Final_Finance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import os \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn import ensemble \n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.metrics import brier_score_loss, precision_score, recall_score,f1_score, roc_auc_score, accuracy_score \n",
    "from sklearn.metrics import confusion_matrix, roc_curve\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import random\n",
    "\n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "PartDRawData = \"c:\\\\FIP\\\\PartD_Prescriber_PUF_NPI_Drug_17.txt\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_pd = pd.read_csv(PartDRawData,sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25209130, 21)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_pd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_Drug_pd1= partD_pd.loc[:,['npi','nppes_provider_city','nppes_provider_state', \\\n",
    "                                               'nppes_provider_last_org_name', \\\n",
    "                                               'nppes_provider_first_name', \\\n",
    "                                               'specialty_description',\\\n",
    "                                               'drug_name', \\\n",
    "                                               'generic_name',\\\n",
    "                                               'total_drug_cost',\\\n",
    "                                               'total_claim_count',\\\n",
    "                                               'total_day_supply']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_pd1 = partD_Drug_pd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_Drug_pd = partD_pd.loc[:,['npi','drug_name','total_drug_cost','total_claim_count','total_day_supply','specialty_description']]\n",
    "partD_Drug_pd['npi'] = partD_Drug_pd.npi.astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_Spec_pd1= partD_pd.loc[:,['npi','specialty_description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>specialty_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [npi, specialty_description]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_Spec_pd1.head(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>drug_name</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>specialty_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>AMLODIPINE BESYLATE</td>\n",
       "      <td>59.21</td>\n",
       "      <td>13</td>\n",
       "      <td>390</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>ATORVASTATIN CALCIUM</td>\n",
       "      <td>259.48</td>\n",
       "      <td>27</td>\n",
       "      <td>765</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CEPHALEXIN</td>\n",
       "      <td>98.99</td>\n",
       "      <td>17</td>\n",
       "      <td>123</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CIPROFLOXACIN HCL</td>\n",
       "      <td>120.43</td>\n",
       "      <td>12</td>\n",
       "      <td>95</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>DOXYCYCLINE HYCLATE</td>\n",
       "      <td>300.76</td>\n",
       "      <td>17</td>\n",
       "      <td>105</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi             drug_name  total_drug_cost  total_claim_count  \\\n",
       "0  1003000126   AMLODIPINE BESYLATE            59.21                 13   \n",
       "1  1003000126  ATORVASTATIN CALCIUM           259.48                 27   \n",
       "2  1003000126            CEPHALEXIN            98.99                 17   \n",
       "3  1003000126     CIPROFLOXACIN HCL           120.43                 12   \n",
       "4  1003000126   DOXYCYCLINE HYCLATE           300.76                 17   \n",
       "\n",
       "   total_day_supply specialty_description  \n",
       "0               390     Internal Medicine  \n",
       "1               765     Internal Medicine  \n",
       "2               123     Internal Medicine  \n",
       "3                95     Internal Medicine  \n",
       "4               105     Internal Medicine  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_Drug_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_pd0= partD_pd.loc[:,['npi','nppes_provider_city','nppes_provider_state', \\\n",
    "                                               'nppes_provider_last_org_name', \\\n",
    "                                               'nppes_provider_first_name','specialty_description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>nppes_provider_last_org_name</th>\n",
       "      <th>nppes_provider_first_name</th>\n",
       "      <th>specialty_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi nppes_provider_city nppes_provider_state  \\\n",
       "0  1003000126          CUMBERLAND                   MD   \n",
       "1  1003000126          CUMBERLAND                   MD   \n",
       "2  1003000126          CUMBERLAND                   MD   \n",
       "3  1003000126          CUMBERLAND                   MD   \n",
       "4  1003000126          CUMBERLAND                   MD   \n",
       "\n",
       "  nppes_provider_last_org_name nppes_provider_first_name specialty_description  \n",
       "0                    ENKESHAFI                   ARDALAN     Internal Medicine  \n",
       "1                    ENKESHAFI                   ARDALAN     Internal Medicine  \n",
       "2                    ENKESHAFI                   ARDALAN     Internal Medicine  \n",
       "3                    ENKESHAFI                   ARDALAN     Internal Medicine  \n",
       "4                    ENKESHAFI                   ARDALAN     Internal Medicine  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_pd0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_catfpd = partD_pd0.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>nppes_provider_last_org_name</th>\n",
       "      <th>nppes_provider_first_name</th>\n",
       "      <th>specialty_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>OH</td>\n",
       "      <td>KHALIL</td>\n",
       "      <td>RASHID</td>\n",
       "      <td>Anesthesiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>NV</td>\n",
       "      <td>ESCOBAR</td>\n",
       "      <td>JULIO</td>\n",
       "      <td>Dentist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>NASHVILLE</td>\n",
       "      <td>TN</td>\n",
       "      <td>BLAKEMORE</td>\n",
       "      <td>ROSIE</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>BROOKVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>GIRARDI</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>Family Practice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           npi nppes_provider_city nppes_provider_state  \\\n",
       "0   1003000126          CUMBERLAND                   MD   \n",
       "16  1003000142              TOLEDO                   OH   \n",
       "38  1003000167              DAYTON                   NV   \n",
       "40  1003000282           NASHVILLE                   TN   \n",
       "42  1003000407          BROOKVILLE                   PA   \n",
       "\n",
       "   nppes_provider_last_org_name nppes_provider_first_name  \\\n",
       "0                     ENKESHAFI                   ARDALAN   \n",
       "16                       KHALIL                    RASHID   \n",
       "38                      ESCOBAR                     JULIO   \n",
       "40                    BLAKEMORE                     ROSIE   \n",
       "42                      GIRARDI                     DAVID   \n",
       "\n",
       "   specialty_description  \n",
       "0      Internal Medicine  \n",
       "16        Anesthesiology  \n",
       "38               Dentist  \n",
       "40    Nurse Practitioner  \n",
       "42       Family Practice  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_catfpd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_dict = {'nppes_provider_first_name':'first_name', 'nppes_provider_last_org_name':'last_name','nppes_provider_city':'city','nppes_provider_state':'state','specialty_description':'Speciality'}\n",
    "partD_catfpd = partD_catfpd.rename(columns=rename_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>Speciality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>OH</td>\n",
       "      <td>KHALIL</td>\n",
       "      <td>RASHID</td>\n",
       "      <td>Anesthesiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>NV</td>\n",
       "      <td>ESCOBAR</td>\n",
       "      <td>JULIO</td>\n",
       "      <td>Dentist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>NASHVILLE</td>\n",
       "      <td>TN</td>\n",
       "      <td>BLAKEMORE</td>\n",
       "      <td>ROSIE</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>BROOKVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>GIRARDI</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>Family Practice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           npi        city state  last_name first_name          Speciality\n",
       "0   1003000126  CUMBERLAND    MD  ENKESHAFI    ARDALAN   Internal Medicine\n",
       "16  1003000142      TOLEDO    OH     KHALIL     RASHID      Anesthesiology\n",
       "38  1003000167      DAYTON    NV    ESCOBAR      JULIO             Dentist\n",
       "40  1003000282   NASHVILLE    TN  BLAKEMORE      ROSIE  Nurse Practitioner\n",
       "42  1003000407  BROOKVILLE    PA    GIRARDI      DAVID     Family Practice"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_catfpd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_cols = ['npi']\n",
    "\n",
    "agg_dict = {'total_drug_cost':['sum','mean','max'], \\\n",
    "           'total_claim_count':['sum','mean','max'],\\\n",
    "           'total_day_supply':['sum','mean','max']}\n",
    "\n",
    "\n",
    "\n",
    "partD_pd2 = partD_pd1.groupby(group_cols).agg(agg_dict).astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_drug_cost</th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_claim_count</th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_day_supply</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1003000126</td>\n",
       "      <td>12374.60</td>\n",
       "      <td>773.412500</td>\n",
       "      <td>9326.11</td>\n",
       "      <td>385.0</td>\n",
       "      <td>24.062500</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8053.0</td>\n",
       "      <td>503.312500</td>\n",
       "      <td>1530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1003000142</td>\n",
       "      <td>113868.77</td>\n",
       "      <td>5175.853182</td>\n",
       "      <td>52305.09</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>83.909091</td>\n",
       "      <td>338.0</td>\n",
       "      <td>55569.0</td>\n",
       "      <td>2525.863636</td>\n",
       "      <td>11835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1003000167</td>\n",
       "      <td>151.75</td>\n",
       "      <td>75.875000</td>\n",
       "      <td>89.80</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1003000282</td>\n",
       "      <td>659.99</td>\n",
       "      <td>329.995000</td>\n",
       "      <td>383.12</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>736.000000</td>\n",
       "      <td>852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1003000407</td>\n",
       "      <td>73501.23</td>\n",
       "      <td>1204.938197</td>\n",
       "      <td>12322.78</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>37.475410</td>\n",
       "      <td>241.0</td>\n",
       "      <td>41901.0</td>\n",
       "      <td>686.901639</td>\n",
       "      <td>2369.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1992998736</td>\n",
       "      <td>21715.30</td>\n",
       "      <td>603.202778</td>\n",
       "      <td>5140.46</td>\n",
       "      <td>729.0</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43230.0</td>\n",
       "      <td>1200.833333</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992998819</td>\n",
       "      <td>1850.33</td>\n",
       "      <td>1850.330000</td>\n",
       "      <td>1850.33</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992998926</td>\n",
       "      <td>981344.44</td>\n",
       "      <td>65422.962667</td>\n",
       "      <td>324463.75</td>\n",
       "      <td>377.0</td>\n",
       "      <td>25.133333</td>\n",
       "      <td>109.0</td>\n",
       "      <td>24287.0</td>\n",
       "      <td>1619.133333</td>\n",
       "      <td>9810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992999031</td>\n",
       "      <td>2317795.96</td>\n",
       "      <td>60994.630526</td>\n",
       "      <td>731701.81</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>38.236842</td>\n",
       "      <td>223.0</td>\n",
       "      <td>50619.0</td>\n",
       "      <td>1332.078947</td>\n",
       "      <td>12858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992999122</td>\n",
       "      <td>689229.13</td>\n",
       "      <td>3961.086954</td>\n",
       "      <td>45476.25</td>\n",
       "      <td>12354.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>741.0</td>\n",
       "      <td>729260.0</td>\n",
       "      <td>4191.149425</td>\n",
       "      <td>55591.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>917999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_drug_cost                          total_claim_count  \\\n",
       "                       sum          mean        max               sum   \n",
       "npi                                                                     \n",
       "1003000126        12374.60    773.412500    9326.11             385.0   \n",
       "1003000142       113868.77   5175.853182   52305.09            1846.0   \n",
       "1003000167          151.75     75.875000      89.80              38.0   \n",
       "1003000282          659.99    329.995000     383.12              54.0   \n",
       "1003000407        73501.23   1204.938197   12322.78            2286.0   \n",
       "...                    ...           ...        ...               ...   \n",
       "1992998736        21715.30    603.202778    5140.46             729.0   \n",
       "1992998819         1850.33   1850.330000    1850.33              23.0   \n",
       "1992998926       981344.44  65422.962667  324463.75             377.0   \n",
       "1992999031      2317795.96  60994.630526  731701.81            1453.0   \n",
       "1992999122       689229.13   3961.086954   45476.25           12354.0   \n",
       "\n",
       "                             total_day_supply                        \n",
       "                 mean    max              sum         mean      max  \n",
       "npi                                                                  \n",
       "1003000126  24.062500   52.0           8053.0   503.312500   1530.0  \n",
       "1003000142  83.909091  338.0          55569.0  2525.863636  11835.0  \n",
       "1003000167  19.000000   24.0            157.0    78.500000    122.0  \n",
       "1003000282  27.000000   32.0           1472.0   736.000000    852.0  \n",
       "1003000407  37.475410  241.0          41901.0   686.901639   2369.0  \n",
       "...               ...    ...              ...          ...      ...  \n",
       "1992998736  20.250000   51.0          43230.0  1200.833333   3600.0  \n",
       "1992998819  23.000000   23.0            187.0   187.000000    187.0  \n",
       "1992998926  25.133333  109.0          24287.0  1619.133333   9810.0  \n",
       "1992999031  38.236842  223.0          50619.0  1332.078947  12858.0  \n",
       "1992999122  71.000000  741.0         729260.0  4191.149425  55591.0  \n",
       "\n",
       "[917999 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_pd2.head(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "level0 = partD_pd2.columns.get_level_values(0)\n",
    "level1 = partD_pd2.columns.get_level_values(1)\n",
    "partD_pd2.columns = level0 + '_' + level1\n",
    "partD_fpd = partD_pd2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
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       "      <td>83.909091</td>\n",
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       "      <td>89.80</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>122.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>659.99</td>\n",
       "      <td>329.995000</td>\n",
       "      <td>383.12</td>\n",
       "      <td>54.0</td>\n",
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       "      <td>736.000000</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>73501.23</td>\n",
       "      <td>1204.938197</td>\n",
       "      <td>12322.78</td>\n",
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       "      <td>41901.0</td>\n",
       "      <td>686.901639</td>\n",
       "      <td>2369.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi  total_drug_cost_sum  total_drug_cost_mean  total_drug_cost_max  \\\n",
       "0  1003000126             12374.60            773.412500              9326.11   \n",
       "1  1003000142            113868.77           5175.853182             52305.09   \n",
       "2  1003000167               151.75             75.875000                89.80   \n",
       "3  1003000282               659.99            329.995000               383.12   \n",
       "4  1003000407             73501.23           1204.938197             12322.78   \n",
       "\n",
       "   total_claim_count_sum  total_claim_count_mean  total_claim_count_max  \\\n",
       "0                  385.0               24.062500                   52.0   \n",
       "1                 1846.0               83.909091                  338.0   \n",
       "2                   38.0               19.000000                   24.0   \n",
       "3                   54.0               27.000000                   32.0   \n",
       "4                 2286.0               37.475410                  241.0   \n",
       "\n",
       "   total_day_supply_sum  total_day_supply_mean  total_day_supply_max  \n",
       "0                8053.0             503.312500                1530.0  \n",
       "1               55569.0            2525.863636               11835.0  \n",
       "2                 157.0              78.500000                 122.0  \n",
       "3                1472.0             736.000000                 852.0  \n",
       "4               41901.0             686.901639                2369.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_fpd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                       918009\n",
       "total_drug_cost_sum       918009\n",
       "total_drug_cost_mean      918009\n",
       "total_drug_cost_max       918009\n",
       "total_claim_count_sum     918009\n",
       "total_claim_count_mean    918009\n",
       "total_claim_count_max     918009\n",
       "total_day_supply_sum      918009\n",
       "total_day_supply_mean     918009\n",
       "total_day_supply_max      918009\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_fpd.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_allpd = pd.merge(partD_fpd,partD_catfpd, how ='left',on = 'npi')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
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       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
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       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>Speciality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>12374.60</td>\n",
       "      <td>773.412500</td>\n",
       "      <td>9326.11</td>\n",
       "      <td>385.0</td>\n",
       "      <td>24.062500</td>\n",
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       "      <td>8053.0</td>\n",
       "      <td>503.312500</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>113868.77</td>\n",
       "      <td>5175.853182</td>\n",
       "      <td>52305.09</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>83.909091</td>\n",
       "      <td>338.0</td>\n",
       "      <td>55569.0</td>\n",
       "      <td>2525.863636</td>\n",
       "      <td>11835.0</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>OH</td>\n",
       "      <td>KHALIL</td>\n",
       "      <td>RASHID</td>\n",
       "      <td>Anesthesiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>151.75</td>\n",
       "      <td>75.875000</td>\n",
       "      <td>89.80</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>122.0</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>NV</td>\n",
       "      <td>ESCOBAR</td>\n",
       "      <td>JULIO</td>\n",
       "      <td>Dentist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>659.99</td>\n",
       "      <td>329.995000</td>\n",
       "      <td>383.12</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>736.000000</td>\n",
       "      <td>852.0</td>\n",
       "      <td>NASHVILLE</td>\n",
       "      <td>TN</td>\n",
       "      <td>BLAKEMORE</td>\n",
       "      <td>ROSIE</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>73501.23</td>\n",
       "      <td>1204.938197</td>\n",
       "      <td>12322.78</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>37.475410</td>\n",
       "      <td>241.0</td>\n",
       "      <td>41901.0</td>\n",
       "      <td>686.901639</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>BROOKVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>GIRARDI</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>Family Practice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi  total_drug_cost_sum  total_drug_cost_mean  total_drug_cost_max  \\\n",
       "0  1003000126             12374.60            773.412500              9326.11   \n",
       "1  1003000142            113868.77           5175.853182             52305.09   \n",
       "2  1003000167               151.75             75.875000                89.80   \n",
       "3  1003000282               659.99            329.995000               383.12   \n",
       "4  1003000407             73501.23           1204.938197             12322.78   \n",
       "\n",
       "   total_claim_count_sum  total_claim_count_mean  total_claim_count_max  \\\n",
       "0                  385.0               24.062500                   52.0   \n",
       "1                 1846.0               83.909091                  338.0   \n",
       "2                   38.0               19.000000                   24.0   \n",
       "3                   54.0               27.000000                   32.0   \n",
       "4                 2286.0               37.475410                  241.0   \n",
       "\n",
       "   total_day_supply_sum  total_day_supply_mean  total_day_supply_max  \\\n",
       "0                8053.0             503.312500                1530.0   \n",
       "1               55569.0            2525.863636               11835.0   \n",
       "2                 157.0              78.500000                 122.0   \n",
       "3                1472.0             736.000000                 852.0   \n",
       "4               41901.0             686.901639                2369.0   \n",
       "\n",
       "         city state  last_name first_name          Speciality  \n",
       "0  CUMBERLAND    MD  ENKESHAFI    ARDALAN   Internal Medicine  \n",
