Analyzing Medicare Part D Prescriber Data - 2013 - Fraud - DistanceMatrix - Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data has a size of (23645873, 21)\n"
     ]
    }
   ],
   "source": [
    "part_d_df = pd.read_csv('data/part_d_2013.csv', low_memory=False)\n",
    "exclusion_df = pd.read_csv('data/exclusion_list_2014.csv', low_memory=False)\n",
    "print(\"The data has a size of {}\".format(part_d_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_last_org_name</th>\n",
       "      <th>nppes_provider_first_name</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>...</th>\n",
       "      <th>total_30_day_fill_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>bene_count_ge65_suppress_flag</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>ge65_suppress_flag</th>\n",
       "      <th>total_30_day_fill_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003049925</td>\n",
       "      <td>BEST</td>\n",
       "      <td>DAWN</td>\n",
       "      <td>GLENNS FERRY</td>\n",
       "      <td>ID</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>NEXIUM</td>\n",
       "      <td>ESOMEPRAZOLE MAGNESIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>720</td>\n",
       "      <td>5353.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003049925</td>\n",
       "      <td>BEST</td>\n",
       "      <td>DAWN</td>\n",
       "      <td>GLENNS FERRY</td>\n",
       "      <td>ID</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>OMEPRAZOLE</td>\n",
       "      <td>OMEPRAZOLE</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3240</td>\n",
       "      <td>1493.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>956.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi nppes_provider_last_org_name nppes_provider_first_name  \\\n",
       "0  1003049925                         BEST                      DAWN   \n",
       "1  1003049925                         BEST                      DAWN   \n",
       "\n",
       "  nppes_provider_city nppes_provider_state specialty_description  \\\n",
       "0        GLENNS FERRY                   ID    Nurse Practitioner   \n",
       "1        GLENNS FERRY                   ID    Nurse Practitioner   \n",
       "\n",
       "  description_flag   drug_name            generic_name  bene_count  ...  \\\n",
       "0                S      NEXIUM  ESOMEPRAZOLE MAGNESIUM         NaN  ...   \n",
       "1                S  OMEPRAZOLE              OMEPRAZOLE        18.0  ...   \n",
       "\n",
       "   total_30_day_fill_count  total_day_supply  total_drug_cost  \\\n",
       "0                     24.0               720          5353.41   \n",
       "1                    108.0              3240          1493.91   \n",
       "\n",
       "   bene_count_ge65  bene_count_ge65_suppress_flag total_claim_count_ge65  \\\n",
       "0              NaN                              *                    NaN   \n",
       "1              NaN                              #                   56.0   \n",
       "\n",
       "   ge65_suppress_flag total_30_day_fill_count_ge65  total_day_supply_ge65  \\\n",
       "0                   *                          NaN                    NaN   \n",
       "1                 NaN                         70.0                 2100.0   \n",
       "\n",
       "   total_drug_cost_ge65  \n",
       "0                   NaN  \n",
       "1                956.83  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_d_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "566"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusion_df.head(2)\n",
    "len(exclusion_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data has a size of (23233763, 16)\n"
     ]
    }
   ],
   "source": [
    "# reorganize data columns\n",
    "part_d_df = part_d_df[['npi', 'nppes_provider_city','nppes_provider_state', 'specialty_description', 'description_flag',\\\n",
    "                        'drug_name', 'generic_name', 'bene_count', 'total_claim_count','total_day_supply', \\\n",
    "                        'total_drug_cost', 'bene_count_ge65', 'total_claim_count_ge65', 'total_day_supply_ge65',\\\n",
    "                        'total_drug_cost_ge65']]\n",
    "\n",
    "# assigning exclusion (False = 0, True = 1)\n",
    "exclusion_npi = exclusion_df['NPI'].values.tolist()\n",
    "\n",
    "part_d_df['is_excluded'] = 0\n",
    "part_d_df.loc[part_d_df['npi'].isin(exclusion_npi), 'is_excluded'] = 1\n",
    "\n",
    "# get US data to analyze\n",
    "states = ['AK', 'AL', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', \\\n",
    "          'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', \\\n",
    "          'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', \\\n",
    "          'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', \\\n",
    "          'SD', 'TN', 'TX', 'UT', 'VT', 'VA', 'WA', 'WV', 'WI',' WY']\n",
    "\n",
    "part_d_df = part_d_df[part_d_df['nppes_provider_state'].isin(states)]\n",
    "\n",
    "print(\"The data has a size of {}\".format(part_d_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "565"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(exclusion_df['NPI'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(exclusion_df['SPECIALTY'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5748"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(part_d_df.loc[part_d_df['npi'].isin(exclusion_npi)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24496</th>\n",
       "      <td>1003801044</td>\n",
       "      <td>KENNER</td>\n",
       "      <td>LA</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>S</td>\n",
       "      <td>CIPROFLOXACIN HCL</td>\n",
       "      <td>CIPROFLOXACIN HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>104</td>\n",
       "      <td>71.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24497</th>\n",
       "      <td>1003801044</td>\n",
       "      <td>KENNER</td>\n",
       "      <td>LA</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>S</td>\n",
       "      <td>DILTIAZEM 24HR CD</td>\n",
       "      <td>DILTIAZEM HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>990</td>\n",
       "      <td>840.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>840.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              npi nppes_provider_city nppes_provider_state  \\\n",
       "24496  1003801044              KENNER                   LA   \n",
       "24497  1003801044              KENNER                   LA   \n",
       "\n",
       "      specialty_description description_flag          drug_name  \\\n",
       "24496     Internal Medicine                S  CIPROFLOXACIN HCL   \n",
       "24497     Internal Medicine                S  DILTIAZEM 24HR CD   \n",
       "\n",
       "            generic_name  bene_count  total_claim_count  total_day_supply  \\\n",
       "24496  CIPROFLOXACIN HCL         NaN                 11               104   \n",
       "24497      DILTIAZEM HCL         NaN                 25               990   \n",
       "\n",
       "       total_drug_cost  bene_count_ge65  total_claim_count_ge65  \\\n",
       "24496            71.18              NaN                     NaN   \n",
       "24497           840.47              NaN                    25.0   \n",
       "\n",
       "       total_day_supply_ge65  total_drug_cost_ge65  is_excluded  \n",
       "24496                    NaN                   NaN            1  \n",
       "24497                  990.0                840.47            1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_d_df.loc[part_d_df['is_excluded']==1].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nppes_provider_state</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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>865190</td>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>2282144</td>\n",
       "      <td>328622</td>\n",
       "      <td>1367606</td>\n",
       "      <td>1367606</td>\n",
       "      <td>1367606</td>\n",
       "      <td>2282144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>571115</td>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>1626307</td>\n",
       "      <td>217906</td>\n",
       "      <td>976201</td>\n",
       "      <td>976201</td>\n",
       "      <td>976201</td>\n",
       "      <td>1626307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>663148</td>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>1570903</td>\n",
       "      <td>221025</td>\n",
       "      <td>906968</td>\n",
       "      <td>906968</td>\n",
       "      <td>906968</td>\n",
       "      <td>1570903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>568033</td>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>1436509</td>\n",
       "      <td>184143</td>\n",
       "      <td>810143</td>\n",
       "      <td>810143</td>\n",
       "      <td>810143</td>\n",
       "      <td>1436509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>417863</td>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>1214631</td>\n",
       "      <td>157632</td>\n",
       "      <td>742763</td>\n",
       "      <td>742763</td>\n",
       "      <td>742763</td>\n",
       "      <td>1214631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>377699</td>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>990148</td>\n",
       "      <td>138853</td>\n",
       "      <td>583992</td>\n",
       "      <td>583992</td>\n",
       "      <td>583992</td>\n",
       "      <td>990148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>317780</td>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>866187</td>\n",
       "      <td>112443</td>\n",
       "      <td>500601</td>\n",
       "      <td>500601</td>\n",
       "      <td>500601</td>\n",
       "      <td>866187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>276049</td>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>831102</td>\n",
       "      <td>108017</td>\n",
       "      <td>503557</td>\n",
       "      <td>503557</td>\n",
       "      <td>503557</td>\n",
       "      <td>831102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>317050</td>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>804781</td>\n",
       "      <td>111952</td>\n",
       "      <td>432274</td>\n",
       "      <td>432274</td>\n",
       "      <td>432274</td>\n",
       "      <td>804781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>230367</td>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>648095</td>\n",
       "      <td>90371</td>\n",
       "      <td>393830</td>\n",
       "      <td>393830</td>\n",
       "      <td>393830</td>\n",
       "      <td>648095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>256027</td>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>648055</td>\n",
       "      <td>87723</td>\n",
       "      <td>377284</td>\n",
       "      <td>377284</td>\n",
       "      <td>377284</td>\n",
       "      <td>648055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>248772</td>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>626872</td>\n",
       "      <td>86689</td>\n",
       "      <td>355112</td>\n",
       "      <td>355112</td>\n",
       "      <td>355112</td>\n",
       "      <td>626872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>193076</td>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>541302</td>\n",
       "      <td>79807</td>\n",
       "      <td>325071</td>\n",
       "      <td>325071</td>\n",
       "      <td>325071</td>\n",
       "      <td>541302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>168380</td>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>538990</td>\n",
       "      <td>80093</td>\n",
       "      <td>327214</td>\n",
       "      <td>327214</td>\n",
       "      <td>327214</td>\n",
       "      <td>538990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>197313</td>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>529990</td>\n",
       "      <td>70137</td>\n",
       "      <td>305532</td>\n",
       "      <td>305532</td>\n",
       "      <td>305532</td>\n",
       "      <td>529990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>182415</td>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>519437</td>\n",
       "      <td>66397</td>\n",
       "      <td>301587</td>\n",
       "      <td>301587</td>\n",
       "      <td>301587</td>\n",
       "      <td>519437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>144520</td>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>458386</td>\n",
       "      <td>55237</td>\n",
       "      <td>270421</td>\n",
       "      <td>270421</td>\n",
       "      <td>270421</td>\n",
       "      <td>458386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>142459</td>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>452618</td>\n",
       "      <td>60248</td>\n",
       "      <td>260983</td>\n",
       "      <td>260983</td>\n",
       "      <td>260983</td>\n",
       "      <td>452618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>172318</td>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>442688</td>\n",
       "      <td>62835</td>\n",
       "      <td>255641</td>\n",
       "      <td>255641</td>\n",
       "      <td>255641</td>\n",
       "      <td>442688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>175269</td>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>419824</td>\n",
       "      <td>63505</td>\n",
       "      <td>243683</td>\n",
       "      <td>243683</td>\n",
       "      <td>243683</td>\n",
       "      <td>419824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          npi  nppes_provider_city  specialty_description  \\\n",
       "nppes_provider_state                                                        \n",
       "CA                    2282144              2282144                2282144   \n",
       "NY                    1626307              1626307                1626307   \n",
       "FL                    1570903              1570903                1570903   \n",
       "TX                    1436509              1436509                1436509   \n",
       "PA                    1214631              1214631                1214631   \n",
       "OH                     990148               990148                 990148   \n",
       "NC                     866187               866187                 866187   \n",
       "IL                     831102               831102                 831102   \n",
       "MI                     804781               804781                 804781   \n",
       "NJ                     648095               648095                 648095   \n",
       "GA                     648055               648055                 648055   \n",
       "TN                     626872               626872                 626872   \n",
       "MO                     541302               541302                 541302   \n",
       "MA                     538990               538990                 538990   \n",
       "IN                     529990               529990                 529990   \n",
       "VA                     519437               519437                 519437   \n",
       "WI                     458386               458386                 458386   \n",
       "WA                     452618               452618                 452618   \n",
       "KY                     442688               442688                 442688   \n",
       "AL                     419824               419824                 419824   \n",
       "\n",
       "                      description_flag  drug_name  generic_name  bene_count  \\\n",
       "nppes_provider_state                                                          \n",
       "CA                             2282144    2282144       2282144      865190   \n",
       "NY                             1626307    1626307       1626307      571115   \n",
       "FL                             1570903    1570903       1570903      663148   \n",
       "TX                             1436509    1436509       1436509      568033   \n",
       "PA                             1214631    1214631       1214631      417863   \n",
       "OH                              990148     990148        990148      377699   \n",
       "NC                              866187     866187        866187      317780   \n",
       "IL                              831102     831102        831102      276049   \n",
       "MI                              804781     804781        804781      317050   \n",
       "NJ                              648095     648095        648095      230367   \n",
       "GA                              648055     648055        648055      256027   \n",
       "TN                              626872     626872        626872      248772   \n",
       "MO                              541302     541302        541302      193076   \n",
       "MA                              538990     538990        538990      168380   \n",
       "IN                              529990     529990        529990      197313   \n",
       "VA                              519437     519437        519437      182415   \n",
       "WI                              458386     458386        458386      144520   \n",
       "WA                              452618     452618        452618      142459   \n",
       "KY                              442688     442688        442688      172318   \n",
       "AL                              419824     419824        419824      175269   \n",
       "\n",
       "                      total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "nppes_provider_state                                                         \n",
       "CA                              2282144           2282144          2282144   \n",
       "NY                              1626307           1626307          1626307   \n",
       "FL                              1570903           1570903          1570903   \n",
       "TX                              1436509           1436509          1436509   \n",
       "PA                              1214631           1214631          1214631   \n",
       "OH                               990148            990148           990148   \n",
       "NC                               866187            866187           866187   \n",
       "IL                               831102            831102           831102   \n",
       "MI                               804781            804781           804781   \n",
       "NJ                               648095            648095           648095   \n",
       "GA                               648055            648055           648055   \n",
       "TN                               626872            626872           626872   \n",
       "MO                               541302            541302           541302   \n",
       "MA                               538990            538990           538990   \n",
       "IN                               529990            529990           529990   \n",
       "VA                               519437            519437           519437   \n",
