((SUM(presentations.net_cost) - (SUM(presentations.quantity) * deciles.lowest_decile)), 0) AS possible_savings -- the \"diode\"\n",
      "    FROM (\n",
      "      SELECT\n",
      "        *\n",
      "      FROM (\n",
      "          -- Create table for joining individual data\n",
      "        SELECT\n",
      "          practice,\n",
      "          pct,\n",
      "          p.bnf_code AS bnf_code,\n",
      "          t.category AS category,\n",
      "          IF(\n",
      "            LENGTH(RTRIM(p.bnf_code)) = 15 -- excludes devices etc\n",
      "              AND (\n",
      "                SUBSTR(p.bnf_code, 14, 15) != 'A0' -- excludes things without generic equivalents\n",
      "                OR SUBSTR(p.bnf_code, 1, 9) == '0601060U0' OR SUBSTR(p.bnf_code, 1, 9) == '0601060D0'), -- unless they're one of our two exceptions -- see issue #1\n",
      "            CONCAT(SUBSTR(p.bnf_code, 1, 9), 'AA', SUBSTR(p.bnf_code, 14, 2), SUBSTR(p.bnf_code, 14, 2)),\n",
      "            NULL) AS generic_presentation,\n",
      "          net_cost,\n",
      "          quantity\n",
      "        FROM\n",
      "          ebmdatalab.hscic.prescribing_with_merged_codes_2016_09_01 AS p\n",
      "        LEFT JOIN ebmdatalab.hscic.tariff t\n",
      "          ON p.bnf_code = t.bnf_code\n",
      "        LEFT JOIN ebmdatalab.hscic.practices practices\n",
      "          ON p.practice = practices.code\n",
      "        WHERE\n",
      "          practices.setting = 4 AND\n",
      "          month = TIMESTAMP(\"2016-09-01\")\n",
      "          AND LENGTH(RTRIM(p.bnf_code)) >= 15 AND p.bnf_code NOT LIKE '0302000C0____BE' AND p.bnf_code NOT LIKE '0302000C0____BF' AND p.bnf_code NOT LIKE '0302000C0____BH' AND p.bnf_code NOT LIKE '0302000C0____BG' AND p.bnf_code NOT LIKE '0904010H0%' AND p.bnf_code NOT LIKE '0904010H0%' AND p.bnf_code NOT LIKE '1311070S0A____AA' AND p.bnf_code NOT LIKE '1311020L0____BS' AND p.bnf_code NOT LIKE '0301020S0____AA' AND p.bnf_code NOT LIKE '190700000BBCJA0' AND p.bnf_code NOT LIKE '0604011L0BGAAAH' AND p.bnf_code NOT LIKE '1502010J0____BY' AND p.bnf_code NOT LIKE '1201010F0AAAAAA' AND p.bnf_code NOT LIKE '0107010S0AAAGAG' AND p.bnf_code NOT LIKE '060106000BBAAA0' AND p.bnf_code NOT LIKE '190201000AABJBJ' AND p.bnf_code NOT LIKE '190201000AABKBK' AND p.bnf_code NOT LIKE '190201000AABLBL' AND p.bnf_code NOT LIKE '190201000AABMBM' AND p.bnf_code NOT LIKE '190201000AABNBN' AND p.bnf_code NOT LIKE '190202000AAADAD' \n",
      "          ) ) presentations\n",
      "    JOIN (\n",
      "        -- Calculate the top decile of price per dose for each generic presentation\n",
      "      SELECT\n",
      "        generic_presentation,\n",
      "        MAX(lowest_decile) AS lowest_decile\n",
      "      FROM (\n",
      "        SELECT\n",
      "          generic_presentation,\n",
      "          PERCENTILE_CONT(0.1) OVER (PARTITION BY generic_presentation ORDER BY price_per_dose ASC) AS lowest_decile\n",
      "        FROM (\n",
      "            -- Calculate price per dose for each presentation, normalising the codes across brands/generics\n",
      "          SELECT\n",
      "            IF(\n",
      "              LENGTH(RTRIM(p.bnf_code)) = 15 -- excludes devices etc\n",
      "                AND (\n",
      "                  SUBSTR(bnf_code, 14, 15) != 'A0' -- excludes things without generic equivalents\n",
      "                  OR SUBSTR(bnf_code, 1, 9) == '0601060U0' OR SUBSTR(bnf_code, 1, 9) == '0601060D0'), -- unless they're one of our two exceptions -- see issue #1\n",
      "              CONCAT(SUBSTR(bnf_code, 1, 9), 'AA', SUBSTR(bnf_code, 14, 2), SUBSTR(bnf_code, 14, 2)),\n",
      "              NULL) AS generic_presentation,\n",
      "            AVG(net_cost/quantity) AS price_per_dose\n",
      "          FROM\n",
      "            ebmdatalab.hscic.prescribing_with_merged_codes_2016_09_01 AS p\n",
      "          LEFT JOIN ebmdatalab.hscic.practices practices\n",
      "            ON p.practice = practices.code\n",
      "          WHERE\n",
      "            practices.setting = 4 AND\n",
      "            month = TIMESTAMP(\"2016-09-01\")\n",
      "            AND LENGTH(RTRIM(p.bnf_code)) >= 15 AND p.bnf_code NOT LIKE '0302000C0____BE' AND p.bnf_code NOT LIKE '0302000C0____BF' AND p.bnf_code NOT LIKE '0302000C0____BH' AND p.bnf_code NOT LIKE '0302000C0____BG' AND p.bnf_code NOT LIKE '0904010H0%' AND p.bnf_code NOT LIKE '0904010H0%' AND p.bnf_code NOT LIKE '1311070S0A____AA' AND p.bnf_code NOT LIKE '1311020L0____BS' AND p.bnf_code NOT LIKE '0301020S0____AA' AND p.bnf_code NOT LIKE '190700000BBCJA0' AND p.bnf_code NOT LIKE '0604011L0BGAAAH' AND p.bnf_code NOT LIKE '1502010J0____BY' AND p.bnf_code NOT LIKE '1201010F0AAAAAA' AND p.bnf_code NOT LIKE '0107010S0AAAGAG' AND p.bnf_code NOT LIKE '060106000BBAAA0' AND p.bnf_code NOT LIKE '190201000AABJBJ' AND p.bnf_code NOT LIKE '190201000AABKBK' AND p.bnf_code NOT LIKE '190201000AABLBL' AND p.bnf_code NOT LIKE '190201000AABMBM' AND p.bnf_code NOT LIKE '190201000AABNBN' AND p.bnf_code NOT LIKE '190202000AAADAD' \n",