       "1      TOLEDO    OH     KHALIL     RASHID      Anesthesiology  \n",
       "2      DAYTON    NV    ESCOBAR      JULIO             Dentist  \n",
       "3   NASHVILLE    TN  BLAKEMORE      ROSIE  Nurse Practitioner  \n",
       "4  BROOKVILLE    PA    GIRARDI      DAVID     Family Practice  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_allpd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                       918009\n",
       "total_drug_cost_sum       918009\n",
       "total_drug_cost_mean      918009\n",
       "total_drug_cost_max       918009\n",
       "total_claim_count_sum     918009\n",
       "total_claim_count_mean    918009\n",
       "total_claim_count_max     918009\n",
       "total_day_supply_sum      918009\n",
       "total_day_supply_mean     918009\n",
       "total_day_supply_max      918009\n",
       "city                      918009\n",
       "state                     918009\n",
       "last_name                 917983\n",
       "first_name                917997\n",
       "Speciality                918009\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partD_allpd.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "PaymentRawData = \"c:\\\\FIP\\\\OP_DTL_GNRL_PGYR2017_P01172020.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (4,7,9,11,14,16,17,21,22,23,24,28,35,36,37,40,41,42,43,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "payment_pd = pd.read_csv(PaymentRawData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_fpd = payment_pd.loc[:,['Physician_First_Name',\\\n",
    "                                             'Physician_Last_Name', \\\n",
    "                                             'Recipient_City', \\\n",
    "                                             'Recipient_State', \\\n",
    "                                             'Total_Amount_of_Payment_USDollars']]"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Physician_First_Name</th>\n",
       "      <th>Physician_Last_Name</th>\n",
       "      <th>Recipient_City</th>\n",
       "      <th>Recipient_State</th>\n",
       "      <th>Total_Amount_of_Payment_USDollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>MARION</td>\n",
       "      <td>ANTHONY</td>\n",
       "      <td>PLEASANTON</td>\n",
       "      <td>CA</td>\n",
       "      <td>14.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ELISABETH</td>\n",
       "      <td>ANTON</td>\n",
       "      <td>BELLEVUE</td>\n",
       "      <td>WA</td>\n",
       "      <td>10.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>PETER</td>\n",
       "      <td>AHN</td>\n",
       "      <td>PHILADELPHIA</td>\n",
       "      <td>PA</td>\n",
       "      <td>80.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>PETER</td>\n",
       "      <td>AHN</td>\n",
       "      <td>PHILADELPHIA</td>\n",
       "      <td>PA</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>MANSUR</td>\n",
       "      <td>CLEVELAND</td>\n",
       "      <td>OH</td>\n",
       "      <td>31.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Physician_First_Name Physician_Last_Name Recipient_City Recipient_State  \\\n",
       "0               MARION             ANTHONY     PLEASANTON              CA   \n",
       "1            ELISABETH               ANTON       BELLEVUE              WA   \n",
       "2                PETER                 AHN   PHILADELPHIA              PA   \n",
       "3                PETER                 AHN   PHILADELPHIA              PA   \n",
       "4                DAVID              MANSUR      CLEVELAND              OH   \n",
       "\n",
       "   Total_Amount_of_Payment_USDollars  \n",
       "0                              14.32  \n",
       "1                              10.61  \n",
       "2                              80.32  \n",
       "3                             100.00  \n",
       "4                              31.50  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Physician_First_Name                 11193330\n",
       "Physician_Last_Name                  11193162\n",
       "Recipient_City                       11235931\n",
       "Recipient_State                      11235584\n",
       "Total_Amount_of_Payment_USDollars    11235934\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_fpd1 = payment_fpd.groupby(['Physician_First_Name','Physician_Last_Name','Recipient_City','Recipient_State'])\\\n",
    "                                   .agg({'Total_Amount_of_Payment_USDollars':['sum']}).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "level0 = payment_fpd1.columns.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "level1 = payment_fpd1.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_fpd1.columns = level0 + '_' + level1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Physician_First_Name</th>\n",
       "      <th>Physician_Last_Name</th>\n",
       "      <th>Recipient_City</th>\n",
       "      <th>Recipient_State</th>\n",
       "      <th>Total_Amount_of_Payment_USDollars_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>,BIRUTE</td>\n",
       "      <td>PUMPUTIS</td>\n",
       "      <td>VALPARAISO</td>\n",
       "      <td>IN</td>\n",
       "      <td>11.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-RAFIK</td>\n",
       "      <td>GIRGIS</td>\n",
       "      <td>GLENDORA</td>\n",
       "      <td>CA</td>\n",
       "      <td>238.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-cheryl</td>\n",
       "      <td>Chu</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>13.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-rafik</td>\n",
       "      <td>Girgis</td>\n",
       "      <td>Glendora</td>\n",
       "      <td>CA</td>\n",
       "      <td>533.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>.ALEKSANDR</td>\n",
       "      <td>ITKIN</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>CA</td>\n",
       "      <td>45.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1093203</td>\n",
       "      <td>ythan</td>\n",
       "      <td>Webster</td>\n",
       "      <td>Roanoke</td>\n",
       "      <td>VA</td>\n",
       "      <td>74.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1093204</td>\n",
       "      <td>zeeshan</td>\n",
       "      <td>sardar</td>\n",
       "      <td>PHILADELPHIA</td>\n",
       "      <td>PA</td>\n",
       "      <td>289.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1093205</td>\n",
       "      <td>zhuoran</td>\n",
       "      <td>zhao</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>55.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1093206</td>\n",
       "      <td>ziad</td>\n",
       "      <td>Abbud</td>\n",
       "      <td>Neptune</td>\n",
       "      <td>NJ</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1093207</td>\n",
       "      <td>ziad</td>\n",
       "      <td>el-hayek</td>\n",
       "      <td>parma</td>\n",
       "      <td>OH</td>\n",
       "      <td>61.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1093208 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Physician_First_Name Physician_Last_Name Recipient_City  \\\n",
       "0                    ,BIRUTE            PUMPUTIS     VALPARAISO   \n",
       "1                     -RAFIK              GIRGIS       GLENDORA   \n",
       "2                    -cheryl                 Chu    Los Angeles   \n",
       "3                     -rafik              Girgis       Glendora   \n",
       "4                 .ALEKSANDR               ITKIN      SAN DIEGO   \n",
       "...                      ...                 ...            ...   \n",
       "1093203                ythan             Webster        Roanoke   \n",
       "1093204              zeeshan              sardar   PHILADELPHIA   \n",
       "1093205              zhuoran                zhao  San Francisco   \n",
       "1093206                 ziad               Abbud        Neptune   \n",
       "1093207                 ziad            el-hayek          parma   \n",
       "\n",
       "        Recipient_State  Total_Amount_of_Payment_USDollars_sum  \n",
       "0                    IN                                  11.69  \n",
       "1                    CA                                 238.28  \n",
       "2                    CA                                  13.76  \n",
       "3                    CA                                 533.11  \n",
       "4                    CA                                  45.65  \n",
       "...                 ...                                    ...  \n",
       "1093203              VA                                  74.22  \n",
       "1093204              PA                                 289.00  \n",
       "1093205              CA                                  55.38  \n",
       "1093206              NJ                                  16.00  \n",
       "1093207              OH                                  61.35  \n",
       "\n",
       "[1093208 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>,BIRUTE</td>\n",
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       "      <td>11.69</td>\n",
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       "    <tr>\n",
       "      <td>-RAFIK</td>\n",
       "      <td>GIRGIS</td>\n",
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       "      <td>CA</td>\n",
       "      <td>533.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>.ALEKSANDR</td>\n",
       "      <td>ITKIN</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>CA</td>\n",
       "      <td>45.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                         Total_Amount_of_Payment_USDollars_sum\n",
       "Physician_First_Name Physician_Last_Name Recipient_City Recipient_State                                       \n",
       ",BIRUTE              PUMPUTIS            VALPARAISO     IN                                               11.69\n",
       "-RAFIK               GIRGIS              GLENDORA       CA                                              238.28\n",
       "-cheryl              Chu                 Los Angeles    CA                                               13.76\n",
       "-rafik               Girgis              Glendora       CA                                              533.11\n",
       ".ALEKSANDR           ITKIN               SAN DIEGO      CA                                               45.65"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_dict = {'Physician_First_Name':'first_name', 'Physician_Last_Name':'last_name','Recipient_City':'city','Recipient_State':'state','Total_Amount_of_Payment_USDollars_sum':'Total_Payment_Sum'}\n",
    "payment_fpd1 = payment_fpd1.rename(columns=rename_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physician_First_Name</th>\n",
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       "      <td>,BIRUTE</td>\n",
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       "    <tr>\n",
       "      <td>-RAFIK</td>\n",
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       "      <td>238.28</td>\n",
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       "    <tr>\n",
       "      <td>-cheryl</td>\n",
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       "      <td>Glendora</td>\n",
       "      <td>CA</td>\n",
       "      <td>533.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>.ALEKSANDR</td>\n",
       "      <td>ITKIN</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>CA</td>\n",
       "      <td>45.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                         Total_Payment_Sum\n",
       "Physician_First_Name Physician_Last_Name Recipient_City Recipient_State                   \n",
       ",BIRUTE              PUMPUTIS            VALPARAISO     IN                           11.69\n",
       "-RAFIK               GIRGIS              GLENDORA       CA                          238.28\n",
       "-cheryl              Chu                 Los Angeles    CA                           13.76\n",
       "-rafik               Girgis              Glendora       CA                          533.11\n",
       ".ALEKSANDR           ITKIN               SAN DIEGO      CA                           45.65"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_fpd2= payment_fpd1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_dict = {'Physician_First_Name':'first_name', 'Physician_Last_Name':'last_name','Recipient_City':'city','Recipient_State':'state','Total_Amount_of_Payment_USDollars_sum':'Total_Payment_Sum'}\n",
    "payment_fpd2 = payment_fpd2.rename(columns=rename_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_fpd2 = payment_fpd2.sort_values('Total_Payment_Sum',ascending=False)"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>405570</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>GAMMIE</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>MD</td>\n",
       "      <td>26843529.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>955640</td>\n",
       "      <td>STEPHEN</td>\n",
       "      <td>BURKHART</td>\n",
       "      <td>SAN ANTONIO</td>\n",
       "      <td>TX</td>\n",
       "      <td>22969245.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155646</td>\n",
       "      <td>CHARLES</td>\n",
       "      <td>GOODIS</td>\n",
       "      <td>ALBUQUERQUE</td>\n",
       "      <td>NM</td>\n",
       "      <td>22880446.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>557952</td>\n",
       "      <td>KEVIN</td>\n",
       "      <td>FOLEY</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>TN</td>\n",
       "      <td>17038512.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>392912</td>\n",
       "      <td>IVAN</td>\n",
       "      <td>OSORIO</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>KS</td>\n",
       "      <td>8740393.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name         city state  Total_Payment_Sum\n",
       "405570      JAMES    GAMMIE    Baltimore    MD        26843529.80\n",
       "955640    STEPHEN  BURKHART  SAN ANTONIO    TX        22969245.26\n",
       "155646    CHARLES    GOODIS  ALBUQUERQUE    NM        22880446.67\n",
       "557952      KEVIN     FOLEY      Memphis    TN        17038512.75\n",
       "392912       IVAN    OSORIO  KANSAS CITY    KS         8740393.58"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_name            object\n",
      "last_name             object\n",
      "city                  object\n",
      "state                 object\n",
      "Total_Payment_Sum    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(payment_fpd2.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
       "        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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>405570</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>GAMMIE</td>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>MD</td>\n",
       "      <td>26843529.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>955640</td>\n",
       "      <td>STEPHEN</td>\n",
       "      <td>BURKHART</td>\n",
       "      <td>SAN ANTONIO</td>\n",
       "      <td>TX</td>\n",
       "      <td>22969245.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155646</td>\n",
       "      <td>CHARLES</td>\n",
       "      <td>GOODIS</td>\n",
       "      <td>ALBUQUERQUE</td>\n",
       "      <td>NM</td>\n",
       "      <td>22880446.670000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>557952</td>\n",
       "      <td>KEVIN</td>\n",
       "      <td>FOLEY</td>\n",
       "      <td>MEMPHIS</td>\n",
       "      <td>TN</td>\n",
       "      <td>17038512.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>392912</td>\n",
       "      <td>IVAN</td>\n",
       "      <td>OSORIO</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>KS</td>\n",
       "      <td>8740393.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>956957</td>\n",
       "      <td>STEPHEN</td>\n",
       "      <td>GREVIOUS</td>\n",
       "      <td>NORWALK</td>\n",
       "      <td>CT</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>548209</td>\n",
       "      <td>KEITH</td>\n",
       "      <td>VAUX</td>\n",
       "      <td>SAN DIEGO</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>914065</td>\n",
       "      <td>SARA</td>\n",
       "      <td>LITTLE</td>\n",
       "      <td>PORT JERVIS</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210972</td>\n",
       "      <td>DARIUS</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>MORRISTOWN</td>\n",
       "      <td>NJ</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>824614</td>\n",
       "      <td>RAMON</td>\n",
       "      <td>VALDERRAMA</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1093208 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name   last_name         city state   Total_Payment_Sum\n",
       "405570      JAMES      GAMMIE    BALTIMORE    MD          26843529.8\n",
       "955640    STEPHEN    BURKHART  SAN ANTONIO    TX         22969245.26\n",
       "155646    CHARLES      GOODIS  ALBUQUERQUE    NM  22880446.670000006\n",
       "557952      KEVIN       FOLEY      MEMPHIS    TN         17038512.75\n",
       "392912       IVAN      OSORIO  KANSAS CITY    KS          8740393.58\n",
       "...           ...         ...          ...   ...                 ...\n",
       "956957    STEPHEN    GREVIOUS      NORWALK    CT                0.03\n",
       "548209      KEITH        VAUX    SAN DIEGO    CA                0.03\n",
       "914065       SARA      LITTLE  PORT JERVIS    NY                0.03\n",
       "210972     DARIUS       ADAMS   MORRISTOWN    NJ                0.03\n",
       "824614      RAMON  VALDERRAMA     NEW YORK    NY                0.03\n",
       "\n",
       "[1093208 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd2.apply(lambda x: x.astype(str).str.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>405570</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>GAMMIE</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>MD</td>\n",
       "      <td>26843529.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>955640</td>\n",
       "      <td>STEPHEN</td>\n",
       "      <td>BURKHART</td>\n",
       "      <td>SAN ANTONIO</td>\n",
       "      <td>TX</td>\n",
       "      <td>22969245.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155646</td>\n",
       "      <td>CHARLES</td>\n",
       "      <td>GOODIS</td>\n",
       "      <td>ALBUQUERQUE</td>\n",
       "      <td>NM</td>\n",
       "      <td>22880446.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>557952</td>\n",
       "      <td>KEVIN</td>\n",
       "      <td>FOLEY</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>TN</td>\n",
       "      <td>17038512.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>392912</td>\n",
       "      <td>IVAN</td>\n",
       "      <td>OSORIO</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>KS</td>\n",
       "      <td>8740393.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name         city state  Total_Payment_Sum\n",
       "405570      JAMES    GAMMIE    Baltimore    MD        26843529.80\n",
       "955640    STEPHEN  BURKHART  SAN ANTONIO    TX        22969245.26\n",
       "155646    CHARLES    GOODIS  ALBUQUERQUE    NM        22880446.67\n",
       "557952      KEVIN     FOLEY      Memphis    TN        17038512.75\n",
       "392912       IVAN    OSORIO  KANSAS CITY    KS         8740393.58"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_fpd2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pay_partD_fpd = pd.merge (partD_allpd,payment_fpd2, how ='left', on = ['last_name','first_name','city','state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>Speciality</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>12374.60</td>\n",
       "      <td>773.412500</td>\n",
       "      <td>9326.11</td>\n",
       "      <td>385.0</td>\n",
       "      <td>24.062500</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8053.0</td>\n",
       "      <td>503.312500</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>113868.77</td>\n",
       "      <td>5175.853182</td>\n",
       "      <td>52305.09</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>83.909091</td>\n",
       "      <td>338.0</td>\n",
       "      <td>55569.0</td>\n",
       "      <td>2525.863636</td>\n",
       "      <td>11835.0</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>OH</td>\n",
       "      <td>KHALIL</td>\n",
       "      <td>RASHID</td>\n",
       "      <td>Anesthesiology</td>\n",
       "      <td>330.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>151.75</td>\n",
       "      <td>75.875000</td>\n",
       "      <td>89.80</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>122.0</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>NV</td>\n",
       "      <td>ESCOBAR</td>\n",
       "      <td>JULIO</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>127.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>659.99</td>\n",
       "      <td>329.995000</td>\n",
       "      <td>383.12</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>736.000000</td>\n",
       "      <td>852.0</td>\n",
       "      <td>NASHVILLE</td>\n",
       "      <td>TN</td>\n",
       "      <td>BLAKEMORE</td>\n",
       "      <td>ROSIE</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>73501.23</td>\n",
       "      <td>1204.938197</td>\n",
       "      <td>12322.78</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>37.475410</td>\n",
       "      <td>241.0</td>\n",
       "      <td>41901.0</td>\n",
       "      <td>686.901639</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>BROOKVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>GIRARDI</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>39.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi  total_drug_cost_sum  total_drug_cost_mean  total_drug_cost_max  \\\n",
       "0  1003000126             12374.60            773.412500              9326.11   \n",
       "1  1003000142            113868.77           5175.853182             52305.09   \n",
       "2  1003000167               151.75             75.875000                89.80   \n",
       "3  1003000282               659.99            329.995000               383.12   \n",
       "4  1003000407             73501.23           1204.938197             12322.78   \n",
       "\n",
       "   total_claim_count_sum  total_claim_count_mean  total_claim_count_max  \\\n",
       "0                  385.0               24.062500                   52.0   \n",
       "1                 1846.0               83.909091                  338.0   \n",
       "2                   38.0               19.000000                   24.0   \n",
       "3                   54.0               27.000000                   32.0   \n",
       "4                 2286.0               37.475410                  241.0   \n",
       "\n",
       "   total_day_supply_sum  total_day_supply_mean  total_day_supply_max  \\\n",
       "0                8053.0             503.312500                1530.0   \n",
       "1               55569.0            2525.863636               11835.0   \n",
       "2                 157.0              78.500000                 122.0   \n",