       "WI                               458386            458386           458386   \n",
       "WA                               452618            452618           452618   \n",
       "KY                               442688            442688           442688   \n",
       "AL                               419824            419824           419824   \n",
       "\n",
       "                      bene_count_ge65  total_claim_count_ge65  \\\n",
       "nppes_provider_state                                            \n",
       "CA                             328622                 1367606   \n",
       "NY                             217906                  976201   \n",
       "FL                             221025                  906968   \n",
       "TX                             184143                  810143   \n",
       "PA                             157632                  742763   \n",
       "OH                             138853                  583992   \n",
       "NC                             112443                  500601   \n",
       "IL                             108017                  503557   \n",
       "MI                             111952                  432274   \n",
       "NJ                              90371                  393830   \n",
       "GA                              87723                  377284   \n",
       "TN                              86689                  355112   \n",
       "MO                              79807                  325071   \n",
       "MA                              80093                  327214   \n",
       "IN                              70137                  305532   \n",
       "VA                              66397                  301587   \n",
       "WI                              55237                  270421   \n",
       "WA                              60248                  260983   \n",
       "KY                              62835                  255641   \n",
       "AL                              63505                  243683   \n",
       "\n",
       "                      total_day_supply_ge65  total_drug_cost_ge65  is_excluded  \n",
       "nppes_provider_state                                                            \n",
       "CA                                  1367606               1367606      2282144  \n",
       "NY                                   976201                976201      1626307  \n",
       "FL                                   906968                906968      1570903  \n",
       "TX                                   810143                810143      1436509  \n",
       "PA                                   742763                742763      1214631  \n",
       "OH                                   583992                583992       990148  \n",
       "NC                                   500601                500601       866187  \n",
       "IL                                   503557                503557       831102  \n",
       "MI                                   432274                432274       804781  \n",
       "NJ                                   393830                393830       648095  \n",
       "GA                                   377284                377284       648055  \n",
       "TN                                   355112                355112       626872  \n",
       "MO                                   325071                325071       541302  \n",
       "MA                                   327214                327214       538990  \n",
       "IN                                   305532                305532       529990  \n",
       "VA                                   301587                301587       519437  \n",
       "WI                                   270421                270421       458386  \n",
       "WA                                   260983                260983       452618  \n",
       "KY                                   255641                255641       442688  \n",
       "AL                                   243683                243683       419824  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 20 states which has the most prescribers\n",
    "part_d_df_state_count = part_d_df.groupby('nppes_provider_state').count().sort_values('npi', ascending=False).iloc[0:20]\n",
    "part_d_df_state_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_excluded</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>0</th>\n",
       "      <td>23228015</td>\n",
       "      <td>23228012</td>\n",
       "      <td>23228015</td>\n",
       "      <td>23228015</td>\n",
       "      <td>23228015</td>\n",
       "      <td>23228015</td>\n",
       "      <td>23228015</td>\n",
       "      <td>8467676</td>\n",
       "      <td>23228015</td>\n",
       "      <td>23228015</td>\n",
       "      <td>23228015</td>\n",
       "      <td>3144022</td>\n",
       "      <td>13618857</td>\n",
       "      <td>13618857</td>\n",
       "      <td>13618857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>1893</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>5748</td>\n",
       "      <td>854</td>\n",
       "      <td>3280</td>\n",
       "      <td>3280</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  npi  nppes_provider_city  nppes_provider_state  \\\n",
       "is_excluded                                                        \n",
       "0            23228015             23228012              23228015   \n",
       "1                5748                 5748                  5748   \n",
       "\n",
       "             specialty_description  description_flag  drug_name  generic_name  \\\n",
       "is_excluded                                                                     \n",
       "0                         23228015          23228015   23228015      23228015   \n",
       "1                             5748              5748       5748          5748   \n",
       "\n",
       "             bene_count  total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "is_excluded                                                                     \n",
       "0               8467676           23228015          23228015         23228015   \n",
       "1                  1893               5748              5748             5748   \n",
       "\n",
       "             bene_count_ge65  total_claim_count_ge65  total_day_supply_ge65  \\\n",
       "is_excluded                                                                   \n",
       "0                    3144022                13618857               13618857   \n",
       "1                        854                    3280                   3280   \n",
       "\n",
       "             total_drug_cost_ge65  \n",
       "is_excluded                        \n",
       "0                        13618857  \n",
       "1                            3280  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclusion and non-exclusion prescribers\n",
    "prescribers = part_d_df.groupby('is_excluded').count().sort_values('npi', ascending=False).iloc[0:2]\n",
    "prescribers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export exclusion providers data to do statistics analysis, run once when needed by uncommenting two lines below\n",
    "part_d_df_exclusion = part_d_df[part_d_df['is_excluded'] == 1]\n",
    "#part_d_df_exclusion.to_csv('exclusion2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(part_d_df_exclusion['npi'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_d_df_exclusion[['npi']].to_csv('2013_part_d_e_npi.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "637"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(part_d_df_exclusion['drug_name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(part_d_df_exclusion['specialty_description'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Internal Medicine', 'Family Practice', 'General Surgery',\n",
       "       'Psychiatry & Neurology', 'Allergy/Immunology', 'Specialist',\n",
       "       'Psychiatry', 'Cardiology', 'Ophthalmology', 'Anesthesiology',\n",
       "       'Emergency Medicine', 'Gastroenterology',\n",
       "       'Oral Surgery (dentists only)', 'Dentist', 'Podiatry', 'CRNA',\n",
       "       'Physician Assistant', 'Family Medicine', 'General Practice',\n",
       "       'Pulmonary Disease', 'Geriatric Medicine', 'Obstetrics/Gynecology',\n",
       "       'Nurse Practitioner', 'Pediatric Medicine', 'Orthopedic Surgery',\n",
       "       'Physical Medicine and Rehabilitation', 'Urology', 'Neurosurgery',\n",
       "       'Dermatology'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_d_df_exclusion['specialty_description'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1841303310</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>136</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>294</td>\n",
       "      <td>55</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1902829112</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>123</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>39</td>\n",
       "      <td>215</td>\n",
       "      <td>215</td>\n",
       "      <td>215</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1942202841</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>103</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>25</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1114972262</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>94</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>37</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1851338248</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>61</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>11</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1902845480</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>91</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>33</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1700981917</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>60</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>17</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1265469472</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>41</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1205813441</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>52</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1760558548</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>30</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>13</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi  nppes_provider_city  nppes_provider_state  \\\n",
       "0  1841303310                  294                   294   \n",
       "1  1902829112                  289                   289   \n",
       "2  1942202841                  240                   240   \n",
       "3  1114972262                  213                   213   \n",
       "4  1851338248                  189                   189   \n",
       "5  1902845480                  174                   174   \n",
       "6  1700981917                  146                   146   \n",
       "7  1265469472                  145                   145   \n",
       "8  1205813441                  144                   144   \n",
       "9  1760558548                  132                   132   \n",
       "\n",
       "   specialty_description  description_flag  drug_name  generic_name  \\\n",
       "0                    294               294        294           294   \n",
       "1                    289               289        289           289   \n",
       "2                    240               240        240           240   \n",
       "3                    213               213        213           213   \n",
       "4                    189               189        189           189   \n",
       "5                    174               174        174           174   \n",
       "6                    146               146        146           146   \n",
       "7                    145               145        145           145   \n",
       "8                    144               144        144           144   \n",
       "9                    132               132        132           132   \n",
       "\n",
       "   bene_count  total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "0         136                294               294              294   \n",
       "1         123                289               289              289   \n",
       "2         103                240               240              240   \n",
       "3          94                213               213              213   \n",
       "4          61                189               189              189   \n",
       "5          91                174               174              174   \n",
       "6          60                146               146              146   \n",
       "7          41                145               145              145   \n",
       "8          52                144               144              144   \n",
       "9          30                132               132              132   \n",
       "\n",
       "   bene_count_ge65  total_claim_count_ge65  total_day_supply_ge65  \\\n",
       "0               55                     193                    193   \n",
       "1               39                     215                    215   \n",
       "2               25                     158                    158   \n",
       "3               37                     118                    118   \n",
       "4               11                     121                    121   \n",
       "5               33                     105                    105   \n",
       "6               17                      93                     93   \n",
       "7                3                      91                     91   \n",
       "8               24                      86                     86   \n",
       "9               13                      94                     94   \n",
       "\n",
       "   total_drug_cost_ge65  is_excluded  \n",
       "0                   193          294  \n",
       "1                   215          289  \n",
       "2                   158          240  \n",
       "3                   118          213  \n",
       "4                   121          189  \n",
       "5                   105          174  \n",
       "6                    93          146  \n",
       "7                    91          145  \n",
       "8                    86          144  \n",
       "9                    94          132  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 NPIs which are the most excluded prescribers\n",
    "part_d_df_exclusion_npi_count = part_d_df_exclusion.groupby('npi').count().sort_values('drug_name', ascending=False).iloc[0:10]\n",
    "part_d_df_exclusion_npi_count.reset_index(level=[\"npi\"], inplace=True)\n",
    "part_d_df_exclusion_npi_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>269</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>957</td>\n",
       "      <td>114</td>\n",
       "      <td>526</td>\n",
       "      <td>526</td>\n",
       "      <td>526</td>\n",
       "      <td>957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NJ</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>251</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>55</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TN</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>181</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>103</td>\n",
       "      <td>349</td>\n",
       "      <td>349</td>\n",
       "      <td>349</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PA</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>209</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>544</td>\n",
       "      <td>73</td>\n",
       "      <td>349</td>\n",
       "      <td>349</td>\n",
       "      <td>349</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MO</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>167</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>61</td>\n",
       "      <td>261</td>\n",
       "      <td>261</td>\n",
       "      <td>261</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MI</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>123</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>93</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NY</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>122</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>71</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TX</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>72</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>35</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IL</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>81</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "      <td>27</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>VA</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>69</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>55</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AL</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>91</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>33</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OH</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>59</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>30</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WV</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>41</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MD</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>31</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>31</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MA</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>42</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>29</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>WI</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>26</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>WA</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FL</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GA</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LA</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nppes_provider_state  npi  nppes_provider_city  specialty_description  \\\n",
       "0                    CA  957                  957                    957   \n",
       "1                    NJ  756                  756                    756   \n",
       "2                    TN  567                  567                    567   \n",
       "3                    PA  544                  544                    544   \n",
       "4                    MO  439                  439                    439   \n",
       "5                    MI  381                  381                    381   \n",
       "6                    NY  344                  344                    344   \n",
       "7                    TX  300                  300                    300   \n",
       "8                    IL  242                  242                    242   \n",
       "9                    VA  214                  214                    214   \n",