      "          GROUP BY practice, generic_presentation)\n",
      "          )\n",
      "      WHERE generic_presentation IS NOT NULL\n",
      "      GROUP BY\n",
      "        generic_presentation) deciles\n",
      "    ON\n",
      "      deciles.generic_presentation = presentations.generic_presentation\n",
      "    GROUP BY\n",
      "      presentations.practice,\n",
      "      presentations.pct,\n",
      "      generic_presentation,\n",
      "      deciles.lowest_decile\n",
      "  )\n",
      "\n",
      "  GROUP BY\n",
      "    generic_presentation,\n",
      "    deciles.lowest_decile,\n",
      "    presentations.pct, \n",
      "    \n",
      "    \n",
      ") savings\n",
      "LEFT JOIN\n",
      "  (SELECT\n",
      "    *\n",
      "  FROM ebmdatalab.hscic.bnf, (\n",
      "    SELECT\n",
      "      'Glucose Blood Testing Reagents' AS presentation,\n",
      "      'Glucose Blood Testing Reagents' AS chemical,\n",
      "      '0601060D0AAA0A0' AS presentation_code),\n",
      "    (\n",
      "    SELECT\n",
      "      'Urine Testing Reagents' AS presentation,\n",
      "      'Urine Testing Reagents' AS chemical,\n",
      "      '0601060U0AAA0A0' AS presentation_code)) bnf\n",
      "ON\n",
      "  bnf.presentation_code = savings.generic_presentation\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print price_utils.get_savings(group_by='pct', limit=None, sql_only=True, month=month, order_by_savings=False, namespace='hscic', prescribing_table='prescribing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f935c7e44d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T8pgxJW/wecuxLi/0w8G/u+4oB+rbeGajt3+RkxVg3vRCqmYWMW9GEfNmFJKf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\n9CIiKU5BLyKS4hT0IiIpTkEvIpLi/j8GoJ+aeRFyGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935c7e4e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.options.display.float_format = None\n",
    "asd = pd.DataFrame(d.items())\n",
    "asd.columns = ['threshold', 'sum']\n",
    "s = asd.sort_values('threshold')\n",
    "s.plot(x='threshold', y='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Visualising distributions of drug prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Drugs with the greatest saving potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7uPYOp6cvWKo2Ho+zdksLW3Z1jMYVj8fZ3xY97JdMR4bKc1EsHj/sW25798Do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\n86PpxM8XdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935d7b87d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F9T/u/lbOjkpERHJuV5qiNb2rZHPFmTuKM7d2hTh3hRhhl4ozZ1VSutNbREQSUcIQEZFE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\nooQhIiKJ/P9aoT9JmM2hHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92de6c0490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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So1CXPS4fRWx8cnhm9aLHBkKlvnJ5Fwf1L0987ZxKvSuMS1+zspudY1Pcfv9T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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92d40f32d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A3Vt6um95efV+ZyYblJaWDNu8gfKX7ZS/7NTc3JqRgNCbb+ufBr4NYGZTgTGEtoALo/0X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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92d0ded490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/statsmodels/nonparametric/kde.py:494: RuntimeWarning: invalid value encountered in divide\n",
      "  binned = fast_linbin(X,a,b,gridsize)/(delta*nobs)\n",
      "/usr/local/lib/python2.7/dist-packages/statsmodels/nonparametric/kdetools.py:32: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "image/png": 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GMyb/XeCCiNiPckW/oynfIPxaRBwF3AWcO+5KJEldN2LIN180Gewyqnt2vxxJUjfVsT8q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\nIS9JFTPkJali/x89K+zBZ84CJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9351ff9850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subjects = \"\"\"0601060D0%\tGlucose Blood Testing Reagents\n",
    "0302000N0AABGBG\tFluticasone/Salmeterol_Inh 250/25mcg120D\n",
    "1404000H0AAAFAF\tInfluenza_Vac Inact 0.5ml Pfs\n",
    "0408010AEAAACAC\tPregabalin_Cap 75mg\n",
    "0302000K0AAAMAM\tBudesonide/Formoterol Inh B/A 200/6(120D\"\"\"\n",
    "\n",
    "for subject in subjects.split(\"\\n\"):\n",
    "    code, title = subject.split(\"\\t\", 1)\n",
    "    sql = \"\"\"  SELECT\n",
    "        *\n",
    "      FROM\n",
    "        ebmdatalab.hscic.prescribing_with_merged_codes_2016_09_01\n",
    "      WHERE\n",
    "        bnf_code LIKE '%s'\"\"\" % code\n",
    "    df = pd.io.gbq.read_gbq(\n",
    "        sql, project_id=\"ebmdatalab\", verbose=False, dialect='standard')\n",
    "    df['ppq'] = df['net_cost'] / df['quantity']\n",
    "    df = df.sort_values('ppq')\n",