       "3                1472.0             736.000000                 852.0   \n",
       "4               41901.0             686.901639                2369.0   \n",
       "\n",
       "         city state  last_name first_name          Speciality  \\\n",
       "0  CUMBERLAND    MD  ENKESHAFI    ARDALAN   Internal Medicine   \n",
       "1      TOLEDO    OH     KHALIL     RASHID      Anesthesiology   \n",
       "2      DAYTON    NV    ESCOBAR      JULIO             Dentist   \n",
       "3   NASHVILLE    TN  BLAKEMORE      ROSIE  Nurse Practitioner   \n",
       "4  BROOKVILLE    PA    GIRARDI      DAVID     Family Practice   \n",
       "\n",
       "   Total_Payment_Sum  \n",
       "0                NaN  \n",
       "1             330.39  \n",
       "2             127.16  \n",
       "3                NaN  \n",
       "4              39.23  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_partD_fpd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                       918009\n",
       "total_drug_cost_sum       918009\n",
       "total_drug_cost_mean      918009\n",
       "total_drug_cost_max       918009\n",
       "total_claim_count_sum     918009\n",
       "total_claim_count_mean    918009\n",
       "total_claim_count_max     918009\n",
       "total_day_supply_sum      918009\n",
       "total_day_supply_mean     918009\n",
       "total_day_supply_max      918009\n",
       "city                      918009\n",
       "state                     918009\n",
       "last_name                 917983\n",
       "first_name                917997\n",
       "Speciality                918009\n",
       "Total_Payment_Sum         382721\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_partD_fpd.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "IELErawdata = \"c:\\\\FIP\\\\LEIE.csv\"\n",
    "IELE_pd = pd.read_csv(IELErawdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>LASTNAME</th>\n",
       "      <th>FIRSTNAME</th>\n",
       "      <th>MIDNAME</th>\n",
       "      <th>BUSNAME</th>\n",
       "      <th>GENERAL</th>\n",
       "      <th>SPECIALTY</th>\n",
       "      <th>UPIN</th>\n",
       "      <th>NPI</th>\n",
       "      <th>DOB</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>EXCLTYPE</th>\n",
       "      <th>EXCLDATE</th>\n",
       "      <th>REINDATE</th>\n",
       "      <th>WAIVERDATE</th>\n",
       "      <th>WVRSTATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>#1 MARKETING SERVICE, INC</td>\n",
       "      <td>OTHER BUSINESS</td>\n",
       "      <td>SOBER HOME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239 BRIGHTON BEACH AVENUE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11235</td>\n",
       "      <td>1128a1</td>\n",
       "      <td>20200319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>14 LAWRENCE AVE PHARMACY</td>\n",
       "      <td>PHARMACY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14 LAWRENCE AVENUE</td>\n",
       "      <td>SMITHTOWN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11787</td>\n",
       "      <td>1128a1</td>\n",
       "      <td>19880830</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>143 MEDICAL EQUIPMENT CO</td>\n",
       "      <td>DME COMPANY</td>\n",
       "      <td>DME - OXYGEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>701 NW 36 AVENUE</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>FL</td>\n",
       "      <td>33125</td>\n",
       "      <td>1128b7</td>\n",
       "      <td>19970620</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>184TH STREET PHARMACY CORP</td>\n",
       "      <td>OTHER BUSINESS</td>\n",
       "      <td>PHARMACY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1922348218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69 E 184TH ST</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>NY</td>\n",
       "      <td>10468</td>\n",
       "      <td>1128a1</td>\n",
       "      <td>20180419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1951 FLATBUSH AVENUE PHARMACY</td>\n",
       "      <td>PHARMACY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1951 FLATBUSH AVE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11234</td>\n",
       "      <td>1128b5</td>\n",
       "      <td>20090319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LASTNAME FIRSTNAME MIDNAME                        BUSNAME         GENERAL  \\\n",
       "0      NaN       NaN              #1 MARKETING SERVICE, INC  OTHER BUSINESS   \n",
       "1      NaN       NaN               14 LAWRENCE AVE PHARMACY        PHARMACY   \n",
       "2      NaN       NaN               143 MEDICAL EQUIPMENT CO     DME COMPANY   \n",
       "3      NaN       NaN             184TH STREET PHARMACY CORP  OTHER BUSINESS   \n",
       "4      NaN       NaN          1951 FLATBUSH AVENUE PHARMACY        PHARMACY   \n",
       "\n",
       "      SPECIALTY UPIN         NPI  DOB                    ADDRESS       CITY  \\\n",
       "0    SOBER HOME  NaN           0  NaN  239 BRIGHTON BEACH AVENUE   BROOKLYN   \n",
       "1           NaN  NaN           0  NaN         14 LAWRENCE AVENUE  SMITHTOWN   \n",
       "2  DME - OXYGEN  NaN           0  NaN           701 NW 36 AVENUE      MIAMI   \n",
       "3      PHARMACY  NaN  1922348218  NaN              69 E 184TH ST      BRONX   \n",
       "4           NaN  NaN           0  NaN          1951 FLATBUSH AVE   BROOKLYN   \n",
       "\n",
       "  STATE    ZIP EXCLTYPE  EXCLDATE  REINDATE  WAIVERDATE WVRSTATE  \n",
       "0    NY  11235   1128a1  20200319         0           0      NaN  \n",
       "1    NY  11787   1128a1  19880830         0           0      NaN  \n",
       "2    FL  33125   1128b7  19970620         0           0      NaN  \n",
       "3    NY  10468   1128a1  20180419         0           0      NaN  \n",
       "4    NY  11234   1128b5  20090319         0           0      NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IELE_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "npifraud_pd0 = IELE_pd.loc[:,['NPI','EXCLTYPE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
       "\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>EXCLTYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1128a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1128a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1128b7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1922348218</td>\n",
       "      <td>1128a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1128b5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI EXCLTYPE\n",
       "0           0   1128a1\n",
       "1           0   1128a1\n",
       "2           0   1128b7\n",
       "3  1922348218   1128a1\n",
       "4           0   1128b5"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npifraud_pd0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "npifraud_pd1 = npifraud_pd0.query('NPI !=0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NPI         5489\n",
       "EXCLTYPE    5489\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npifraud_pd1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_dict = {'NPI':'npi', 'EXCLTYPE':'is_fraud'}\n",
    "npi_fraud_pd = npifraud_pd1.rename(columns=rename_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
       "\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>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1922348218</td>\n",
       "      <td>1128a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1942476080</td>\n",
       "      <td>1128b8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1275600959</td>\n",
       "      <td>1128a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1891731758</td>\n",
       "      <td>1128b8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1851631543</td>\n",
       "      <td>1128b7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           npi is_fraud\n",
       "3   1922348218   1128a1\n",
       "23  1942476080   1128b8\n",
       "27  1275600959   1128a1\n",
       "30  1891731758   1128b8\n",
       "58  1851631543   1128b7"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npi_fraud_pd.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "npi_fraud_pd['is_fraud'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1922348218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1942476080</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1275600959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1891731758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1851631543</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           npi  is_fraud\n",
       "3   1922348218         1\n",
       "23  1942476080         1\n",
       "27  1275600959         1\n",
       "30  1891731758         1\n",
       "58  1851631543         1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npi_fraud_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "npi         int64\n",
      "is_fraud    int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(npi_fraud_pd.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features Engineering \n",
    "Features_pd1 = pd.merge(pay_partD_fpd,npi_fraud_pd, how ='left',on = 'npi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                       918012\n",
       "total_drug_cost_sum       918012\n",
       "total_drug_cost_mean      918012\n",
       "total_drug_cost_max       918012\n",
       "total_claim_count_sum     918012\n",
       "total_claim_count_mean    918012\n",
       "total_claim_count_max     918012\n",
       "total_day_supply_sum      918012\n",
       "total_day_supply_mean     918012\n",
       "total_day_supply_max      918012\n",
       "city                      918012\n",
       "state                     918012\n",
       "last_name                 917986\n",
       "first_name                918000\n",
       "Speciality                918012\n",
       "Total_Payment_Sum         382724\n",
       "is_fraud                     368\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Features_pd1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>9.180120e+05</td>\n",
       "      <td>9.180120e+05</td>\n",
       "      <td>9.180120e+05</td>\n",
       "      <td>9.180120e+05</td>\n",
       "      <td>918012.000000</td>\n",
       "      <td>918012.000000</td>\n",
       "      <td>918012.000000</td>\n",
       "      <td>9.180120e+05</td>\n",
       "      <td>918012.000000</td>\n",
       "      <td>918012.000000</td>\n",
       "      <td>3.827240e+05</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.499678e+09</td>\n",
       "      <td>1.264841e+05</td>\n",
       "      <td>3.697505e+03</td>\n",
       "      <td>3.343104e+04</td>\n",
       "      <td>1412.076618</td>\n",
       "      <td>33.300242</td>\n",
       "      <td>142.531894</td>\n",
       "      <td>6.181806e+04</td>\n",
       "      <td>1157.336640</td>\n",
       "      <td>6452.158384</td>\n",
       "      <td>2.890240e+03</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.878975e+08</td>\n",
       "      <td>3.567932e+05</td>\n",
       "      <td>1.737574e+04</td>\n",
       "      <td>1.209231e+05</td>\n",
       "      <td>3259.292029</td>\n",
       "      <td>26.095845</td>\n",
       "      <td>243.770002</td>\n",
       "      <td>1.366230e+05</td>\n",
       "      <td>1312.099134</td>\n",
       "      <td>11466.275680</td>\n",
       "      <td>5.962324e+04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.003000e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.245682e+09</td>\n",
       "      <td>5.658800e+02</td>\n",
       "      <td>1.800950e+02</td>\n",
       "      <td>2.917800e+02</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>303.500000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>5.438750e+01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.497957e+09</td>\n",
       "      <td>7.413850e+03</td>\n",
       "      <td>8.005217e+02</td>\n",
       "      <td>3.649825e+03</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>25.705882</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>5.695000e+03</td>\n",
       "      <td>748.666667</td>\n",
       "      <td>1594.000000</td>\n",
       "      <td>1.852700e+02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.740696e+09</td>\n",
       "      <td>1.003794e+05</td>\n",
       "      <td>2.756595e+03</td>\n",
       "      <td>2.246262e+04</td>\n",
       "      <td>1252.000000</td>\n",
       "      <td>41.163996</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>5.149000e+04</td>\n",
       "      <td>1608.847348</td>\n",
       "      <td>7232.000000</td>\n",
       "      <td>6.433600e+02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.993000e+09</td>\n",
       "      <td>2.629355e+07</td>\n",
       "      <td>5.899316e+06</td>\n",
       "      <td>1.388153e+07</td>\n",
       "      <td>446242.000000</td>\n",
       "      <td>2053.000000</td>\n",
       "      <td>21913.000000</td>\n",
       "      <td>5.616161e+06</td>\n",
       "      <td>137523.666667</td>\n",
       "      <td>375009.000000</td>\n",
       "      <td>2.296925e+07</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "count  9.180120e+05         9.180120e+05          9.180120e+05   \n",
       "mean   1.499678e+09         1.264841e+05          3.697505e+03   \n",
       "std    2.878975e+08         3.567932e+05          1.737574e+04   \n",
       "min    1.003000e+09         0.000000e+00          0.000000e+00   \n",
       "25%    1.245682e+09         5.658800e+02          1.800950e+02   \n",
       "50%    1.497957e+09         7.413850e+03          8.005217e+02   \n",
       "75%    1.740696e+09         1.003794e+05          2.756595e+03   \n",
       "max    1.993000e+09         2.629355e+07          5.899316e+06   \n",
       "\n",
       "       total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "count         9.180120e+05          918012.000000           918012.000000   \n",
       "mean          3.343104e+04            1412.076618               33.300242   \n",
       "std           1.209231e+05            3259.292029               26.095845   \n",
       "min           0.000000e+00              11.000000               11.000000   \n",
       "25%           2.917800e+02              51.000000               17.333333   \n",
       "50%           3.649825e+03             217.000000               25.705882   \n",
       "75%           2.246262e+04            1252.000000               41.163996   \n",
       "max           1.388153e+07          446242.000000             2053.000000   \n",
       "\n",
       "       total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "count          918012.000000          9.180120e+05          918012.000000   \n",
       "mean              142.531894          6.181806e+04            1157.336640   \n",
       "std               243.770002          1.366230e+05            1312.099134   \n",
       "min                11.000000          1.100000e+01              11.000000   \n",
       "25%                23.000000          7.680000e+02             303.500000   \n",
       "50%                53.000000          5.695000e+03             748.666667   \n",
       "75%               163.000000          5.149000e+04            1608.847348   \n",
       "max             21913.000000          5.616161e+06          137523.666667   \n",
       "\n",
       "       total_day_supply_max  Total_Payment_Sum  is_fraud  \n",
       "count         918012.000000       3.827240e+05     368.0  \n",
       "mean            6452.158384       2.890240e+03       1.0  \n",
       "std            11466.275680       5.962324e+04       0.0  \n",
       "min               11.000000       6.000000e-02       1.0  \n",
       "25%              420.000000       5.438750e+01       1.0  \n",
       "50%             1594.000000       1.852700e+02       1.0  \n",
       "75%             7232.000000       6.433600e+02       1.0  \n",
       "max           375009.000000       2.296925e+07       1.0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_pd1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Features_pd1.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>Speciality</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>12374.60</td>\n",
       "      <td>773.412500</td>\n",
       "      <td>9326.11</td>\n",
       "      <td>385.0</td>\n",
       "      <td>24.062500</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8053.0</td>\n",
       "      <td>503.312500</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>113868.77</td>\n",
       "      <td>5175.853182</td>\n",
       "      <td>52305.09</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>83.909091</td>\n",
       "      <td>338.0</td>\n",
       "      <td>55569.0</td>\n",
       "      <td>2525.863636</td>\n",
       "      <td>11835.0</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>OH</td>\n",
       "      <td>KHALIL</td>\n",
       "      <td>RASHID</td>\n",
       "      <td>Anesthesiology</td>\n",
       "      <td>330.39</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>151.75</td>\n",
       "      <td>75.875000</td>\n",
       "      <td>89.80</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>122.0</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>NV</td>\n",
       "      <td>ESCOBAR</td>\n",
       "      <td>JULIO</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>127.16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>659.99</td>\n",
       "      <td>329.995000</td>\n",
       "      <td>383.12</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>736.000000</td>\n",
       "      <td>852.0</td>\n",
       "      <td>NASHVILLE</td>\n",
       "      <td>TN</td>\n",
       "      <td>BLAKEMORE</td>\n",
       "      <td>ROSIE</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>73501.23</td>\n",
       "      <td>1204.938197</td>\n",
       "      <td>12322.78</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>37.475410</td>\n",
       "      <td>241.0</td>\n",
       "      <td>41901.0</td>\n",
       "      <td>686.901639</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>BROOKVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>GIRARDI</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>39.23</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918007</td>\n",
       "      <td>1992999759</td>\n",
       "      <td>15881.82</td>\n",
       "      <td>547.648966</td>\n",
       "      <td>9068.62</td>\n",
       "      <td>573.0</td>\n",
       "      <td>19.758621</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25573.0</td>\n",
       "      <td>881.827586</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>GUYTON</td>\n",
       "      <td>GA</td>\n",
       "      <td>SORIANO</td>\n",
       "      <td>DINO</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918008</td>\n",
       "      <td>1992999791</td>\n",
       "      <td>1362.33</td>\n",
       "      <td>454.110000</td>\n",
       "      <td>782.27</td>\n",
       "      <td>70.0</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>MEMPHIS</td>\n",
       "      <td>TN</td>\n",
       "      <td>THOMAS</td>\n",
       "      <td>SHELIA</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918009</td>\n",
       "      <td>1992999825</td>\n",
       "      <td>3378.47</td>\n",
       "      <td>422.308750</td>\n",
       "      <td>2020.37</td>\n",
       "      <td>167.0</td>\n",
       "      <td>20.875000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3746.0</td>\n",
       "      <td>468.250000</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>SEATTLE</td>\n",
       "      <td>WA</td>\n",
       "      <td>DESCHENES</td>\n",
       "      <td>GEOFFREY</td>\n",
       "      <td>Otolaryngology</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918010</td>\n",
       "      <td>1992999833</td>\n",
       "      <td>305.81</td>\n",
       "      <td>101.936667</td>\n",
       "      <td>122.89</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>390.0</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>NY</td>\n",
       "      <td>SHAW</td>\n",
       "      <td>L. NOAH</td>\n",
       "      <td>Psychoanalyst</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918011</td>\n",
       "      <td>1992999882</td>\n",
       "      <td>619.92</td>\n",
       "      <td>103.320000</td>\n",
       "      <td>126.04</td>\n",
       "      <td>91.0</td>\n",
       "      <td>15.166667</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2642.0</td>\n",
       "      <td>440.333333</td>\n",
       "      <td>570.0</td>\n",
       "      <td>WILKES BARRE</td>\n",
       "      <td>PA</td>\n",
       "      <td>RUDZINSKI-WALTER</td>\n",
       "      <td>MARISSA</td>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918012 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "0       1003000126             12374.60            773.412500   \n",
       "1       1003000142            113868.77           5175.853182   \n",
       "2       1003000167               151.75             75.875000   \n",
       "3       1003000282               659.99            329.995000   \n",
       "4       1003000407             73501.23           1204.938197   \n",
       "...            ...                  ...                   ...   \n",
       "918007  1992999759             15881.82            547.648966   \n",
       "918008  1992999791              1362.33            454.110000   \n",
       "918009  1992999825              3378.47            422.308750   \n",
       "918010  1992999833               305.81            101.936667   \n",
       "918011  1992999882               619.92            103.320000   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "0                   9326.11                  385.0               24.062500   \n",
       "1                  52305.09                 1846.0               83.909091   \n",
       "2                     89.80                   38.0               19.000000   \n",
       "3                    383.12                   54.0               27.000000   \n",
       "4                  12322.78                 2286.0               37.475410   \n",
       "...                     ...                    ...                     ...   \n",
       "918007              9068.62                  573.0               19.758621   \n",
       "918008               782.27                   70.0               23.333333   \n",
       "918009              2020.37                  167.0               20.875000   \n",
       "918010               122.89                   35.0               11.666667   \n",
       "918011               126.04                   91.0               15.166667   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "0                        52.0                8053.0             503.312500   \n",
       "1                       338.0               55569.0            2525.863636   \n",
       "2                        24.0                 157.0              78.500000   \n",
       "3                        32.0                1472.0             736.000000   \n",
       "4                       241.0               41901.0             686.901639   \n",