       "10                   AL  176                  176                    176   \n",
       "11                   OH  159                  159                    159   \n",
       "12                   WV  148                  148                    148   \n",
       "13                   MD  106                  106                    106   \n",
       "14                   MA   77                   77                     77   \n",
       "15                   WI   71                   71                     71   \n",
       "16                   WA   65                   65                     65   \n",
       "17                   FL   52                   52                     52   \n",
       "18                   GA   44                   44                     44   \n",
       "19                   LA   39                   39                     39   \n",
       "\n",
       "    description_flag  drug_name  generic_name  bene_count  total_claim_count  \\\n",
       "0                957        957           957         269                957   \n",
       "1                756        756           756         251                756   \n",
       "2                567        567           567         181                567   \n",
       "3                544        544           544         209                544   \n",
       "4                439        439           439         167                439   \n",
       "5                381        381           381         123                381   \n",
       "6                344        344           344         122                344   \n",
       "7                300        300           300          72                300   \n",
       "8                242        242           242          81                242   \n",
       "9                214        214           214          69                214   \n",
       "10               176        176           176          91                176   \n",
       "11               159        159           159          59                159   \n",
       "12               148        148           148          41                148   \n",
       "13               106        106           106          31                106   \n",
       "14                77         77            77          42                 77   \n",
       "15                71         71            71          26                 71   \n",
       "16                65         65            65          21                 65   \n",
       "17                52         52            52          16                 52   \n",
       "18                44         44            44           3                 44   \n",
       "19                39         39            39           8                 39   \n",
       "\n",
       "    total_day_supply  total_drug_cost  bene_count_ge65  \\\n",
       "0                957              957              114   \n",
       "1                756              756               55   \n",
       "2                567              567              103   \n",
       "3                544              544               73   \n",
       "4                439              439               61   \n",
       "5                381              381               93   \n",
       "6                344              344               71   \n",
       "7                300              300               35   \n",
       "8                242              242               27   \n",
       "9                214              214               55   \n",
       "10               176              176               33   \n",
       "11               159              159               30   \n",
       "12               148              148                3   \n",
       "13               106              106               31   \n",
       "14                77               77               29   \n",
       "15                71               71                7   \n",
       "16                65               65                2   \n",
       "17                52               52               11   \n",
       "18                44               44                2   \n",
       "19                39               39                3   \n",
       "\n",
       "    total_claim_count_ge65  total_day_supply_ge65  total_drug_cost_ge65  \\\n",
       "0                      526                    526                   526   \n",
       "1                      428                    428                   428   \n",
       "2                      349                    349                   349   \n",
       "3                      349                    349                   349   \n",
       "4                      261                    261                   261   \n",
       "5                      187                    187                   187   \n",
       "6                      189                    189                   189   \n",
       "7                      159                    159                   159   \n",
       "8                      144                    144                   144   \n",
       "9                      119                    119                   119   \n",
       "10                     106                    106                   106   \n",
       "11                      95                     95                    95   \n",
       "12                      91                     91                    91   \n",
       "13                      55                     55                    55   \n",
       "14                      47                     47                    47   \n",
       "15                      29                     29                    29   \n",
       "16                      28                     28                    28   \n",
       "17                      27                     27                    27   \n",
       "18                      37                     37                    37   \n",
       "19                      17                     17                    17   \n",
       "\n",
       "    is_excluded  \n",
       "0           957  \n",
       "1           756  \n",
       "2           567  \n",
       "3           544  \n",
       "4           439  \n",
       "5           381  \n",
       "6           344  \n",
       "7           300  \n",
       "8           242  \n",
       "9           214  \n",
       "10          176  \n",
       "11          159  \n",
       "12          148  \n",
       "13          106  \n",
       "14           77  \n",
       "15           71  \n",
       "16           65  \n",
       "17           52  \n",
       "18           44  \n",
       "19           39  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 20 states which has the most excluded prescribers\n",
    "part_d_df_exclusion_state_count = part_d_df_exclusion.groupby('nppes_provider_state').count().sort_values('npi', ascending=False).iloc[0:20]\n",
    "part_d_df_exclusion_state_count.reset_index(level=[\"nppes_provider_state\"], inplace=True)\n",
    "part_d_df_exclusion_state_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>801</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>2378</td>\n",
       "      <td>305</td>\n",
       "      <td>1442</td>\n",
       "      <td>1442</td>\n",
       "      <td>1442</td>\n",
       "      <td>2378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Family Practice</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>367</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>1414</td>\n",
       "      <td>176</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Psychiatry</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>172</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>115</td>\n",
       "      <td>225</td>\n",
       "      <td>225</td>\n",
       "      <td>225</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Emergency Medicine</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>139</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>59</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cardiology</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>66</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>7</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>68</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Geriatric Medicine</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>39</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>23</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>General Practice</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>50</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>32</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>General Surgery</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>25</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>24</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Anesthesiology</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>31</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>25</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Specialist</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gastroenterology</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Podiatry</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ophthalmology</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dentist</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Allergy/Immunology</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Orthopedic Surgery</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Obstetrics/Gynecology</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Psychiatry &amp; Neurology</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     specialty_description   npi  nppes_provider_city  nppes_provider_state  \\\n",
       "0        Internal Medicine  2378                 2378                  2378   \n",
       "1          Family Practice  1414                 1414                  1414   \n",
       "2               Psychiatry   379                  379                   379   \n",
       "3       Emergency Medicine   302                  302                   302   \n",
       "4               Cardiology   222                  222                   222   \n",
       "5      Physician Assistant   186                  186                   186   \n",
       "6       Geriatric Medicine   167                  167                   167   \n",
       "7         General Practice   158                  158                   158   \n",
       "8          General Surgery   128                  128                   128   \n",
       "9           Anesthesiology    81                   81                    81   \n",
       "10      Nurse Practitioner    58                   58                    58   \n",
       "11              Specialist    55                   55                    55   \n",
       "12        Gastroenterology    47                   47                    47   \n",
       "13                Podiatry    28                   28                    28   \n",
       "14           Ophthalmology    24                   24                    24   \n",
       "15                 Dentist    22                   22                    22   \n",
       "16      Allergy/Immunology    21                   21                    21   \n",
       "17      Orthopedic Surgery    16                   16                    16   \n",
       "18   Obstetrics/Gynecology    14                   14                    14   \n",
       "19  Psychiatry & Neurology    11                   11                    11   \n",
       "\n",
       "    description_flag  drug_name  generic_name  bene_count  total_claim_count  \\\n",
       "0               2378       2378          2378         801               2378   \n",
       "1               1414       1414          1414         367               1414   \n",
       "2                379        379           379         172                379   \n",
       "3                302        302           302         139                302   \n",
       "4                222        222           222          66                222   \n",
       "5                186        186           186          68                186   \n",
       "6                167        167           167          39                167   \n",
       "7                158        158           158          50                158   \n",
       "8                128        128           128          25                128   \n",
       "9                 81         81            81          42                 81   \n",
       "10                58         58            58          25                 58   \n",
       "11                55         55            55          17                 55   \n",
       "12                47         47            47          13                 47   \n",
       "13                28         28            28          12                 28   \n",
       "14                24         24            24          10                 24   \n",
       "15                22         22            22          17                 22   \n",
       "16                21         21            21          13                 21   \n",
       "17                16         16            16           7                 16   \n",
       "18                14         14            14           1                 14   \n",
       "19                11         11            11           0                 11   \n",
       "\n",
       "    total_day_supply  total_drug_cost  bene_count_ge65  \\\n",
       "0               2378             2378              305   \n",
       "1               1414             1414              176   \n",
       "2                379              379              115   \n",
       "3                302              302               59   \n",
       "4                222              222                7   \n",
       "5                186              186               27   \n",
       "6                167              167               23   \n",
       "7                158              158               32   \n",
       "8                128              128               24   \n",
       "9                 81               81               31   \n",
       "10                58               58                2   \n",
       "11                55               55               10   \n",
       "12                47               47                6   \n",
       "13                28               28                5   \n",
       "14                24               24                2   \n",
       "15                22               22                4   \n",
       "16                21               21                2   \n",
       "17                16               16                6   \n",
       "18                14               14                3   \n",
       "19                11               11                6   \n",
       "\n",
       "    total_claim_count_ge65  total_day_supply_ge65  total_drug_cost_ge65  \\\n",
       "0                     1442                   1442                  1442   \n",
       "1                      761                    761                   761   \n",
       "2                      225                    225                   225   \n",
       "3                      165                    165                   165   \n",
       "4                      137                    137                   137   \n",
       "5                       81                     81                    81   \n",
       "6                       87                     87                    87   \n",
       "7                       93                     93                    93   \n",
       "8                       85                     85                    85   \n",
       "9                       47                     47                    47   \n",
       "10                      22                     22                    22   \n",
       "11                      22                     22                    22   \n",
       "12                      35                     35                    35   \n",
       "13                       9                      9                     9   \n",
       "14                       9                      9                     9   \n",
       "15                       4                      4                     4   \n",
       "16                      11                     11                    11   \n",
       "17                       9                      9                     9   \n",
       "18                       8                      8                     8   \n",
       "19                       9                      9                     9   \n",
       "\n",
       "    is_excluded  \n",
       "0          2378  \n",
       "1          1414  \n",
       "2           379  \n",
       "3           302  \n",
       "4           222  \n",
       "5           186  \n",
       "6           167  \n",
       "7           158  \n",
       "8           128  \n",
       "9            81  \n",
       "10           58  \n",
       "11           55  \n",
       "12           47  \n",
       "13           28  \n",
       "14           24  \n",
       "15           22  \n",
       "16           21  \n",
       "17           16  \n",
       "18           14  \n",
       "19           11  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 20 specialties which has the most excluded prescribers\n",
    "part_d_df_exclusion_specialty_count = part_d_df_exclusion.groupby('specialty_description').count().sort_values('npi', ascending=False).iloc[0:20]\n",
    "part_d_df_exclusion_specialty_count.reset_index(level=[\"specialty_description\"], inplace=True)\n",
    "part_d_df_exclusion_specialty_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After full US dataset analysis, will use NJ data to do detailed analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_d_df_small=part_d_df[part_d_df['nppes_provider_state']=='NJ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "648095"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(part_d_df_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_excluded</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>0</th>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>230116</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>647339</td>\n",
       "      <td>90316</td>\n",
       "      <td>393402</td>\n",
       "      <td>393402</td>\n",
       "      <td>393402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>251</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>756</td>\n",
       "      <td>55</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi  nppes_provider_city  nppes_provider_state  \\\n",
       "is_excluded                                                      \n",
       "0            647339               647339                647339   \n",
       "1               756                  756                   756   \n",
       "\n",