    "    cheapest = float(df[['quantity', 'net_cost', 'ppq']].head(1)['ppq'].iloc[0])\n",
    "    expensivest = float(df[['quantity', 'net_cost', 'ppq']].tail(1)['ppq'].iloc[0])\n",
    "    ratio = int(expensivest / cheapest)\n",
    "    sns.distplot(df['ppq'], rug=True)\n",
    "    sns.plt.title(\"%s\\nRatio: %s\" % (title, ratio))\n",
    "    sns.plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Some branded drugs that show significant variation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BxBhjTFwskBhjjImLBRJjjDFxsUBijDEmLhZIjDHGxMUCiTHGmLhYIDHGGBMX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\nzBr5E15aCWY9r+ePAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93566becd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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30miZ8qGQ48Fd0V1EpKFUBvdS23rlUEjQTbJFRJJIZXAvjZap1iyjTlURkcbS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\nFNxFRFqQgruISAv6/wEY3kx/ZtPWKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935bc9b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1opKyzAbgaGBJ8R1mtiuw3N0Xu3sBuAY4srYh1r/8iHHuqrmLSLbGLMu4ex7o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\nuKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9357c51ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1lcV09w7S3Wvt+saYERb0p5Hu3gG2vdzKgvoKFjdWANbEY4wZzYL+NPL0iy0M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\nivlm6dwaVi2dybaXW3kgQZK0R7bs5/9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935c4b7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M9sr6Tdx3iLp5/juBDSKyDvgKrsl9ZP1BjpdYv+9PAtXesbgGd2K62Xvvi0Cl913cAbxf\n3YQRGKCOB9lvrNM4n8Bd6T+kqq1RlvkK7sLsVVwQ+SVwq7iZf9H28w7gv7z6+hFwdViXbWg5XxeR\nV4AHgf/yuv5+AZwf0hX0aeAicbPnvgGIV5efxs2CCkT4Lr+KN7YT5iW84BPi9GCXi1fXZ3D0d/QN\n4GYRWYsLuF8DviYi5+ICzje84/wTuN9hAfAP3OzE76vqCtxFwxqvPv4N93sPfv6BDPb+T3GtuQ24\nC7CFwLNey+wB3DnlOVwgeBE3YSfUI7h6DY4zRTyOBjhH/QD4iHeu+AguoH5ARN4WYVsRyxrlcwW/\nj+B3Ehz/uBu4Q0Q+hbswmyUi23GTgv4Wto3TcMEmKp89j8QYAyAidwLPqOotgy7Mke7V13HdPrtH\ns2wmMUREcBdJs0IG+49hKVKMGadE5GMicq/XPVKJmwk34JVnKK9l9CNcq9ykp//GjcdEDSJggcSY\n8ex3uKm+W4FncCeMVQOucazvAieIdxOmSR/eONZcXHfXgKxryxhjTFysRWKMMSYug2b/TQRxuZm2\nAb246WuZuLstPxFlxkroukeyhYrIx4BKVf1qHGWpBP6EG2xaEPJ6CW7WzYleGe9Ul3ogbhJjVt7R\nIiFZTYe5/qO4tBavDrrw4NvKwt2V/2mGmd3Wm8X1H7j5+jeq6mXxlmssiUgPMDd8QFuGmFU53u81\nxn0kXRbcWCTTMR+23agZrZNJUgYS3HS381R1PxzJfPtX3PTO/x1k3ffhpsKtV9WfDrLsgLz7HZ7C\nTf+bFfb2d4EaVX2H92WvEZHnNSyd+HB40xxXxLudOA27z1MjpK2Owz24aaZx98F6049TKoh4In52\ndfnKRmRbI+hC3F3x943R/kZSshzzoYIZrd8m7gbxNSLybPDcmCySNZAEn2UAHLlD9iHcvPGBsoj+\nJyHZQnH3KVSr6gfFZVW9FRcQunEDi3/0tvcY7v6L8KuJAC778FT6p4gGNw97rVe+JhFZAwhHE9Qd\nw7vqGTTTrAyelfeYrLjecodxLaQFuJvIrlXVTjk2e+knVfVxb7334JL3BbxyfTBsu1m4OfL3qeoP\nw957O+7eiEyvTj+hqv8SkZ24+zROwc2JD+C+z/le2f8pIlfiMpkW4Fqf16lqpBQXN6rqSnGZoWMi\nLlHhH3DH9wMcTRoZ+vyIJbg7lotxmXh/rKo/G6QeF+GeaTIFN0j9flVdHfK9PoXLJZULvFdVnxGX\nJubWkP38RFV/KgNnm70Md4d3NxFydoV8Tj8uZ9z8aPsfYN2B9h8ti/OAGYHF3bgcUxZcGTzb7bg5\n5sXd5B2tpRwxozVhiSMTLSXGSLyWwXW4gw2iZxH9JSHZQr1lg1cZvwKe8Na5HPiJuDvBUdWLIzVJ\nVbVRVbdGKpOqPqaqh7zyLcAdQJHSe4eLNdPsQFl5o2XFfQtwlapW4360wR9IePbSX3jlnoW7Se1c\nr14Kcem7Q93iPm7/H5Tnp8Bl3pXxRwkLtqr6E/WeGYK7mXINLtfUHNyJ/t9VdR7uwUgRfxiqujLS\n64P4OfBD7zM9T/8ke8F6/Qrwc1U9HnfT3MVeyxci1KN34lsB/E7d804+DNwjR9PQnISXZ8rbf/BG\ny6+G7ecibz8Rs8162/s18GFVPQ53R3m01Dahx0i0/UcTbf8RM/9Gez10g6r6CjFmwWXwbLfj5phX\n1ZcjBREZOKN1UknmQPKUdzfmdlwK8Edx3UmDZRGFsLQA3hXGJbgfGF5f85O4ZviwiUtRELxD9bsa\n9ryGKIaTaTaWrLjg8hAFE8KtwLuSIXr20kuA5/Ro6pTrcHfHg7uS/DCuXkOzpIY6iLsbd4aqPq+q\nn420kPcj+h7uarEXdxX8ZEh9/QqXHSDWdCRRiUguLjtuMJvp3UB7hEUP4dJvn6SqDap6lR6dKx+p\nHhcCFar6O2+7L+DSRgTruFlVg6la1nD0O4q2n2jZZucDucGrZ1zLO1q9hL4ebf/RRNt/tMy/sWYE\njuSYLLg6eLZbO+YHzmidVJI5kJznRfXluKbpnd5BNWAW0SjKwSXXC3ktPPPskHk/gvm47rJ3ikgs\nj+wcTqbZWLPyhnYNHeZoQsVo2UvDM5F269H0KsEHPh0IeS3cFbhuntXi8oAd88wCL4jfjntkZzAN\n/wTgPO9CYSOu1XCYYxMUDkcZLs1HaD2HZ9AFdxPdeuBOEdntnUCCItXjBKBQ+mdzrggpc7TvKNp+\nomVwLaN/pupYs2YPNXNzxP1rlMy/0V6PoVwRs+DK4Nlu7Zh36aMyJHJG66SSrGMk4H3x6p6edwsu\nur/Fey9qFtEo6oCAiJR6LRgYOIvqoMQ9oOY+76qpXkTuAC7FXWkMV7wDk6HZi8uABhk4e2kdIc8Z\nEJfcMd/7bweuG+5JEblSjz7O9gh1qdLf7617Pe7HMz1ssW+6RTX00bY1wKOqGinDa7wOe+UpVpeV\n14eri/Cyt+O6f74sIifjuh8e894+ph69MjdphAFuGeBmvCj7eZzo2WYX4p5wGFTJ6AxYR80gq1Ey\n/0Z7fZj7v4nYs90OJG2PeR04o3VSSeYWSagf4B6YE4z+4VlE53M0U2Z4Fk68puFDuEyxwQyX5wCP\nEZt+g/+e9+ElsvP6vN+IN/geh3i7di4VkRIRycQF3WeInr20ADcQfZaIzPBOuL/g6JMnG1V1L+5z\n/lxE+l05icgkEXnE+yGCG7QMhC1zsVeOj4aV82HgHHHJChGR5SLyI0aAd/X7Gkf71N+BG3zuR1xq\nkGBQ2Ii7Sg2WP7we/6UuC/RecUn0gp//dvEy0UYTZT9+omdw3Qb0iktkCK7+RyOQRNy/RMn8G+31\nCNsdLAtuUAWxZ7sdSLof85EyWh8T4BItWQNJvy9HXTbVb+NmmcCxWURvwGURPYOQbKFh2/kIcIHX\n1P0b7lGe+8DN2hKXRrsfEbncW/53wHSvWfqo9/b7cA8z2oQb66jDewqhiNwksXVzDSfT7EDLPI77\n/HtwM8Ju0+jZS5/2Pv9/4saLNuO6EG4O3aCqPou76vp52Ot1uCyzL4vIem+Z4A8yWMbP404qL4vL\nPLpRRD6lqgdwg6L/EJft9Ce4bL39iEilt94mb5vBcbMpIjJVXGbaSD4KfF7cg5xOIcKzXXADqrd7\n+18F/FTdY3Mj1WNw5tS1uEejbsLNkHpUB7mvaYD9RMs224s78f3WW6eX6F0Z8QSYaPuPlPn3E1Fe\nj5QReLAsuEFDyXab1se8iJwqIg9G+Xxf4tiM1jHdNzSWYkqRIu5h9afjrkA+pSH5eETkAlwztRfX\nnPuA19S/C9c37APWqmqkgy4teQFtmcZ5H8sQ9/lbYKuq3jRW+0w0EfmzqkZLBz/cbY67ekxV9l0l\nj0HHSLwm9jxVPdPrv72NozMjwHvegaru95q+l+L6Gp8apT7wVJCH95wOMzq87sS/J7ocxpjYurYu\nwrvLWlU3AxPEzSkPOlmP3mVZy9FZCHFP5UxVqvqkeveYjKFUuoM4buqe2R7+AJ6RMK7qMcXZd5Uk\nBu3aEpFf4h7pep/3/3/h+unCHwU7BZcP6zTgBOBnuD7KMtxdm7EObBtjjEkhwxlsP6alIS5R273A\nR1T1MC6A3KCqbwHeC/xG+s+FNsYYkyZiObnX4G7UCZoKHEkY5k2FewD4QvBuXHUZWoPP8t4hIgdw\nd7TuiraTQCAQ8PnGbW+YMcYMV8JPnLEEkkdw02tvFZcMb5+qtoW8fzNws6oGp8UiItcBU1T1ByJS\nhbvvY99AO/H5fNTWtgy0iBmCiopiq88RYnU5sqw+R1ZFRfHgC42yWKf/3gSch5tz/THc3Z+NuCDT\ngHvOsw83+HU78BfvzwRcZs8bVPXhQXYTsINr5NiPdeRYXY4sq8+RVVFRnBItElT1i2Evhd4IFu3O\n3vC068YYY9JQst7ZbowxJkVYIDHGGBMXCyTGGGPiYoHEGGNMXCyQGGOMiYsFEmOMMXGxQGKMMSYu\nFkiMMcbExQKJMcaYuFggMcYYExdL7W4A6O7uxu/3J7oYIyIzM5Ps7OxEF8OYccMCiQHgoadXkZmT\n+CyiIyGLDt543vJEF8OYccMCiQEgN6+Q3OKyRBdjRAQ6GxJdBGPGFRsjMcYYExcLJMYYY+JigcQY\nY0xcLJAYY4yJiwUSY4wxcbFAYowxJi4WSIwxxsTFAokxxpi4WCAxxhgTFwskxhhj4mKBxBhjTFws\nkBhjjImLBRJjjDFxsUBijDEmLhZIjDHGxMUCiTHGmLhYIDHGGBMXCyTGGGPiYoHEGGNMXCyQGGOM\niYsFEmOMMXGxQGKMMSYuFkiMMcbEJSuWhUTkZuB0wA98SlVXhbx3AXAT0Auoqn5gsHWMMcakj0Fb\nJCJyLjBPVc8EPgD8JGyRXwBXqeo5QImIXBrDOsYYY9JELF1bFwErAFR1MzBBRIpC3j9ZVfd7/64F\nymNYxxhjTJqIJZBU4QJEUJ33GgCq2gogIlOAS4AHBlvHGGNM+ohpjCSML/wFEakE7gU+oqqHRWTQ\ndSKpqCgeRnFMNEOpz+LiXHKL8kaxNGMoJ2/EjyU7NkeW1Wd6iSWQ1NC/NTEVCHZlISLFuFbIF1T1\n8VjWiaa2tiWG4phYVFQUD6k+W1q66KZzFEs0dgKdnSN6LA21Ls3ArD5HVjIE5Vi6th4BrgYQkWXA\nPlVtC3n/ZuBmVX10COsYY4xJE4O2SFT1BRFZLSLPAX3Ax0TkeqARFzDeBcwVkQ8CAeB2Vf21iKwJ\nXWf0PoIxxphEimmMRFW/GPbSupB/50dZ5wvDLZQxxpjUYXe2G2OMiYsFEmOMMXGxQGKMMSYuFkiM\nMcbExQKJMcaYuFggMcYYExcLJMYYY+JigcQYY0xcLJAYY4yJiwUSY4wxcbFAYowxJi4WSIwxxsTF\nAokxxpi4WCAxxhgTFwskxhhj4jKcZ7abcaiptZs9h1rIysygtCiHsuI8cnMyE10sY0wSsEBiBlRT\n18arW+uoa+r/PPesTB/LFlQgMybg8/kSVDpjTDKwQGKi2rq3iRc3HIAATJ1UwOwpJfh8Phpbu9iy\np5GXNh1i96FWzjlhCvm5digZM17Zr99EtHZbHa9uqycnO4MLl02jcmJBv/cXzpjICxsOsK+2jcdX\n7+WNy2eQnWVDbsaMR/bLN8fYUdPMq9vqKcrP5rLTZh4TRAAK8rK4cNk05leX0tDcxbNr9xMIBBJQ\nWmNMolkgMf20tHezcsNBsjJ9XHxKNaVFOVGX9fl8nLZ4MlVlBew51MqaLXVjWFJjTLKwQGKO8PsD\nPPPafnr6/Jx+3GRKCqMHkaCMDB/nnTSV4oJsNuxsoLaxYwxKaoxJJhZIzBHrd9RT19TJnKklzJla\nGvN6udmZnLmkCoCVGw/ity4uY8YVCyQGgI5uP+t3NpCXk8nyxZVDXn9yWQFzppbQ0NzFlt2No1BC\nY0yyskBiANiwp53evgBL55WTkzW8Gw1PlgqyszJ4ZWsdHV29I1xCY0yyskBiONDQzo4DXZQUZDO/\nesKwt5Ofm8WJ8ybR0+tn/Y6GESyhMSaZWSAx/O3p7QSAkxZUkJER313qC2ZMoCAvi617G+nstlaJ\nMeOBBZJxbl9dG6u1lrKiLGZMLop7e5kZPo6bVUZvX4DNu2ysxJjxwALJOPfoy3sAWFSdP2I5s+ZP\nLyU3O5PNuw7T3ds3Its0xiQvCyTjWHN7N8+vP0DFhDymlmWP2HazMjNYPGsi3b1+m8FlzDhggWQc\ne+qVffT2+bn4lOlkjHAGX5kxgeysDDbtOozfb/eVGJPOLJCMUz29fp5Ys4/83EzOPn7KiG8/JzuT\nudNK6OjqY8+h1hHfvjEmeVggGade2nSQ5rZuzl06ddRSwC+Y7qYSb9lj3VvGpDMLJOPUM6/VAHDh\nsupR28eEolwqJ+azv76dlvbuUduPMSaxLJCMQwcb2tmyt4lFMydSMSF/VPd1tFXSNKr7McYkTkx9\nGiJyM3A64Ac+paqrQt7LBX4JHKeqp3qvnQfcBawHfMBaVf3kCJfdDNOz6/YDcPYJIz82Em7m5CJe\nzs5k+74mTpxfTmaGXbsYk24GDSQici4wT1XPFJGFwG3AmSGLfA94BVgctupTqnrNiJXUjAi/P8Dz\n6w+Qn5thDu02AAAgAElEQVTJsgUVo76/zMwM5k4rYePrh9lzqI1ZVcWjvk9jzNiK5fLwImAFgKpu\nBiaISOgt0F8Ivh9mZOeTmhGx4fUGDrd0cdqiyeRmDy8541DNneZS0u+oaR6T/RljxlYsgaQKqA35\nf533GgCq2hZlvcUiskJE/iUiF8dRRjOCnl3rurXOGoNuraCJxblMLM6lpraVrm67092YdDOcDutY\nWhpbgRtU9S3Ae4HfiMjozDE1MWvv7OWVrXVMKS9gzpSSMd337Kkl+AOw60DLmO7XGDP6Yjm51xDS\nAgGmAvsHWkFVa3CD7ajqDhE5AEwDdg20XkWF9Z+PpPD6fGLVbnr7/Fx46gwqK/sHkuLiXHKL8kat\nLMfPncQarWXXwVZOXlw1+ArxyMkb8WPJjs2RZfWZXmIJJI8ANwC3isgyYF+E7iwfIS0VEbkOmKKq\nPxCRKqAS2DfYjmpr7Wp1pFRUFB9Tn4+/tBuA42ZMOOa9lpYuuukc1TJVlRWwv76N/bUtFOWPXG6v\ncIHOzhE9liLVpRk+q8+RlQxBedBAoqoviMhqEXkO6AM+JiLXA42qeo+I3AlMBxaIyBPAr4B7gdtF\n5EogG/iwqtrDKRKotaOHDTsbmDG5iKqygoSUYfbUYg40tLOzppnj55YnpAzGmJEX07iFqn4x7KV1\nIe9Fm+J7xXALZUbemi219PkDLF80OWFlmDm5mJUbD7FzvwUSY9KJ3R02Try06SAAyxdWJqwMOdmZ\nTC0voLG1m6ZWS5liTLqwQDIONLd1s2nXYeZMLWHSKKdEGcxM74bE3Qetj9yYdGGBZBxYs6WWQCCx\nrZGg6soiMnywywKJMWnDAsk4sGaLu5/0ZEl8IMnNzqSqvJCG5i7LCGxMmrBAkubaO3vZtOswMycX\nU146eveJDMXMKpdhZ9dBe+CVMenAAkmaW7ujjj5/gGULJiW6KEdMryzC54Pddpe7MWnBAkmaW7Ol\nDoCTxiDTb6zycrKYXFZAXVMnrR09iS6OMSZOFkjSWE9vH+t21FM5MZ9pkwoTXZx+Zkx23Vt77Xnu\nxqQ8CyRpbOPrh+nq7mPZ/Ap8vuTK6l9d4QWSWgskxqQ6CyRpLDhbayweYDVURfnZTCzO5UB9Bz29\n/kQXxxgTBwskacrvD/DatjpKCnOYM21sU8bHqrqyCH8gQE1dtEfaGGNSgQWSNLV1z2Ga23tYOrec\njCTr1gqaXuHGbWycxJjUZoEkTb200eXWWjoveab9hisvzSM/N5O9tW34A4FEF8cYM0wWSNLUyxsP\nkJXpY/GsiYkuSlQ+n49pFUV09fRR1zi6z0IxxoweCyRpqL6pk501zSycOZG8nOR+wvH0SpsGbEyq\ns0CShtZudzchLp2bvN1aQVPKC8jM8Nk0YGNSmAWSNPTa9noAls5L/odHZWVmUOU9o8SSOBqTmiyQ\npJmu7j42vn6YWVNKmFSa2GePxGp68ObEQzYN2JhUZIEkzWzc1UBvn59TFyfukbpDVV3pTQO27i1j\nUpIFkjSzbkcDACcvTJ1AUpCXTVlJLgcb2unu7Ut0cYwxQ2SBJI0EAgHWba+nIDeLhTOTd9pvJNUV\nRfgDUFPXnuiiGGOGyAJJGqmpb6e+uZPjZpeRmZlaX61NAzYmdaXW2cYMaJ03W+v4Ock/WytcWUku\n+blZ7K1txe+3u9yNSSUWSNLIuh3BQFKW4JIMnc/no7qikO4eP7VNHYkujjFmCCyQpInO7l627Glk\n5uRiSotyE12cYan2urf22TRgY1KKBZI0sen1w/T5Axw/N/VaI0FVZQVk2F3uxqQcCyRp4mi3VuqN\njwRlZ2VQVebucrdnuRuTOiyQpIFAIMC6HW7a75ypyfkQq1hVe88o2Vdr3VvGpAoLJGnATfvtYsmc\nMjIzUvsrnXYkkFj3ljGpIrXPOgZI7Wm/4YoLcigtzGF/fTu9ffYsd2NSgQWSNBAcH1mSBoEEXKuk\nzx/gYIPd5W5MKrBAkuI6ukKm/RbmJLo4I6I6mA3YxkmMSQkWSFLc5l2pP+03XOXEfLKzMthX20