       "...                       ...                   ...                    ...   \n",
       "918007                   38.0               25573.0             881.827586   \n",
       "918008                   42.0                2070.0             690.000000   \n",
       "918009                   45.0                3746.0             468.250000   \n",
       "918010                   13.0                1050.0             350.000000   \n",
       "918011                   19.0                2642.0             440.333333   \n",
       "\n",
       "        total_day_supply_max          city state         last_name first_name  \\\n",
       "0                     1530.0    CUMBERLAND    MD         ENKESHAFI    ARDALAN   \n",
       "1                    11835.0        TOLEDO    OH            KHALIL     RASHID   \n",
       "2                      122.0        DAYTON    NV           ESCOBAR      JULIO   \n",
       "3                      852.0     NASHVILLE    TN         BLAKEMORE      ROSIE   \n",
       "4                     2369.0    BROOKVILLE    PA           GIRARDI      DAVID   \n",
       "...                      ...           ...   ...               ...        ...   \n",
       "918007                2520.0        GUYTON    GA           SORIANO       DINO   \n",
       "918008                1230.0       MEMPHIS    TN            THOMAS     SHELIA   \n",
       "918009                1271.0       SEATTLE    WA         DESCHENES   GEOFFREY   \n",
       "918010                 390.0      NEW YORK    NY              SHAW    L. NOAH   \n",
       "918011                 570.0  WILKES BARRE    PA  RUDZINSKI-WALTER    MARISSA   \n",
       "\n",
       "                 Speciality  Total_Payment_Sum  is_fraud  \n",
       "0         Internal Medicine               0.00       0.0  \n",
       "1            Anesthesiology             330.39       0.0  \n",
       "2                   Dentist             127.16       0.0  \n",
       "3        Nurse Practitioner               0.00       0.0  \n",
       "4           Family Practice              39.23       1.0  \n",
       "...                     ...                ...       ...  \n",
       "918007   Nurse Practitioner               0.00       0.0  \n",
       "918008     Registered Nurse               0.00       0.0  \n",
       "918009       Otolaryngology               0.00       0.0  \n",
       "918010        Psychoanalyst               0.00       0.0  \n",
       "918011  Physician Assistant               0.00       0.0  \n",
       "\n",
       "[918012 rows x 17 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_pd1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                       368\n",
       "total_drug_cost_sum       368\n",
       "total_drug_cost_mean      368\n",
       "total_drug_cost_max       368\n",
       "total_claim_count_sum     368\n",
       "total_claim_count_mean    368\n",
       "total_claim_count_max     368\n",
       "total_day_supply_sum      368\n",
       "total_day_supply_mean     368\n",
       "total_day_supply_max      368\n",
       "city                      368\n",
       "state                     368\n",
       "last_name                 368\n",
       "first_name                368\n",
       "Speciality                368\n",
       "Total_Payment_Sum         368\n",
       "is_fraud                  368\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_pd1[Features_pd1['is_fraud']==1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd=Features_pd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the features\n",
    "FeaturesAll_pd['total_drug_cost_sum'] = FeaturesAll_pd['total_drug_cost_sum'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['total_claim_count_sum'] = FeaturesAll_pd['total_claim_count_sum'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['total_day_supply_sum'] = FeaturesAll_pd['total_day_supply_sum'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['Total_Payment_Sum'] = FeaturesAll_pd['Total_Payment_Sum'].map(lambda x: np.log10(x + 1.0))\n",
    "\n",
    "FeaturesAll_pd['total_drug_cost_mean'] = FeaturesAll_pd['total_drug_cost_mean'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['total_claim_count_mean'] = FeaturesAll_pd['total_claim_count_mean'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['total_day_supply_mean'] = FeaturesAll_pd['total_day_supply_mean'].map(lambda x: np.log10(x + 1.0))\n",
    "\n",
    "FeaturesAll_pd['total_drug_cost_max'] = FeaturesAll_pd['total_drug_cost_max'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['total_claim_count_max'] = FeaturesAll_pd['total_claim_count_max'].map(lambda x: np.log10(x + 1.0))\n",
    "FeaturesAll_pd['total_day_supply_max'] = FeaturesAll_pd['total_day_supply_max'].map(lambda x: np.log10(x + 1.0))\n",
    "\n",
    "\n",
    "FeaturesAll_pd['claim_max-mean'] = FeaturesAll_pd['total_claim_count_max'] - FeaturesAll_pd['total_claim_count_mean']\n",
    "\n",
    "FeaturesAll_pd['supply_max-mean'] = FeaturesAll_pd['total_day_supply_max'] - FeaturesAll_pd['total_day_supply_mean']\n",
    "\n",
    "FeaturesAll_pd['drug_max-mean'] = FeaturesAll_pd['total_drug_cost_max'] - FeaturesAll_pd['total_drug_cost_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>Speciality</th>\n",
       "      <th>Total_Payment_Sum</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>claim_max-mean</th>\n",
       "      <th>supply_max-mean</th>\n",
       "      <th>drug_max-mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>4.092566</td>\n",
       "      <td>2.888972</td>\n",
       "      <td>3.969747</td>\n",
       "      <td>2.586587</td>\n",
       "      <td>1.399024</td>\n",
       "      <td>1.724276</td>\n",
       "      <td>3.906012</td>\n",
       "      <td>2.702700</td>\n",
       "      <td>3.184975</td>\n",
       "      <td>CUMBERLAND</td>\n",
       "      <td>MD</td>\n",
       "      <td>ENKESHAFI</td>\n",
       "      <td>ARDALAN</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.325251</td>\n",
       "      <td>0.482275</td>\n",
       "      <td>1.080775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>5.056408</td>\n",
       "      <td>3.714066</td>\n",
       "      <td>4.718552</td>\n",
       "      <td>3.266467</td>\n",
       "      <td>1.928954</td>\n",
       "      <td>2.530200</td>\n",
       "      <td>4.744840</td>\n",
       "      <td>3.402582</td>\n",
       "      <td>4.073205</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>OH</td>\n",
       "      <td>KHALIL</td>\n",
       "      <td>RASHID</td>\n",
       "      <td>Anesthesiology</td>\n",
       "      <td>2.520339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.601246</td>\n",
       "      <td>0.670623</td>\n",
       "      <td>1.004486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>2.183981</td>\n",
       "      <td>1.885785</td>\n",
       "      <td>1.958086</td>\n",
       "      <td>1.591065</td>\n",
       "      <td>1.301030</td>\n",
       "      <td>1.397940</td>\n",
       "      <td>2.198657</td>\n",
       "      <td>1.900367</td>\n",
       "      <td>2.089905</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>NV</td>\n",
       "      <td>ESCOBAR</td>\n",
       "      <td>JULIO</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>2.107752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.189538</td>\n",
       "      <td>0.072301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>2.820195</td>\n",
       "      <td>2.519821</td>\n",
       "      <td>2.584467</td>\n",
       "      <td>1.740363</td>\n",
       "      <td>1.447158</td>\n",
       "      <td>1.518514</td>\n",
       "      <td>3.168203</td>\n",
       "      <td>2.867467</td>\n",
       "      <td>2.930949</td>\n",
       "      <td>NASHVILLE</td>\n",
       "      <td>TN</td>\n",
       "      <td>BLAKEMORE</td>\n",
       "      <td>ROSIE</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071356</td>\n",
       "      <td>0.063482</td>\n",
       "      <td>0.064645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>4.866301</td>\n",
       "      <td>3.081325</td>\n",
       "      <td>4.090744</td>\n",
       "      <td>3.359266</td>\n",
       "      <td>1.585183</td>\n",
       "      <td>2.383815</td>\n",
       "      <td>4.622235</td>\n",
       "      <td>2.837526</td>\n",
       "      <td>3.374748</td>\n",
       "      <td>BROOKVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>GIRARDI</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>1.604550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.798632</td>\n",
       "      <td>0.537222</td>\n",
       "      <td>1.009419</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918007</td>\n",
       "      <td>1992999759</td>\n",
       "      <td>4.200928</td>\n",
       "      <td>2.739295</td>\n",
       "      <td>3.957589</td>\n",
       "      <td>2.758912</td>\n",
       "      <td>1.317198</td>\n",
       "      <td>1.591065</td>\n",
       "      <td>4.407799</td>\n",
       "      <td>2.945876</td>\n",
       "      <td>3.401573</td>\n",
       "      <td>GUYTON</td>\n",
       "      <td>GA</td>\n",
       "      <td>SORIANO</td>\n",
       "      <td>DINO</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.273866</td>\n",
       "      <td>0.455697</td>\n",
       "      <td>1.218295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918008</td>\n",
       "      <td>1992999791</td>\n",
       "      <td>3.134601</td>\n",
       "      <td>2.658116</td>\n",
       "      <td>2.893911</td>\n",
       "      <td>1.851258</td>\n",
       "      <td>1.386202</td>\n",
       "      <td>1.633468</td>\n",
       "      <td>3.316180</td>\n",
       "      <td>2.839478</td>\n",
       "      <td>3.090258</td>\n",
       "      <td>MEMPHIS</td>\n",
       "      <td>TN</td>\n",
       "      <td>THOMAS</td>\n",
       "      <td>SHELIA</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.247267</td>\n",
       "      <td>0.250780</td>\n",
       "      <td>0.235795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918009</td>\n",
       "      <td>1992999825</td>\n",
       "      <td>3.528849</td>\n",
       "      <td>2.626657</td>\n",
       "      <td>3.305646</td>\n",
       "      <td>2.225309</td>\n",
       "      <td>1.339948</td>\n",
       "      <td>1.662758</td>\n",
       "      <td>3.573684</td>\n",
       "      <td>2.671404</td>\n",
       "      <td>3.104487</td>\n",
       "      <td>SEATTLE</td>\n",
       "      <td>WA</td>\n",
       "      <td>DESCHENES</td>\n",
       "      <td>GEOFFREY</td>\n",
       "      <td>Otolaryngology</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322810</td>\n",
       "      <td>0.433083</td>\n",
       "      <td>0.678989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918010</td>\n",
       "      <td>1992999833</td>\n",
       "      <td>2.486870</td>\n",
       "      <td>2.012570</td>\n",
       "      <td>2.093036</td>\n",
       "      <td>1.556303</td>\n",
       "      <td>1.102662</td>\n",
       "      <td>1.146128</td>\n",
       "      <td>3.021603</td>\n",
       "      <td>2.545307</td>\n",
       "      <td>2.592177</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>NY</td>\n",
       "      <td>SHAW</td>\n",
       "      <td>L. NOAH</td>\n",
       "      <td>Psychoanalyst</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043466</td>\n",
       "      <td>0.046870</td>\n",
       "      <td>0.080466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>918011</td>\n",
       "      <td>1992999882</td>\n",
       "      <td>2.793036</td>\n",
       "      <td>2.018368</td>\n",
       "      <td>2.103940</td>\n",
       "      <td>1.963788</td>\n",
       "      <td>1.208620</td>\n",
       "      <td>1.301030</td>\n",
       "      <td>3.422097</td>\n",
       "      <td>2.644767</td>\n",
       "      <td>2.756636</td>\n",
       "      <td>WILKES BARRE</td>\n",
       "      <td>PA</td>\n",
       "      <td>RUDZINSKI-WALTER</td>\n",
       "      <td>MARISSA</td>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.092410</td>\n",
       "      <td>0.111869</td>\n",
       "      <td>0.085573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918012 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "0       1003000126             4.092566              2.888972   \n",
       "1       1003000142             5.056408              3.714066   \n",
       "2       1003000167             2.183981              1.885785   \n",
       "3       1003000282             2.820195              2.519821   \n",
       "4       1003000407             4.866301              3.081325   \n",
       "...            ...                  ...                   ...   \n",
       "918007  1992999759             4.200928              2.739295   \n",
       "918008  1992999791             3.134601              2.658116   \n",
       "918009  1992999825             3.528849              2.626657   \n",
       "918010  1992999833             2.486870              2.012570   \n",
       "918011  1992999882             2.793036              2.018368   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "0                  3.969747               2.586587                1.399024   \n",
       "1                  4.718552               3.266467                1.928954   \n",
       "2                  1.958086               1.591065                1.301030   \n",
       "3                  2.584467               1.740363                1.447158   \n",
       "4                  4.090744               3.359266                1.585183   \n",
       "...                     ...                    ...                     ...   \n",
       "918007             3.957589               2.758912                1.317198   \n",
       "918008             2.893911               1.851258                1.386202   \n",
       "918009             3.305646               2.225309                1.339948   \n",
       "918010             2.093036               1.556303                1.102662   \n",
       "918011             2.103940               1.963788                1.208620   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "0                    1.724276              3.906012               2.702700   \n",
       "1                    2.530200              4.744840               3.402582   \n",
       "2                    1.397940              2.198657               1.900367   \n",
       "3                    1.518514              3.168203               2.867467   \n",
       "4                    2.383815              4.622235               2.837526   \n",
       "...                       ...                   ...                    ...   \n",
       "918007               1.591065              4.407799               2.945876   \n",
       "918008               1.633468              3.316180               2.839478   \n",
       "918009               1.662758              3.573684               2.671404   \n",
       "918010               1.146128              3.021603               2.545307   \n",
       "918011               1.301030              3.422097               2.644767   \n",
       "\n",
       "        total_day_supply_max          city state         last_name first_name  \\\n",
       "0                   3.184975    CUMBERLAND    MD         ENKESHAFI    ARDALAN   \n",
       "1                   4.073205        TOLEDO    OH            KHALIL     RASHID   \n",
       "2                   2.089905        DAYTON    NV           ESCOBAR      JULIO   \n",
       "3                   2.930949     NASHVILLE    TN         BLAKEMORE      ROSIE   \n",
       "4                   3.374748    BROOKVILLE    PA           GIRARDI      DAVID   \n",
       "...                      ...           ...   ...               ...        ...   \n",
       "918007              3.401573        GUYTON    GA           SORIANO       DINO   \n",
       "918008              3.090258       MEMPHIS    TN            THOMAS     SHELIA   \n",
       "918009              3.104487       SEATTLE    WA         DESCHENES   GEOFFREY   \n",
       "918010              2.592177      NEW YORK    NY              SHAW    L. NOAH   \n",
       "918011              2.756636  WILKES BARRE    PA  RUDZINSKI-WALTER    MARISSA   \n",
       "\n",
       "                 Speciality  Total_Payment_Sum  is_fraud  claim_max-mean  \\\n",
       "0         Internal Medicine           0.000000       0.0        0.325251   \n",
       "1            Anesthesiology           2.520339       0.0        0.601246   \n",
       "2                   Dentist           2.107752       0.0        0.096910   \n",
       "3        Nurse Practitioner           0.000000       0.0        0.071356   \n",
       "4           Family Practice           1.604550       1.0        0.798632   \n",
       "...                     ...                ...       ...             ...   \n",
       "918007   Nurse Practitioner           0.000000       0.0        0.273866   \n",
       "918008     Registered Nurse           0.000000       0.0        0.247267   \n",
       "918009       Otolaryngology           0.000000       0.0        0.322810   \n",
       "918010        Psychoanalyst           0.000000       0.0        0.043466   \n",
       "918011  Physician Assistant           0.000000       0.0        0.092410   \n",
       "\n",
       "        supply_max-mean  drug_max-mean  \n",
       "0              0.482275       1.080775  \n",
       "1              0.670623       1.004486  \n",
       "2              0.189538       0.072301  \n",
       "3              0.063482       0.064645  \n",
       "4              0.537222       1.009419  \n",
       "...                 ...            ...  \n",
       "918007         0.455697       1.218295  \n",
       "918008         0.250780       0.235795  \n",
       "918009         0.433083       0.678989  \n",
       "918010         0.046870       0.080466  \n",
       "918011         0.111869       0.085573  \n",
       "\n",
       "[918012 rows x 20 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FeaturesAll_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd['npi'] = FeaturesAll_pd.npi.astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = ['npi','last_name', 'Speciality','first_name','city', 'state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_features = ['total_drug_cost_sum', 'total_drug_cost_mean','Total_Payment_Sum',\n",
    "       'total_drug_cost_max', 'total_claim_count_sum',\n",
    "       'total_claim_count_mean', 'total_claim_count_max',\n",
    "       'total_day_supply_sum', 'total_day_supply_mean', 'total_day_supply_max',\n",
    "    'claim_max-mean','supply_max-mean', 'drug_max-mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = ['is_fraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "allvars = categorical_features + numerical_features + target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = FeaturesAll_pd[\"is_fraud\"].values\n",
    "X = FeaturesAll_pd[allvars].drop('is_fraud',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scikit learn \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "#from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(734409, 19)\n",
      "(183603, 19)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "print(X_train.shape)\n",
    "print(X_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3494: 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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "X_train[numerical_features] = X_train.loc[:,numerical_features].fillna(0) \n",
    "X_valid[numerical_features] = X_valid.loc[:,numerical_features].fillna(0) \n",
    "X_train[categorical_features] = X_train.loc[:,categorical_features].fillna('NA') \n",
    "X_valid[categorical_features] = X_valid.loc[:,categorical_features].fillna('NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:494: 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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: 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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:494: 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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "scaler= StandardScaler() \n",
    "X_train[numerical_features] = scaler.fit_transform(X_train[numerical_features].values)\n",
    "X_valid[numerical_features] = scaler.transform(X_valid[numerical_features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_drug_cost_sum       float64\n",
      "total_drug_cost_mean      float64\n",
      "Total_Payment_Sum         float64\n",
      "total_drug_cost_max       float64\n",
      "total_claim_count_sum     float64\n",
      "total_claim_count_mean    float64\n",
      "total_claim_count_max     float64\n",
      "total_day_supply_sum      float64\n",
      "total_day_supply_mean     float64\n",
      "total_day_supply_max      float64\n",
      "claim_max-mean            float64\n",
      "supply_max-mean           float64\n",
      "drug_max-mean             float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train[numerical_features].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "734409\n",
      "183603\n"
     ]
    }
   ],
   "source": [
    "ix_ran = FeaturesAll_pd.index.values\n",
    "np.random.shuffle(ix_ran)\n",
    "\n",
    "df_len = len(FeaturesAll_pd)\n",
    "train_len = int(df_len * 0.8)  # 80% for training\n",
    "\n",
    "\n",
    "ix_train = ix_ran[:train_len]\n",
    "ix_valid = ix_ran[train_len:]\n",
    "\n",
    "df_train = FeaturesAll_pd.ix[ix_train]\n",
    "df_valid = FeaturesAll_pd.ix[ix_valid]\n",
    "\n",
    "print(len(ix_train))\n",
    "print(len(ix_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "npi                        object\n",
      "total_drug_cost_sum       float64\n",
      "total_drug_cost_mean      float64\n",
      "total_drug_cost_max       float64\n",
      "total_claim_count_sum     float64\n",
      "total_claim_count_mean    float64\n",
      "total_claim_count_max     float64\n",
      "total_day_supply_sum      float64\n",
      "total_day_supply_mean     float64\n",
      "total_day_supply_max      float64\n",
      "city                       object\n",
      "state                      object\n",
      "last_name                  object\n",
      "first_name                 object\n",
      "Speciality                 object\n",
      "Total_Payment_Sum         float64\n",
      "is_fraud                  float64\n",
      "claim_max-mean            float64\n",
      "supply_max-mean           float64\n",
      "drug_max-mean             float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drug Weighted_Scores\n",
    "\n",
    "partD_drug_train = pd.merge(partD_Drug_pd,df_train[['npi','is_fraud']], how='inner', on=['npi'])\n",
    "partD_drug_All = pd.merge(partD_Drug_pd,FeaturesAll_pd[['npi','is_fraud']], how='inner', on=['npi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12719\n"
     ]
    }
   ],
   "source": [
    "print(len(partD_drug_train[partD_drug_train['is_fraud']==1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2778\n"
     ]
    }
   ],
   "source": [
    "# get unique drug names\n",
    "drugs = set([ drugx for drugx in partD_drug_train['drug_name'].values if isinstance(drugx, str)])\n",
    "print(len(drugs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records in train set : \n",
      "20165202\n",
      "Total Fraud in train set : \n",
      "12719\n"
     ]
    },
    {
     "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>drug_name</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>AMLODIPINE BESYLATE</td>\n",
       "      <td>59.21</td>\n",
       "      <td>13</td>\n",
       "      <td>390</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>ATORVASTATIN CALCIUM</td>\n",
       "      <td>259.48</td>\n",
       "      <td>27</td>\n",
       "      <td>765</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CEPHALEXIN</td>\n",
       "      <td>98.99</td>\n",
       "      <td>17</td>\n",
       "      <td>123</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>CIPROFLOXACIN HCL</td>\n",
       "      <td>120.43</td>\n",
       "      <td>12</td>\n",
       "      <td>95</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>DOXYCYCLINE HYCLATE</td>\n",
       "      <td>300.76</td>\n",
       "      <td>17</td>\n",
       "      <td>105</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi             drug_name  total_drug_cost  total_claim_count  \\\n",
       "0  1003000126   AMLODIPINE BESYLATE            59.21                 13   \n",
       "1  1003000126  ATORVASTATIN CALCIUM           259.48                 27   \n",
       "2  1003000126            CEPHALEXIN            98.99                 17   \n",
       "3  1003000126     CIPROFLOXACIN HCL           120.43                 12   \n",
       "4  1003000126   DOXYCYCLINE HYCLATE           300.76                 17   \n",
       "\n",
       "   total_day_supply specialty_description  is_fraud  \n",
       "0               390     Internal Medicine       0.0  \n",
       "1               765     Internal Medicine       0.0  \n",
       "2               123     Internal Medicine       0.0  \n",
       "3                95     Internal Medicine       0.0  \n",
       "4               105     Internal Medicine       0.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total records in train set : \")\n",
    "print(len(partD_drug_train))\n",
    "print(\"Total Fraud in train set : \")\n",
    "print(len(partD_drug_train[partD_drug_train['is_fraud']==1]))\n",
    "partD_drug_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['total_drug_cost','total_claim_count','total_day_supply']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "partD_drug_train_Group = partD_drug_train.groupby(['drug_name', 'is_fraud'])\n",
    "partD_drug_All_Group = partD_drug_All.groupby(['drug_name', 'is_fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3593\n"
     ]
    }
   ],
   "source": [
    "drug_keys = partD_drug_train_Group.groups.keys()\n",
    "print(len(drug_keys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('1ST TIER UNIFINE PENTIPS', 0.0), ('1ST TIER UNIFINE PENTIPS PLUS', 0.0), ('ABACAVIR', 0.0), ('ABACAVIR-LAMIVUDINE', 0.0), ('ABACAVIR-LAMIVUDINE-ZIDOVUDINE', 0.0), ('ABELCET', 0.0), ('ABILIFY', 0.0), ('ABILIFY MAINTENA', 0.0), ('ABILIFY MAINTENA', 1.0), ('ABRAXANE', 0.0), ('ABSORICA', 0.0), ('ABSTRAL', 0.0), ('ACAMPROSATE CALCIUM', 0.0), ('ACARBOSE', 0.0), ('ACARBOSE', 1.0), ('ACCOLATE', 0.0), ('ACCUPRIL', 0.0), ('ACCURETIC', 0.0), ('ACEBUTOLOL HCL', 0.0), ('ACEBUTOLOL HCL', 1.0), ('ACETAMIN-CAFF-DIHYDROCODEINE', 0.0), ('ACETAMINOPHEN-CODEINE', 0.0), ('ACETAMINOPHEN-CODEINE', 1.0), ('ACETAZOLAMIDE', 0.0), ('ACETAZOLAMIDE', 1.0), ('ACETAZOLAMIDE SODIUM', 0.0), ('ACETIC ACID', 0.0), ('ACETIC ACID-ALUMINUM', 0.0), ('ACETYLCYSTEINE', 0.0), ('ACIPHEX', 0.0), ('ACIPHEX SPRINKLE', 0.0), ('ACITRETIN', 0.0), ('ACTEMRA', 0.0), ('ACTHIB', 0.0), ('ACTIGALL', 0.0), ('ACTIMMUNE', 0.0), ('ACTIQ', 0.0), ('ACTIVELLA', 0.0), ('ACTONEL', 0.0), ('ACTOPLUS MET', 0.0), ('ACTOPLUS MET XR', 0.0), ('ACTOS', 0.0), ('ACULAR', 0.0), ('ACULAR LS', 0.0), ('ACUVAIL', 0.0), ('ACYCLOVIR', 0.0), ('ACYCLOVIR', 1.0), ('ACYCLOVIR SODIUM', 0.0), ('ACZONE', 0.0), ('ADACEL TDAP', 0.0), ('ADAGEN', 0.0), ('ADAPALENE', 0.0), ('ADCIRCA', 0.0), ('ADDERALL', 0.0), ('ADDERALL XR', 0.0), ('ADEFOVIR DIPIVOXIL', 0.0), ('ADEMPAS', 0.0), ('ADLYXIN', 0.0), ('ADRENALIN', 0.0), ('ADRUCIL', 0.0), ('ADVAIR DISKUS', 0.0), ('ADVAIR DISKUS', 1.0), ('ADVAIR HFA', 0.0), ('ADVAIR HFA', 1.0), ('ADVOCATE PEN NEEDLES', 0.0), ('ADVOCATE SYRINGES', 0.0), ('ADZENYS XR-ODT', 0.0), ('AEROSPAN', 0.0), ('AFEDITAB CR', 0.0), ('AFINITOR', 0.0), ('AFINITOR DISPERZ', 0.0), ('AFREZZA', 0.0), ('AGGRENOX', 0.0), ('AGRYLIN', 0.0), ('AK-POLY-BAC', 0.0), ('AKYNZEO', 0.0), ('ALBENZA', 0.0), ('ALBUMIN (HUMAN)', 0.0), ('ALBUTEROL SULFATE', 0.0), ('ALBUTEROL SULFATE', 1.0), ('ALCLOMETASONE DIPROPIONATE', 0.0), ('ALCOHOL PADS', 0.0), ('ALCOHOL PADS', 1.0), ('ALCOHOL PREP PADS', 0.0), ('ALCOHOL PREP PADS', 1.0), ('ALCOHOL SWAB', 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0.0), ('DEXTROAMPHETAMINE SULFATE ER', 1.0), ('DEXTROAMPHETAMINE-AMPHET ER', 0.0), ('DEXTROAMPHETAMINE-AMPHET ER', 1.0), ('DEXTROAMPHETAMINE-AMPHETAMINE', 0.0), ('DEXTROAMPHETAMINE-AMPHETAMINE', 1.0), ('DEXTROSE 5%-0.2% NACL', 0.0), ('DEXTROSE 5%-0.225% NACL', 0.0), ('DEXTROSE 5%-0.45% NACL', 0.0), ('DEXTROSE 5%-0.45% NACL', 1.0), ('DEXTROSE 5%-0.45% NACL-KCL', 0.0), ('DEXTROSE 5%-0.9% NACL', 0.0), ('DEXTROSE 5%-1/2NS-KCL', 0.0), ('DEXTROSE 5%-NS-KCL', 0.0), ('DEXTROSE 5%-POTASSIUM CHLORIDE', 0.0), ('DEXTROSE IN LACTATED RINGERS', 0.0), ('DEXTROSE IN LACTATED RINGERS', 1.0), ('DEXTROSE IN WATER', 0.0), ('DIALYVITE', 0.0), ('DIALYVITE 3000', 0.0), ('DIAMOX SEQUELS', 0.0), ('DIANEAL WITH 1.5% DEXTROSE', 0.0), ('DIASTAT', 0.0), ('DIASTAT ACUDIAL', 0.0), ('DIAZEPAM', 0.0), ('DIAZEPAM', 1.0), ('DIBENZYLINE', 0.0), ('DICLOFENAC POTASSIUM', 0.0), ('DICLOFENAC POTASSIUM', 1.0), ('DICLOFENAC SODIUM', 0.0), ('DICLOFENAC SODIUM', 1.0), ('DICLOFENAC SODIUM ER', 0.0), ('DICLOFENAC SODIUM ER', 1.0), 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INSULIN', 0.0), ('EASY TOUCH INSULIN SAFETY', 0.0), ('EASY TOUCH INSULIN SYRINGE', 0.0), ('EASY TOUCH PEN NEEDLE', 0.0), ('EASY TOUCH PEN NEEDLE', 1.0), ('EASY TOUCH SHEATHLOCK INSULIN', 0.0), ('EASY-TOUCH INSULIN SYRINGE', 0.0), ('EC-NAPROSYN', 0.0), ('ECONAZOLE NITRATE', 0.0), ('ECONAZOLE NITRATE', 1.0), ('EDARBI', 0.0), ('EDARBYCLOR', 0.0), ('EDARBYCLOR', 1.0), ('EDECRIN', 0.0), ('EDLUAR', 0.0), ('EDURANT', 0.0), ('EFFER-K', 0.0), ('EFFEXOR XR', 0.0), ('EFFIENT', 0.0), ('EFFIENT', 1.0), ('EGRIFTA', 0.0), ('ELAPRASE', 0.0), ('ELELYSO', 0.0), ('ELESTAT', 0.0), ('ELESTRIN', 0.0), ('ELETRIPTAN HBR', 0.0), ('ELIDEL', 0.0), ('ELIGARD', 0.0), ('ELINEST', 0.0), ('ELIQUIS', 0.0), ('ELIQUIS', 1.0), ('ELITE-OB', 0.0), ('ELIXOPHYLLIN', 0.0), ('ELLA', 0.0), ('ELMIRON', 0.0), ('ELMIRON', 1.0), ('ELOCON', 0.0), ('EMBEDA', 0.0), ('EMBEDA', 1.0), ('EMCYT', 0.0), ('EMEND', 0.0), ('EMFLAZA', 0.0), ('EMOQUETTE', 0.0), ('EMPLICITI', 0.0), ('EMSAM', 0.0), ('EMTRIVA', 0.0), ('ENABLEX', 0.0), ('ENALAPRIL 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0.0), ('FLUCYTOSINE', 0.0), ('FLUDARABINE PHOSPHATE', 0.0), ('FLUDROCORTISONE ACETATE', 0.0), ('FLUDROCORTISONE ACETATE', 1.0), ('FLUMAZENIL', 0.0), ('FLUNISOLIDE', 0.0), ('FLUOCINOLONE ACETONIDE', 0.0), ('FLUOCINOLONE ACETONIDE OIL', 0.0), ('FLUOCINONIDE', 0.0), ('FLUOCINONIDE', 1.0), ('FLUOCINONIDE-E', 0.0), ('FLUORIDE', 0.0), ('FLUOROMETHOLONE', 0.0), ('FLUOROURACIL', 0.0), ('FLUOXETINE DR', 0.0), ('FLUOXETINE HCL', 0.0), ('FLUOXETINE HCL', 1.0), ('FLUPHENAZINE DECANOATE', 0.0), ('FLUPHENAZINE DECANOATE', 1.0), ('FLUPHENAZINE HCL', 0.0), ('FLUPHENAZINE HCL', 1.0), ('FLURANDRENOLIDE', 0.0), ('FLURAZEPAM HCL', 0.0), ('FLURAZEPAM HCL', 1.0), ('FLURBIPROFEN', 0.0), ('FLURBIPROFEN SODIUM', 0.0), ('FLUTAMIDE', 0.0), ('FLUTICASONE PROPIONATE', 0.0), ('FLUTICASONE PROPIONATE', 1.0), ('FLUTICASONE-SALMETEROL', 0.0), ('FLUVASTATIN ER', 0.0), ('FLUVASTATIN ER', 1.0), ('FLUVASTATIN SODIUM', 0.0), ('FLUVASTATIN SODIUM', 1.0), ('FLUVOXAMINE MALEATE', 0.0), ('FLUVOXAMINE MALEATE', 1.0), 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ULTRAMICROSIZE', 0.0), ('GUANFACINE HCL', 0.0), ('GUANFACINE HCL', 1.0), ('GUANFACINE HCL ER', 0.0), ('GUANFACINE HCL ER', 1.0), ('GYNAZOLE 1', 0.0), ('H.P. 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('HUMALOG MIX 75-25', 0.0), ('HUMALOG MIX 75-25', 1.0), ('HUMALOG MIX 75-25 KWIKPEN', 0.0), ('HUMALOG MIX 75-25 KWIKPEN', 1.0), ('HUMATROPE', 0.0), ('HUMIRA', 0.0), ('HUMIRA', 1.0), ('HUMIRA PEN', 0.0), ('HUMIRA PEN', 1.0), ('HUMIRA PEN PSORIASIS-UVEITIS', 0.0), ('HUMULIN 70-30', 0.0), ('HUMULIN 70-30', 1.0), ('HUMULIN 70/30 KWIKPEN', 0.0), ('HUMULIN 70/30 KWIKPEN', 1.0), ('HUMULIN N', 0.0), ('HUMULIN N', 1.0), ('HUMULIN N KWIKPEN', 0.0), ('HUMULIN R', 0.0), ('HUMULIN R', 1.0), ('HUMULIN R U-500', 0.0), ('HUMULIN R U-500 KWIKPEN', 0.0), ('HYDRALAZINE HCL', 0.0), ('HYDRALAZINE HCL', 1.0), ('HYDREA', 0.0), ('HYDROCHLOROTHIAZIDE', 0.0), ('HYDROCHLOROTHIAZIDE', 1.0), ('HYDROCODONE-ACETAMINOPHEN', 0.0), ('HYDROCODONE-ACETAMINOPHEN', 1.0), ('HYDROCODONE-HOMATROPINE MBR', 0.0), ('HYDROCODONE-IBUPROFEN', 0.0), ('HYDROCODONE-IBUPROFEN', 1.0), ('HYDROCORTISONE', 0.0), ('HYDROCORTISONE', 1.0), ('HYDROCORTISONE BUTYRATE', 0.0), ('HYDROCORTISONE VALERATE', 0.0), ('HYDROCORTISONE VALERATE', 1.0), 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0.0), ('LEVETIRACETAM ER', 1.0), ('LEVETIRACETAM-NACL', 0.0), ('LEVITRA', 0.0), ('LEVOBUNOLOL HCL', 0.0), ('LEVOCARNITINE', 0.0), ('LEVOCARNITINE', 1.0), ('LEVOCETIRIZINE DIHYDROCHLORIDE', 0.0), ('LEVOCETIRIZINE DIHYDROCHLORIDE', 1.0), ('LEVOFLOXACIN', 0.0), ('LEVOFLOXACIN', 1.0), ('LEVOFLOXACIN-D5W', 0.0), ('LEVOLEUCOVORIN CALCIUM', 0.0), ('LEVONEST', 0.0), ('LEVONORGESTREL-ETH ESTRADIOL', 0.0), ('LEVORA-28', 0.0), ('LEVORPHANOL TARTRATE', 0.0), ('LEVORPHANOL TARTRATE', 1.0), ('LEVOTHYROXINE SODIUM', 0.0), ('LEVOTHYROXINE SODIUM', 1.0), ('LEVOXYL', 0.0), ('LEVOXYL', 1.0), ('LEVSIN', 0.0), ('LEVSIN-SL', 0.0), ('LEXAPRO', 0.0), ('LEXAPRO', 1.0), ('LEXIVA', 0.0), ('LIALDA', 0.0), ('LIALDA', 1.0), ('LIBRAX', 0.0), ('LIDOCAINE', 0.0), ('LIDOCAINE', 1.0), ('LIDOCAINE HCL', 0.0), ('LIDOCAINE HCL VISCOUS', 0.0), ('LIDOCAINE HCL VISCOUS', 1.0), ('LIDOCAINE-HYDROCORTISONE', 0.0), ('LIDOCAINE-PRILOCAINE', 0.0), ('LIDOCAINE-PRILOCAINE', 1.0), ('LIDODERM', 0.0), ('LIDORX', 0.0), ('LINDANE', 0.0), ('LINEZOLID', 0.0), ('LINEZOLID-0.9% NACL', 0.0), ('LINZESS', 0.0), ('LINZESS', 1.0), ('LIORESAL INTRATHECAL', 0.0), ('LIOTHYRONINE SODIUM', 0.0), ('LIOTHYRONINE SODIUM', 1.0), ('LIPITOR', 0.0), ('LIPOFEN', 0.0), ('LIPOFEN', 1.0), ('LISINOPRIL', 0.0), ('LISINOPRIL', 1.0), ('LISINOPRIL-HYDROCHLOROTHIAZIDE', 0.0), ('LISINOPRIL-HYDROCHLOROTHIAZIDE', 1.0), ('LITE TOUCH', 0.0), ('LITE TOUCH', 1.0), ('LITETOUCH INSULIN SYRINGE', 0.0), ('LITHIUM', 0.0), ('LITHIUM CARBONATE', 0.0), ('LITHIUM CARBONATE', 1.0), ('LITHIUM CARBONATE ER', 0.0), ('LITHIUM CARBONATE ER', 1.0), ('LITHOBID', 0.0), ('LIVALO', 0.0), ('LIVALO', 1.0), ('LO LOESTRIN FE', 0.0), ('LOCOID', 0.0), ('LODOSYN', 0.0), ('LOESTRIN FE', 0.0), ('LOMEDIA 24 FE', 0.0), ('LOMOTIL', 0.0), ('LONSURF', 0.0), ('LOPERAMIDE', 0.0), ('LOPERAMIDE', 1.0), ('LOPID', 0.0), ('LOPINAVIR-RITONAVIR', 0.0), ('LOPREEZA', 0.0), ('LOPRESSOR', 0.0), ('LOPROX', 0.0), ('LORAZEPAM', 0.0), ('LORAZEPAM', 1.0), ('LORAZEPAM INTENSOL', 0.0), ('LORCET', 0.0), ('LORCET HD', 0.0), ('LORCET PLUS', 0.0), ('LORTAB', 0.0), ('LORYNA', 0.0), ('LORZONE', 0.0), ('LOSARTAN POTASSIUM', 0.0), ('LOSARTAN POTASSIUM', 1.0), ('LOSARTAN-HYDROCHLOROTHIAZIDE', 0.0), ('LOSARTAN-HYDROCHLOROTHIAZIDE', 1.0), ('LOTEMAX', 0.0), ('LOTENSIN HCT', 0.0), ('LOTREL', 0.0), ('LOTRONEX', 0.0), ('LOTRONEX', 1.0), ('LOVASTATIN', 0.0), ('LOVASTATIN', 1.0), ('LOVAZA', 0.0), ('LOVENOX', 0.0), ('LOW-OGESTREL', 0.0), ('LOW-OGESTREL', 1.0), ('LOXAPINE', 0.0), ('LUCENTIS', 0.0), ('LUDENT FLUORIDE', 0.0), ('LUMIGAN', 0.0), ('LUMIGAN', 1.0), ('LUMIZYME', 0.0), ('LUNESTA', 0.0), ('LUPRON DEPOT', 0.0), ('LUPRON DEPOT-PED', 0.0), ('LUTERA', 0.0), ('LUXIQ', 0.0), ('LUZU', 0.0), ('LYNPARZA', 0.0), ('LYRICA', 0.0), ('LYRICA', 1.0), ('LYSODREN', 0.0), ('M-M-R II VACCINE', 0.0), ('MACROBID', 0.0), ('MAGELLAN INSULIN SAFETY SYRNG', 0.0), ('MAGELLAN INSULIN SYRINGE', 0.0), ('MAGNEBIND 400 RX', 0.0), ('MAGNESIUM SULFATE', 0.0), ('MAGNESIUM SULFATE-D5W', 0.0), ('MAKENA', 0.0), ('MALARONE', 0.0), ('MALATHION', 0.0), ('MANNITOL', 0.0), ('MAPROTILINE HCL', 0.0), ('MARCAINE', 0.0), ('MARINOL', 0.0), ('MARLISSA', 0.0), ('MARPLAN', 0.0), ('MATULANE', 0.0), ('MATZIM LA', 0.0), ('MATZIM LA', 1.0), ('MAVYRET', 0.0), ('MAXALT', 0.0), ('MAXALT MLT', 0.0), ('MAXI-COMFORT', 0.0), ('MAXIDEX', 0.0), ('MAXIPIME', 0.0), ('MAXITROL', 0.0), ('MAXZIDE', 0.0), ('MAXZIDE-25 MG', 0.0), ('MECLIZINE HCL', 0.0), ('MECLIZINE HCL', 1.0), ('MECLOFENAMATE SODIUM', 0.0), ('MEDROL', 0.0), ('MEDROXYPROGESTERONE ACETATE', 0.0), ('MEDROXYPROGESTERONE ACETATE', 1.0), ('MEFENAMIC ACID', 0.0), ('MEFLOQUINE HCL', 0.0), ('MEGESTROL ACETATE', 0.0), ('MEGESTROL ACETATE', 1.0), ('MEKINIST', 0.0), ('MELOXICAM', 0.0), ('MELOXICAM', 1.0), ('MEMANTINE HCL', 0.0), ('MEMANTINE HCL', 1.0), ('MENACTRA', 0.0), ('MENEST', 0.0), ('MENOMUNE-A-C-Y-W-135', 0.0), ('MENOSTAR', 0.0), ('MENTAX', 0.0), ('MENVEO A-C-Y-W-135-DIP', 0.0), ('MEPERIDINE HCL', 0.0), ('MEPERIDINE HCL', 1.0), ('MEPROBAMATE', 0.0), ('MEPRON', 0.0), ('MERCAPTOPURINE', 0.0), ('MEROPENEM', 0.0), ('MEROPENEM', 1.0), ('MEROPENEM-0.9% NACL', 0.0), ('MESALAMINE', 0.0), ('MESALAMINE', 1.0), ('MESNA', 0.0), ('MESNEX', 0.0), ('MESTINON', 0.0), ('METADATE CD', 0.0), ('METADATE ER', 0.0), ('METAPROTERENOL SULFATE', 0.0), ('METAXALONE', 0.0), ('METFORMIN HCL', 0.0), ('METFORMIN HCL', 1.0), ('METFORMIN HCL ER', 0.0), ('METFORMIN HCL ER', 1.0), ('METHADONE HCL', 0.0), ('METHADONE HCL', 1.0), ('METHADONE INTENSOL', 0.0), ('METHADOSE', 0.0), ('METHAMPHETAMINE HCL', 0.0), ('METHAZOLAMIDE', 0.0), ('METHENAMINE HIPPURATE', 0.0), ('METHENAMINE MANDELATE', 0.0), ('METHERGINE', 0.0), ('METHIMAZOLE', 0.0), ('METHIMAZOLE', 1.0), ('METHOCARBAMOL', 0.0), ('METHOCARBAMOL', 1.0), ('METHOTREXATE', 0.0), ('METHOTREXATE', 1.0), ('METHOTREXATE SODIUM', 0.0), ('METHOXSALEN', 0.0), ('METHSCOPOLAMINE BROMIDE', 0.0), ('METHYCLOTHIAZIDE', 0.0), ('METHYLDOPA', 0.0), ('METHYLDOPA-HYDROCHLOROTHIAZIDE', 0.0), ('METHYLPHENIDATE ER', 0.0), ('METHYLPHENIDATE ER', 1.0), ('METHYLPHENIDATE HCL', 0.0), ('METHYLPHENIDATE HCL', 1.0), ('METHYLPHENIDATE HCL CD', 0.0), ('METHYLPHENIDATE HCL ER', 0.0), ('METHYLPHENIDATE LA', 0.0), ('METHYLPREDNISOLONE', 0.0), ('METHYLPREDNISOLONE', 1.0), ('METHYLPREDNISOLONE ACETATE', 0.0), ('METHYLPREDNISOLONE SODIUM SUCC', 0.0), ('METHYLTESTOSTERONE', 0.0), ('METOCLOPRAMIDE HCL', 0.0), ('METOCLOPRAMIDE HCL', 1.0), ('METOLAZONE', 0.0), ('METOLAZONE', 1.0), ('METOPROLOL SUCCINATE', 0.0), ('METOPROLOL SUCCINATE', 1.0), ('METOPROLOL TARTRATE', 0.0), ('METOPROLOL TARTRATE', 1.0), ('METOPROLOL-HYDROCHLOROTHIAZIDE', 0.0), ('METOPROLOL-HYDROCHLOROTHIAZIDE', 1.0), ('METRO IV', 0.0), ('METROGEL', 0.0), ('METROLOTION', 0.0), ('METRONIDAZOLE', 0.0), ('METRONIDAZOLE', 1.0), ('MEXILETINE HCL', 0.0), ('MEXILETINE HCL', 1.0), ('MIACALCIN', 0.0), ('MIBELAS 24 FE', 0.0), ('MICARDIS', 0.0), ('MICARDIS HCT', 0.0), ('MICORT-HC', 0.0), ('MICROGESTIN', 0.0), ('MICROGESTIN', 1.0), ('MICROGESTIN 24 FE', 0.0), ('MICROGESTIN FE', 0.0), ('MICROZIDE', 0.0), ('MIDAZOLAM HCL', 0.0), ('MIDODRINE HCL', 0.0), ('MIDODRINE HCL', 1.0), ('MIGERGOT', 0.0), ('MIGLITOL', 0.0), ('MIGRANAL', 0.0), ('MILLIPRED', 0.0), ('MILRINONE IN 5% DEXTROSE', 0.0), ('MILRINONE LACTATE', 0.0), ('MIMVEY', 0.0), ('MIMVEY LO', 0.0), ('MINASTRIN 24 FE', 0.0), ('MINI ULTRA-THIN II', 0.0), ('MINIPRESS', 0.0), ('MINITRAN', 0.0), ('MINIVELLE', 0.0), ('MINOCIN', 0.0), ('MINOCYCLINE HCL', 0.0), ('MINOCYCLINE HCL', 1.0), ('MINOCYCLINE HCL ER', 0.0), ('MINOXIDIL', 0.0), ('MINOXIDIL', 1.0), ('MIRAPEX', 0.0), ('MIRAPEX ER', 0.0), ('MIRCETTE', 0.0), ('MIRTAZAPINE', 0.0), ('MIRTAZAPINE', 1.0), ('MIRVASO', 0.0), ('MISOPROSTOL', 0.0), ('MISOPROSTOL', 1.0), ('MITIGARE', 0.0), ('MITOMYCIN', 0.0), ('MOBIC', 0.0), ('MODAFINIL', 0.0), ('MODAFINIL', 1.0), ('MOEXIPRIL HCL', 0.0), ('MOEXIPRIL HCL', 1.0), ('MOEXIPRIL-HYDROCHLOROTHIAZIDE', 0.0), ('MOLINDONE HCL', 0.0), ('MOMETASONE FUROATE', 0.0), ('MOMETASONE FUROATE', 1.0), ('MONO-LINYAH', 0.0), ('MONOJECT', 0.0), ('MONOJECT INSULIN SAFETY SYRNG', 0.0), ('MONOJECT INSULIN SYRINGE', 0.0), ('MONONESSA', 0.0), ('MONTELUKAST SODIUM', 0.0), ('MONTELUKAST SODIUM', 1.0), ('MONUROL', 0.0), ('MORPHABOND ER', 0.0), ('MORPHINE SULFATE', 0.0), ('MORPHINE SULFATE', 1.0), ('MORPHINE SULFATE ER', 0.0), ('MORPHINE SULFATE ER', 1.0), ('MOVANTIK', 0.0), ('MOVANTIK', 1.0), ('MOVIPREP', 0.0), ('MOXEZA', 0.0), ('MOXIFLOXACIN', 0.0), ('MOXIFLOXACIN HCL', 0.0), ('MOZOBIL', 0.0), ('MS CONTIN', 0.0), ('MULTAQ', 0.0), ('MULTAQ', 1.0), ('MULTIVITAMIN WITH FLUORIDE', 0.0), ('MULTIVITAMINS W-FLUORIDE-IRON', 0.0), ('MULTIVITAMINS WITH FLUORIDE', 0.0), ('MUPIROCIN', 0.0), ('MUPIROCIN', 1.0), ('MVC-FLUORIDE', 0.0), ('MYALEPT', 0.0), ('MYAMBUTOL', 0.0), ('MYCAMINE', 0.0), ('MYCOPHENOLATE MOFETIL', 0.0), ('MYCOPHENOLATE MOFETIL', 1.0), ('MYCOPHENOLIC ACID', 0.0), ('MYFORTIC', 0.0), ('MYOBLOC', 0.0), ('MYORISAN', 0.0), ('MYRBETRIQ', 0.0), ('MYRBETRIQ', 1.0), ('MYSOLINE', 0.0), ('MYTESI', 0.0), ('MYZILRA', 0.0), ('NABI-HB', 0.0), ('NABUMETONE', 0.0), ('NABUMETONE', 1.0), ('NADOLOL', 0.0), ('NADOLOL', 1.0), ('NADOLOL-BENDROFLUMETHIAZIDE', 0.0), ('NAFCILLIN', 0.0), ('NAFCILLIN SODIUM', 0.0), ('NAFTIFINE HCL', 0.0), ('NAFTIN', 0.0), ('NAGLAZYME', 0.0), ('NALBUPHINE HCL', 0.0), ('NALBUPHINE HCL', 1.0), ('NALFON', 0.0), ('NALOXONE HCL', 0.0), ('NALTREXONE HCL', 0.0), ('NALTREXONE HCL', 1.0), ('NAMENDA', 0.0), ('NAMENDA XR', 0.0), ('NAMENDA XR', 1.0), ('NAMZARIC', 0.0), ('NAMZARIC', 1.0), ('NAPHAZOLINE HCL', 0.0), ('NAPRELAN', 0.0), ('NAPROXEN', 0.0), ('NAPROXEN', 1.0), ('NAPROXEN SODIUM', 0.0), ('NAPROXEN SODIUM', 1.0), ('NAPROXEN SODIUM CR', 0.0), ('NAPROXEN SODIUM CR', 1.0), ('NAPROXEN SODIUM DS', 0.0), ('NAPROXEN SODIUM ER', 0.0), ('NARATRIPTAN HCL', 0.0), ('NARCAN', 0.0), ('NARCAN', 1.0), ('NARDIL', 0.0), ('NASONEX', 0.0), ('NASONEX', 1.0), ('NATACYN', 0.0), ('NATAZIA', 0.0), ('NATEGLINIDE', 0.0), ('NATEGLINIDE', 1.0), ('NATESTO', 0.0), ('NATPARA', 0.0), ('NEBUPENT', 0.0), ('NECON', 0.0), ('NEFAZODONE HCL', 0.0), ('NEO-POLYCIN', 0.0), ('NEO-POLYCIN HC', 0.0), ('NEOMYCIN SULFATE', 0.0), ('NEOMYCIN-BACITRACIN-POLY-HC', 0.0), ('NEOMYCIN-BACITRACIN-POLYMYXIN', 0.0), ('NEOMYCIN-POLYMYXIN B', 0.0), ('NEOMYCIN-POLYMYXIN-DEXAMETH', 0.0), ('NEOMYCIN-POLYMYXIN-DEXAMETH', 1.0), ('NEOMYCIN-POLYMYXIN-GRAMICIDIN', 0.0), ('NEOMYCIN-POLYMYXIN-GRAMICIDIN', 1.0), ('NEOMYCIN-POLYMYXIN-HC', 0.0), ('NEOMYCIN-POLYMYXIN-HC', 1.0), ('NEOMYCIN-POLYMYXIN-HYDROCORT', 0.0), ('NEOMYCIN-POLYMYXIN-HYDROCORT', 1.0), ('NEORAL', 0.0), ('NEOSPORIN G.U. IRRIGANT', 0.0), ('NEPHRO-VITE RX', 0.0), ('NESINA', 0.0), ('NEULASTA', 0.0), ('NEUPOGEN', 0.0), ('NEUPRO', 0.0), ('NEUPRO', 1.0), ('NEURONTIN', 0.0), ('NEVANAC', 0.0), ('NEVIRAPINE', 0.0), ('NEVIRAPINE ER', 0.0), ('NEXAVAR', 0.0), ('NEXAVAR', 1.0), ('NEXIUM', 0.0), ('NEXIUM', 1.0), ('NEXIUM I.V.', 0.0), ('NIACIN ER', 0.0), ('NIACIN ER', 1.0), ('NIACOR', 0.0), ('NIACOR', 1.0), ('NIASPAN', 0.0), ('NICARDIPINE HCL', 0.0), ('NICOTROL', 0.0), ('NICOTROL NS', 0.0), ('NIFEDICAL XL', 0.0), ('NIFEDICAL XL', 1.0), ('NIFEDIPINE', 0.0), ('NIFEDIPINE ER', 0.0), ('NIFEDIPINE ER', 1.0), ('NIKKI', 0.0), ('NILANDRON', 0.0), ('NILUTAMIDE', 0.0), ('NIMODIPINE', 0.0), ('NINLARO', 0.0), ('NISOLDIPINE', 0.0), ('NISOLDIPINE', 1.0), ('NITRO-BID', 0.0), ('NITRO-DUR', 0.0), ('NITROFURANTOIN', 0.0), ('NITROFURANTOIN', 1.0), ('NITROFURANTOIN MONO-MACRO', 0.0), ('NITROFURANTOIN MONO-MACRO', 1.0), ('NITROGLYCERIN', 0.0), ('NITROGLYCERIN', 1.0), ('NITROGLYCERIN PATCH', 0.0), ('NITROGLYCERIN PATCH', 1.0), ('NITROLINGUAL', 0.0), ('NITROSTAT', 0.0), ('NITROSTAT', 1.0), ('NIVA-PLUS', 0.0), ('NIZATIDINE', 0.0), ('NIZORAL', 0.0), ('NORA-BE', 0.0), ('NORCO', 0.0), ('NORCO', 1.0), ('NORDITROPIN FLEXPRO', 0.0), ('NORETHIN-ETH ESTRA-FERROUS FUM', 0.0), ('NORETHINDRON-ETHINYL ESTRADIOL', 0.0), ('NORETHINDRONE', 0.0), ('NORETHINDRONE', 1.0), ('NORETHINDRONE ACETATE', 0.0), ('NORETHINDRONE ACETATE', 1.0), ('NORGESTIMATE-ETHINYL ESTRADIOL', 0.0), ('NORITATE', 0.0), ('NORPACE', 0.0), ('NORPACE CR', 0.0), ('NORPRAMIN', 0.0), ('NORTHERA', 0.0), ('NORTREL', 0.0), ('NORTRIPTYLINE HCL', 0.0), ('NORTRIPTYLINE HCL', 1.0), ('NORVASC', 0.0), ('NORVASC', 1.0), ('NORVIR', 0.0), ('NORVIR', 1.0), ('NOVOFINE', 0.0), ('NOVOFINE', 1.0), ('NOVOFINE 32', 0.0), ('NOVOFINE 32', 1.0), ('NOVOFINE AUTOCOVER', 0.0), ('NOVOFINE AUTOCOVER', 1.0), ('NOVOFINE PLUS', 0.0), ('NOVOLIN 70-30', 0.0), ('NOVOLIN 70-30', 1.0), ('NOVOLIN N', 0.0), ('NOVOLIN N', 1.0), ('NOVOLIN R', 0.0), ('NOVOLIN R', 1.0), ('NOVOLOG', 0.0), ('NOVOLOG', 1.0), ('NOVOLOG FLEXPEN', 0.0), ('NOVOLOG FLEXPEN', 1.0), ('NOVOLOG MIX 70-30', 0.0), ('NOVOLOG MIX 70-30', 1.0), ('NOVOLOG MIX 70-30 FLEXPEN', 0.0), ('NOVOLOG MIX 70-30 FLEXPEN', 1.0), ('NOVOTWIST', 0.0), ('NOXAFIL', 0.0), ('NP THYROID', 0.0), ('NPLATE', 0.0), ('NUCALA', 0.0), ('NUCYNTA', 0.0), ('NUCYNTA', 1.0), ('NUCYNTA ER', 0.0), ('NUCYNTA ER', 1.0), ('NUEDEXTA', 0.0), ('NUEDEXTA', 1.0), ('NULOJIX', 0.0), ('NULYTELY WITH FLAVOR PACKS', 0.0), ('NUPLAZID', 0.0), ('NUTRILIPID', 0.0), ('NUTROPIN AQ NUSPIN', 0.0), ('NUVARING', 0.0), ('NUVIGIL', 0.0), ('NYAMYC', 0.0), ('NYAMYC', 1.0), ('NYSTATIN', 0.0), ('NYSTATIN', 1.0), ('NYSTATIN-TRIAMCINOLONE', 0.0), ('NYSTATIN-TRIAMCINOLONE', 1.0), ('NYSTOP', 0.0), ('NYSTOP', 1.0), ('O-CAL FA', 0.0), ('O-CAL PRENATAL', 0.0), ('OB COMPLETE', 0.0), ('OCALIVA', 0.0), ('OCELLA', 0.0), ('OCREVUS', 0.0), ('OCTAGAM', 0.0), ('OCTREOTIDE ACETATE', 0.0), ('ODEFSEY', 0.0), ('ODEFSEY', 1.0), ('ODOMZO', 0.0), ('OFEV', 0.0), ('OFLOXACIN', 0.0), ('OFLOXACIN', 1.0), ('OGESTREL', 0.0), ('OLANZAPINE', 0.0), ('OLANZAPINE', 1.0), ('OLANZAPINE ODT', 0.0), ('OLANZAPINE ODT', 1.0), ('OLANZAPINE-FLUOXETINE HCL', 0.0), ('OLMESARTAN MEDOXOMIL', 0.0), ('OLMESARTAN MEDOXOMIL', 1.0), ('OLMESARTAN-AMLODIPINE-HCTZ', 0.0), ('OLMESARTAN-AMLODIPINE-HCTZ', 1.0), ('OLMESARTAN-HYDROCHLOROTHIAZIDE', 0.0), ('OLMESARTAN-HYDROCHLOROTHIAZIDE', 1.0), ('OLOPATADINE HCL', 0.0), ('OLOPATADINE HCL', 1.0), ('OLUX', 0.0), ('OLYSIO', 0.0), ('OMEGA-3 ACID ETHYL ESTERS', 0.0), ('OMEGA-3 ACID ETHYL ESTERS', 1.0), ('OMEPRAZOLE', 0.0), ('OMEPRAZOLE', 1.0), ('OMEPRAZOLE-SODIUM BICARBONATE', 0.0), ('OMEPRAZOLE-SODIUM BICARBONATE', 1.0), ('OMNARIS', 0.0), ('OMNIPOD', 0.0), ('OMNIPRED', 0.0), ('OMNITROPE', 0.0), ('ONDANSETRON HCL', 0.0), ('ONDANSETRON HCL', 1.0), ('ONDANSETRON ODT', 0.0), ('ONDANSETRON ODT', 1.0), ('ONFI', 0.0), ('ONFI', 1.0), ('ONGLYZA', 0.0), ('ONGLYZA', 1.0), ('ONZETRA XSAIL', 0.0), ('OPANA', 0.0), ('OPANA ER', 0.0), ('OPANA ER', 1.0), ('OPDIVO', 0.0), ('OPDIVO', 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('SOMA', 0.0), ('SOMATULINE DEPOT', 0.0), ('SOMAVERT', 0.0), ('SONATA', 0.0), ('SOOLANTRA', 0.0), ('SORBITOL', 0.0), ('SORIATANE', 0.0), ('SORILUX', 0.0), ('SORINE', 0.0), ('SOTALOL', 0.0), ('SOTALOL', 1.0), ('SOTALOL AF', 0.0), ('SOVALDI', 0.0), ('SPIRIVA', 0.0), ('SPIRIVA', 1.0), ('SPIRIVA RESPIMAT', 0.0), ('SPIRIVA RESPIMAT', 1.0), ('SPIRONOLACTONE', 0.0), ('SPIRONOLACTONE', 1.0), ('SPIRONOLACTONE-HCTZ', 0.0), ('SPIRONOLACTONE-HCTZ', 1.0), ('SPORANOX', 0.0), ('SPRINTEC', 0.0), ('SPRITAM', 0.0), ('SPRYCEL', 0.0), ('SPS', 0.0), ('SRONYX', 0.0), ('SSD', 0.0), ('SSD', 1.0), ('STALEVO 100', 0.0), ('STALEVO 