       "             specialty_description  description_flag  drug_name  generic_name  \\\n",
       "is_excluded                                                                     \n",
       "0                           647339            647339     647339        647339   \n",
       "1                              756               756        756           756   \n",
       "\n",
       "             bene_count  total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "is_excluded                                                                     \n",
       "0                230116             647339            647339           647339   \n",
       "1                   251                756               756              756   \n",
       "\n",
       "             bene_count_ge65  total_claim_count_ge65  total_day_supply_ge65  \\\n",
       "is_excluded                                                                   \n",
       "0                      90316                  393402                 393402   \n",
       "1                         55                     428                    428   \n",
       "\n",
       "             total_drug_cost_ge65  \n",
       "is_excluded                        \n",
       "0                          393402  \n",
       "1                             428  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclusion and non-exclusion prescribers\n",
    "prescribers = part_d_df_small.groupby('is_excluded').count().sort_values('npi', ascending=False).iloc[0:2]\n",
    "prescribers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>152</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>35</td>\n",
       "      <td>276</td>\n",
       "      <td>276</td>\n",
       "      <td>276</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Geriatric Medicine</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>27</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cardiology</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gastroenterology</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Family Practice</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Specialist</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  specialty_description  npi  nppes_provider_city  nppes_provider_state  \\\n",
       "0     Internal Medicine  449                  449                   449   \n",
       "1    Geriatric Medicine   94                   94                    94   \n",
       "2            Cardiology   63                   63                    63   \n",
       "3    Nurse Practitioner   50                   50                    50   \n",
       "4      Gastroenterology   47                   47                    47   \n",
       "5       Family Practice   29                   29                    29   \n",
       "6            Specialist   24                   24                    24   \n",
       "\n",
       "   description_flag  drug_name  generic_name  bene_count  total_claim_count  \\\n",
       "0               449        449           449         152                449   \n",
       "1                94         94            94          27                 94   \n",
       "2                63         63            63          22                 63   \n",
       "3                50         50            50          20                 50   \n",
       "4                47         47            47          13                 47   \n",
       "5                29         29            29           7                 29   \n",
       "6                24         24            24          10                 24   \n",
       "\n",
       "   total_day_supply  total_drug_cost  bene_count_ge65  total_claim_count_ge65  \\\n",
       "0               449              449               35                     276   \n",
       "1                94               94                2                      40   \n",
       "2                63               63                3                      38   \n",
       "3                50               50                0                      18   \n",
       "4                47               47                6                      35   \n",
       "5                29               29                0                       9   \n",
       "6                24               24                9                      12   \n",
       "\n",
       "   total_day_supply_ge65  total_drug_cost_ge65  is_excluded  \n",
       "0                    276                   276          449  \n",
       "1                     40                    40           94  \n",
       "2                     38                    38           63  \n",
       "3                     18                    18           50  \n",
       "4                     35                    35           47  \n",
       "5                      9                     9           29  \n",
       "6                     12                    12           24  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top specialties which has the most excluded prescribers in small dataset\n",
    "part_d_df_small_exclusion = part_d_df_small[part_d_df_small['is_excluded'] == 1]\n",
    "part_d_df_small_exclusion_specialty_count = part_d_df_small_exclusion.groupby('specialty_description').count().sort_values('npi', ascending=False).iloc[0:20]\n",
    "part_d_df_small_exclusion_specialty_count.reset_index(level=[\"specialty_description\"], inplace=True)\n",
    "part_d_df_small_exclusion_specialty_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# add a new field called drug_count to the dataframe\n",
    "part_d_df_small[\"drug_count\"] = 1\n",
    "\n",
    "# use the groupby() and agg() methods to create a new dataframe. use numpy sum method to populate values\n",
    "prescriber_specialty_df = part_d_df_small.groupby([\"npi\",\"specialty_description\"]).agg({\"total_claim_count\": np.sum,  \"drug_count\": np.sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>drug_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npi</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1003002072</th>\n",
       "      <th>Urology</th>\n",
       "      <td>237</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003002858</th>\n",
       "      <th>Nurse Practitioner</th>\n",
       "      <td>1793</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003012410</th>\n",
       "      <th>Family Practice</th>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003019910</th>\n",
       "      <th>Dentist</th>\n",
       "      <td>229</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003020975</th>\n",
       "      <th>Internal Medicine</th>\n",
       "      <td>1205</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  total_claim_count  drug_count\n",
       "npi        specialty_description                               \n",
       "1003002072 Urology                              237          10\n",
       "1003002858 Nurse Practitioner                  1793          48\n",
       "1003012410 Family Practice                      216          14\n",
       "1003019910 Dentist                              229           4\n",
       "1003020975 Internal Medicine                   1205          43"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22856, 4)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to merge the tables we need to flatten the indices.\n",
    "prescriber_specialty_df.reset_index(level=[\"npi\",\"specialty_description\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>drug_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003002072</td>\n",
       "      <td>Urology</td>\n",
       "      <td>237</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003002858</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>1793</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003012410</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>216</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003019910</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>229</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003020975</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>1205</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi specialty_description  total_claim_count  drug_count\n",
       "0  1003002072               Urology                237          10\n",
       "1  1003002858    Nurse Practitioner               1793          48\n",
       "2  1003012410       Family Practice                216          14\n",
       "3  1003019910               Dentist                229           4\n",
       "4  1003020975     Internal Medicine               1205          43"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19809bd10f0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H0t9Xpn5J6pTULWmFpIO394mNBq95zWtoaWnhS1/6UtGhmFmTqiVJjIuIrZuYImIVMG6QZTYBfxkRfwwcBpwp6QDgs8CNETEDuDG1AeaQXdd6BjAXuLCG+Ead8ePHc9BBB3kUYWaFqSVJLJN0kaSj0+1bZCfTVRURqyPirjT9LPAAMAU4kWzTFenvSWn6ROCyyNwKTJA0uYYYzcx2mKvAblNLkvhzYCUwj6zY3/3AJ4a6sKRpwBuB24BJEbEaskQCvCrNNgV4tGyx3tRnZjZiOjs7ueeee+js7Cw6lMLVclzlWOD/RcS/AEgaA+w0lAUl7Qb8EPhMRPyhdDZjf7P20/eyiztLmku2OYqpU6cOJQQzsyFZt24dN998MwBLly7liSeeaOoT6moZSdwI7FLW3oXsKnUDkjSOLEF8NyL+O3WvKW1GSn8fT/29wH5li7cBj/VdZ0QsiIj2iGifOHFiDU/BzGxgnZ2dW6vARkTTjyZqSRI7R8RzpUaaHrA0qbIhw0XAA6URSHINUDqVsYPs0qil/g+mo5wOA54pbZYyMxsJpVFEydKlS4sJpE7UsrnpeUkHl3ZESzoEeGGQZY4AzgDulbQ89f0t8GXgCkkfAR4B3p/uWwi8A+gG1gMfriE+M7MdVhpFVGs3m1qSxGeAKyWVNv9MBk4ZaIGI+Bn972cAeFs/8wdwZg0xmZkNq7a2Nnp7eyvazayWUuF3APuTHeX0SeCPI2LrIbCSZg9/eGZmI+vcc8+taJ933nnFBFInaioVHhEbI+K+iLg3Ijb2ufsrwxiXmVkhZs6cuXX00NbW1vQns+7I9ST6qnpcq5lZIzn33HPZddddm34UAbXtkxhMc+/dMbNRY+bMmSxatKjoMOrCcI4kzMxslBnOJNEzjOsyM7M6UMv1JN7TT/czwL0R8XhE9He/mZk1sFpGEh8Bvg2clm7fAs4Gfi7pjBxiMzMrhKvAblNLkthCdm7EeyPivcABwEvAm4Bz8gjOzKwIXV1drFixgq6ursFnHuVqSRLTImJNWftxYGZEPAn0PWfCzKwhrVu3jh//+MdEBNdee23TjyZqSRI/lXStpA5JpaJ8t0jaFXg6n/DMzEZWV1cXmzZtAmDTpk1NP5qoJUmcCVwKzCK7eNBlwJkR8XxEHJNDbGZmI+66666raDf7+RJDPropFd/7QbqZmY1KmzdvHrDdbGo5BPZZtp1V/QpgHPB8ROyRR2BmZkUobWqq1m42tYwkdi9vSzoJOHTYIzIzK9DYsWMrEsPYscNZvajxbPcZ1xHxP8CxwxiLmVnhWlpaBmw3m+0947oFaMdF/cxslNl3333p6empaDezWlLku8puJwDPAicOtICkiyU9Lum+sr5zJf1O0vJ0e0fZfZ+T1C3pQUkn1PZUzMx23O9///sB281mSCMJSWOAFRFxfo3rvxT4BtnhsuXOj4iv9XmMA4BTgdcD+wI3SJoZEc19aIGZjah99tmnYiSxzz77FBdMHRjSSCL9o353rSuPiFuAJ4c4+4nA5RHxUkQ8DHTjHeNmNsLWrFkzYLvZ1LK56ReSviHprZIOLt2283E/JWlF2hz1ytQ3BXi0bJ7e1GdmNmKOP/74ivYJJzT3lu9aksSbyTYFfR74GvD19LdWFwKvJTtze3VaD/R/+dN+d4xLmitpmaRla9eu3Y4QzMz619HRMWC72Qy6T0LS2WnyWrJ/2uX/zGs+uqm8SKCkb6X1QjZy2K9s1jbgsSrrWAAsAGhvb/cRVmY2bJ58snIL+VNPPcXee+9dUDTFG8pIYvd0OwT4c2Ay2Y7lj5OVC6+JpMllzZOB0pFP1wCnStpJ0quBGcDtta7fzGxHzJ8/v6J93nnnFRRJfRh0JBERnweQtBg4OCKeTe1zgSsHWlbS94CjgVZJvcA/AkdLmkU2CukhSzZExEpJVwD3A5vIigf6yCYzG1HlRzb11242tZxvPhXYUNbeAEwbaIGI+EA/3RcNMP8XgS/WEJOZ2bByWY5KtTz77wC3S7qKbBRwMtDchdbNbNRxgb9KtRT4+6KkRcBbU9eHI+LufMIyM7N6UNM4KiLuAu7KKRYzM6szzV3e0MzMBtTce2TMrKF1dnbS3d2d++PMmzdv2NY1ffr0YV1f3jySMDMr0/doJh/dZGbWoPL4Rb5q1So++tGPbm0vWLCA6dOnD/vjNAqPJMzMysycOXPr6KG1tbWpEwQ4SZiZvcxrXvMaWlpa+OpXv1p0KIVzkjAz62P8+PEcdNBBTT+KACcJMzMbgJOEmZlV5aObrCGM1PHww+mhhx4C8jkCJy+Ndgy/5c9JwhpCd3c3q+67i6m7NU71+FdszAbqL/bcUXAkQ/PIc2OKDsHqkJOENYypu23m79ufKzqMUWv+st2KDsHqkPdJmJlZVU4SZmZWVa5JQtLFkh6XdF9Z316Slkh6KP19ZeqXpE5J3ZJWSDo4z9jMzGxweY8kLgXe3qfvs8CNETEDuDG1AeYAM9JtLnBhzrGZmdkgck0SEXEL8GSf7hPZdtnTLuCksv7LInMrMEHS5DzjMzOzgRVxdNOkiFgNEBGrJb0q9U8BHi2brzf1re67AklzyUYbTJ06dUgP6uPsR46PtTcbPerpEFj10xf9zRgRC4AFAO3t7f3O01d3dzd333s/W8bvtf0RjjBtyJ7anb/+fcGRDF3L+r4DRzNrZEUkiTWSJqdRxGTg8dTfC+xXNl8b8NhwPvCW8Xvx4gHvHM5VWh87339t0SGY2TAq4hDYa4CONN0BXF3W/8F0lNNhwDOlzVJmZlaMXEcSkr4HHA20SuoF/hH4MnCFpI8AjwDvT7MvBN4BdAPrgQ/nGZuZmQ0u1yQRER+octfb+pk3gDPzjMfMzGrjM67NzKwqJwkzM6vKScLMzKpykjAzs6qcJMzMrConCTMzq8pJwszMqnKSMDOzquqpwJ+ZjUKuwDxy8qjA7CRhZrnq7u7m7pV3w4SiI6nBluzP3b+7u9g4avF0Pqt1kjCz/E2ALUdvKTqKUa1laT57D7xPwszMqvJIwhpCb28vzz87hvnLdis6lFHrt8+OYdfe3qLDsDrjkYSZmVXlkYQ1hLa2Nl7ctJq/b3+u6FBGrfnLdmPntraiw7A645GEmZlVVdhIQlIP8CywGdgUEe2S9gK+D0wDeoD/HRFPDcfj9fb20rL+GV+DOWct65+gt3dT0WGY2TApeiRxTETMioj21P4scGNEzABuTG0zMytIve2TOJHsmtgAXcBS4JzhWHFbWxtrXhrLiwe8czhWZ1XsfP+1tLXtU3QYZjZMikwSASyWFMB/RMQCYFJErAaIiNWSXlVgfGY2DHp7e+GZ/E72suRp6I3hP4S5yCRxREQ8lhLBEkm/GuqCkuYCcwGmTp2aV3xmZk2vsCQREY+lv49Lugo4FFgjaXIaRUwGHq+y7AJgAUB7e3uMVMxmVru2tjbWaq3LcuSsZWkLbVOG/xDmQsZ/knaVtHtpGjgeuA+4BuhIs3UAVxcRn5mZZYoaSUwCrpJUiuG/IuI6SXcAV0j6CPAI8P6C4jMzMwpKEhHxG+AN/fQ/Abxt5CMyM7P++HADMzOryknCzMyqcpIwM7Oq6u2MazMbjZ5usJPpSsWGG+nyJU8DU4Z/tU4SZpar6dOnFx1CzR566CEAZkyZUXAkNZiSz2vtJGFmuZo3b17RIdSsFHNnZ2fBkRSvgcZ/ZmY20pwkzMysqqba3NSy/smGuuiQXvwDALHzHgVHMnQt658EXCrcbLRomiTRmDvPngVgxmsb6Z/uPg35WptZ/5omSXjnmZlZ7bxPwszMqmqakYQ1vkeeG8P8ZY1zdtOa9dlvsEnjG+M6Co88N4aZRQdhdcdJwhpCI+7n2JBOyNp5WmOckDWTxnydLV9OEtYQvE/JrBjeJ2FmZlU5SZiZWVV1lyQkvV3Sg5K6JX226HjMzJpZXSUJSWOAfwPmAAcAH5B0QLFRmZk1L0VE0TFsJelw4NyIOCG1PwcQEV+qtkx7e3ssW7ZshCLsX2dnJ93d3cO+3q3limcM/9Ex06dPb8idwcMtr/cO/P6NBH/3tp+kOyOifbD56u3opinAo2XtXuBNBcVSuF122aXoEGwH+P1rXH7vtqm3kcT7gRMi4qOpfQZwaESc1We+ucBcgKlTpx7y29/+dsRjNTNrZEMdSdTVPgmykcN+Ze024LG+M0XEgohoj4j2iRMnjlhwZmbNpt6SxB3ADEmvlvQK4FTgmoJjMjNrWnW1TyIiNkn6FHA9MAa4OCJWFhyWmVnTqqskARARC4GFRcdhZmb1t7nJzMzqiJOEmZlV5SRhZmZVOUmYmVlVdXUy3faQtBYYzWfTtQLrig7Ctpvfv8Y12t+7P4qIQU80a/gkMdpJWjaUsyKtPvn9a1x+7zLe3GRmZlU5SZiZWVVOEvVvQdEB2A7x+9e4/N7hfRJmZjYAjyTMzKwqJ4kykjZLWl52mzYM6/yEpA+m6Uslva+GZZem633fI+nnkl43DPFMkPTJsva+kn6wo+sdSZKeG8I8n5E0fgRimSbpvir9IekLZX2tkjZK+kaNj9EjqTVN/2KQec+TdFwt629EkvaRdLmkX0u6X9JCSTO3c10fKr0n5d/XAeav6Xvc6JwkKr0QEbPKbj07usKI+GZEXLYDqzgtIt4AdAH/3PfOdF3wWkwAtiaJiHgsIkbjB/4zQE1JYjtey8H8BnhnWfv9wA5VNY6INw9y/z9ExA078hj1TpKAq4ClEfHaiDgA+Ftg0lCWlVT1/94wfF9HHSeJQaRfhD+VdFe6vTn1Hy3pZklXSFol6cuSTpN0u6R7Jb02zXeupL/qs863SbqqrD1b0n8PEsotwPQ0f4+kf5D0M+D9kj4m6Y404vhh6Re0pEmSrkr996TYvwy8No2U/rn8l7CkMZK+luJfIems1H9Ieq53Srpe0uRheXF3UHoPlkr6gaRfSfpu+icwD9gXuEnSTWne4yX9Mr2HV0raLfX3fS2XSvpKeh9XSXprmq/fz8EgXgAekFQ61v4U4Iqy+Cem9+uOdDsi9e8tabGkuyX9B6CyZZ4rm/6b9F7dI+nLqW/rr9z03D6f4r1X0v6pf1dJF6fHvFvSidv3DhTmGGBjRHyz1BERy4G7Jd1Y9nxPhK3v3QOS/h24C9hP0ofT+3szcERpPeXfV0mzJN2avgtXSXpl30DSd/nu9HgXS9op9b8jfSZ/JqlT0rWSWiQ9JGlimqdFUrfSKLFuRYRv6QZsBpan21Wpbzywc5qeASxL00cDTwOTgZ2A3wGfT/d9GvjXNH0u8Fdp+lLgfWRf+l8BE1P/fwHv6ieepUB7mv5r4Ptpugf4m7L59i6bng+claa/D3wmTY8B9gSmAfeVzb+1Dfw58ENgbGrvBYwDflEW6ylk1/ko8n16ruw9eIbsCoYtwC+Bt5S9Rq1pupUsye6a2ucA/1DltVwKfD1NvwO4YZDPQcXr2fd1Bd4NfC3FeCPwIeAbZe97Kd6pwANpurMsvj8Fouy5lJ77nPS+jC+9V+WfsbLnVvosfBL4dpr+J+D0ND0BWFV6bRrhBswDzu+nfyywR9l73k32XZsGbAEOS/dNBh4BJgKvAH5e9p6cy7bv6wrgqDR9Htu+05eSfY93Bh4FZqb+y8hGsKX+V6f+7wHXpul/ZNt38njgh0W/noPd6u56EgV7ISJm9ekbB3xD0iyyJFK+3fOOiFgNIOnXwOLUfy/Zr51+RURI+g5wuqRLgMOBattBvyvpBdIXvqz/+2XTB0qaT/aF343sok0Ax5bWGxGbgWf6+zVU5jjgmxGxKS3zpKQDgQOBJZIgSzarB1jHSLs9InoBJC0n+4fwsz7zHAYcAPw8PYdXkCWUku/3mb80qrszrQ8G/hwM5DrgC8Cafh7nOOCAFBPAHpJ2B44E3gMQET+W9FQ/6z0OuCQi1qf5nqzy+OXP5T1p+njg3do2wt2ZlKSG+JzqlYB/knQkWVKYwrZNUL+NiFvT9JvINlWtBZD0ffq8n5L2BCZExM2pqwu4ss/jvQ54OCJWlc1zJtkPjd9ExMOp/3vA3DR9MXA18K/AnwGXbPezHSFOEoP7C7Iv+BvIfq2+WHbfS2XTW8raWxj8tb0E+FFa35Wlf8yCaHGfAAAGnElEQVT9OC0ilvXT/3zZ9KXASRFxj6QPkf3C3h4i+9Xat29lRBy+nevMW/l7sJn+X3cBSyLiA1XW8Xyfdmmd5esb6HNQVURskHQn8JfA64F3ld3dAhweES9UBJsljcGOTe/vvepPf89FwHsj4sEhLF+PVpL9ku/rNLLRwSERsVFSD1kChJe/x8Nx7L9q7CciHpW0RtKxZMnqtGGII1feJzG4PYHVEbEFOIPsl/QOi4jHgMeAvyf7J78jdgdWSxpH5YfuRrJNSKX9DXsAz6b5+7MY+ISksWmZvYAHgYmSDk994yS9fgfjHQnlz/NW4AhJpX0641X7kTA78jn4OnBORDzRp38x8KlSI41SINs0dlrqmwP0N/pbDPyZtu1/2quGeK4HzlLKRpLeWMOy9eAnwE6SPlbqkPQnwB8Bj6cEcUxq9+c24Oi072cc2QEFFSLiGeCp0j4psvf85j6z/QqYVvpclc3zK+A12nZ05Cl9lvs28J/AFWmEX9ecJAb370CHpFvJhqR9f5HsiO8Cj0bE/Tu4nv9L9sFfQvYBLfk0cIyke8k2N7w+/aP6uaT7JPU9WurbZNtqV0i6B/g/EbGB7FfbV1LfcmAoO22LtgBYJOmmtFnhQ8D3JK0gSxr717i+7f4cRMTKiOjq5655QHvaMXo/8InU/3ngSEl3kW0aeqSfdV4HXAMsS5vZ/qrvPAP4AtnmsxXKDlr4wiDz15XINuifDMxWdgjsSrJ9CQvJXs9lZEn2V1WWX53m/yVwA9nO7P50AP+cPjOzyPZLlK/nReDDwJXpO7aFbHPtC2T7gK5TdkDEGrJ9ZyXXkG0WrvtNTeAzrgul7NjsuyPioqJjMbPhI2m3iHgujdb+DXgoIs5P97WT7Xh/64ArqRMeSRQkbac+iGzYaWajy8fSCG8l2abK/wCQ9FmyIwg/V2BsNfFIwszMqvJIwszMqnKSMDOzqpwkzMysKicJMzOryknCmoKyUtITBpmnR1k574py6jv4uEu1rcDfsBvi86oomz6UZcxKnCSsKUTEOyLi6SHOXlFOvR4p0zLE51VRNr3G18KanJOE1Q1lJax/rKz09X2STkm/7kulu28vK61Rrcz2bpIu0bZy5+9N/eUX7vkfZWXPV0qa208ofcupf0dl5bSVlSR/d5XnsIuyi+GsSIXjdim7r1q58i8ru3DOCklfS30vK/Ou/ktel0Y/05SVpu5K6/lBKj/SX9n08tfi7PRa3yfpM6mv9DjfSq/RYkm7YM2p6DK0vvlWugHvBb5V1t6TrPrt36X2B9lWcrlame2vkEo6p/Yr098etpXbLpXV3oWsnPfe5fPw8nLqRwH/UxbTw6Ry6v08h7NJpdTJTpbcBLRTpVw5WTn2B9l2ztKE9LdamfetJa/7iTmAI1L/xWwreb31ufdZ5hCyisW7kpWJWAm8Ma1rEzArzX8FqbS4b81380jC6sm9wHFp5PDWyIqsQVZqufS3VI32OLLS3cvJauGUymwfR1YGAYCI6K/M9rxUh+pWYD+y60NUFVm56OmSXgV8gOwaANWq9h5JOos+IlaQXZMAKsuVLyerC/RHwB/IKsp+W9J7gPVp/mOBC9N6Npe9FuUlr/t6NCJ+nqb/E3jLQM8r3X9VRDwfEc+RlRUvlYp4OLIL+UBlyXRrMi4VbnUjIlZJOoTsYj9fklS6Pkd5WYDSdLUy2wOW0JZ0NFkiOTwi1ktayrZy0gP5DlnRuFPJrgMw4FPp76GpUq5c0qHA29K6P0WWIKoZqLBg38cdSrnxavqWYPfmpiblkYTVDUn7Ausj4j/JruZ2cLrrlLK/pYsFVSuz3be/b5ntPYGnUoLYn+wXfl/9lVO/lGwHMBEx0HWqy8t8H0i2yQmqlCtP+yX2jIiFaf2l59FfmffBTFUq6U424ildfKlaefhbgJNSLLuSVVb96RAex5qIk4TVk/8F3J42x/wd2aVYIbt2wG1kpc//IvVVK7M9H3hl2hF7Dy+/QuB1wFhl5Z+/QPbPu0L0U049ItaQXbltsPLOFwK7pfX/DXB7Wr5aufLdgWtT381lz+9lZd4HeVxSfB1pXXulWKCsbHqf53kXWfK7nazU/Lcj4u4hPI41ERf4s7qm7Opi7RGxruA4xpPtMzm4bP9A3VB2gZtrI+LAgkOxUcYjCbNBSDqO7AI2F9RjgjDLk0cSZttB0glkh9uWezgiTi4iHrO8OEmYmVlV3txkZmZVOUmYmVlVThJmZlaVk4SZmVXlJGFmZlX9f4nFdzuiHjrJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show that different medical specialties prescribe different numbers of drugs\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "\n",
    "sb.boxplot(x=\"specialty_description\", y=\"drug_count\", \n",
    "           data=prescriber_specialty_df\n",
    "           [\n",
    "           (prescriber_specialty_df[\"specialty_description\"] == \"Internal Medicine\") |\n",
    "           (prescriber_specialty_df[\"specialty_description\"] == \"Family Practice\") |\n",
    "           (prescriber_specialty_df[\"specialty_description\"] == \"Cardiology\")\n",
    "           ]\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User pandas crosstab function to create a dataframe where each row is a prescriber identified by their NPI, \n",
    "# the columns are the drug generic names, the values are the sum of total_claim_count\n",
    "npi_generic_cross_df = pd.crosstab(part_d_df_small[\"npi\"], part_d_df_small[\"generic_name\"], values=part_d_df_small[\"total_claim_count\"], \n",
    "                                  aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To replace the NaN (not a number) from the cells with zero the fillna() method is used.\n",
    "npi_generic_cross_df = npi_generic_cross_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>generic_name</th>\n",
       "      <th>0.9 % SODIUM CHLORIDE</th>\n",
       "      <th>AA 4.25%/CALCIUM/LYTES/DEX 10%</th>\n",
       "      <th>ABACAVIR SULFATE</th>\n",
       "      <th>ABACAVIR SULFATE/LAMIVUDINE</th>\n",
       "      <th>ABACAVIR/LAMIVUDINE/ZIDOVUDINE</th>\n",
       "      <th>ABATACEPT</th>\n",
       "      <th>ABATACEPT/MALTOSE</th>\n",
       "      <th>ABIRATERONE ACETATE</th>\n",
       "      <th>ACAMPROSATE CALCIUM</th>\n",
       "      <th>ACARBOSE</th>\n",
       "      <th>...</th>\n",
       "      <th>ZALEPLON</th>\n",
       "      <th>ZIDOVUDINE</th>\n",
       "      <th>ZILEUTON</th>\n",
       "      <th>ZINC ACETATE</th>\n",
       "      <th>ZIPRASIDONE HCL</th>\n",
       "      <th>ZOLEDRONIC ACID</th>\n",
       "      <th>ZOLMITRIPTAN</th>\n",