bA\nnuVuTNKzQJLigt1aJ8xJ/qchxiojw8fUSYW0dvTQ1GYPuzIm2VkgSWGBQIC1O+opzEv9ab/hgtOA\nLYmjMcnPAkkKq6lro6G5i+Nml5GR4Ut0cUbU1El2P4kxqSIrloVE5GbgdMAPfEpVV4W8lwv8EjhO\nVU+NZR0zMoIPsUqHab/h8nOzmFSax6HGDrp6+sjNzkx0kYwxUQzaIhGRc4F5qnom8AHgJ2GLfA94\nBQgMYR0zAtJt2m+46opCAgHX8jLGJK9YurYuAlYAqOpmYIKIFIW8/4Xg+0NYx8TpyLTfqvSZ9htu\nmjcN2Lq3jElusQSSKqA25P913msAqGqkX/mA65j4bQpO+03T1ggEH3aVyb7aNvw2DdiYpDWcwfbh\njOqm10hwEjg67Td9A4nP52NaRRFdPX3UN3UmujjGmChiGWyvoX9rYiqwfxTWoaKiOIbimEAgwIbX\nD1NckM3ypdPIjDJjayj1WVycS25R3kgVccTMnz6RbXubqG3sZE71xNhWyskb8WPJjs2RZfWZXmIJ\nJI8ANwC3isgyYF+E7iwf/VsdsaxzjNralljKPO7tq22lrrGD5YsqaaiPfJ9FRUXxkOqzpaWLbpLv\nqn9CYTYZPthR08TiWbEFkkBn54geS0OtSzMwq8+RlQxBedBAoqoviMhqEXkO6AM+JiLXA42qeo+I\n3AlMBxaIyBPAr1T1DhFZE7rOaH6I8WZtGjzEKlbZWRlMLitgf3077Z29FOTFNGPdGDOGYvpVquoX\nw15aF/LeNVHW+UIc5TIDCKaNT9dpv+GmVRSyv76dfbWtzJ8+IdHFMcaEsTvbU0x7Zw9b9zYxe0pJ\n2k77DWfZgI1JbtZPkGLW72ygzx9g6dzx0RoBKCnMobggm/31bfT5/Sn/FMihCAQCtLQ0J7oYI6a4\nOL1ywhnHAkmKWet1ay2dlz7ZfmNRXVHEpl2HOdjQcSQP13jQ0tLMoyu3kV+Q+p+5o72NS06bR2Wl\nBZN0Y4Ekhfj9AdZur2dCUQ4zJo+vRAHTKgrZtOswe2tbx1UgAcgvKKSgMPEzc4yJZvz0EaSBHfub\nae3o4YS55fh84+sez8llBWRnZbDnYKs97MqYJGOBJIWs3V4HwNK546tbCyAzw8e0SYW0dfZyuKUr\n0cUxxoSwQJJCXttWT1ZmBotivDEv3Uz3uvP22MOujEkqFkhSRENzJ3sOtbJwxgTycsbn0Na0SYVk\n+CyQGJNsLJCkiNfG6WytUDnZmUwuK6ChuYu2jp5EF8cY47FAkiLWbnPjIyeMo/tHIplead1bxiQb\nCyQpoKunj427DjN1UiEVE/ITXZyEskBiTPKxQJICNu86TE+vf1zdzR5NYX425SW5HGhop7unL9HF\nMcZggSQlBO9mH+/dWkEzJhcTCFirxJhkYYEkyQUCAV7bXkdBbhbzqksTXZykMGOyu8t710ELJMYk\nAwskSW5vbRsNzV0cP7d8XCUrHEhpUQ4TinKoqWujp9ef6OIYM+7ZmSnJBe9mt26t/mZMLsbvD7C3\n1lolxiSaBZIk98rWOjJ8vnHxNMShmFnlurd2H7BHthqTaBZIktjhli521DSzYHopRfnZiS5OUplQ\nlENJQTb76tro7bPuLWMSyQJJEnt1ay0AyxZUJLgkycfn8zGzqpjevgD77MmJxiSUBZIktmaLBZKB\nzPC6t1637i1jEsoCSZJq7+xh8+5GZlYVU1aSl+jiJKWy4lxKCnPYe6jVZm8Zk0AWSJLUa9vr6fMH\nrDUyAJ/Px+wpxfT5A+w5ZK0SYxLFAkmSeiXYrTV//Gb7jcXsKe753ztrLJAYkygWSJJQd08f63Y0\nMHli/rh7PvlQlRTmUF6SR019G53dvYkujjHjkgWSJLRuRwNdPX0sk4px92z24Zg9xeXe2mWD7sYk\nhAWSJLRKDwGwfOHkBJckNcya4mZv7bDuLWMSwgJJkunu6ePVrXVUTMhjhveMcjOwgrxsqsoKqG3s\noLmtO9HFMWbcsUCSZILdWqcunGzdWkMwr9oNum/f15Tgkhgz/lggSTIvbz4IwKkLKxNcktQyY3Ix\n2VkZbK9pxh8IJLo4xowrFkiSSHdPH69tq6dyQr51aw1RVmYGs6qKae/s5cDhnkQXx5hxxQJJElm3\no56unj5OWVhp3VrDEHzw17b9nQkuiTHjiwWSJLJyk5utZd1awzOpNI/Sohz21HXR2mGtEmPGigWS\nJNHe2curW+uYUl5g3VrD5PP5mDetFH8Anl9/INHFMWbcsECSJFbrIXr7/JxxXJV1a8Vh7rQSMnzw\nxJq9NuhuzBixQJIkXtjgrqBPX2w3IcYjLyeL2ZNzOXS4g/U7GhJdHGPGBQskSaChuRPd3ciC6lIm\nTchPdHFSnkxzdfj46r0JLokx40NWLAuJyM3A6YAf+JSqrgp572Lgm0Av8KCqfkNEzgPuAtYDPmCt\nqn5ypAufLlZuPEgAOP24qkQXJS2UF2czb1op63bUc/BwO5MnFiS6SMaktUEDiYicC8xT1TNFZCFw\nG3BmyCI/Bi4B9gNPi8jd3utPqeo1I13gdBMIBHhhwwEyM3ycYrO1RsxFJ1ezbV8TT6zexzsunp/o\n4hiT1mLp2roIWAGgqpuBCSJSBCAis4F6Va1R1QDwgLc8uJaIGcSugy3srW1j6bxJFOVnJ7o4aeNk\nqWBCUQ7/WltjU4GNGWWxBJIqoDbk/3Xea5HeOwRM8f69WERWiMi/vO4vE8G/XtsPwDknTBlkSTMU\nWZkZvOHUGXR199lYiTGjLKYxkjADtTSC720BblDVu0RkDvCkiMxV1QGfPFRRUTyM4qSuzu5eXtp0\nkPLSPC5YPpPMzJGd+zCU+iwuziW3KE2eDZ+TR0VFMVdfIjy4chePr97LdZctoiBv+C2+RB2bOTl+\nigobKBzB76bP76eto4f2zl56ev309vmP/O33B8jI8JGZkeH97SM7K4OCvCwK8rLJy8kc9vT0DLqZ\nNMnV43j7rae7WAJJDUdbIABTceMhwfdCL6WnATWquh832I6q7hCRA957uwbaUW3t+HqexHPr9tPe\n2cuFy6ppaGgb0W1XVBQPqT5bWrroJj1SiwQ6O4989otOrmbFMzu5+zHlstNmDmt7Q63LkdTc3EJr\nWxf+IX43gUCAts5eGpo7aWjuorG1i7aOXto6e+js7ht2eXw+yM/JoiAvi9KiHEoLcygryaOsJI+8\nnMwB121v66KuroXS0tJx91sfTckQlGMJJI8ANwC3isgyYJ+qtgGo6i4RKRaRGbigcjlwnYhcB0xR\n1R+ISBVQCewblU+Qwp55rQawbq3RdNHJ1Ty0cjcPv7SHi5ZVk5M98MkuVfkDARqaO9lf186BhnYa\nmrvo6ukfMDIyfBR6AaAwL5v83EyyMjO8Pz6yMjPI8PnwBwL4/QH6vL97ev10dPXR2d1LR1cvHV19\n1Dd3UtfUP7gVF2QzuayAqrICpk4qHDSwmPQxaCBR1RdEZLWIPAf0AR8TkeuBRlW9B/gIcAcQAP6i\nqtu8FsjtInIlkA18eLBurfFmf30bW/Y2sWjmRCrs3pFRU5iXzUUnV/PPF3bx+Jq9w26VJCO/P8D+\n+jZ2HWxl76HWfi2Novxsqsryj7QWykpy4+qWirTvlvYemtq6qG/uor6pk9rGDrbtbWLb3iZ8QMXE\nfGZOLmbWlGLyc4fTi25SRUzfrqp+MeyldSHvPUv/6cCoaitwRdylS2NPv+paI+cunZrgkqS/Ny6f\nwVOv7OP+53dx9vFTKC7ISXSR4tLY0sXWvU3s3N98JHjk5WQyr7qUKeUFTCkvIC9ndE/cGRk+17VV\nlMOMya5rxR8IcLi5i5r6NvYeauXQ4Q4OHe5glR5i2qRCZlbmWtqaNGWXCQnQ2d3LM2v3U1qYw8lS\nkejipL2i/Gz+7azZ3PH4Vu599nXe+YYFiS7SkAUCAfYcamXzrkYONLQDkJudicyYwOwpxUyakE9G\ngnO0Zfh8lJfmUV6ax/Fzyuno6uX1/S3sqGlib20be2vb0D1tXH1RDyfNLSM7y7q+0oUFkgR4Yf0B\nOrp6ecOps8ka4ZlaJrILl03jiTV7eerVfVx48jSmlBcmukgx8QcC7KntQPfW0djqnkdfVVaAzJhA\ndWURmRnJe7tWfm4Wi2ZNZNGsidQ3d7Jh+yH21nXyi3+so7Qwh8tOm8EFy6ZZQEkDdhYbY4FAgMdW\n7yUzw8f5J1q31ljJyszg7efPpc8f4I7HtxFI8i4Wvz/Ayo0H+e4dG1m5+TBNbd3MnVrCFWfN4g3L\npzOzqjipg0i48pI8Tlkwka+++3iuvnA+nT193PHENr74qxd5Yf0B6/JKcdYiGWMbdx1mf307px83\nmdKi3EQXZ1xZtqCCRTMnsm5HPc+vP8BZxyfnbLl1O+r56xPbqKlrI8MHsybnc5JUpfzYDriZXde/\neTbnHl/FAy/u4rFVe7n1/o08tnov737jAmZVlSS6iGYYrEUyxh5f5e6yvvjk6Qkuyfjj8/l432UL\nyc3J5C+PbeVwS1eii9TPwYZ2fnzXa/zwztfYX9/GWcdX8cXrlnDKgolpEURCFeVnc80F87jpQ6ex\nfFElO/c38/XfreJPjyid3TbBM9VYIBlD++raeHVbHXOnljBnql15JcKkCflcc8E82rt6+cNDm5Oi\ni6ujq5e7ntzGl3+9kte21yPTJ3DD+5bzH29ezKTS9G61TirN58NXLuGz157I5LICnlizj6/e9hJb\n9zYmumhmCKxraww98IK7sf9NZ6TPvQyp6PwTp7Jq8yFe217Po6v28oZTE9M69AcCvLD+AHc/tZ2m\ntm7KS3K55sL5nCIV4+4pmYtnlfG19y9nxTM7eGjlbr79pzVcetoM3nLOHLKz7Ho32VkgGSO1jR2s\n3HiQaZMKWTpvUqKLM675fD4+cPlibvzdy/z1ia1MnVTAktnlY1qGnfub+fOjW9hR00x2VgZXnj2b\nS0+bQW6a3nkfi+ysDN5+wTxOnD+J39y/iQdX7mbtjno+ePniI/eqmORkoX6MPLRyN/5AgDedMTPh\n8/0NTCzO5eNXHU9mho9frNjAQe/ejNHW1NrFbf/cxNd/v4odNc2curCSmz54OleePXtcB5FQ86sn\ncMP7T+WCk6axr7aNb/xhFU++si8puiFNZBZIxkBTaxfPrN3PpNI8li+yh1cli7nTSrn+0oW0d/Xy\n/TteGdVg0tXTx33P7eTzv3qRZ9ftp7qiiM9ddxIfecsSykvTJOvyCMrLyeLdbxQ+9fal5OVk8ceH\nlVvv30hXHAknzeixrq0xcP8Lu+jt8/Om02eSmWGxO5mcdfwUGlu7+NvTO/j2n9fwmWtPpLqiaMS2\n7/cHeHbdflY8s4PG1m6KC7K55vy5nHviVDsWYnDC3HJueN+p/HzFel7ccJDdB1v56FuWMHVSatxQ\nOl7YkTzKDjV28NQr+6iYkMfZluU3Kb35jFlcd/F8mtq6+c6f17BaD8W9zUAgwKvb6vjqb1/idw9u\npr2zl8vPnMm3//MMLlhWbUFkCMpK8vjcO5dx8SnV1NS18fXfr2LlxoOJLpYJYS2SUbbimR30+QO8\n9dw5lg4liV18ynTyc7P4w8PKT/+xnrOOr+Lai+ZTOMSHYfX09vHy5kM8uHI3+2rb8AFnnzCFt54z\nh4nF6T2VdzRlZWZw3cULmF89gdse2MQv793A6weaufr8uRaUk4AFklG0+2ALKzccZMbkIpYvmpzo\n4phBnHX8FGZPKeHW+zfy3LoDrNJazls6lWvesHDApntPr5/t+5p4afMhXtp4kPauXjJ8Pk4/bjJv\nOn3miHaVjXenLqykuqKQ//v7Oh5+aQ+7D7by4SuPS7sbNlONBZJREggEuPup7QSAq8+bazO1UsTU\nSYV86d0n89iqvTzy8m4eeXkPj7y8h8oJ+cyvLmViSS55OVn09fmpb+7i0OF2ttc009PrB6C0KIdL\nT5zBhSdNY5I9Z2ZUTCkv5MvvOYVf37+RV7bWcePvVvHxq45nZpVNEU4UCySjZM2WOtbvbOC4WRM5\nbnZZootjhiArM4NLT5vBxadU8+KGg6x7vYH12+t5bv2BiMtXVxSycOZETphTzqJZE62rZQzk52bx\nsauO5/7nXmfFszu56U+ree+lCzljSdXgK5sRZ4FkFHR19/GXx7eQmeHjuksWjLu7lNNFVmaGG9+4\naAEHDzazv76N1g73zPPMDB8TS/IoK861p/8lSIbPxxVnz2ZGVTG33reBW+/fyOsHWnj7BXNtPHKM\n2S9gFNz7/E4amru4/MyZKfPcCzOwjAwf02ysIymdOG8S/3v9qdzyt7U8umoPew618OErl1BSaOMm\nY8XC9gjbe6iVR17aQ3lJHm8+Y1aii2PMuFBVVsCX33MKyxZUsHl3Izf+/mV27m9OdLHGDQskI6in\n18+t92+kzx/gXW9YYCkvjBlD+blZfPStS7jq3Dkcbu7iW39aw3Pr9ie6WOOCBZIRtOLZHew51Mq5\nS6daYkZjEiDD5+PyM2fxybefQHZWBr/55yb+/OgWevv8iS5aWrNAMkK27GnkoRd3UzEhj2svmpfo\n4hgzrp0wdxJfee8pTJtUyOOr9/L9O16lqa070cVKWxZIRkBTaxe/vHcD+OCDlx9HXo7NYTAm0SZP\nLOBL7zmZU6SCLXsaufF3Nm4yWiyQxKm3z89PV6zncEsXbztvLvOqSxNdJGOMJy8ni4+8ZQlXnz+X\nxhY3bmIp6UeeBZI4/fnRLWzb28TyRZVcdtqMRBfHGBPG5/PxptNn8ulrlpKbncEfH1Z+cvdamq2r\na8RYIInDP194nadfrWF6ZRHvu2yR3XhoTBJbMqecr71/OYtmTuS17fV85TcreXVbXaKLlRYskAzT\n46v38rend1Bekssnrz6B3Byb6mtMsisryeMz157ItRfOo72rj5/cvZY/PLTZHpgVJxsVHoZ/vVbD\nnx/dQklhDp+99iTKSuwJd8akigyfjzcsn8HiWWX86r4NPPVqDet21POOixdw0vxJ1rMwDNYiGYJA\nIMD9z7/O7x7cTGFeFp/99xOZXFaQ6GIZY4ahurKI/73+VC4/cyaNrd3839/X8eO713KosSPRRUs5\nFkhi1Nvn50+PbuHv/3LdWV9418lUV6ZP7qUHX2mMabl7n9155N9/emRLv/+bsZHKdf7tOzYkugj9\nZGdlcNW5c7nxP9zYydrt9fzvr1dy77M76e6x7q5YWddWDBqaO/nFPRvYtq+J6ooiPn3N0rR72l1z\ne2w/msbWozNd/P5Av/+bsZHKdX6goTPRRYhoSnkhn732RF7adIg7ntjKimd38vRrNVx59mzOOr7K\nHg0wCAskAwgEAqzWWn7/0GbaOntZvqiS6y9daGnDjUlDPp+P0xZP5oS55dz//Os8tnovv3twMw++\nuIs3nT6TM5ZUWXr6KOyMGEVdYwd/enQLa7fXk5WZwXsuFc5bOtUG4oxJc/m5Wbz9gnlcfMp07n1u\nJ8+u3c9vH9zMPc/t5KJl1ZyzdCpF+dmJLmZSsUASpqm1i3++uIunXqmht8/PopkTec8bxQbVjRln\nJhbncv2lC/m3M2fx0Eu7+derNdz11HZWPLuT5YsqOfv4KSyYPsEuLrFAcsTugy089co+nl9/gO5e\nP5NK83jruXM4ffFkO1CMGcfKSvK47uIFXHn2bJ5du58n1uzluXUHeG7dASaV5nGyVHDivEnMqy4d\nt2MpMQUSEbkZOB3wA59S1VUh710MfBPoBR5U1W8Mtk6yqG/qZJUe4uXNh9hR45K5uQdSzeTsE6ZY\nf6gx5ojCvGzeuHwGl5w6Hd3dyPPr9rNKa3n4pT08/NIeCvOyOGFuOSfMncT86tJxdX/ZoIFERM4F\n5qnqmSKyELgNODNkkR8DlwD7gadF5G6gcpB1xlwgEKCuqZPXD7SwZXcjm3YfpqauDQCfD5bMKePC\nk6o5YW45GRnWAjHGRJbh87Fo5kTX7X1pH5t3N/Lq1jpe3VbHCxsO8sKGg4DrGpsztYQ5U0uYXVVC\nVXkBpYU5adnDEUuL5CJgBYCqbhaRCSJSpKqtIjIbqFfVGgAR+SdwMVARbZ3R+RguUNQ2dtDU1k2z\n96eprZv65k4ONnSwv76Nts7eI8vnZGewZE4ZyxZUsGxBBSUF9nxnY8zQZGdlcvycco6fU8673rCA\n3Qdb2fB6A9v3NbGjppnVWstqrT2yfG5OJpMn5jN5YgEVE/IpKcyhuCCbkgL3d2FeNtlZGWRnZZCV\nmUFWpu9I4AkEAkkbhGIJJFVAaLdUnffaNu/v2pD3aoG5QPkA64yKvz6xjUde3hPxvQyfj4qJ+Rw3\nu4yZVcXMnVrKnKkl1nVljBkxPp+PmVXFzKwqBtyJv6G5i+01Tew+2MrBw+0cbGjnQH07uw/Gfk2d\n4fPhDwTIzcnk89ctO7L9ZDKcwfaBQmK090Y9jC6dN4n2rl6K87MpLcyhxPszsTiXign5FjRi0N50\naFjLxbreWMnN6KW5uWnEtpeT46e5uWXEtjcULS3NdLS3HfN6e1tiyhOPSJ8jnfl8PspL8ygvzWP5\noslHXvcHAjS2dFHX1ElLew8t7d00t3fT0t5De2cPPX0Benv99PT20dMXwO8PkOGDgrxsiguSc9px\nLIGkBteaCJqKGw8Jvjcl5L1pwD6ga4B1ovFVVAw/0lZUFHPuKfY8kFBDqc/7fnBlTMtdf3Xkf6e7\n0tLEPbDsxBMX9/v/9W9JUEFGwIevc3/H81tPB5MrQRJdiBEUy2X6I8DVACKyDNinqm0AqroLKBaR\nGSKSBVzuLf9otHWMMcakF18sj5wUkZuA84A+4GPAMqBRVe8RkbOB7wIB4G5V/WGkdVR13eh8BGOM\nMYkUUyAxxhhjorERaGOMMXGxQGKMMSYuFkiMMcbEJWmSNorI9cDXOXrT4qOq+q0EFiklpUKOs1Qh\nIjanFLYAAAOoSURBVOcBdwHrcfdCrVXVTya2VKlHRJbgMl3crKo/E5Fq4I+4C9n9wLtVtSeRZUwl\nEerzt8DJuBu/Ab6nqg+OZZmSJpB47lDV/0l0IVJVDHnRzNA9parXJLoQqUpECoCfAI+FvHwjcIuq\n/l1Evgm8H/hlIsqXaqLUJ8DnVfWBBBQJsK6tdNMvLxowQUTS58HyiZGcyY1SRydwGf1vSD4fuM/7\n9324/HwmNpHqM+GSrUVyvog8AGQD/62qrya6QClmoLxoZngWi8gKoAy4UVXDrwTNAFTVD3SJ9LuP\nuzCkK+sQ/bNjmAFEqU+Aj4vIZ4CDwMdVtWEsy5WQQCIi/wF8AHcTo8/7+y/AV1X1QRE5HfgDcEIi\nypdG7Go6PluBG1T1LhGZAzwpInNVtXewFU3M7BiN3x9wWdjXisjngK8B/zWWBUhIIFHV3wC/GeD9\nF0Vkkoj4VNXumIzdQHnRzBB5j0e4y/v3DhE5gMsntyuhBUt9LSKSq6pduPqsSXSBUpmqPhny33uB\nn411GZJmjERE/ltErvX+vQSotSAyZFHzopmhE5HrvO4CRKQK98C2fYktVVp4DHib9++3AQ8lsCwp\nT0Tu9p4NBW78af1YlyFpUqSIyDSOTgnMBD5tU1eHznKcjRxvosLtwATcuN0NqvpwYkuVWrwLmh8A\nM4EeXCB+J/B7IBfXunufqvYlrJApJEp93gJ8AWgDWnH1WRd1I6MgaQKJMcaY1JQ0XVvGGGNSkwUS\nY4wxcbFAYowxJi4WSIwxxsTFAokxxpi4WCAxxhgTFwskxhhj4mKBxBhjTFySLfuvMWPOe4DVN3B3\nWc8GDuPuFP4H8ABwIi6x6LWqul9EPgR8AtgDrAPOUNVzElF2Y5KBtUiMcZYBn1XVs4B6XM6i2cBt\nqnou8DTwGREpBm4CzlPVy3BpPiw9hBnXLJAY42xQ1QPev58H3oRLzR18Js5zwGJgPvC6qtZ7rz8y\ntsU0JvlYIDHGCf0tBJ+RE+k1H/2foeEf/aIZk9wskBjjLBSRyd6/zwbuASaKyNKQ19binjY5S0TK\nvNf/bWyLaUzyscF2Y5yNwLdEZD5ujOQZ3AOX3isiJ+BaIdeqapOIfAX4l4jsIwHPfjAm2VggMcbp\nVNX3B/8jIjMBVPXT4Quq6k+Bn3rLnQecOlaFNCYZWdeWMdHZbCxjYmAPtjLGGBMXa5EYY4yJiwUS\nY4wxcbFAYowxJi4WSIwxxsTFAokxxpi4WCAxxhgTl/8PIKJkMTZslx0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93564bc350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bsBjoBG5S1VVBz+0C9nrP+YEPq+rBQYjVGGNMD3pN6CKyDJihqktFZDZwD7A0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\ngrCEbowxCcISujHGJAhL6MYYkyD+P8dKQmnGGG/MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93583794d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Nc8zGGGOiFFWfvKreHvLUhqBpDwIPxjIoY4wxsWFXvBpjTAqzJG+MMSnMkrwx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\n94YL9mS7V1fXUFvXeOIzx0vouuzOZ77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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9356441390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IvC4i6d7c9D5OoidiDAj9YZoA+GecZ0mnCt/zfeCWnswpYbwJ80qgx3UyBjcCG/qq8MGU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\nfYd1wUfFpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935d04d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MxG/jsbDWX5Fgjwuqy/14V/xY6F/iEnmdmW9BXHrQe4HHVfWiEMrya1XNlhPlXGBYVfcU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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935be4cd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subjects = \"\"\"0902021S0BBAJAZ\tSodium chloride 0.9% infusion 1litre Viaflex bags (Baxter Healthcare Ltd)\n",
    "1502010J0BEAABY\tEmla 5% cream (AstraZeneca UK Ltd)\n",
    "0401010ADBCBZBP\tBio-Melatonin 3mg tablets (Imported (Denmark))\n",
    "0902021X0BGACAZ\tWater for infusion 1litre Viaflo bags (Baxter Healthcare Ltd)\n",
    "0902021S0BBAXAY\tSodium chloride 0.9% infusion 500ml Viaflo bags (Baxter Healthcare Ltd)\n",
    "0604030Q0BBAFAI\tDHEA 25mg tablets (Imported (United States))\n",
    "0602010Z0BEABAJ\tErfa Thyroid 60mg (1 grain) tablets (Imported (Canada))\n",
    "0407020A0BPAABN\tPecFent 100micrograms/dose nasal spray (Kyowa Kirin Ltd)\n",
    "1302010N0BDAAAA\tVaseline Pure Petroleum jelly (Unilever UK Home & Personal Care)\"\"\"\n",
    "\n",
    "for subject in subjects.split(\"\\n\"):\n",
    "    code, title = subject.split(\"\\t\", 1)\n",
    "    sql = \"\"\"  SELECT\n",
    "        *\n",
    "      FROM\n",
    "        ebmdatalab.hscic.prescribing_with_merged_codes_2016_09_01\n",
    "      WHERE\n",
    "        bnf_code = '%s'\"\"\" % code\n",
    "    df = pd.io.gbq.read_gbq(\n",
    "        sql, project_id=\"ebmdatalab\", verbose=False, dialect='standard')\n",
    "    df['ppq'] = df['net_cost'] / df['quantity']\n",
    "    df = df.sort_values('ppq')\n",
    "    cheapest = float(df[['quantity', 'net_cost', 'ppq']].head(1)['ppq'].iloc[0])\n",
    "    expensivest = float(df[['quantity', 'net_cost', 'ppq']].tail(1)['ppq'].iloc[0])\n",
    "    ratio = int(expensivest / cheapest)\n",
    "\n",
    "    sql = \"select MIN(VMPP.QTYVAL) from dmd_product inner join AMP on dmd_product.DMDID = AMP.APID  inner join VMPP on AMP.VPID = VMPP.VPID where BNF_CODE = '%s'\" % code\n",
    "    min_pack_size = int(pd.read_sql_query(sql, con).iloc[0])\n",
    "    dispensed_in_packs = len(df[df.quantity < min_pack_size])\n",
    "    sns.distplot(df['ppq'], rug=True)\n",
    "    sns.plt.title(\"%s\\nRatio: %s, min pack size %s, dispensed in less than pack size: %s\" % (title, ratio, min_pack_size, dispensed_in_packs))\n",
    "    sns.plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Some NP8 drugs identified in our \"scalping\" exercise\n",
    "\n",
    "See [this notebook](https://github.com/ebmdatalab/price-per-dose/blob/master/How%20would%20I%20scalp%20the%20NHS%3F.ipynb) for background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IK554PTd2pXYTxqRPNjL1nYdVhK6dpOH0HvWzX9VK7j1dHEtjv9wFxNY64XD27x7G6sbM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\npJCSu4hICim5i4ik0P8HzSdmZKfUYFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93565cd190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZpOQuIpJBSu4iIhmk5C4ikkFK7iIiGaTkLiKSQRU9Q9XMlgOLgQJwuruvKrPO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\n5C4ikkFK7iIiGfT/AcXne6R86HTtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92d1fa7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0JbCIjEYJoIrpOgARGY0SQBVL3gZSJQARKaYEUMUKGoHVBiAiRZQAqljBWEAq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\nVjieVuByYEfCBR9fd/eK9Yw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935bc88310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wj49bh5dmz+OZlxdQXZWhbq1hAGy07khGjhoNzGVFc5ZX31vB8uUt1PzrfT6+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\ni4gkSOEuIpKg/wUfzQK7hxovZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92d21665d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jzOwkYIq7X2Zmk4GdgcW1NtTRsWYgsWfGqpXre88A1q/vZMOmTgDWrN3UFO+p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\nCPX/Adwz5OxRLG2JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f935c9927d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cvT8m56zs9+HlGMvOrK8kXERI+FeY2TuyldNc7n4ZcJmZbUuorZ9IaObJ7tv2hFaBw+OV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\nPU6ielcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93565b5950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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17x7ix+csBODq23xVLf7GB16qYYlEpC+U0yyzPvDPzPO347QX4/8LMvPeAjap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\nJEjhLiKSIIW7iEiC/g+fDiv8hKssdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93596d7650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-dd78c82f0f31>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ppq'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'net_cost'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'quantity'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ppq'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     \u001b[0mcheapest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'quantity'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'net_cost'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'ppq'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ppq'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m     \u001b[0mexpensivest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'quantity'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'net_cost'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'ppq'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ppq'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mratio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpensivest\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mcheapest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seb/.virtualenvs/price-per-pill/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1309\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1310\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seb/.virtualenvs/price-per-pill/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1625\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1626\u001b[0m                 \u001b[1;31m# validate the location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1627\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1628\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1629\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seb/.virtualenvs/price-per-pill/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1539\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1540\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1541\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1542\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "subjects = \"\"\"0901020D0AAAHAH\tCyanocobalamin_Tab 1mg M/R\n",
    "0905013G0AAAYAY\tMag Glycerophos_Tab Chble 97.2mg S/F\n",
    "0906050P0AAABAB\tVit E_Cap 200u\n",
    "0906040G0AABTBT\tColecal_Oral Dps\n",
    "0202010V0AAANAN\tMetolazone_Tab 2.5mg\n",
    "0906050P0AAAFAF\tVit E_Cap 400u\n",
    "0906040G0AABIBI\tColecal_Cap 400u\n",
    "0906040G0AACRCR\tColecal_Tab 20 000u\n",
    "0906040G0AABRBR\tColecal_Tab 400u\n",
    "0906024N0AAAGAG\tPyridox HCl_Tab 10mg\n",
    "0906040G0AABHBH\tColecal_Cap 1 000u\n",
    "0407010Q0AAAAAA\tCo-Proxamol_Tab 32.5mg/325mg\"\"\"\n",
    "\n",
    "for subject in subjects.split(\"\\n\"):\n",
    "    code, title = subject.split(\"\\t\", 1)\n",
    "    sql = \"\"\"  SELECT\n",
    "        *\n",
    "      FROM\n",
    "        ebmdatalab.hscic.prescribing_with_merged_codes_2016_09_01\n",
    "      WHERE\n",
    "        bnf_code = '%s'\"\"\" % code\n",
    "    df = pd.io.gbq.read_gbq(\n",
    "        sql, project_id=\"ebmdatalab\", verbose=False, dialect='standard')\n",
    "    df['ppq'] = df['net_cost'] / df['quantity']\n",
    "    df = df.sort_values('ppq')\n",
    "    cheapest = float(df[['quantity', 'net_cost', 'ppq']].head(1)['ppq'].iloc[0])\n",
    "    expensivest = float(df[['quantity', 'net_cost', 'ppq']].tail(1)['ppq'].iloc[0])\n",
    "    ratio = int(expensivest / cheapest)\n",
    "\n",
    "    sql = \"select MIN(VMPP.QTYVAL) from dmd_product inner join AMP on dmd_product.DMDID = AMP.APID  inner join VMPP on AMP.VPID = VMPP.VPID where BNF_CODE = '%s'\" % code\n",
    "    min_pack_size = int(pd.read_sql_query(sql, con).iloc[0])\n",
    "    dispensed_in_packs = len(df[df.quantity < min_pack_size])\n",
    "    sns.distplot(df['ppq'], rug=True)\n",
    "    sns.plt.title(\"%s\\nRatio: %s, min pack size %s, dispensed in less than pack size: %s\" % (title, ratio, min_pack_size, dispensed_in_packs))\n",
    "    sns.plt.show()"
   ]
  }
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