125', 0.0), ('STALEVO 150', 0.0), ('STALEVO 200', 0.0), ('STALEVO 50', 0.0), ('STALEVO 75', 0.0), ('STAMARIL', 0.0), ('STARLIX', 0.0), ('STAVUDINE', 0.0), ('STELARA', 0.0), ('STERILE PADS', 0.0), ('STIMATE', 0.0), ('STIOLTO RESPIMAT', 0.0), ('STIOLTO RESPIMAT', 1.0), ('STIVARGA', 0.0), ('STRATTERA', 0.0), ('STRATTERA', 1.0), ('STRENSIQ', 0.0), ('STREPTOMYCIN SULFATE', 0.0), ('STRIANT', 0.0), ('STRIBILD', 0.0), ('STRIBILD', 1.0), ('STRIVERDI RESPIMAT', 0.0), ('STROMECTOL', 0.0), ('SUBOXONE', 0.0), ('SUBOXONE', 1.0), ('SUBSYS', 0.0), ('SUBSYS', 1.0), ('SUCRAID', 0.0), ('SUCRALFATE', 0.0), ('SUCRALFATE', 1.0), ('SULAR', 0.0), ('SULFACETAMIDE SODIUM', 0.0), ('SULFACETAMIDE-PREDNISOLONE', 0.0), ('SULFADIAZINE', 0.0), ('SULFAMETHOXAZOLE-TRIMETHOPRIM', 0.0), ('SULFAMETHOXAZOLE-TRIMETHOPRIM', 1.0), ('SULFAMYLON', 0.0), ('SULFASALAZINE', 0.0), ('SULFASALAZINE', 1.0), ('SULFASALAZINE DR', 0.0), ('SULFATRIM', 0.0), ('SULINDAC', 0.0), ('SULINDAC', 1.0), ('SUMATRIPTAN', 0.0), ('SUMATRIPTAN', 1.0), ('SUMATRIPTAN SUCCINATE', 0.0), ('SUMATRIPTAN SUCCINATE', 1.0), ('SUMAVEL DOSEPRO', 0.0), ('SUPRAX', 0.0), ('SUPREP', 0.0), ('SURE COMFORT', 0.0), ('SURE COMFORT', 1.0), ('SURE COMFORT ALCOHOL', 0.0), ('SURE-FINE PEN NEEDLES', 0.0), ('SURE-JECT INSULIN SYRINGE', 0.0), ('SURE-PREP ALCOHOL PREP PADS', 0.0), ('SURMONTIL', 0.0), ('SUSTIVA', 0.0), ('SUTENT', 0.0), ('SUTENT', 1.0), ('SYEDA', 0.0), ('SYLATRON', 0.0), ('SYMBICORT', 0.0), ('SYMBICORT', 1.0), ('SYMBYAX', 0.0), ('SYMLINPEN 120', 0.0), ('SYMLINPEN 60', 0.0), ('SYNALGOS-DC', 0.0), ('SYNAREL', 0.0), ('SYNERA', 0.0), ('SYNERCID', 0.0), ('SYNJARDY', 0.0), ('SYNJARDY XR', 0.0), ('SYNRIBO', 0.0), ('SYNTHROID', 0.0), ('SYNTHROID', 1.0), ('SYPRINE', 0.0), ('TABLOID', 0.0), ('TACLONEX', 0.0), ('TACROLIMUS', 0.0), ('TAFINLAR', 0.0), ('TAGRISSO', 0.0), ('TALTZ AUTOINJECTOR', 0.0), ('TALTZ AUTOINJECTOR (2 PACK)', 0.0), ('TALTZ SYRINGE', 0.0), ('TAMIFLU', 0.0), ('TAMOXIFEN CITRATE', 0.0), ('TAMOXIFEN CITRATE', 1.0), ('TAMSULOSIN HCL', 0.0), ('TAMSULOSIN HCL', 1.0), ('TANZEUM', 0.0), ('TAPAZOLE', 0.0), ('TARCEVA', 0.0), ('TARCEVA', 1.0), ('TARGRETIN', 0.0), ('TARKA', 0.0), ('TARON-C DHA', 0.0), ('TASIGNA', 0.0), ('TASIGNA', 1.0), ('TAZAROTENE', 0.0), ('TAZICEF', 0.0), ('TAZORAC', 0.0), ('TAZTIA XT', 0.0), ('TECENTRIQ', 0.0), ('TECFIDERA', 0.0), ('TECHLITE PEN NEEDLE', 0.0), ('TEFLARO', 0.0), ('TEGRETOL', 0.0), ('TEGRETOL', 1.0), ('TEGRETOL XR', 0.0), ('TEKTURNA', 0.0), ('TEKTURNA', 1.0), ('TEKTURNA HCT', 0.0), ('TELMISARTAN', 0.0), ('TELMISARTAN', 1.0), ('TELMISARTAN-AMLODIPINE', 0.0), ('TELMISARTAN-HYDROCHLOROTHIAZID', 0.0), ('TELMISARTAN-HYDROCHLOROTHIAZID', 1.0), ('TEMAZEPAM', 0.0), ('TEMAZEPAM', 1.0), ('TENCON', 0.0), ('TENIVAC', 0.0), ('TENORETIC 100', 0.0), ('TENORETIC 50', 0.0), ('TENORMIN', 0.0), ('TERAZOSIN HCL', 0.0), ('TERAZOSIN HCL', 1.0), ('TERBINAFINE HCL', 0.0), ('TERBINAFINE HCL', 1.0), ('TERBUTALINE SULFATE', 0.0), ('TERCONAZOLE', 0.0), ('TERCONAZOLE', 1.0), ('TERUMO INSULIN SYRINGE', 0.0), ('TESTIM', 0.0), ('TESTOPEL', 0.0), ('TESTOSTERONE', 0.0), ('TESTOSTERONE CYPIONATE', 0.0), ('TESTOSTERONE CYPIONATE', 1.0), ('TESTOSTERONE ENANTHATE', 0.0), ('TETANUS DIPHTHERIA TOXOIDS', 0.0), ('TETRABENAZINE', 0.0), ('TETRACAINE HCL', 0.0), ('TETRACYCLINE HCL', 0.0), ('THALOMID', 0.0), ('THEO-24', 0.0), ('THEOPHYLLINE', 0.0), ('THEOPHYLLINE', 1.0), ('THEOPHYLLINE ANHYDROUS', 0.0), ('THEOPHYLLINE ANHYDROUS', 1.0), ('THIOLA', 0.0), ('THIORIDAZINE HCL', 0.0), ('THIORIDAZINE HCL', 1.0), ('THIOTHIXENE', 0.0), ('THIOTHIXENE', 1.0), ('TIAGABINE HCL', 0.0), ('TIAGABINE HCL', 1.0), ('TIAZAC', 0.0), ('TIGAN', 0.0), ('TIGECYCLINE', 0.0), ('TIKOSYN', 0.0), ('TIKOSYN', 1.0), ('TILIA FE', 0.0), ('TIMOLOL MALEATE', 0.0), ('TIMOLOL MALEATE', 1.0), ('TIMOPTIC OCUDOSE', 0.0), ('TIMOPTIC-XE', 0.0), ('TINIDAZOLE', 0.0), ('TINIDAZOLE', 1.0), ('TIROSINT', 0.0), ('TIVICAY', 0.0), ('TIVICAY', 1.0), ('TIVORBEX', 0.0), ('TIZANIDINE HCL', 0.0), ('TIZANIDINE HCL', 1.0), ('TOBI PODHALER', 0.0), ('TOBRADEX', 0.0), ('TOBRADEX ST', 0.0), ('TOBRAMYCIN', 0.0), ('TOBRAMYCIN', 1.0), ('TOBRAMYCIN SULFATE', 0.0), ('TOBRAMYCIN-DEXAMETHASONE', 0.0), ('TOBRAMYCIN-DEXAMETHASONE', 1.0), ('TOBREX', 0.0), ('TOFRANIL', 0.0), ('TOLAK', 0.0), ('TOLAZAMIDE', 0.0), ('TOLBUTAMIDE', 0.0), ('TOLCAPONE', 0.0), ('TOLMETIN SODIUM', 0.0), ('TOLTERODINE TARTRATE', 0.0), ('TOLTERODINE TARTRATE', 1.0), ('TOLTERODINE TARTRATE ER', 0.0), ('TOLTERODINE TARTRATE ER', 1.0), ('TOPAMAX', 0.0), ('TOPCARE CLICKFINE', 0.0), ('TOPCARE ULTRA COMFORT', 0.0), ('TOPICORT', 0.0), ('TOPIRAMATE', 0.0), ('TOPIRAMATE', 1.0), ('TOPIRAMATE ER', 0.0), ('TOPROL XL', 0.0), ('TOPROL XL', 1.0), ('TORSEMIDE', 0.0), ('TORSEMIDE', 1.0), ('TOUJEO SOLOSTAR', 0.0), ('TOUJEO SOLOSTAR', 1.0), ('TOVIAZ', 0.0), ('TOVIAZ', 1.0), ('TPN ELECTROLYTES', 0.0), ('TRACLEER', 0.0), ('TRADJENTA', 0.0), ('TRADJENTA', 1.0), ('TRAMADOL HCL', 0.0), ('TRAMADOL HCL', 1.0), ('TRAMADOL HCL ER', 0.0), ('TRAMADOL HCL ER', 1.0), ('TRAMADOL HCL-ACETAMINOPHEN', 0.0), ('TRAMADOL HCL-ACETAMINOPHEN', 1.0), ('TRANDOLAPRIL', 0.0), ('TRANDOLAPRIL-VERAPAMIL ER', 0.0), ('TRANEXAMIC ACID', 0.0), ('TRANSDERM-SCOP', 0.0), ('TRANSDERM-SCOP', 1.0), ('TRANXENE T-TAB', 0.0), ('TRANYLCYPROMINE SULFATE', 0.0), ('TRAVASOL', 0.0), ('TRAVATAN Z', 0.0), ('TRAVATAN Z', 1.0), ('TRAZODONE HCL', 0.0), ('TRAZODONE HCL', 1.0), ('TREANDA', 0.0), ('TRELSTAR', 0.0), ('TREMFYA', 0.0), ('TRESIBA FLEXTOUCH U-100', 0.0), ('TRESIBA FLEXTOUCH U-100', 1.0), ('TRESIBA FLEXTOUCH U-200', 0.0), ('TRESIBA FLEXTOUCH U-200', 1.0), ('TRETINOIN', 0.0), ('TRETINOIN MICROSPHERE', 0.0), ('TREXALL', 0.0), ('TREXIMET', 0.0), ('TREZIX', 0.0), ('TRI-ESTARYLLA', 0.0), ('TRI-LEGEST FE', 0.0), ('TRI-LINYAH', 0.0), ('TRI-LO-ESTARYLLA', 0.0), ('TRI-LO-MARZIA', 0.0), ('TRI-LO-SPRINTEC', 0.0), ('TRI-PREVIFEM', 0.0), ('TRI-SPRINTEC', 0.0), ('TRI-SPRINTEC', 1.0), ('TRIAMCINOLONE ACETONIDE', 0.0), ('TRIAMCINOLONE ACETONIDE', 1.0), ('TRIAMTERENE-HYDROCHLOROTHIAZID', 0.0), ('TRIAMTERENE-HYDROCHLOROTHIAZID', 1.0), ('TRIAZOLAM', 0.0), ('TRIAZOLAM', 1.0), ('TRIBENZOR', 0.0), ('TRIBENZOR', 1.0), ('TRICITRATES', 0.0), ('TRICOR', 0.0), ('TRIDERM', 0.0), ('TRIFLUOPERAZINE HCL', 0.0), ('TRIFLUOPERAZINE HCL', 1.0), ('TRIFLURIDINE', 0.0), ('TRIGLIDE', 0.0), ('TRIHEXYPHENIDYL HCL', 0.0), ('TRIHEXYPHENIDYL HCL', 1.0), ('TRILEPTAL', 0.0), ('TRILEPTAL', 1.0), ('TRILIPIX', 0.0), ('TRILYTE WITH FLAVOR PACKETS', 0.0), ('TRILYTE WITH FLAVOR PACKETS', 1.0), ('TRIMETHOBENZAMIDE HCL', 0.0), ('TRIMETHOPRIM', 0.0), ('TRIMETHOPRIM', 1.0), ('TRIMIPRAMINE MALEATE', 0.0), ('TRINATAL RX 1', 0.0), ('TRINESSA', 0.0), ('TRINESSA LO', 0.0), ('TRINTELLIX', 0.0), ('TRINTELLIX', 1.0), ('TRISTART DHA', 0.0), ('TRIUMEQ', 0.0), ('TRIUMEQ', 1.0), ('TRIVORA-28', 0.0), ('TRIZIVIR', 0.0), ('TROKENDI XR', 0.0), ('TROPICAMIDE', 0.0), ('TROSPIUM CHLORIDE', 0.0), ('TROSPIUM CHLORIDE ER', 0.0), ('TRUEPLUS INSULIN SYRINGE', 0.0), ('TRUEPLUS PEN NEEDLE', 0.0), ('TRULANCE', 0.0), ('TRULICITY', 0.0), ('TRULICITY', 1.0), ('TRUMENBA', 0.0), ('TRUSOPT', 0.0), ('TRUVADA', 0.0), ('TRUVADA', 1.0), ('TUDORZA PRESSAIR', 0.0), ('TUSSIONEX', 0.0), ('TWINRIX', 0.0), ('TWYNSTA', 0.0), ('TYBOST', 0.0), ('TYGACIL', 0.0), ('TYGACIL', 1.0), ('TYKERB', 0.0), ('TYLENOL-CODEINE NO.3', 0.0), ('TYLENOL-CODEINE NO.4', 0.0), ('TYMLOS', 0.0), ('TYPHIM VI', 0.0), ('TYSABRI', 0.0), ('TYVASO REFILL KIT', 0.0), ('UCERIS', 0.0), ('ULORIC', 0.0), ('ULORIC', 1.0), ('ULTICARE', 0.0), ('ULTICARE INSULIN SYRINGE', 0.0), ('ULTICARE PEN NEEDLE', 0.0), ('ULTICARE PEN NEEDLE', 1.0), ('ULTILET ALCOHOL SWAB', 0.0), ('ULTILET INSULIN SYRINGE', 0.0), ('ULTRA COMFORT', 0.0), ('ULTRA COMFORT', 1.0), ('ULTRA-FINE MINI PEN NEEDLE', 0.0), ('ULTRA-FINE MINI PEN NEEDLE', 1.0), ('ULTRA-FINE NANO PEN NEEDLE', 0.0), ('ULTRA-FINE NANO PEN NEEDLE', 1.0), ('ULTRA-FINE ORIGINAL PEN NEEDLE', 0.0), ('ULTRA-FINE SHORT PEN NEEDLE', 0.0), ('ULTRA-FINE SHORT PEN NEEDLE', 1.0), ('ULTRA-THIN II', 0.0), ('ULTRACET', 0.0), ('ULTRAM', 0.0), ('ULTRAM', 1.0), ('ULTRAVATE', 0.0), ('UNASYN', 0.0), ('UNIFINE PENTIPS', 0.0), ('UNIFINE PENTIPS', 1.0), ('UNIFINE PENTIPS PLUS', 0.0), ('UNIFINE PENTIPS PLUS', 1.0), ('UNITHROID', 0.0), ('UPTRAVI', 0.0), ('UREA', 0.0), ('URECHOLINE', 0.0), ('URIBEL', 0.0), ('URIN D.S.', 0.0), ('UROCIT-K', 0.0), ('UROGESIC-BLUE', 0.0), ('UROXATRAL', 0.0), ('URSO', 0.0), ('URSO FORTE', 0.0), ('URSODIOL', 0.0), ('URSODIOL', 1.0), ('UTIBRON NEOHALER', 0.0), ('VAGIFEM', 0.0), ('VAGIFEM', 1.0), ('VALACYCLOVIR', 0.0), ('VALACYCLOVIR', 1.0), ('VALCHLOR', 0.0), ('VALCYTE', 0.0), ('VALGANCICLOVIR HCL', 0.0), ('VALIUM', 0.0), ('VALPROATE SODIUM', 0.0), ('VALPROIC ACID', 0.0), ('VALPROIC ACID', 1.0), ('VALSARTAN', 0.0), ('VALSARTAN', 1.0), ('VALSARTAN-HYDROCHLOROTHIAZIDE', 0.0), ('VALSARTAN-HYDROCHLOROTHIAZIDE', 1.0), ('VALTREX', 0.0), ('VANATOL LQ', 0.0), ('VANCOCIN HCL', 0.0), ('VANCOMYCIN', 0.0), ('VANCOMYCIN HCL', 0.0), ('VANCOMYCIN HCL', 1.0), ('VANCOMYCIN HCL-0.9% NACL', 0.0), ('VANCOMYCIN HCL-D5W', 0.0), ('VANDAZOLE', 0.0), ('VANISHPOINT', 0.0), ('VANOS', 0.0), ('VAQTA', 0.0), ('VARIVAX VACCINE', 0.0), ('VARUBI', 0.0), ('VASCEPA', 0.0), ('VASCEPA', 1.0), ('VASERETIC', 0.0), ('VASOTEC', 0.0), ('VECAMYL', 0.0), ('VECTIBIX', 0.0), ('VECTICAL', 0.0), ('VELCADE', 0.0), ('VELETRI', 0.0), ('VELIVET', 0.0), ('VELPHORO', 0.0), ('VELPHORO', 1.0), ('VELTASSA', 0.0), ('VEMLIDY', 0.0), ('VENCLEXTA', 0.0), ('VENLAFAXINE HCL', 0.0), ('VENLAFAXINE HCL', 1.0), ('VENLAFAXINE HCL ER', 0.0), ('VENLAFAXINE HCL ER', 1.0), ('VENTAVIS', 0.0), ('VENTOLIN HFA', 0.0), ('VENTOLIN HFA', 1.0), ('VERAMYST', 0.0), ('VERAPAMIL ER', 0.0), ('VERAPAMIL ER', 1.0), ('VERAPAMIL ER PM', 0.0), ('VERAPAMIL HCL', 0.0), ('VERAPAMIL HCL', 1.0), ('VERAPAMIL SR', 0.0), ('VERAPAMIL SR', 1.0), ('VEREGEN', 0.0), ('VERELAN', 0.0), ('VERELAN PM', 0.0), ('VERSACLOZ', 0.0), ('VESICARE', 0.0), ('VESICARE', 1.0), ('VESTURA', 0.0), ('VEXOL', 0.0), ('VFEND', 0.0), ('VFEND IV', 0.0), ('VGO 20', 0.0), ('VGO 30', 0.0), ('VGO 40', 0.0), ('VIAGRA', 0.0), ('VIBATIV', 0.0), ('VIBERZI', 0.0), ('VIBRAMYCIN', 0.0), ('VICODIN', 0.0), ('VICODIN ES', 0.0), ('VICODIN HP', 0.0), ('VICTOZA 2-PAK', 0.0), ('VICTOZA 2-PAK', 1.0), ('VICTOZA 3-PAK', 0.0), ('VICTOZA 3-PAK', 1.0), ('VIDEX EC', 0.0), ('VIEKIRA PAK', 0.0), ('VIEKIRA XR', 0.0), ('VIENVA', 0.0), ('VIGAMOX', 0.0), ('VIGAMOX', 1.0), ('VIIBRYD', 0.0), ('VIIBRYD', 1.0), ('VIMIZIM', 0.0), ('VIMOVO', 0.0), ('VIMPAT', 0.0), ('VIMPAT', 1.0), ('VINCRISTINE SULFATE', 0.0), ('VINORELBINE TARTRATE', 0.0), ('VIOKACE', 0.0), ('VIORELE', 0.0), ('VIRACEPT', 0.0), ('VIRAMUNE', 0.0), ('VIRAMUNE XR', 0.0), ('VIREAD', 0.0), ('VIREAD', 1.0), ('VIRT-C DHA', 0.0), ('VIRT-CAPS', 0.0), ('VIRT-PN', 0.0), ('VIRT-PN DHA', 0.0), ('VISTARIL', 0.0), ('VITAFOL GUMMIES', 0.0), ('VITAFOL ULTRA', 0.0), ('VITAFOL-OB+DHA', 0.0), ('VITAFOL-ONE', 0.0), ('VITAMIN D2', 0.0), ('VIVELLE-DOT', 0.0), ('VIVITROL', 0.0), ('VIVLODEX', 0.0), ('VOL-CARE RX', 0.0), ('VOL-NATE', 0.0), ('VOL-PLUS', 0.0), ('VOLTAREN', 0.0), ('VOLTAREN', 1.0), ('VORICONAZOLE', 0.0), ('VOSEVI', 0.0), ('VOTRIENT', 0.0), ('VP-PNV-DHA', 0.0), ('VP-VITE RX', 0.0), ('VPRIV', 0.0), ('VRAYLAR', 0.0), ('VRAYLAR', 1.0), ('VUSION', 0.0), ('VYFEMLA', 0.0), ('VYTORIN', 0.0), ('VYTORIN', 1.0), ('VYVANSE', 0.0), ('VYVANSE', 1.0), ('WARFARIN SODIUM', 0.0), ('WARFARIN SODIUM', 1.0), ('WATER', 0.0), ('WEBCOL', 0.0), ('WELCHOL', 0.0), ('WELCHOL', 1.0), ('WELLBUTRIN SR', 0.0), ('WELLBUTRIN XL', 0.0), ('WERA', 0.0), ('WYMZYA FE', 0.0), ('XADAGO', 0.0), ('XALATAN', 0.0), ('XALATAN', 1.0), ('XALKORI', 0.0), ('XANAX', 0.0), ('XANAX XR', 0.0), ('XARELTO', 0.0), ('XARELTO', 1.0), ('XARTEMIS XR', 0.0), ('XELJANZ', 0.0), ('XELJANZ XR', 0.0), ('XENAZINE', 0.0), ('XENICAL', 0.0), ('XEOMIN', 0.0), ('XERMELO', 0.0), ('XGEVA', 0.0), ('XIAFLEX', 0.0), ('XIFAXAN', 0.0), ('XIFAXAN', 1.0), ('XIGDUO XR', 0.0), ('XIIDRA', 0.0), ('XODOL 10-300', 0.0), ('XODOL 7.5-300', 0.0), ('XOLAIR', 0.0), ('XOLAIR', 1.0), ('XOPENEX', 0.0), ('XOPENEX CONCENTRATE', 0.0), ('XOPENEX HFA', 0.0), ('XOPENEX HFA', 1.0), ('XTAMPZA ER', 0.0), ('XTAMPZA ER', 1.0), ('XTANDI', 0.0), ('XTANDI', 1.0), ('XULANE', 0.0), ('XULTOPHY 100-3.6', 0.0), ('XYLOCAINE', 0.0), ('XYLOCAINE-MPF', 0.0), ('XYLON 10', 0.0), ('XYREM', 0.0), ('XYZAL', 0.0), ('YASMIN 28', 0.0), ('YAZ', 0.0), ('YF-VAX', 0.0), ('YOSPRALA', 0.0), ('YUVAFEM', 0.0), ('ZAFIRLUKAST', 0.0), ('ZALEPLON', 0.0), ('ZALEPLON', 1.0), ('ZANAFLEX', 0.0), ('ZANTAC', 0.0), ('ZARAH', 0.0), ('ZARONTIN', 0.0), ('ZARXIO', 0.0), ('ZATEAN-PN DHA', 0.0), ('ZATEAN-PN PLUS', 0.0), ('ZAVESCA', 0.0), ('ZEBUTAL', 0.0), ('ZEGERID', 0.0), ('ZEJULA', 0.0), ('ZELAPAR', 0.0), ('ZELBORAF', 0.0), ('ZEMAIRA', 0.0), ('ZEMBRACE SYMTOUCH', 0.0), ('ZEMPLAR', 0.0), ('ZENCHENT', 0.0), ('ZENCHENT FE', 0.0), ('ZENPEP', 0.0), ('ZENZEDI', 0.0), ('ZEPATIER', 0.0), ('ZERBAXA', 0.0), ('ZERIT', 0.0), ('ZESTORETIC', 0.0), ('ZESTRIL', 0.0), ('ZETIA', 0.0), ('ZETIA', 1.0), ('ZETONNA', 0.0), ('ZIAC', 0.0), ('ZIAGEN', 0.0), ('ZIDOVUDINE', 0.0), ('ZILEUTON ER', 0.0), ('ZINBRYTA', 0.0), ('ZIOPTAN', 0.0), ('ZIPRASIDONE HCL', 0.0), ('ZIPRASIDONE HCL', 1.0), ('ZIPSOR', 0.0), ('ZIRGAN', 0.0), ('ZITHROMAX', 0.0), ('ZOCOR', 0.0), ('ZOFRAN', 0.0), ('ZOFRAN ODT', 0.0), ('ZOHYDRO ER', 0.0), ('ZOHYDRO ER', 1.0), ('ZOLADEX', 0.0), ('ZOLEDRONIC ACID', 0.0), ('ZOLINZA', 0.0), ('ZOLMITRIPTAN', 0.0), ('ZOLMITRIPTAN ODT', 0.0), ('ZOLOFT', 0.0), ('ZOLOFT', 1.0), ('ZOLPIDEM TARTRATE', 0.0), ('ZOLPIDEM TARTRATE', 1.0), ('ZOLPIDEM TARTRATE ER', 0.0), ('ZOLPIDEM TARTRATE ER', 1.0), ('ZOMACTON', 0.0), ('ZOMETA', 0.0), ('ZOMIG', 0.0), ('ZOMIG ZMT', 0.0), ('ZONALON', 0.0), ('ZONEGRAN', 0.0), ('ZONISAMIDE', 0.0), ('ZONISAMIDE', 1.0), ('ZONTIVITY', 0.0), ('ZONTIVITY', 1.0), ('ZORTRESS', 0.0), ('ZORVOLEX', 0.0), ('ZORVOLEX', 1.0), ('ZOSTAVAX', 0.0), ('ZOSTAVAX', 1.0), ('ZOSYN', 0.0), ('ZOSYN', 1.0), ('ZOVIA 1-35E', 0.0), ('ZOVIA 1-50E', 0.0), ('ZOVIRAX', 0.0), ('ZUBSOLV', 0.0), ('ZUBSOLV', 1.0), ('ZUPLENZ', 0.0), ('ZURAMPIC', 0.0), ('ZYCLARA', 0.0), ('ZYDELIG', 0.0), ('ZYFLO', 0.0), ('ZYFLO CR', 0.0), ('ZYKADIA', 0.0), ('ZYLET', 0.0), ('ZYLOPRIM', 0.0), ('ZYMAXID', 0.0), ('ZYPREXA', 0.0), ('ZYPREXA RELPREVV', 0.0), ('ZYPREXA ZYDIS', 0.0), ('ZYPREXA ZYDIS', 1.0), ('ZYTIGA', 0.0), ('ZYTIGA', 1.0), ('ZYVOX', 0.0)])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_with_isfraud = [drugx for drugx in drugs if ((drugx,0.0) in drug_keys ) & ( (drugx,1.0) in drug_keys)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "re_drug_tt = dict()\n",
    "for drugx in drug_with_isfraud:\n",
    "    for colx in cols:\n",
    "        fraud_0 = partD_drug_train_Group.get_group((drugx,0.0))[colx].values\n",
    "        fraud_1 = partD_drug_train_Group.get_group((drugx,1.0))[colx].values\n",
    "        # print len(fraud_0), len(fraud_1)\n",
    "        if (len(fraud_0)>2) & (len(fraud_1)>2) :\n",
    "            tt = ttest_ind(fraud_0, fraud_1)\n",
    "            re_drug_tt[(drugx, colx)] = tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145\n"
     ]
    }
   ],
   "source": [
    "#Setting Probilities\n",
    "Prob_005 = [(key, p) for (key, (t, p)) in re_drug_tt.items() if p <=0.05]  \n",
    "print(len(Prob_005))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IBU\n"
     ]
    },
    {
     "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>drug_name</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>961</td>\n",
       "      <td>1003002817</td>\n",
       "      <td>IBU</td>\n",
       "      <td>95.76</td>\n",
       "      <td>20</td>\n",
       "      <td>363</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3167</td>\n",
       "      <td>1003008855</td>\n",
       "      <td>IBU</td>\n",
       "      <td>50.96</td>\n",
       "      <td>12</td>\n",
       "      <td>262</td>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4435</td>\n",
       "      <td>1003011214</td>\n",
       "      <td>IBU</td>\n",
       "      <td>155.95</td>\n",
       "      <td>20</td>\n",
       "      <td>580</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5177</td>\n",
       "      <td>1003012857</td>\n",
       "      <td>IBU</td>\n",
       "      <td>144.24</td>\n",
       "      <td>34</td>\n",
       "      <td>282</td>\n",
       "      <td>Oral Surgery (Dentist only)</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5568</td>\n",
       "      <td>1003013608</td>\n",
       "      <td>IBU</td>\n",
       "      <td>87.11</td>\n",
       "      <td>12</td>\n",
       "      <td>420</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             npi drug_name  total_drug_cost  total_claim_count  \\\n",
       "961   1003002817       IBU            95.76                 20   \n",
       "3167  1003008855       IBU            50.96                 12   \n",
       "4435  1003011214       IBU           155.95                 20   \n",
       "5177  1003012857       IBU           144.24                 34   \n",
       "5568  1003013608       IBU            87.11                 12   \n",
       "\n",
       "      total_day_supply        specialty_description  is_fraud  \n",
       "961                363              Family Practice       0.0  \n",
       "3167               262          Physician Assistant       0.0  \n",
       "4435               580            Internal Medicine       0.0  \n",
       "5177               282  Oral Surgery (Dentist only)       0.0  \n",
       "5568               420           Nurse Practitioner       0.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inx=100\n",
    "drug_name = Prob_005[inx][0][0]\n",
    "print(drug_name)\n",
    "df_bar = pd.concat([partD_drug_All_Group.get_group((Prob_005[inx][0][0],0.0)), partD_drug_All_Group.get_group((Prob_005[inx][0][0],1.0))])\n",
    "df_bar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "Feture_DrugWeighted = []\n",
    "new_col_all =[]\n",
    "for i, p005x in enumerate(Prob_005):\n",
    "    #if i>4:\n",
    "    #   break\n",
    "    drug_name = p005x[0][0]\n",
    "    cat_name = p005x[0][1] \n",
    "    \n",
    "    new_col = drug_name+'_'+cat_name\n",
    "    new_col_all.append(new_col)\n",
    "\n",
    "    drug_0 = partD_drug_All_Group.get_group((drug_name,0.0))[['npi', cat_name]]\n",
    "    drug_1 = partD_drug_All_Group.get_group((drug_name,1.0))[['npi', cat_name]]\n",
    "\n",
    "    drug_01 = pd.concat([drug_0, drug_1])\n",
    "    drug_01.rename(columns={cat_name: new_col}, inplace=True)\n",
    "    Feture_DrugWeighted.append(drug_01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "npi_col = FeaturesAll_pd[['npi']]\n",
    "\n",
    "w_npi = []\n",
    "\n",
    "for n, nx in enumerate(Feture_DrugWeighted):\n",
    "      nggx = pd.merge(npi_col, nx.drop_duplicates(['npi']), on='npi', how='left')\n",
    "      w_npi.append(nggx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd1 = FeaturesAll_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "for wx in w_npi:\n",
    "    col_n = wx.columns[1]\n",
    "    FeaturesAll_pd1[col_n] = wx[col_n].values\n",
    "    \n",
    "wx = w_npi[0]\n",
    "wx.columns[1]\n",
    "col_n = wx.columns[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
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       "      <th>OXYCODONE-ACETAMINOPHEN_total_claim_count</th>\n",
       "      <th>OXYCODONE-ACETAMINOPHEN_total_day_supply</th>\n",
       "      <th>DIAZEPAM_total_claim_count</th>\n",
       "      <th>DIAZEPAM_total_day_supply</th>\n",
       "      <th>ALLOPURINOL_total_drug_cost</th>\n",
       "      <th>ALLOPURINOL_total_day_supply</th>\n",
       "      <th>OXYCONTIN_total_drug_cost</th>\n",
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       "    </tr>\n",
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       "      <td>178678</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>4.866301</td>\n",
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       "      <td>481077</td>\n",
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       "      <td>3.957589</td>\n",
       "      <td>2.758912</td>\n",
       "      <td>1.317198</td>\n",
       "      <td>1.591065</td>\n",
       "      <td>4.407799</td>\n",
       "      <td>2.945876</td>\n",
       "      <td>3.401573</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>352034</td>\n",
       "      <td>1992999791</td>\n",
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       "      <td>3.573684</td>\n",
       "      <td>2.671404</td>\n",
       "      <td>3.104487</td>\n",
       "      <td>...</td>\n",
       "      <td>92.37</td>\n",
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       "    <tr>\n",
       "      <td>606940</td>\n",
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       "      <td>3.021603</td>\n",
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       "      <td>2.592177</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>869023</td>\n",
       "      <td>1992999882</td>\n",
       "      <td>2.793036</td>\n",
       "      <td>2.018368</td>\n",
       "      <td>2.103940</td>\n",
       "      <td>1.963788</td>\n",
       "      <td>1.208620</td>\n",
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       "      <td>3.422097</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918012 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "593816  1003000126             4.092566              2.888972   \n",
       "493584  1003000142             5.056408              3.714066   \n",
       "761450  1003000167             2.183981              1.885785   \n",
       "160806  1003000282             2.820195              2.519821   \n",
       "178678  1003000407             4.866301              3.081325   \n",
       "...            ...                  ...                   ...   \n",
       "481077  1992999759             4.200928              2.739295   \n",
       "352034  1992999791             3.134601              2.658116   \n",
       "583453  1992999825             3.528849              2.626657   \n",
       "606940  1992999833             2.486870              2.012570   \n",
       "869023  1992999882             2.793036              2.018368   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "593816             3.969747               2.586587                1.399024   \n",
       "493584             4.718552               3.266467                1.928954   \n",
       "761450             1.958086               1.591065                1.301030   \n",
       "160806             2.584467               1.740363                1.447158   \n",
       "178678             4.090744               3.359266                1.585183   \n",
       "...                     ...                    ...                     ...   \n",
       "481077             3.957589               2.758912                1.317198   \n",
       "352034             2.893911               1.851258                1.386202   \n",
       "583453             3.305646               2.225309                1.339948   \n",
       "606940             2.093036               1.556303                1.102662   \n",
       "869023             2.103940               1.963788                1.208620   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "593816               1.724276              3.906012               2.702700   \n",
       "493584               2.530200              4.744840               3.402582   \n",
       "761450               1.397940              2.198657               1.900367   \n",