       "      <th>ZOLPIDEM TARTRATE</th>\n",
       "      <th>ZONISAMIDE</th>\n",
       "      <th>ZOSTER VACCINE LIVE/PF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npi</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1003002072</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "generic_name  0.9 % SODIUM CHLORIDE  AA 4.25%/CALCIUM/LYTES/DEX 10%  \\\n",
       "npi                                                                   \n",
       "1003002072                      0.0                             0.0   \n",
       "1003002858                      0.0                             0.0   \n",
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       "1003019910                      0.0                             0.0   \n",
       "1003020975                      0.0                             0.0   \n",
       "\n",
       "generic_name  ABACAVIR SULFATE  ABACAVIR SULFATE/LAMIVUDINE  \\\n",
       "npi                                                           \n",
       "1003002072                 0.0                          0.0   \n",
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       "1003020975                 0.0                          0.0   \n",
       "\n",
       "generic_name  ABACAVIR/LAMIVUDINE/ZIDOVUDINE  ABATACEPT  ABATACEPT/MALTOSE  \\\n",
       "npi                                                                          \n",
       "1003002072                               0.0        0.0                0.0   \n",
       "1003002858                               0.0        0.0                0.0   \n",
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       "1003019910                               0.0        0.0                0.0   \n",
       "1003020975                               0.0        0.0                0.0   \n",
       "\n",
       "generic_name  ABIRATERONE ACETATE  ACAMPROSATE CALCIUM  ACARBOSE  ...  \\\n",
       "npi                                                               ...   \n",
       "1003002072                    0.0                  0.0       0.0  ...   \n",
       "1003002858                    0.0                  0.0       0.0  ...   \n",
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       "1003019910                    0.0                  0.0       0.0  ...   \n",
       "1003020975                    0.0                  0.0       0.0  ...   \n",
       "\n",
       "generic_name  ZALEPLON  ZIDOVUDINE  ZILEUTON  ZINC ACETATE  ZIPRASIDONE HCL  \\\n",
       "npi                                                                           \n",
       "1003002072         0.0         0.0       0.0           0.0              0.0   \n",
       "1003002858         0.0         0.0       0.0           0.0              0.0   \n",
       "1003012410         0.0         0.0       0.0           0.0              0.0   \n",
       "1003019910         0.0         0.0       0.0           0.0              0.0   \n",
       "1003020975         0.0         0.0       0.0           0.0              0.0   \n",
       "\n",
       "generic_name  ZOLEDRONIC ACID  ZOLMITRIPTAN  ZOLPIDEM TARTRATE  ZONISAMIDE  \\\n",
       "npi                                                                          \n",
       "1003002072                0.0           0.0                0.0         0.0   \n",
       "1003002858                0.0           0.0               17.0         0.0   \n",
       "1003012410                0.0           0.0                0.0         0.0   \n",
       "1003019910                0.0           0.0                0.0         0.0   \n",
       "1003020975                0.0           0.0                0.0         0.0   \n",
       "\n",
       "generic_name  ZOSTER VACCINE LIVE/PF  \n",
       "npi                                   \n",
       "1003002072                       0.0  \n",
       "1003002858                       0.0  \n",
       "1003012410                       0.0  \n",
       "1003019910                       0.0  \n",
       "1003020975                       0.0  \n",
       "\n",
       "[5 rows x 1169 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npi_generic_cross_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use npi to merge two tables prescriber_specialty_df and npi_generic_cross_df\n",
    "prescriber_specialty_generic_df = pd.merge(prescriber_specialty_df, npi_generic_cross_df.reset_index(level=[\"npi\"]), on=\"npi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>npi</th>\n",
       "      <th>specialty_description</th>\n",
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       "      <td>Urology</td>\n",
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       "      <td>Family Practice</td>\n",
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       "          npi specialty_description  total_claim_count  drug_count  \\\n",
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       "3  1003019910               Dentist                229           4   \n",
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       "\n",
       "   ...  ZALEPLON  ZIDOVUDINE  ZILEUTON  ZINC ACETATE  ZIPRASIDONE HCL  \\\n",
       "0  ...       0.0         0.0       0.0           0.0              0.0   \n",
       "1  ...       0.0         0.0       0.0           0.0              0.0   \n",
       "2  ...       0.0         0.0       0.0           0.0              0.0   \n",
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       "4  ...       0.0         0.0       0.0           0.0              0.0   \n",
       "\n",
       "   ZOLEDRONIC ACID  ZOLMITRIPTAN  ZOLPIDEM TARTRATE  ZONISAMIDE  \\\n",
       "0              0.0           0.0                0.0         0.0   \n",
       "1              0.0           0.0               17.0         0.0   \n",
       "2              0.0           0.0                0.0         0.0   \n",
       "3              0.0           0.0                0.0         0.0   \n",
       "4              0.0           0.0                0.0         0.0   \n",
       "\n",
       "   ZOSTER VACCINE LIVE/PF  \n",
       "0                     0.0  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                     0.0  \n",
       "\n",
       "[5 rows x 1173 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_generic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "prescriber_specialty_generic_df = prescriber_specialty_generic_df.sort_values(by=[\"specialty_description\", \"npi\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Addiction Medicine</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
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       "      <td>Allergy/Immunology</td>\n",
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       "             npi specialty_description  total_claim_count  drug_count  \\\n",
       "2016  1083895114    Addiction Medicine               3088          40   \n",
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       "\n",
       "      ABACAVIR SULFATE/LAMIVUDINE  ABACAVIR/LAMIVUDINE/ZIDOVUDINE  ABATACEPT  \\\n",
       "2016                          0.0                             0.0        0.0   \n",
       "128                           0.0                             0.0        0.0   \n",
       "386                           0.0                             0.0        0.0   \n",
       "409                           0.0                             0.0        0.0   \n",
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       "\n",
       "      ...  ZALEPLON  ZIDOVUDINE  ZILEUTON  ZINC ACETATE  ZIPRASIDONE HCL  \\\n",
       "2016  ...       0.0         0.0       0.0           0.0             44.0   \n",
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       "      ZOLEDRONIC ACID  ZOLMITRIPTAN  ZOLPIDEM TARTRATE  ZONISAMIDE  \\\n",
       "2016              0.0           0.0              177.0         0.0   \n",
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       "128                      0.0  \n",
       "386                      0.0  \n",
       "409                      0.0  \n",
       "682                      0.0  \n",
       "\n",
       "[5 rows x 1173 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_generic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Since the data has been resorted by specialty_desc and npi, index needs to be updated to reflect new order of data\n",
    "prescriber_specialty_generic_df=prescriber_specialty_generic_df.reset_index(drop=True)\n",
    "prescriber_specialty_generic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "(22856, 1173)"
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     "execution_count": 65,
     "metadata": {},
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   "source": [
    "prescriber_specialty_generic_df.shape"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699840264</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114072444</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093805012</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831276880</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043311145</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            nppes_provider_city  nppes_provider_state  specialty_description  \\\n",
       "npi                                                                            \n",
       "1942202841                  240                   240                    240   \n",
       "1215045869                  124                   124                    124   \n",
       "1417959503                   94                    94                     94   \n",
       "1053393405                   63                    63                     63   \n",
       "1285702829                   58                    58                     58   \n",
       "1699840264                   50                    50                     50   \n",
       "1114072444                   47                    47                     47   \n",
       "1093805012                   29                    29                     29   \n",
       "1831276880                   25                    25                     25   \n",
       "1043311145                   24                    24                     24   \n",
       "\n",
       "            description_flag  drug_name  generic_name  bene_count  \\\n",
       "npi                                                                 \n",
       "1942202841               240        240           240         103   \n",
       "1215045869               124        124           124          41   \n",
       "1417959503                94         94            94          27   \n",
       "1053393405                63         63            63          22   \n",
       "1285702829                58         58            58           8   \n",
       "1699840264                50         50            50          20   \n",
       "1114072444                47         47            47          13   \n",
       "1093805012                29         29            29           7   \n",
       "1831276880                25         25            25           0   \n",
       "1043311145                24         24            24          10   \n",
       "\n",
       "            total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "npi                                                                \n",
       "1942202841                240               240              240   \n",
       "1215045869                124               124              124   \n",
       "1417959503                 94                94               94   \n",
       "1053393405                 63                63               63   \n",
       "1285702829                 58                58               58   \n",
       "1699840264                 50                50               50   \n",
       "1114072444                 47                47               47   \n",
       "1093805012                 29                29               29   \n",
       "1831276880                 25                25               25   \n",
       "1043311145                 24                24               24   \n",
       "\n",
       "            bene_count_ge65  total_claim_count_ge65  total_day_supply_ge65  \\\n",
       "npi                                                                          \n",
       "1942202841               25                     158                    158   \n",
       "1215045869                6                      61                     61   \n",
       "1417959503                2                      40                     40   \n",
       "1053393405                3                      38                     38   \n",
       "1285702829                4                      43                     43   \n",
       "1699840264                0                      18                     18   \n",
       "1114072444                6                      35                     35   \n",
       "1093805012                0                       9                      9   \n",
       "1831276880                0                      13                     13   \n",
       "1043311145                9                      12                     12   \n",
       "\n",
       "            total_drug_cost_ge65  is_excluded  \n",
       "npi                                            \n",
       "1942202841                   158          240  \n",
       "1215045869                    61          124  \n",
       "1417959503                    40           94  \n",
       "1053393405                    38           63  \n",
       "1285702829                    43           58  \n",
       "1699840264                    18           50  \n",
       "1114072444                    35           47  \n",
       "1093805012                     9           29  \n",
       "1831276880                    13           25  \n",
       "1043311145                    12           24  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 NPIs which are the most excluded prescribers\n",
    "part_d_df_small_exclusion_npi_count = part_d_df_small_exclusion.groupby('npi').count().sort_values('drug_name', ascending=False).iloc[0:10]\n",
    "part_d_df_small_exclusion_npi_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>drug_count</th>\n",
       "      <th>0.9 % SODIUM CHLORIDE</th>\n",
       "      <th>AA 4.25%/CALCIUM/LYTES/DEX 10%</th>\n",
       "      <th>ABACAVIR SULFATE</th>\n",
       "      <th>ABACAVIR SULFATE/LAMIVUDINE</th>\n",
       "      <th>ABACAVIR/LAMIVUDINE/ZIDOVUDINE</th>\n",
       "      <th>ABATACEPT</th>\n",
       "      <th>...</th>\n",
       "      <th>ZALEPLON</th>\n",
       "      <th>ZIDOVUDINE</th>\n",
       "      <th>ZILEUTON</th>\n",
       "      <th>ZINC ACETATE</th>\n",
       "      <th>ZIPRASIDONE HCL</th>\n",
       "      <th>ZOLEDRONIC ACID</th>\n",
       "      <th>ZOLMITRIPTAN</th>\n",
       "      <th>ZOLPIDEM TARTRATE</th>\n",
       "      <th>ZONISAMIDE</th>\n",
       "      <th>ZOSTER VACCINE LIVE/PF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>2222</td>\n",
       "      <td>63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 1173 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            npi specialty_description  total_claim_count  drug_count  \\\n",
       "409  1053393405            Cardiology               2222          63   \n",
       "\n",
       "     0.9 % SODIUM CHLORIDE  AA 4.25%/CALCIUM/LYTES/DEX 10%  ABACAVIR SULFATE  \\\n",
       "409                    0.0                             0.0               0.0   \n",
       "\n",
       "     ABACAVIR SULFATE/LAMIVUDINE  ABACAVIR/LAMIVUDINE/ZIDOVUDINE  ABATACEPT  \\\n",
       "409                          0.0                             0.0        0.0   \n",
       "\n",
       "     ...  ZALEPLON  ZIDOVUDINE  ZILEUTON  ZINC ACETATE  ZIPRASIDONE HCL  \\\n",
       "409  ...       0.0         0.0       0.0           0.0              0.0   \n",
       "\n",
       "     ZOLEDRONIC ACID  ZOLMITRIPTAN  ZOLPIDEM TARTRATE  ZONISAMIDE  \\\n",
       "409              0.0           0.0               42.0         0.0   \n",
       "\n",
       "     ZOSTER VACCINE LIVE/PF  \n",
       "409                     0.0  \n",
       "\n",
       "[1 rows x 1173 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Based on analyzed result, the interested excluded npi is 1942202841\n",
    "# we need to find out the position of 1942202841 based on this script, the result is 12569\n",
    "# update the position of the rest of the scripts using 12569\n",
    "prescriber_specialty_generic_df.loc[prescriber_specialty_generic_df['npi'] == 1053393405]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#part_d_df_small_exclusion[part_d_df_small_exclusion['specialty_description'] == 'Cardiology']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1281987</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ACTONEL</td>\n",
       "      <td>RISEDRONATE SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>762</td>\n",
       "      <td>3655.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>3655.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281988</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ALENDRONATE SODIUM</td>\n",
       "      <td>ALENDRONATE SODIUM</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1404</td>\n",
       "      <td>508.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>508.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281989</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ALPRAZOLAM</td>\n",
       "      <td>ALPRAZOLAM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>390</td>\n",
       "      <td>51.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281990</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>AMLODIPINE BESYLATE</td>\n",
       "      <td>AMLODIPINE BESYLATE</td>\n",
       "      <td>63.0</td>\n",
       "      <td>213</td>\n",
       "      <td>6810</td>\n",
       "      <td>1499.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>1355.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281991</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>AMLODIPINE BESYLATE-BENAZEPRIL</td>\n",
       "      <td>AMLODIPINE BESYLATE/BENAZEPRIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>450</td>\n",
       "      <td>520.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>520.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi nppes_provider_city nppes_provider_state  \\\n",
       "1281987  1053393405          UNION CITY                   NJ   \n",
       "1281988  1053393405          UNION CITY                   NJ   \n",
       "1281989  1053393405          UNION CITY                   NJ   \n",
       "1281990  1053393405          UNION CITY                   NJ   \n",
       "1281991  1053393405          UNION CITY                   NJ   \n",
       "\n",
       "        specialty_description description_flag  \\\n",
       "1281987            Cardiology                S   \n",
       "1281988            Cardiology                S   \n",
       "1281989            Cardiology                S   \n",
       "1281990            Cardiology                S   \n",
       "1281991            Cardiology                S   \n",
       "\n",
       "                              drug_name                    generic_name  \\\n",
       "1281987                         ACTONEL              RISEDRONATE SODIUM   \n",
       "1281988              ALENDRONATE SODIUM              ALENDRONATE SODIUM   \n",
       "1281989                      ALPRAZOLAM                      ALPRAZOLAM   \n",
       "1281990             AMLODIPINE BESYLATE             AMLODIPINE BESYLATE   \n",
       "1281991  AMLODIPINE BESYLATE-BENAZEPRIL  AMLODIPINE BESYLATE/BENAZEPRIL   \n",
       "\n",
       "         bene_count  total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "1281987         NaN                 27               762          3655.90   \n",
       "1281988        14.0                 46              1404           508.17   \n",
       "1281989         NaN                 13               390            51.48   \n",
       "1281990        63.0                213              6810          1499.13   \n",
       "1281991         NaN                 15               450           520.79   \n",
       "\n",
       "         bene_count_ge65  total_claim_count_ge65  total_day_supply_ge65  \\\n",
       "1281987              NaN                    27.0                  762.0   \n",
       "1281988             14.0                    46.0                 1404.0   \n",
       "1281989              NaN                     NaN                    NaN   \n",
       "1281990              NaN                   186.0                 6000.0   \n",
       "1281991              NaN                    15.0                  450.0   \n",
       "\n",
       "         total_drug_cost_ge65  is_excluded  \n",
       "1281987               3655.90            1  \n",
       "1281988                508.17            1  \n",
       "1281989                   NaN            1  \n",
       "1281990               1355.12            1  \n",
       "1281991                520.79            1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_d_df_exclusion.loc[part_d_df_exclusion['npi'] == 1053393405].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shift from using the Pandas dataframe object to the `numpy.array`. Slice off the columns that are not related to the number of filled prescriptions and convert this to a matrix. The `iloc()` method slices a dataframe by the column and the method `as_matrix()` converts the dataframe into a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "pf_matrix = prescriber_specialty_generic_df.iloc[: , 4: ].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0., ..., 177.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "       ...,\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,   0.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22856, 1169)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "identifiers_matrix = prescriber_specialty_generic_df.iloc[:,0:2].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "generic_array = prescriber_specialty_generic_df.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['0.9 % SODIUM CHLORIDE', 'AA 4.25%/CALCIUM/LYTES/DEX 10%',\n",
       "       'ABACAVIR SULFATE', 'ABACAVIR SULFATE/LAMIVUDINE',\n",
       "       'ABACAVIR/LAMIVUDINE/ZIDOVUDINE', 'ABATACEPT', 'ABATACEPT/MALTOSE',\n",
       "       'ABIRATERONE ACETATE', 'ACAMPROSATE CALCIUM', 'ACARBOSE',\n",
       "       ...\n",
       "       'ZALEPLON', 'ZIDOVUDINE', 'ZILEUTON', 'ZINC ACETATE', 'ZIPRASIDONE HCL',\n",
       "       'ZOLEDRONIC ACID', 'ZOLMITRIPTAN', 'ZOLPIDEM TARTRATE', 'ZONISAMIDE',\n",
       "       'ZOSTER VACCINE LIVE/PF'],\n",
       "      dtype='object', length=1169)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generic_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to compute the distance between a vector representing the drugs that a prescriber prescribes \n",
    "# we need to import the scipy.spatial library.\n",
    "import scipy.spatial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the claim count of prescriptions by the total number of claims for the prescriber. Each entry now represent the percentage of total claims that are associated with the prescriber."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3088.],\n",
       "       [ 112.],\n",
       "       [ 323.],\n",
       "       ...,\n",
       "       [  81.],\n",
       "       [ 198.],\n",
       "       [ 253.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(np.sum(pf_matrix,1), (pf_matrix.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_pf_matrix = pf_matrix / np.reshape(np.sum(pf_matrix,1), (pf_matrix.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22856, 1169)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_pf_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the euclidean distance to compute the distance between two prescribers. The results of the pdist() function need to be placed into square form. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "prescriber_dist = scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(normalized_pf_matrix, \"euclidean\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22856, 22856)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_dist.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have sorted the prescribers by their specialty then we expect to see some spatial patterns in the distance matrix. A matrix can be visualized using the `matplotlib` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19809af4198>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(prescriber_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                      1053393405\n",
       "specialty_description    Cardiology\n",
       "total_claim_count              2222\n",
       "drug_count                       63\n",
       "Name: 409, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_generic_df.iloc[409,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "providers_sorted = np.lexsort((prescriber_dist[:,409].tolist(),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ...]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_dist[:,409].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  409,   395, 11899,   850, 12621, 10245, 13773, 11032, 10591,\n",
       "       10202, 12384,  1101,  9843, 10763, 11485,  9955], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "providers_sorted[0:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12270318, 0.12241352, 0.4429385 , 0.12897594, 0.47028465,\n",
       "       0.38825998, 0.51801186, 0.41266651, 0.39843181, 0.38709718,\n",
       "       0.46054846, 0.1318678 , 0.37689257, 0.40331791, 0.42697094,\n",
       "       0.38001302])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(prescriber_dist[409])[providers_sorted[0:16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12270318, 0.12241352, 0.4429385 , 0.12897594, 0.47028465,\n",
       "       0.38825998, 0.51801186, 0.41266651, 0.39843181, 0.38709718,\n",
       "       0.46054846, 0.1318678 , 0.37689257, 0.40331791, 0.42697094,\n",
       "       0.38001302])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The prescriber referenced at position 6072 is an Internal Medicine. \n",
    "# We can now calculate the prescribers most similar to this Internal Medicine.\n",
    "prescriber_dist[409,:]\n",
    "np.sort(prescriber_dist[409])[providers_sorted[0:16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting the distances we can see that prescribers most like are probably also Internal Medicine.\n",
    "#plt.scatter(np.arange(prescriber_dist.shape[0]),prescriber_dist[6072,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2, ..., 22853, 22854, 22855])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x198108850b8>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dis_range = np.arange(prescriber_dist.shape[0])\n",
    "#plt.scatter(np.arange(prescriber_dist.shape[0]),np.sort(prescriber_dist[6072,:]))\n",
    "plt.scatter(dis_range,np.sort(prescriber_dist[409,:]), color='blue', alpha=0.01, s=7)\n",
    "plt.scatter(dis_range[providers_sorted[0:1]],np.sort(prescriber_dist[409])[providers_sorted[0:1]], color='red', alpha=1, s=14, edgecolor='red')\n",
    "plt.scatter(dis_range[providers_sorted[0:16]],np.sort(prescriber_dist[409])[providers_sorted[0:16]], color='white', alpha=1, s=14, edgecolor='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#providers_sorted = np.lexsort((prescriber_dist[:,12569].tolist(),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1053393405, 'Cardiology'],\n",
       "       [1043233513, 'Cardiology'],\n",
       "       [1750350682, 'Internal Medicine'],\n",
       "       [1508966219, 'Cardiology'],\n",
       "       [1952414849, 'Internal Medicine'],\n",
       "       [1275592735, 'Internal Medicine'],\n",
       "       [1013244730, 'Nurse Practitioner'],\n",
       "       [1508848979, 'Internal Medicine'],\n",
       "       [1386688083, 'Internal Medicine'],\n",
       "       [1265449326, 'Internal Medicine'],\n",
       "       [1881730026, 'Internal Medicine'],\n",
       "       [1811050404, 'Cardiology'],\n",
       "       [1164469540, 'Internal Medicine'],\n",
       "       [1437144391, 'Internal Medicine'],\n",
       "       [1649287756, 'Internal Medicine'],\n",
       "       [1194760447, 'Internal Medicine']], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prescriber_specialty_generic_df.iloc[:,0:2].as_matrix()[providers_sorted[0:16],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  54,  716,  717,  240, 1141,  485, 1008,  597,  823,   83,  388,\n",
       "        986,  651,  689,   30,  364], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.lexsort(((-1 * pf_matrix[409,:]).tolist(),))[0:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AMLODIPINE BESYLATE', 'METOPROLOL SUCCINATE', 'METOPROLOL TARTRATE',\n",
       "       'CLOPIDOGREL BISULFATE', 'VALSARTAN/HYDROCHLOROTHIAZIDE', 'GABAPENTIN',\n",
       "       'SIMVASTATIN', 'ISOSORBIDE MONONITRATE', 'OMEPRAZOLE',\n",
       "       'ATORVASTATIN CALCIUM', 'ESOMEPRAZOLE MAGNESIUM',\n",
       "       'ROSUVASTATIN CALCIUM', 'LISINOPRIL', 'METFORMIN HCL',\n",
       "       'ALENDRONATE SODIUM', 'ENALAPRIL MALEATE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generic_array[np.lexsort(((-1 * pf_matrix[409,:]).tolist(),))][0:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>is_excluded</th>\n",
       "      <th>drug_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1281990</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>AMLODIPINE BESYLATE</td>\n",
       "      <td>AMLODIPINE BESYLATE</td>\n",
       "      <td>63.0</td>\n",
       "      <td>213</td>\n",
       "      <td>6810</td>\n",
       "      <td>1499.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>1355.12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282027</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>METOPROLOL SUCCINATE</td>\n",
       "      <td>METOPROLOL SUCCINATE</td>\n",
       "      <td>28.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3540</td>\n",
       "      <td>2816.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>2396.22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282028</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>METOPROLOL TARTRATE</td>\n",
       "      <td>METOPROLOL TARTRATE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3105</td>\n",
       "      <td>491.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281999</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>CLOPIDOGREL</td>\n",
       "      <td>CLOPIDOGREL BISULFATE</td>\n",
       "      <td>22.0</td>\n",
       "      <td>84</td>\n",
       "      <td>3060</td>\n",
       "      <td>4354.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>3469.53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282011</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>GABAPENTIN</td>\n",
       "      <td>GABAPENTIN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2445</td>\n",
       "      <td>598.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>479.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282040</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>SIMVASTATIN</td>\n",
       "      <td>SIMVASTATIN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>78</td>\n",
       "      <td>2640</td>\n",
       "      <td>757.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282034</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>OMEPRAZOLE</td>\n",
       "      <td>OMEPRAZOLE</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68</td>\n",
       "      <td>2091</td>\n",
       "      <td>928.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281993</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ATORVASTATIN CALCIUM</td>\n",
       "      <td>ATORVASTATIN CALCIUM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>64</td>\n",
       "      <td>2160</td>\n",
       "      <td>1964.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1695.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282031</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>NEXIUM</td>\n",
       "      <td>ESOMEPRAZOLE MAGNESIUM</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1800</td>\n",
       "      <td>12637.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282001</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>CRESTOR</td>\n",
       "      <td>ROSUVASTATIN CALCIUM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1860</td>\n",
       "      <td>10428.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282026</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>16.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1581</td>\n",
       "      <td>364.58</td>\n",
       "      <td>16.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>364.58</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282022</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>LISINOPRIL</td>\n",
       "      <td>LISINOPRIL</td>\n",
       "      <td>18.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1710</td>\n",
       "      <td>290.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>242.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282019</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ISOSORBIDE MONONITRATE ER</td>\n",
       "      <td>ISOSORBIDE MONONITRATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>1530</td>\n",
       "      <td>728.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>351.54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282045</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>VALSARTAN-HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>VALSARTAN/HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48</td>\n",
       "      <td>1611</td>\n",
       "      <td>4029.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281988</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ALENDRONATE SODIUM</td>\n",
       "      <td>ALENDRONATE SODIUM</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1404</td>\n",
       "      <td>508.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>508.17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282006</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ENALAPRIL MALEATE</td>\n",
       "      <td>ENALAPRIL MALEATE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>45</td>\n",
       "      <td>1470</td>\n",
       "      <td>265.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282010</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>FUROSEMIDE</td>\n",
       "      <td>FUROSEMIDE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42</td>\n",
       "      <td>1316</td>\n",
       "      <td>129.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282020</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>JANUVIA</td>\n",
       "      <td>SITAGLIPTIN PHOSPHATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>1260</td>\n",
       "      <td>10748.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>10748.20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282049</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ZOLPIDEM TARTRATE</td>\n",
       "      <td>ZOLPIDEM TARTRATE</td>\n",
       "      <td>19.0</td>\n",
       "      <td>42</td>\n",
       "      <td>1260</td>\n",
       "      <td>321.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282016</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>IBUPROFEN</td>\n",
       "      <td>IBUPROFEN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42</td>\n",
       "      <td>1280</td>\n",
       "      <td>236.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282023</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>LOSARTAN POTASSIUM</td>\n",
       "      <td>LOSARTAN POTASSIUM</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1412</td>\n",
       "      <td>1670.37</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1412.0</td>\n",
       "      <td>1670.37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282004</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>DIOVAN HCT</td>\n",
       "      <td>VALSARTAN/HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1140</td>\n",
       "      <td>5519.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282025</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>LOVAZA</td>\n",
       "      <td>OMEGA-3 ACID ETHYL ESTERS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>1170</td>\n",
       "      <td>5718.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>5718.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282003</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>DIOVAN</td>\n",
       "      <td>VALSARTAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>1170</td>\n",
       "      <td>4543.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281996</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>CARVEDILOL</td>\n",
       "      <td>CARVEDILOL</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1080</td>\n",
       "      <td>316.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282017</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ISOSORBIDE DINITRATE</td>\n",
       "      <td>ISOSORBIDE DINITRATE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30</td>\n",
       "      <td>975</td>\n",
       "      <td>1285.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281992</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ATENOLOL</td>\n",
       "      <td>ATENOLOL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>990</td>\n",
       "      <td>159.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>159.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282042</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>TAMSULOSIN HCL</td>\n",
       "      <td>TAMSULOSIN HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>870</td>\n",
       "      <td>364.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>364.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281987</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ACTONEL</td>\n",
       "      <td>RISEDRONATE SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>762</td>\n",