       "160806               1.518514              3.168203               2.867467   \n",
       "178678               2.383815              4.622235               2.837526   \n",
       "...                       ...                   ...                    ...   \n",
       "481077               1.591065              4.407799               2.945876   \n",
       "352034               1.633468              3.316180               2.839478   \n",
       "583453               1.662758              3.573684               2.671404   \n",
       "606940               1.146128              3.021603               2.545307   \n",
       "869023               1.301030              3.422097               2.644767   \n",
       "\n",
       "        total_day_supply_max  ... OXYCODONE-ACETAMINOPHEN_total_drug_cost  \\\n",
       "593816              3.184975  ...                                  103.65   \n",
       "493584              4.073205  ...                                 5403.10   \n",
       "761450              2.089905  ...                                    0.00   \n",
       "160806              2.930949  ...                                    0.00   \n",
       "178678              3.374748  ...                                    0.00   \n",
       "...                      ...  ...                                     ...   \n",
       "481077              3.401573  ...                                    0.00   \n",
       "352034              3.090258  ...                                  474.10   \n",
       "583453              3.104487  ...                                   92.37   \n",
       "606940              2.592177  ...                                    0.00   \n",
       "869023              2.756636  ...                                    0.00   \n",
       "\n",
       "       OXYCODONE-ACETAMINOPHEN_total_claim_count  \\\n",
       "593816                                      14.0   \n",
       "493584                                     254.0   \n",
       "761450                                       0.0   \n",
       "160806                                       0.0   \n",
       "178678                                       0.0   \n",
       "...                                          ...   \n",
       "481077                                       0.0   \n",
       "352034                                      12.0   \n",
       "583453                                      17.0   \n",
       "606940                                       0.0   \n",
       "869023                                       0.0   \n",
       "\n",
       "       OXYCODONE-ACETAMINOPHEN_total_day_supply DIAZEPAM_total_claim_count  \\\n",
       "593816                                     65.0                        0.0   \n",
       "493584                                   6915.0                        0.0   \n",
       "761450                                      0.0                        0.0   \n",
       "160806                                      0.0                        0.0   \n",
       "178678                                      0.0                        0.0   \n",
       "...                                         ...                        ...   \n",
       "481077                                      0.0                        0.0   \n",
       "352034                                    360.0                        0.0   \n",
       "583453                                     81.0                        0.0   \n",
       "606940                                      0.0                        0.0   \n",
       "869023                                      0.0                        0.0   \n",
       "\n",
       "       DIAZEPAM_total_day_supply  ALLOPURINOL_total_drug_cost  \\\n",
       "593816                       0.0                         0.00   \n",
       "493584                       0.0                         0.00   \n",
       "761450                       0.0                         0.00   \n",
       "160806                       0.0                       276.87   \n",
       "178678                       0.0                         0.00   \n",
       "...                          ...                          ...   \n",
       "481077                       0.0                         0.00   \n",
       "352034                       0.0                         0.00   \n",
       "583453                       0.0                         0.00   \n",
       "606940                       0.0                         0.00   \n",
       "869023                       0.0                         0.00   \n",
       "\n",
       "        ALLOPURINOL_total_day_supply  OXYCONTIN_total_drug_cost  \\\n",
       "593816                           0.0                        0.0   \n",
       "493584                           0.0                        0.0   \n",
       "761450                           0.0                        0.0   \n",
       "160806                         852.0                        0.0   \n",
       "178678                           0.0                        0.0   \n",
       "...                              ...                        ...   \n",
       "481077                           0.0                        0.0   \n",
       "352034                           0.0                        0.0   \n",
       "583453                           0.0                        0.0   \n",
       "606940                           0.0                        0.0   \n",
       "869023                           0.0                        0.0   \n",
       "\n",
       "        OXYCONTIN_total_claim_count  OXYCONTIN_total_day_supply  \n",
       "593816                          0.0                         0.0  \n",
       "493584                          0.0                         0.0  \n",
       "761450                          0.0                         0.0  \n",
       "160806                          0.0                         0.0  \n",
       "178678                          0.0                         0.0  \n",
       "...                             ...                         ...  \n",
       "481077                          0.0                         0.0  \n",
       "352034                          0.0                         0.0  \n",
       "583453                          0.0                         0.0  \n",
       "606940                          0.0                         0.0  \n",
       "869023                          0.0                         0.0  \n",
       "\n",
       "[918012 rows x 165 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wx[col_n].values)\n",
    "FeaturesAll_pd1.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MORPHINE SULFATE ER_total_drug_cost',\n",
       " 'MORPHINE SULFATE ER_total_claim_count',\n",
       " 'MORPHINE SULFATE ER_total_day_supply',\n",
       " 'SODIUM CHLORIDE_total_claim_count',\n",
       " 'SODIUM CHLORIDE_total_day_supply',\n",
       " 'FENTANYL_total_drug_cost',\n",
       " 'FENTANYL_total_claim_count',\n",
       " 'FENTANYL_total_day_supply',\n",
       " 'SULFAMETHOXAZOLE-TRIMETHOPRIM_total_drug_cost',\n",
       " 'SULFAMETHOXAZOLE-TRIMETHOPRIM_total_claim_count',\n",
       " 'ESCITALOPRAM OXALATE_total_day_supply',\n",
       " 'AMOXICILLIN_total_drug_cost',\n",
       " 'AMOXICILLIN_total_day_supply',\n",
       " 'ECONAZOLE NITRATE_total_drug_cost',\n",
       " 'ECONAZOLE NITRATE_total_claim_count',\n",
       " 'ECONAZOLE NITRATE_total_day_supply',\n",
       " 'ONDANSETRON ODT_total_day_supply',\n",
       " 'ACYCLOVIR_total_drug_cost',\n",
       " 'AMITRIPTYLINE HCL_total_drug_cost',\n",
       " 'BUTALBITAL-ACETAMINOPHEN-CAFFE_total_drug_cost',\n",
       " 'BUTALBITAL-ACETAMINOPHEN-CAFFE_total_claim_count',\n",
       " 'BENAZEPRIL-HYDROCHLOROTHIAZIDE_total_drug_cost',\n",
       " 'BENAZEPRIL-HYDROCHLOROTHIAZIDE_total_claim_count',\n",
       " 'BENAZEPRIL-HYDROCHLOROTHIAZIDE_total_day_supply',\n",
       " 'IBUPROFEN_total_drug_cost',\n",
       " 'IBUPROFEN_total_claim_count',\n",
       " 'IBUPROFEN_total_day_supply',\n",
       " 'LYRICA_total_drug_cost',\n",
       " 'LYRICA_total_claim_count',\n",
       " 'LYRICA_total_day_supply',\n",
       " 'PROAIR HFA_total_drug_cost',\n",
       " 'PROAIR HFA_total_claim_count',\n",
       " 'PROAIR HFA_total_day_supply',\n",
       " 'ASPIRIN-DIPYRIDAMOLE ER_total_claim_count',\n",
       " 'AZELASTINE HCL_total_claim_count',\n",
       " 'OXYCODONE HCL_total_drug_cost',\n",
       " 'OXYCODONE HCL_total_claim_count',\n",
       " 'OXYCODONE HCL_total_day_supply',\n",
       " 'GLIPIZIDE_total_drug_cost',\n",
       " 'GLIPIZIDE_total_day_supply',\n",
       " 'CEPHALEXIN_total_drug_cost',\n",
       " 'CEPHALEXIN_total_claim_count',\n",
       " 'METOPROLOL SUCCINATE_total_drug_cost',\n",
       " 'METOPROLOL SUCCINATE_total_day_supply',\n",
       " 'CARISOPRODOL_total_claim_count',\n",
       " 'CARISOPRODOL_total_day_supply',\n",
       " 'NAPROXEN_total_drug_cost',\n",
       " 'NAPROXEN_total_claim_count',\n",
       " 'NAPROXEN_total_day_supply',\n",
       " 'LOSARTAN POTASSIUM_total_day_supply',\n",
       " 'PROMETHAZINE HCL_total_drug_cost',\n",
       " 'PROMETHAZINE HCL_total_claim_count',\n",
       " 'PROMETHAZINE HCL_total_day_supply',\n",
       " 'MELOXICAM_total_claim_count',\n",
       " 'CHLORHEXIDINE GLUCONATE_total_claim_count',\n",
       " 'CHLORHEXIDINE GLUCONATE_total_day_supply',\n",
       " 'ALPRAZOLAM_total_drug_cost',\n",
       " 'ALPRAZOLAM_total_claim_count',\n",
       " 'ALPRAZOLAM_total_day_supply',\n",
       " 'ATENOLOL_total_drug_cost',\n",
       " 'ATENOLOL_total_day_supply',\n",
       " 'OXYMORPHONE HCL ER_total_drug_cost',\n",
       " 'SERTRALINE HCL_total_drug_cost',\n",
       " 'SERTRALINE HCL_total_day_supply',\n",
       " 'HYDROCHLOROTHIAZIDE_total_drug_cost',\n",
       " 'HYDROCHLOROTHIAZIDE_total_day_supply',\n",
       " 'AZITHROMYCIN_total_claim_count',\n",
       " 'CICLOPIROX_total_drug_cost',\n",
       " 'INSULIN SYRINGE_total_day_supply',\n",
       " 'TIZANIDINE HCL_total_drug_cost',\n",
       " 'TIZANIDINE HCL_total_claim_count',\n",
       " 'TIZANIDINE HCL_total_day_supply',\n",
       " 'FLUCONAZOLE_total_claim_count',\n",
       " 'TESTOSTERONE CYPIONATE_total_drug_cost',\n",
       " 'TESTOSTERONE CYPIONATE_total_claim_count',\n",
       " 'SIMVASTATIN_total_drug_cost',\n",
       " 'SIMVASTATIN_total_day_supply',\n",
       " 'COMBIVENT RESPIMAT_total_drug_cost',\n",
       " 'COMBIVENT RESPIMAT_total_claim_count',\n",
       " 'COMBIVENT RESPIMAT_total_day_supply',\n",
       " 'ZOLPIDEM TARTRATE_total_claim_count',\n",
       " 'HYDROCODONE-ACETAMINOPHEN_total_drug_cost',\n",
       " 'HYDROCODONE-ACETAMINOPHEN_total_claim_count',\n",
       " 'HYDROCODONE-ACETAMINOPHEN_total_day_supply',\n",
       " 'CLINDAMYCIN HCL_total_drug_cost',\n",
       " 'CLINDAMYCIN HCL_total_day_supply',\n",
       " 'METHADONE HCL_total_drug_cost',\n",
       " 'METHADONE HCL_total_claim_count',\n",
       " 'METHADONE HCL_total_day_supply',\n",
       " 'DOXEPIN HCL_total_drug_cost',\n",
       " 'DOXEPIN HCL_total_claim_count',\n",
       " 'DOXEPIN HCL_total_day_supply',\n",
       " 'DICLOFENAC SODIUM_total_drug_cost',\n",
       " 'FLUOCINONIDE_total_drug_cost',\n",
       " 'FLUOCINONIDE_total_day_supply',\n",
       " 'LEVOTHYROXINE SODIUM_total_day_supply',\n",
       " 'DEXTROAMPHETAMINE-AMPHETAMINE_total_claim_count',\n",
       " 'DEXTROAMPHETAMINE-AMPHETAMINE_total_day_supply',\n",
       " 'CYCLOBENZAPRINE HCL_total_claim_count',\n",
       " 'CYCLOBENZAPRINE HCL_total_day_supply',\n",
       " 'IBU_total_claim_count',\n",
       " 'IBU_total_day_supply',\n",
       " 'DIGOXIN_total_drug_cost',\n",
       " 'DIGOXIN_total_claim_count',\n",
       " 'DIGOXIN_total_day_supply',\n",
       " 'BUPRENORPHINE HCL_total_drug_cost',\n",
       " 'BUPRENORPHINE HCL_total_claim_count',\n",
       " 'BUPRENORPHINE HCL_total_day_supply',\n",
       " 'MODAFINIL_total_claim_count',\n",
       " 'MODAFINIL_total_day_supply',\n",
       " 'GABAPENTIN_total_drug_cost',\n",
       " 'GABAPENTIN_total_claim_count',\n",
       " 'GABAPENTIN_total_day_supply',\n",
       " 'ACETAMINOPHEN-CODEINE_total_drug_cost',\n",
       " 'ACETAMINOPHEN-CODEINE_total_claim_count',\n",
       " 'ACETAMINOPHEN-CODEINE_total_day_supply',\n",
       " 'LIDOCAINE_total_drug_cost',\n",
       " 'LIDOCAINE_total_claim_count',\n",
       " 'LIDOCAINE_total_day_supply',\n",
       " 'CILOSTAZOL_total_drug_cost',\n",
       " 'CILOSTAZOL_total_claim_count',\n",
       " 'CILOSTAZOL_total_day_supply',\n",
       " 'HYDROMORPHONE HCL_total_drug_cost',\n",
       " 'HYDROMORPHONE HCL_total_claim_count',\n",
       " 'HYDROMORPHONE HCL_total_day_supply',\n",
       " 'VENLAFAXINE HCL ER_total_drug_cost',\n",
       " 'VENLAFAXINE HCL ER_total_day_supply',\n",
       " 'TRAMADOL HCL_total_drug_cost',\n",
       " 'TRAMADOL HCL_total_day_supply',\n",
       " 'DRONABINOL_total_drug_cost',\n",
       " 'DRONABINOL_total_day_supply',\n",
       " 'VENTOLIN HFA_total_drug_cost',\n",
       " 'VENTOLIN HFA_total_claim_count',\n",
       " 'VENTOLIN HFA_total_day_supply',\n",
       " 'JENTADUETO_total_claim_count',\n",
       " 'OXYCODONE-ACETAMINOPHEN_total_drug_cost',\n",
       " 'OXYCODONE-ACETAMINOPHEN_total_claim_count',\n",
       " 'OXYCODONE-ACETAMINOPHEN_total_day_supply',\n",
       " 'DIAZEPAM_total_claim_count',\n",
       " 'DIAZEPAM_total_day_supply',\n",
       " 'ALLOPURINOL_total_drug_cost',\n",
       " 'ALLOPURINOL_total_day_supply',\n",
       " 'OXYCONTIN_total_drug_cost',\n",
       " 'OXYCONTIN_total_claim_count',\n",
       " 'OXYCONTIN_total_day_supply']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_col_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>MORPHINE SULFATE ER_total_drug_cost</th>\n",
       "      <th>MORPHINE SULFATE ER_total_claim_count</th>\n",
       "      <th>MORPHINE SULFATE ER_total_day_supply</th>\n",
       "      <th>SODIUM CHLORIDE_total_claim_count</th>\n",
       "      <th>SODIUM CHLORIDE_total_day_supply</th>\n",
       "      <th>FENTANYL_total_drug_cost</th>\n",
       "      <th>FENTANYL_total_claim_count</th>\n",
       "      <th>FENTANYL_total_day_supply</th>\n",
       "      <th>SULFAMETHOXAZOLE-TRIMETHOPRIM_total_drug_cost</th>\n",
       "      <th>SULFAMETHOXAZOLE-TRIMETHOPRIM_total_claim_count</th>\n",
       "      <th>...</th>\n",
       "      <th>OXYCODONE-ACETAMINOPHEN_total_drug_cost</th>\n",
       "      <th>OXYCODONE-ACETAMINOPHEN_total_claim_count</th>\n",
       "      <th>OXYCODONE-ACETAMINOPHEN_total_day_supply</th>\n",
       "      <th>DIAZEPAM_total_claim_count</th>\n",
       "      <th>DIAZEPAM_total_day_supply</th>\n",
       "      <th>ALLOPURINOL_total_drug_cost</th>\n",
       "      <th>ALLOPURINOL_total_day_supply</th>\n",
       "      <th>OXYCONTIN_total_drug_cost</th>\n",
       "      <th>OXYCONTIN_total_claim_count</th>\n",
       "      <th>OXYCONTIN_total_day_supply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>54323.000000</td>\n",
       "      <td>54323.000000</td>\n",
       "      <td>54323.000000</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>54401.000000</td>\n",
       "      <td>54401.000000</td>\n",
       "      <td>54401.000000</td>\n",
       "      <td>130160.000000</td>\n",
       "      <td>130160.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>155808.000000</td>\n",
       "      <td>155808.000000</td>\n",
       "      <td>155808.000000</td>\n",
       "      <td>95056.000000</td>\n",
       "      <td>95056.000000</td>\n",
       "      <td>159787.000000</td>\n",
       "      <td>159787.000000</td>\n",
       "      <td>3.140600e+04</td>\n",
       "      <td>31406.000000</td>\n",
       "      <td>31406.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3452.790606</td>\n",
       "      <td>54.497506</td>\n",
       "      <td>1555.618265</td>\n",
       "      <td>26.629790</td>\n",
       "      <td>204.819760</td>\n",
       "      <td>4208.584526</td>\n",
       "      <td>41.626349</td>\n",
       "      <td>1144.098914</td>\n",
       "      <td>162.473906</td>\n",
       "      <td>27.859258</td>\n",
       "      <td>...</td>\n",
       "      <td>2233.121837</td>\n",
       "      <td>58.918515</td>\n",
       "      <td>1291.449207</td>\n",
       "      <td>33.961833</td>\n",
       "      <td>946.048845</td>\n",
       "      <td>887.407572</td>\n",
       "      <td>2989.408181</td>\n",
       "      <td>2.163437e+04</td>\n",
       "      <td>35.995383</td>\n",
       "      <td>998.872986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>7213.213882</td>\n",
       "      <td>105.796107</td>\n",
       "      <td>3103.331318</td>\n",
       "      <td>28.313438</td>\n",
       "      <td>410.881602</td>\n",
       "      <td>6219.280544</td>\n",
       "      <td>58.910810</td>\n",
       "      <td>1689.869639</td>\n",
       "      <td>224.888726</td>\n",
       "      <td>31.310718</td>\n",
       "      <td>...</td>\n",
       "      <td>6759.251845</td>\n",
       "      <td>127.546684</td>\n",
       "      <td>3641.605361</td>\n",
       "      <td>38.907649</td>\n",
       "      <td>1138.152739</td>\n",
       "      <td>881.734829</td>\n",
       "      <td>2893.621125</td>\n",
       "      <td>3.828520e+04</td>\n",
       "      <td>50.959666</td>\n",
       "      <td>1484.428045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>42.390000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>146.250000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.352000e+01</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>720.815000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1380.350000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>63.960000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>230.807500</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>312.450000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>5.725085e+03</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1383.230000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>2372.240000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>660.000000</td>\n",
       "      <td>101.450000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>632.270000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>599.630000</td>\n",
       "      <td>2040.000000</td>\n",
       "      <td>1.085448e+04</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>3133.460000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1341.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>4591.850000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1185.000000</td>\n",
       "      <td>180.362500</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1734.247500</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>1164.505000</td>\n",
       "      <td>3932.000000</td>\n",
       "      <td>2.212691e+04</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>262484.290000</td>\n",
       "      <td>2860.000000</td>\n",
       "      <td>84456.000000</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>11158.000000</td>\n",
       "      <td>163847.160000</td>\n",
       "      <td>1525.000000</td>\n",
       "      <td>45544.000000</td>\n",
       "      <td>20350.420000</td>\n",
       "      <td>3063.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>343899.420000</td>\n",
       "      <td>7174.000000</td>\n",
       "      <td>214660.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>37151.000000</td>\n",
       "      <td>26886.450000</td>\n",
       "      <td>90312.000000</td>\n",
       "      <td>1.602998e+06</td>\n",
       "      <td>1051.000000</td>\n",
       "      <td>31447.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MORPHINE SULFATE ER_total_drug_cost  \\\n",
       "count                         54323.000000   \n",
       "mean                           3452.790606   \n",
       "std                            7213.213882   \n",
       "min                              42.390000   \n",
       "25%                             720.815000   \n",
       "50%                            1383.230000   \n",
       "75%                            3133.460000   \n",
       "max                          262484.290000   \n",
       "\n",
       "       MORPHINE SULFATE ER_total_claim_count  \\\n",
       "count                           54323.000000   \n",
       "mean                               54.497506   \n",
       "std                               105.796107   \n",
       "min                                11.000000   \n",
       "25%                                15.000000   \n",
       "50%                                24.000000   \n",
       "75%                                48.000000   \n",
       "max                              2860.000000   \n",
       "\n",
       "       MORPHINE SULFATE ER_total_day_supply  \\\n",
       "count                          54323.000000   \n",
       "mean                            1555.618265   \n",
       "std                             3103.331318   \n",
       "min                               21.000000   \n",
       "25%                              404.000000   \n",
       "50%                              690.000000   \n",
       "75%                             1341.000000   \n",
       "max                            84456.000000   \n",
       "\n",
       "       SODIUM CHLORIDE_total_claim_count  SODIUM CHLORIDE_total_day_supply  \\\n",
       "count                        6680.000000                       6680.000000   \n",
       "mean                           26.629790                        204.819760   \n",
       "std                            28.313438                        410.881602   \n",
       "min                            11.000000                         11.000000   \n",
       "25%                            13.000000                         50.000000   \n",
       "50%                            18.000000                        108.000000   \n",
       "75%                            29.000000                        255.000000   \n",
       "max                           521.000000                      11158.000000   \n",
       "\n",
       "       FENTANYL_total_drug_cost  FENTANYL_total_claim_count  \\\n",
       "count              54401.000000                54401.000000   \n",
       "mean                4208.584526                   41.626349   \n",
       "std                 6219.280544                   58.910810   \n",
       "min                  146.250000                   11.000000   \n",
       "25%                 1380.350000                   15.000000   \n",
       "50%                 2372.240000                   24.000000   \n",
       "75%                 4591.850000                   44.000000   \n",
       "max               163847.160000                 1525.000000   \n",
       "\n",
       "       FENTANYL_total_day_supply  \\\n",
       "count               54401.000000   \n",
       "mean                 1144.098914   \n",
       "std                  1689.869639   \n",
       "min                    30.000000   \n",
       "25%                   400.000000   \n",
       "50%                   660.000000   \n",
       "75%                  1185.000000   \n",