       "      <td>3655.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>3655.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282030</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>MONTELUKAST SODIUM</td>\n",
       "      <td>MONTELUKAST SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>771</td>\n",
       "      <td>973.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282005</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>DONEPEZIL HCL</td>\n",
       "      <td>DONEPEZIL HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>651</td>\n",
       "      <td>844.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>844.55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282013</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>GLYBURIDE-METFORMIN HCL</td>\n",
       "      <td>GLYBURIDE/METFORMIN HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>660</td>\n",
       "      <td>343.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>343.26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281997</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>CELEBREX</td>\n",
       "      <td>CELECOXIB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>660</td>\n",
       "      <td>4158.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>4158.53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282037</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>765</td>\n",
       "      <td>278.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>278.17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282024</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>LOSARTAN-HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>LOSARTAN/HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>720</td>\n",
       "      <td>606.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>606.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282014</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>660</td>\n",
       "      <td>78.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282038</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>PROAIR HFA</td>\n",
       "      <td>ALBUTEROL SULFATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>520</td>\n",
       "      <td>895.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>895.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282000</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>CLOTRIMAZOLE-BETAMETHASONE</td>\n",
       "      <td>CLOTRIMAZOLE/BETAMETHASONE DIP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>491</td>\n",
       "      <td>1030.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>1030.47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282007</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>EXFORGE</td>\n",
       "      <td>AMLODIPINE/VALSARTAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>540</td>\n",
       "      <td>2757.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>2757.98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281995</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>BYSTOLIC</td>\n",
       "      <td>NEBIVOLOL HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>540</td>\n",
       "      <td>1300.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1300.95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282032</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>NITROLINGUAL</td>\n",
       "      <td>NITROGLYCERIN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>540</td>\n",
       "      <td>4942.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>4942.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282044</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>TRIAMCINOLONE ACETONIDE</td>\n",
       "      <td>TRIAMCINOLONE ACETONIDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>468</td>\n",
       "      <td>295.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>295.76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281994</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>480</td>\n",
       "      <td>145.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282015</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>IBANDRONATE SODIUM</td>\n",
       "      <td>IBANDRONATE SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>696</td>\n",
       "      <td>2428.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282041</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>SPIRIVA</td>\n",
       "      <td>TIOTROPIUM BROMIDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>570</td>\n",
       "      <td>5114.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>5114.29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281991</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>AMLODIPINE BESYLATE-BENAZEPRIL</td>\n",
       "      <td>AMLODIPINE BESYLATE/BENAZEPRIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>450</td>\n",
       "      <td>520.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>520.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282029</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>MICARDIS HCT</td>\n",
       "      <td>TELMISARTAN/HYDROCHLOROTHIAZID</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>420</td>\n",
       "      <td>1943.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1943.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282036</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>POLYETHYLENE GLYCOL 3350</td>\n",
       "      <td>POLYETHYLENE GLYCOL 3350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>352</td>\n",
       "      <td>171.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>171.54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282002</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>DILTIAZEM HCL</td>\n",
       "      <td>DILTIAZEM HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>420</td>\n",
       "      <td>59.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282046</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>VOLTAREN</td>\n",
       "      <td>DICLOFENAC SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>338</td>\n",
       "      <td>525.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281998</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>CLONIDINE HCL</td>\n",
       "      <td>CLONIDINE HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>390</td>\n",
       "      <td>101.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>101.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282043</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>370</td>\n",
       "      <td>101.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>101.54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281989</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>ALPRAZOLAM</td>\n",
       "      <td>ALPRAZOLAM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>390</td>\n",
       "      <td>51.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282035</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>OXAPROZIN</td>\n",
       "      <td>OXAPROZIN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>480</td>\n",
       "      <td>2461.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282047</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>WARFARIN SODIUM</td>\n",
       "      <td>WARFARIN SODIUM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>349</td>\n",
       "      <td>78.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>78.69</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282039</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>SERTRALINE HCL</td>\n",
       "      <td>SERTRALINE HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>330</td>\n",
       "      <td>154.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>154.59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282009</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>FLUTICASONE PROPIONATE</td>\n",
       "      <td>FLUTICASONE PROPIONATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>505</td>\n",
       "      <td>524.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>524.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282012</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>GLIMEPIRIDE</td>\n",
       "      <td>GLIMEPIRIDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>330</td>\n",
       "      <td>73.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>73.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282048</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>WELCHOL</td>\n",
       "      <td>COLESEVELAM HCL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>330</td>\n",
       "      <td>3948.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>3948.35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282033</th>\n",
       "      <td>1053393405</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>S</td>\n",
       "      <td>NITROSTAT</td>\n",
       "      <td>NITROGLYCERIN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>330</td>\n",
       "      <td>311.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi nppes_provider_city nppes_provider_state  \\\n",
       "1281990  1053393405          UNION CITY                   NJ   \n",
       "1282027  1053393405          UNION CITY                   NJ   \n",
       "1282028  1053393405          UNION CITY                   NJ   \n",
       "1281999  1053393405          UNION CITY                   NJ   \n",
       "1282011  1053393405          UNION CITY                   NJ   \n",
       "1282040  1053393405          UNION CITY                   NJ   \n",
       "1282034  1053393405          UNION CITY                   NJ   \n",
       "1281993  1053393405          UNION CITY                   NJ   \n",
       "1282031  1053393405          UNION CITY                   NJ   \n",
       "1282001  1053393405          UNION CITY                   NJ   \n",
       "1282026  1053393405          UNION CITY                   NJ   \n",
       "1282022  1053393405          UNION CITY                   NJ   \n",
       "1282019  1053393405          UNION CITY                   NJ   \n",
       "1282045  1053393405          UNION CITY                   NJ   \n",
       "1281988  1053393405          UNION CITY                   NJ   \n",
       "1282006  1053393405          UNION CITY                   NJ   \n",
       "1282010  1053393405          UNION CITY                   NJ   \n",
       "1282020  1053393405          UNION CITY                   NJ   \n",
       "1282049  1053393405          UNION CITY                   NJ   \n",
       "1282016  1053393405          UNION CITY                   NJ   \n",
       "1282023  1053393405          UNION CITY                   NJ   \n",
       "1282004  1053393405          UNION CITY                   NJ   \n",
       "1282025  1053393405          UNION CITY                   NJ   \n",
       "1282003  1053393405          UNION CITY                   NJ   \n",
       "1281996  1053393405          UNION CITY                   NJ   \n",
       "1282017  1053393405          UNION CITY                   NJ   \n",
       "1281992  1053393405          UNION CITY                   NJ   \n",
       "1282042  1053393405          UNION CITY                   NJ   \n",
       "1281987  1053393405          UNION CITY                   NJ   \n",
       "1282030  1053393405          UNION CITY                   NJ   \n",
       "...             ...                 ...                  ...   \n",
       "1282005  1053393405          UNION CITY                   NJ   \n",
       "1282013  1053393405          UNION CITY                   NJ   \n",
       "1281997  1053393405          UNION CITY                   NJ   \n",
       "1282037  1053393405          UNION CITY                   NJ   \n",
       "1282024  1053393405          UNION CITY                   NJ   \n",
       "1282014  1053393405          UNION CITY                   NJ   \n",
       "1282038  1053393405          UNION CITY                   NJ   \n",
       "1282000  1053393405          UNION CITY                   NJ   \n",
       "1282007  1053393405          UNION CITY                   NJ   \n",
       "1281995  1053393405          UNION CITY                   NJ   \n",
       "1282032  1053393405          UNION CITY                   NJ   \n",
       "1282044  1053393405          UNION CITY                   NJ   \n",
       "1281994  1053393405          UNION CITY                   NJ   \n",
       "1282015  1053393405          UNION CITY                   NJ   \n",
       "1282041  1053393405          UNION CITY                   NJ   \n",
       "1281991  1053393405          UNION CITY                   NJ   \n",
       "1282029  1053393405          UNION CITY                   NJ   \n",
       "1282036  1053393405          UNION CITY                   NJ   \n",
       "1282002  1053393405          UNION CITY                   NJ   \n",
       "1282046  1053393405          UNION CITY                   NJ   \n",
       "1281998  1053393405          UNION CITY                   NJ   \n",
       "1282043  1053393405          UNION CITY                   NJ   \n",
       "1281989  1053393405          UNION CITY                   NJ   \n",
       "1282035  1053393405          UNION CITY                   NJ   \n",
       "1282047  1053393405          UNION CITY                   NJ   \n",
       "1282039  1053393405          UNION CITY                   NJ   \n",
       "1282009  1053393405          UNION CITY                   NJ   \n",
       "1282012  1053393405          UNION CITY                   NJ   \n",
       "1282048  1053393405          UNION CITY                   NJ   \n",
       "1282033  1053393405          UNION CITY                   NJ   \n",
       "\n",
       "        specialty_description description_flag  \\\n",
       "1281990            Cardiology                S   \n",
       "1282027            Cardiology                S   \n",
       "1282028            Cardiology                S   \n",
       "1281999            Cardiology                S   \n",
       "1282011            Cardiology                S   \n",
       "1282040            Cardiology                S   \n",
       "1282034            Cardiology                S   \n",
       "1281993            Cardiology                S   \n",
       "1282031            Cardiology                S   \n",
       "1282001            Cardiology                S   \n",
       "1282026            Cardiology                S   \n",
       "1282022            Cardiology                S   \n",
       "1282019            Cardiology                S   \n",
       "1282045            Cardiology                S   \n",
       "1281988            Cardiology                S   \n",
       "1282006            Cardiology                S   \n",
       "1282010            Cardiology                S   \n",
       "1282020            Cardiology                S   \n",
       "1282049            Cardiology                S   \n",
       "1282016            Cardiology                S   \n",
       "1282023            Cardiology                S   \n",
       "1282004            Cardiology                S   \n",
       "1282025            Cardiology                S   \n",
       "1282003            Cardiology                S   \n",
       "1281996            Cardiology                S   \n",
       "1282017            Cardiology                S   \n",
       "1281992            Cardiology                S   \n",
       "1282042            Cardiology                S   \n",
       "1281987            Cardiology                S   \n",
       "1282030            Cardiology                S   \n",
       "...                       ...              ...   \n",
       "1282005            Cardiology                S   \n",
       "1282013            Cardiology                S   \n",
       "1281997            Cardiology                S   \n",
       "1282037            Cardiology                S   \n",
       "1282024            Cardiology                S   \n",
       "1282014            Cardiology                S   \n",
       "1282038            Cardiology                S   \n",
       "1282000            Cardiology                S   \n",
       "1282007            Cardiology                S   \n",
       "1281995            Cardiology                S   \n",
       "1282032            Cardiology                S   \n",
       "1282044            Cardiology                S   \n",
       "1281994            Cardiology                S   \n",
       "1282015            Cardiology                S   \n",
       "1282041            Cardiology                S   \n",
       "1281991            Cardiology                S   \n",
       "1282029            Cardiology                S   \n",
       "1282036            Cardiology                S   \n",
       "1282002            Cardiology                S   \n",
       "1282046            Cardiology                S   \n",
       "1281998            Cardiology                S   \n",
       "1282043            Cardiology                S   \n",
       "1281989            Cardiology                S   \n",
       "1282035            Cardiology                S   \n",
       "1282047            Cardiology                S   \n",
       "1282039            Cardiology                S   \n",
       "1282009            Cardiology                S   \n",
       "1282012            Cardiology                S   \n",
       "1282048            Cardiology                S   \n",
       "1282033            Cardiology                S   \n",
       "\n",
       "                              drug_name                    generic_name  \\\n",
       "1281990             AMLODIPINE BESYLATE             AMLODIPINE BESYLATE   \n",
       "1282027            METOPROLOL SUCCINATE            METOPROLOL SUCCINATE   \n",
       "1282028             METOPROLOL TARTRATE             METOPROLOL TARTRATE   \n",
       "1281999                     CLOPIDOGREL           CLOPIDOGREL BISULFATE   \n",
       "1282011                      GABAPENTIN                      GABAPENTIN   \n",
       "1282040                     SIMVASTATIN                     SIMVASTATIN   \n",
       "1282034                      OMEPRAZOLE                      OMEPRAZOLE   \n",
       "1281993            ATORVASTATIN CALCIUM            ATORVASTATIN CALCIUM   \n",
       "1282031                          NEXIUM          ESOMEPRAZOLE MAGNESIUM   \n",
       "1282001                         CRESTOR            ROSUVASTATIN CALCIUM   \n",
       "1282026                   METFORMIN HCL                   METFORMIN HCL   \n",
       "1282022                      LISINOPRIL                      LISINOPRIL   \n",
       "1282019       ISOSORBIDE MONONITRATE ER          ISOSORBIDE MONONITRATE   \n",
       "1282045   VALSARTAN-HYDROCHLOROTHIAZIDE   VALSARTAN/HYDROCHLOROTHIAZIDE   \n",
       "1281988              ALENDRONATE SODIUM              ALENDRONATE SODIUM   \n",
       "1282006               ENALAPRIL MALEATE               ENALAPRIL MALEATE   \n",
       "1282010                      FUROSEMIDE                      FUROSEMIDE   \n",
       "1282020                         JANUVIA           SITAGLIPTIN PHOSPHATE   \n",
       "1282049               ZOLPIDEM TARTRATE               ZOLPIDEM TARTRATE   \n",
       "1282016                       IBUPROFEN                       IBUPROFEN   \n",
       "1282023              LOSARTAN POTASSIUM              LOSARTAN POTASSIUM   \n",
       "1282004                      DIOVAN HCT   VALSARTAN/HYDROCHLOROTHIAZIDE   \n",
       "1282025                          LOVAZA       OMEGA-3 ACID ETHYL ESTERS   \n",
       "1282003                          DIOVAN                       VALSARTAN   \n",
       "1281996                      CARVEDILOL                      CARVEDILOL   \n",
       "1282017            ISOSORBIDE DINITRATE            ISOSORBIDE DINITRATE   \n",
       "1281992                        ATENOLOL                        ATENOLOL   \n",
       "1282042                  TAMSULOSIN HCL                  TAMSULOSIN HCL   \n",
       "1281987                         ACTONEL              RISEDRONATE SODIUM   \n",
       "1282030              MONTELUKAST SODIUM              MONTELUKAST SODIUM   \n",
       "...                                 ...                             ...   \n",
       "1282005                   DONEPEZIL HCL                   DONEPEZIL HCL   \n",
       "1282013         GLYBURIDE-METFORMIN HCL         GLYBURIDE/METFORMIN HCL   \n",
       "1281997                        CELEBREX                       CELECOXIB   \n",
       "1282037              PRAVASTATIN SODIUM              PRAVASTATIN SODIUM   \n",
       "1282024    LOSARTAN-HYDROCHLOROTHIAZIDE    LOSARTAN/HYDROCHLOROTHIAZIDE   \n",
       "1282014             HYDROCHLOROTHIAZIDE             HYDROCHLOROTHIAZIDE   \n",
       "1282038                      PROAIR HFA               ALBUTEROL SULFATE   \n",
       "1282000      CLOTRIMAZOLE-BETAMETHASONE  CLOTRIMAZOLE/BETAMETHASONE DIP   \n",
       "1282007                         EXFORGE            AMLODIPINE/VALSARTAN   \n",
       "1281995                        BYSTOLIC                   NEBIVOLOL HCL   \n",
       "1282032                    NITROLINGUAL                   NITROGLYCERIN   \n",
       "1282044         TRIAMCINOLONE ACETONIDE         TRIAMCINOLONE ACETONIDE   \n",
       "1281994                   BUSPIRONE HCL                   BUSPIRONE HCL   \n",
       "1282015              IBANDRONATE SODIUM              IBANDRONATE SODIUM   \n",
       "1282041                         SPIRIVA              TIOTROPIUM BROMIDE   \n",
       "1281991  AMLODIPINE BESYLATE-BENAZEPRIL  AMLODIPINE BESYLATE/BENAZEPRIL   \n",
       "1282029                    MICARDIS HCT  TELMISARTAN/HYDROCHLOROTHIAZID   \n",
       "1282036        POLYETHYLENE GLYCOL 3350        POLYETHYLENE GLYCOL 3350   \n",
       "1282002                   DILTIAZEM HCL                   DILTIAZEM HCL   \n",
       "1282046                        VOLTAREN               DICLOFENAC SODIUM   \n",
       "1281998                   CLONIDINE HCL                   CLONIDINE HCL   \n",
       "1282043                    TRAMADOL HCL                    TRAMADOL HCL   \n",
       "1281989                      ALPRAZOLAM                      ALPRAZOLAM   \n",
       "1282035                       OXAPROZIN                       OXAPROZIN   \n",
       "1282047                 WARFARIN SODIUM                 WARFARIN SODIUM   \n",
       "1282039                  SERTRALINE HCL                  SERTRALINE HCL   \n",
       "1282009          FLUTICASONE PROPIONATE          FLUTICASONE PROPIONATE   \n",
       "1282012                     GLIMEPIRIDE                     GLIMEPIRIDE   \n",
       "1282048                         WELCHOL                 COLESEVELAM HCL   \n",
       "1282033                       NITROSTAT                   NITROGLYCERIN   \n",
       "\n",
       "         bene_count  total_claim_count  total_day_supply  total_drug_cost  \\\n",
       "1281990        63.0                213              6810          1499.13   \n",
       "1282027        28.0                110              3540          2816.62   \n",
       "1282028        24.0                 88              3105           491.56   \n",
       "1281999        22.0                 84              3060          4354.49   \n",
       "1282011        33.0                 82              2445           598.16   \n",
       "1282040        27.0                 78              2640           757.54   \n",
       "1282034        20.0                 68              2091           928.75   \n",
       "1281993        20.0                 64              2160          1964.65   \n",
       "1282031        15.0                 60              1800         12637.43   \n",
       "1282001        20.0                 56              1860         10428.26   \n",
       "1282026        16.0                 51              1581           364.58   \n",
       "1282022        18.0                 51              1710           290.31   \n",
       "1282019         NaN                 49              1530           728.50   \n",
       "1282045        12.0                 48              1611          4029.95   \n",
       "1281988        14.0                 46              1404           508.17   \n",
       "1282006        11.0                 45              1470           265.39   \n",
       "1282010        14.0                 42              1316           129.18   \n",
       "1282020         NaN                 42              1260         10748.20   \n",
       "1282049        19.0                 42              1260           321.02   \n",
       "1282016        16.0                 42              1280           236.64   \n",
       "1282023        12.0                 40              1412          1670.37   \n",
       "1282004        11.0                 36              1140          5519.92   \n",
       "1282025         NaN                 35              1170          5718.65   \n",
       "1282003         NaN                 31              1170          4543.36   \n",
       "1281996        11.0                 30              1080           316.46   \n",
       "1282017        11.0                 30               975          1285.04   \n",
       "1281992         NaN                 29               990           159.99   \n",
       "1282042         NaN                 29               870           364.82   \n",
       "1281987         NaN                 27               762          3655.90   \n",
       "1282030         NaN                 26               771           973.31   \n",
       "...             ...                ...               ...              ...   \n",
       "1282005         NaN                 22               651           844.55   \n",
       "1282013         NaN                 22               660           343.26   \n",
       "1281997         NaN                 22               660          4158.53   \n",
       "1282037         NaN                 21               765           278.17   \n",
       "1282024         NaN                 20               720           606.30   \n",
       "1282014         NaN                 20               660            78.04   \n",
       "1282038         NaN                 19               520           895.99   \n",
       "1282000         NaN                 19               491          1030.47   \n",
       "1282007         NaN                 18               540          2757.98   \n",
       "1281995         NaN                 18               540          1300.95   \n",
       "1282032         NaN                 18               540          4942.80   \n",
       "1282044         NaN                 17               468           295.76   \n",
       "1281994         NaN                 16               480           145.35   \n",
       "1282015         NaN                 16               696          2428.72   \n",
       "1282041         NaN                 15               570          5114.29   \n",
       "1281991         NaN                 15               450           520.79   \n",
       "1282029         NaN                 14               420          1943.18   \n",
       "1282036         NaN                 14               352           171.54   \n",
       "1282002         NaN                 14               420            59.76   \n",
       "1282046         NaN                 14               338           525.52   \n",
       "1281998         NaN                 13               390           101.72   \n",
       "1282043         NaN                 13               370           101.54   \n",
       "1281989         NaN                 13               390            51.48   \n",
       "1282035         NaN                 12               480          2461.06   \n",
       "1282047         NaN                 12               349            78.69   \n",
       "1282039         NaN                 11               330           154.59   \n",
       "1282009         NaN                 11               505           524.33   \n",
       "1282012         NaN                 11               330            73.60   \n",
       "1282048         NaN                 11               330          3948.35   \n",
       "1282033         NaN                 11               330           311.11   \n",
       "\n",
       "         bene_count_ge65  total_claim_count_ge65  total_day_supply_ge65  \\\n",
       "1281990              NaN                   186.0                 6000.0   \n",
       "1282027              NaN                    91.0                 2970.0   \n",
       "1282028              NaN                     NaN                    NaN   \n",
       "1281999              NaN                    69.0                 2610.0   \n",
       "1282011              NaN                    66.0                 1965.0   \n",
       "1282040              NaN                     NaN                    NaN   \n",
       "1282034              NaN                     NaN                    NaN   \n",
       "1281993              NaN                    51.0                 1770.0   \n",
       "1282031              NaN                     NaN                    NaN   \n",
       "1282001              NaN                     NaN                    NaN   \n",
       "1282026             16.0                    51.0                 1581.0   \n",
       "1282022              NaN                    40.0                 1380.0   \n",
       "1282019              NaN                    21.0                  690.0   \n",
       "1282045              NaN                     NaN                    NaN   \n",
       "1281988             14.0                    46.0                 1404.0   \n",
       "1282006              NaN                     NaN                    NaN   \n",
       "1282010              NaN                     NaN                    NaN   \n",
       "1282020              NaN                    42.0                 1260.0   \n",
       "1282049              NaN                     NaN                    NaN   \n",
       "1282016              NaN                     NaN                    NaN   \n",
       "1282023             12.0                    40.0                 1412.0   \n",
       "1282004              NaN                     NaN                    NaN   \n",
       "1282025              NaN                    35.0                 1170.0   \n",
       "1282003              NaN                     NaN                    NaN   \n",
       "1281996              NaN                     NaN                    NaN   \n",
       "1282017              NaN                     NaN                    NaN   \n",
       "1281992              NaN                    29.0                  990.0   \n",
       "1282042              NaN                    29.0                  870.0   \n",
       "1281987              NaN                    27.0                  762.0   \n",
       "1282030              NaN                     NaN                    NaN   \n",
       "...                  ...                     ...                    ...   \n",
       "1282005              NaN                    22.0                  651.0   \n",
       "1282013              NaN                    22.0                  660.0   \n",
       "1281997              NaN                    22.0                  660.0   \n",
       "1282037              NaN                    21.0                  765.0   \n",
       "1282024              NaN                    20.0                  720.0   \n",
       "1282014              NaN                     NaN                    NaN   \n",
       "1282038              NaN                    19.0                  520.0   \n",
       "1282000              NaN                    19.0                  491.0   \n",
       "1282007              NaN                    18.0                  540.0   \n",
       "1281995              NaN                    18.0                  540.0   \n",
       "1282032              NaN                    18.0                  540.0   \n",
       "1282044              NaN                    17.0                  468.0   \n",
       "1281994              NaN                     NaN                    NaN   \n",
       "1282015              NaN                     NaN                    NaN   \n",
       "1282041              NaN                    15.0                  570.0   \n",
       "1281991              NaN                    15.0                  450.0   \n",
       "1282029              NaN                    14.0                  420.0   \n",
       "1282036              NaN                    14.0                  352.0   \n",
       "1282002              NaN                     NaN                    NaN   \n",
       "1282046              NaN                     NaN                    NaN   \n",
       "1281998              NaN                    13.0                  390.0   \n",
       "1282043              NaN                    13.0                  370.0   \n",
       "1281989              NaN                     NaN                    NaN   \n",
       "1282035              NaN                     NaN                    NaN   \n",
       "1282047              NaN                    12.0                  349.0   \n",
       "1282039              NaN                    11.0                  330.0   \n",
       "1282009              NaN                    11.0                  505.0   \n",
       "1282012              NaN                    11.0                  330.0   \n",
       "1282048              NaN                    11.0                  330.0   \n",
       "1282033              NaN                     NaN                    NaN   \n",
       "\n",
       "         total_drug_cost_ge65  is_excluded  drug_count  \n",
       "1281990               1355.12            1           1  \n",
       "1282027               2396.22            1           1  \n",
       "1282028                   NaN            1           1  \n",
       "1281999               3469.53            1           1  \n",
       "1282011                479.00            1           1  \n",
       "1282040                   NaN            1           1  \n",
       "1282034                   NaN            1           1  \n",
       "1281993               1695.80            1           1  \n",
       "1282031                   NaN            1           1  \n",
       "1282001                   NaN            1           1  \n",
       "1282026                364.58            1           1  \n",
       "1282022                242.82            1           1  \n",
       "1282019                351.54            1           1  \n",
       "1282045                   NaN            1           1  \n",
       "1281988                508.17            1           1  \n",
       "1282006                   NaN            1           1  \n",
       "1282010                   NaN            1           1  \n",
       "1282020              10748.20            1           1  \n",
       "1282049                   NaN            1           1  \n",
       "1282016                   NaN            1           1  \n",
       "1282023               1670.37            1           1  \n",
       "1282004                   NaN            1           1  \n",
       "1282025               5718.65            1           1  \n",
       "1282003                   NaN            1           1  \n",
       "1281996                   NaN            1           1  \n",
       "1282017                   NaN            1           1  \n",
       "1281992                159.99            1           1  \n",
       "1282042                364.82            1           1  \n",
       "1281987               3655.90            1           1  \n",
       "1282030                   NaN            1           1  \n",
       "...                       ...          ...         ...  \n",
       "1282005                844.55            1           1  \n",
       "1282013                343.26            1           1  \n",
       "1281997               4158.53            1           1  \n",
       "1282037                278.17            1           1  \n",
       "1282024                606.30            1           1  \n",
       "1282014                   NaN            1           1  \n",
       "1282038                895.99            1           1  \n",
       "1282000               1030.47            1           1  \n",
       "1282007               2757.98            1           1  \n",
       "1281995               1300.95            1           1  \n",
       "1282032               4942.80            1           1  \n",
       "1282044                295.76            1           1  \n",
       "1281994                   NaN            1           1  \n",
       "1282015                   NaN            1           1  \n",
       "1282041               5114.29            1           1  \n",
       "1281991                520.79            1           1  \n",
       "1282029               1943.18            1           1  \n",
       "1282036                171.54            1           1  \n",
       "1282002                   NaN            1           1  \n",
       "1282046                   NaN            1           1  \n",
       "1281998                101.72            1           1  \n",
       "1282043                101.54            1           1  \n",
       "1281989                   NaN            1           1  \n",
       "1282035                   NaN            1           1  \n",
       "1282047                 78.69            1           1  \n",
       "1282039                154.59            1           1  \n",
       "1282009                524.33            1           1  \n",
       "1282012                 73.60            1           1  \n",
       "1282048               3948.35            1           1  \n",
       "1282033                   NaN            1           1  \n",
       "\n",
       "[63 rows x 17 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_d_df_small[part_d_df_small[\"npi\"]==1053393405].sort_values(\"total_claim_count\", ascending=False)"
   ]
  }
 ],
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