       "max                 45544.000000   \n",
       "\n",
       "       SULFAMETHOXAZOLE-TRIMETHOPRIM_total_drug_cost  \\\n",
       "count                                  130160.000000   \n",
       "mean                                      162.473906   \n",
       "std                                       224.888726   \n",
       "min                                         0.000000   \n",
       "25%                                        63.960000   \n",
       "50%                                       101.450000   \n",
       "75%                                       180.362500   \n",
       "max                                     20350.420000   \n",
       "\n",
       "       SULFAMETHOXAZOLE-TRIMETHOPRIM_total_claim_count  ...  \\\n",
       "count                                    130160.000000  ...   \n",
       "mean                                         27.859258  ...   \n",
       "std                                          31.310718  ...   \n",
       "min                                          11.000000  ...   \n",
       "25%                                          14.000000  ...   \n",
       "50%                                          19.000000  ...   \n",
       "75%                                          31.000000  ...   \n",
       "max                                        3063.000000  ...   \n",
       "\n",
       "       OXYCODONE-ACETAMINOPHEN_total_drug_cost  \\\n",
       "count                            155808.000000   \n",
       "mean                               2233.121837   \n",
       "std                                6759.251845   \n",
       "min                                  12.500000   \n",
       "25%                                 230.807500   \n",
       "50%                                 632.270000   \n",
       "75%                                1734.247500   \n",
       "max                              343899.420000   \n",
       "\n",
       "       OXYCODONE-ACETAMINOPHEN_total_claim_count  \\\n",
       "count                              155808.000000   \n",
       "mean                                   58.918515   \n",
       "std                                   127.546684   \n",
       "min                                    11.000000   \n",
       "25%                                    16.000000   \n",
       "50%                                    27.000000   \n",
       "75%                                    54.000000   \n",
       "max                                  7174.000000   \n",
       "\n",
       "       OXYCODONE-ACETAMINOPHEN_total_day_supply  DIAZEPAM_total_claim_count  \\\n",
       "count                             155808.000000                95056.000000   \n",
       "mean                                1291.449207                   33.961833   \n",
       "std                                 3641.605361                   38.907649   \n",
       "min                                   11.000000                   11.000000   \n",
       "25%                                  172.000000                   15.000000   \n",
       "50%                                  430.000000                   22.000000   \n",
       "75%                                 1048.000000                   38.000000   \n",
       "max                               214660.000000                 1493.000000   \n",
       "\n",
       "       DIAZEPAM_total_day_supply  ALLOPURINOL_total_drug_cost  \\\n",
       "count               95056.000000                159787.000000   \n",
       "mean                  946.048845                   887.407572   \n",
       "std                  1138.152739                   881.734829   \n",
       "min                    11.000000                     9.550000   \n",
       "25%                   380.000000                   312.450000   \n",
       "50%                   625.000000                   599.630000   \n",
       "75%                  1110.000000                  1164.505000   \n",
       "max                 37151.000000                 26886.450000   \n",
       "\n",
       "       ALLOPURINOL_total_day_supply  OXYCONTIN_total_drug_cost  \\\n",
       "count                 159787.000000               3.140600e+04   \n",
       "mean                    2989.408181               2.163437e+04   \n",
       "std                     2893.621125               3.828520e+04   \n",
       "min                       15.000000               4.352000e+01   \n",
       "25%                     1080.000000               5.725085e+03   \n",
       "50%                     2040.000000               1.085448e+04   \n",
       "75%                     3932.000000               2.212691e+04   \n",
       "max                    90312.000000               1.602998e+06   \n",
       "\n",
       "       OXYCONTIN_total_claim_count  OXYCONTIN_total_day_supply  \n",
       "count                 31406.000000                31406.000000  \n",
       "mean                     35.995383                  998.872986  \n",
       "std                      50.959666                 1484.428045  \n",
       "min                      11.000000                   11.000000  \n",
       "25%                      13.000000                  360.000000  \n",
       "50%                      20.000000                  540.000000  \n",
       "75%                      36.000000                  990.000000  \n",
       "max                    1051.000000                31447.000000  \n",
       "\n",
       "[8 rows x 145 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FeaturesAll_pd1[new_col_all].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd1['drug_mean'] = FeaturesAll_pd1[new_col_all].mean(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd['drug_mean'] = FeaturesAll_pd['drug_mean'].map(lambda x: np.log10(x + 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd1['drug_sum'] = FeaturesAll_pd1[new_col_all].sum(axis=1)\n",
    "FeaturesAll_pd['drug_sum'] = FeaturesAll_pd['drug_sum'].map(lambda x: np.log10(x + 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "FeaturesAll_pd1['drug_variance'] = FeaturesAll_pd1[new_col_all].var(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>...</th>\n",
       "      <th>DIAZEPAM_total_claim_count</th>\n",
       "      <th>DIAZEPAM_total_day_supply</th>\n",
       "      <th>ALLOPURINOL_total_drug_cost</th>\n",
       "      <th>ALLOPURINOL_total_day_supply</th>\n",
       "      <th>OXYCONTIN_total_drug_cost</th>\n",
       "      <th>OXYCONTIN_total_claim_count</th>\n",
       "      <th>OXYCONTIN_total_day_supply</th>\n",
       "      <th>drug_mean</th>\n",
       "      <th>drug_sum</th>\n",
       "      <th>drug_variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>593816</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>4.092566</td>\n",
       "      <td>2.888972</td>\n",
       "      <td>3.969747</td>\n",
       "      <td>2.586587</td>\n",
       "      <td>1.399024</td>\n",
       "      <td>1.724276</td>\n",
       "      <td>3.906012</td>\n",
       "      <td>2.702700</td>\n",
       "      <td>3.184975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.783389</td>\n",
       "      <td>2.476600</td>\n",
       "      <td>1.853793e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493584</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>5.056408</td>\n",
       "      <td>3.714066</td>\n",
       "      <td>4.718552</td>\n",
       "      <td>3.266467</td>\n",
       "      <td>1.928954</td>\n",
       "      <td>2.530200</td>\n",
       "      <td>4.744840</td>\n",
       "      <td>3.402582</td>\n",
       "      <td>4.073205</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.622318</td>\n",
       "      <td>5.099339</td>\n",
       "      <td>9.146782e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>761450</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>2.183981</td>\n",
       "      <td>1.885785</td>\n",
       "      <td>1.958086</td>\n",
       "      <td>1.591065</td>\n",
       "      <td>1.301030</td>\n",
       "      <td>1.397940</td>\n",
       "      <td>2.198657</td>\n",
       "      <td>1.900367</td>\n",
       "      <td>2.089905</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.816573</td>\n",
       "      <td>2.510210</td>\n",
       "      <td>1.843332e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160806</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>2.820195</td>\n",
       "      <td>2.519821</td>\n",
       "      <td>2.584467</td>\n",
       "      <td>1.740363</td>\n",
       "      <td>1.447158</td>\n",
       "      <td>1.518514</td>\n",
       "      <td>3.168203</td>\n",
       "      <td>2.867467</td>\n",
       "      <td>2.930949</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276.87</td>\n",
       "      <td>852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.752383</td>\n",
       "      <td>3.053028</td>\n",
       "      <td>1.653873e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178678</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>4.866301</td>\n",
       "      <td>3.081325</td>\n",
       "      <td>4.090744</td>\n",
       "      <td>3.359266</td>\n",
       "      <td>1.585183</td>\n",
       "      <td>2.383815</td>\n",
       "      <td>4.622235</td>\n",
       "      <td>2.837526</td>\n",
       "      <td>3.374748</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.671752</td>\n",
       "      <td>4.085835</td>\n",
       "      <td>3.488178e+05</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481077</td>\n",
       "      <td>1992999759</td>\n",
       "      <td>4.200928</td>\n",
       "      <td>2.739295</td>\n",
       "      <td>3.957589</td>\n",
       "      <td>2.758912</td>\n",
       "      <td>1.317198</td>\n",
       "      <td>1.591065</td>\n",
       "      <td>4.407799</td>\n",
       "      <td>2.945876</td>\n",
       "      <td>3.401573</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.652837</td>\n",
       "      <td>4.032122</td>\n",
       "      <td>2.807693e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>352034</td>\n",
       "      <td>1992999791</td>\n",
       "      <td>3.134601</td>\n",
       "      <td>2.658116</td>\n",
       "      <td>2.893911</td>\n",
       "      <td>1.851258</td>\n",
       "      <td>1.386202</td>\n",
       "      <td>1.633468</td>\n",
       "      <td>3.316180</td>\n",
       "      <td>2.839478</td>\n",
       "      <td>3.090258</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.591229</td>\n",
       "      <td>3.544481</td>\n",
       "      <td>1.700106e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>583453</td>\n",
       "      <td>1992999825</td>\n",
       "      <td>3.528849</td>\n",
       "      <td>2.626657</td>\n",
       "      <td>3.305646</td>\n",
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       "      <td>1.339948</td>\n",
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       "<p>918012 rows × 168 columns</p>\n",
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      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "593816  1003000126             4.092566              2.888972   \n",
       "493584  1003000142             5.056408              3.714066   \n",
       "761450  1003000167             2.183981              1.885785   \n",
       "160806  1003000282             2.820195              2.519821   \n",
       "178678  1003000407             4.866301              3.081325   \n",
       "...            ...                  ...                   ...   \n",
       "481077  1992999759             4.200928              2.739295   \n",
       "352034  1992999791             3.134601              2.658116   \n",
       "583453  1992999825             3.528849              2.626657   \n",
       "606940  1992999833             2.486870              2.012570   \n",
       "869023  1992999882             2.793036              2.018368   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "593816             3.969747               2.586587                1.399024   \n",
       "493584             4.718552               3.266467                1.928954   \n",
       "761450             1.958086               1.591065                1.301030   \n",
       "160806             2.584467               1.740363                1.447158   \n",
       "178678             4.090744               3.359266                1.585183   \n",
       "...                     ...                    ...                     ...   \n",
       "481077             3.957589               2.758912                1.317198   \n",
       "352034             2.893911               1.851258                1.386202   \n",
       "583453             3.305646               2.225309                1.339948   \n",
       "606940             2.093036               1.556303                1.102662   \n",
       "869023             2.103940               1.963788                1.208620   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "593816               1.724276              3.906012               2.702700   \n",
       "493584               2.530200              4.744840               3.402582   \n",
       "761450               1.397940              2.198657               1.900367   \n",
       "160806               1.518514              3.168203               2.867467   \n",
       "178678               2.383815              4.622235               2.837526   \n",
       "...                       ...                   ...                    ...   \n",
       "481077               1.591065              4.407799               2.945876   \n",
       "352034               1.633468              3.316180               2.839478   \n",
       "583453               1.662758              3.573684               2.671404   \n",
       "606940               1.146128              3.021603               2.545307   \n",
       "869023               1.301030              3.422097               2.644767   \n",
       "\n",
       "        total_day_supply_max  ... DIAZEPAM_total_claim_count  \\\n",
       "593816              3.184975  ...                        NaN   \n",
       "493584              4.073205  ...                        NaN   \n",
       "761450              2.089905  ...                        NaN   \n",
       "160806              2.930949  ...                        NaN   \n",
       "178678              3.374748  ...                        NaN   \n",
       "...                      ...  ...                        ...   \n",
       "481077              3.401573  ...                        NaN   \n",
       "352034              3.090258  ...                        NaN   \n",
       "583453              3.104487  ...                        NaN   \n",
       "606940              2.592177  ...                        NaN   \n",
       "869023              2.756636  ...                        NaN   \n",
       "\n",
       "       DIAZEPAM_total_day_supply ALLOPURINOL_total_drug_cost  \\\n",
       "593816                       NaN                         NaN   \n",
       "493584                       NaN                         NaN   \n",
       "761450                       NaN                         NaN   \n",
       "160806                       NaN                      276.87   \n",
       "178678                       NaN                         NaN   \n",
       "...                          ...                         ...   \n",
       "481077                       NaN                         NaN   \n",
       "352034                       NaN                         NaN   \n",
       "583453                       NaN                         NaN   \n",
       "606940                       NaN                         NaN   \n",
       "869023                       NaN                         NaN   \n",
       "\n",
       "       ALLOPURINOL_total_day_supply OXYCONTIN_total_drug_cost  \\\n",
       "593816                          NaN                       NaN   \n",
       "493584                          NaN                       NaN   \n",
       "761450                          NaN                       NaN   \n",
       "160806                        852.0                       NaN   \n",
       "178678                          NaN                       NaN   \n",
       "...                             ...                       ...   \n",
       "481077                          NaN                       NaN   \n",
       "352034                          NaN                       NaN   \n",
       "583453                          NaN                       NaN   \n",
       "606940                          NaN                       NaN   \n",
       "869023                          NaN                       NaN   \n",
       "\n",
       "        OXYCONTIN_total_claim_count  OXYCONTIN_total_day_supply  drug_mean  \\\n",
       "593816                          NaN                         NaN   1.783389   \n",
       "493584                          NaN                         NaN   3.622318   \n",
       "761450                          NaN                         NaN   1.816573   \n",
       "160806                          NaN                         NaN   2.752383   \n",
       "178678                          NaN                         NaN   2.671752   \n",
       "...                             ...                         ...        ...   \n",
       "481077                          NaN                         NaN   2.652837   \n",
       "352034                          NaN                         NaN   2.591229   \n",
       "583453                          NaN                         NaN   1.999360   \n",
       "606940                          NaN                         NaN   2.367822   \n",
       "869023                          NaN                         NaN   2.317403   \n",
       "\n",
       "        drug_sum  drug_variance  \n",
       "593816  2.476600   1.853793e+03  \n",
       "493584  5.099339   9.146782e+07  \n",
       "761450  2.510210   1.843332e+03  \n",
       "160806  3.053028   1.653873e+05  \n",
       "178678  4.085835   3.488178e+05  \n",
       "...          ...            ...  \n",
       "481077  4.032122   2.807693e+05  \n",
       "352034  3.544481   1.700106e+05  \n",
       "583453  2.840714   5.186767e+03  \n",
       "606940  2.667920   4.977012e+04  \n",
       "869023  3.014697   4.419073e+04  \n",
       "\n",
       "[918012 rows x 168 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FeaturesAll_pd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
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       "      <td>2.545307</td>\n",
       "      <td>2.592177</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>2.367822</td>\n",
       "      <td>2.667920</td>\n",
       "      <td>49770.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>869023</td>\n",
       "      <td>1992999882</td>\n",
       "      <td>2.793036</td>\n",
       "      <td>2.018368</td>\n",
       "      <td>2.103940</td>\n",
       "      <td>1.963788</td>\n",
       "      <td>1.208620</td>\n",
       "      <td>1.301030</td>\n",
       "      <td>3.422097</td>\n",
       "      <td>2.644767</td>\n",
       "      <td>2.756636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.317403</td>\n",
       "      <td>3.014697</td>\n",
       "      <td>44190.731280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183603 rows × 168 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "438548  1790986990             3.717641              3.019004   \n",
       "207538  1790987055             1.936865              1.936865   \n",
       "762303  1790987063             2.798975              2.102756   \n",
       "245938  1790987089             2.409984              1.936229   \n",
       "819406  1790987105             2.854646              2.854646   \n",
       "...            ...                  ...                   ...   \n",
       "481077  1992999759             4.200928              2.739295   \n",
       "352034  1992999791             3.134601              2.658116   \n",
       "583453  1992999825             3.528849              2.626657   \n",
       "606940  1992999833             2.486870              2.012570   \n",
       "869023  1992999882             2.793036              2.018368   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "438548             3.691204               1.826075                1.152288   \n",
       "207538             1.936865               1.342423                1.342423   \n",
       "762303             2.349608               2.206826                1.518514   \n",
       "245938             2.264180               1.690196                1.230449   \n",
       "819406             2.854646               1.763428                1.763428   \n",
       "...                     ...                    ...                     ...   \n",
       "481077             3.957589               2.758912                1.317198   \n",
       "352034             2.893911               1.851258                1.386202   \n",
       "583453             3.305646               2.225309                1.339948   \n",
       "606940             2.093036               1.556303                1.102662   \n",
       "869023             2.103940               1.963788                1.208620   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "438548               1.322219              3.228400               2.530456   \n",
       "207538               1.342423              2.274158               2.274158   \n",
       "762303               1.812913              3.162863               2.465085   \n",
       "245938               1.322219              2.629410               2.154323   \n",
       "819406               1.763428              2.209515               2.209515   \n",
       "...                       ...                   ...                    ...   \n",
       "481077               1.591065              4.407799               2.945876   \n",
       "352034               1.633468              3.316180               2.839478   \n",
       "583453               1.662758              3.573684               2.671404   \n",
       "606940               1.146128              3.021603               2.545307   \n",
       "869023               1.301030              3.422097               2.644767   \n",
       "\n",
       "        total_day_supply_max  ... DIAZEPAM_total_claim_count  \\\n",
       "438548              2.782473  ...                        0.0   \n",
       "207538              2.274158  ...                        0.0   \n",
       "762303              2.806858  ...                        0.0   \n",
       "245938              2.271842  ...                        0.0   \n",
       "819406              2.209515  ...                        0.0   \n",
       "...                      ...  ...                        ...   \n",
       "481077              3.401573  ...                        0.0   \n",
       "352034              3.090258  ...                        0.0   \n",
       "583453              3.104487  ...                        0.0   \n",
       "606940              2.592177  ...                        0.0   \n",
       "869023              2.756636  ...                        0.0   \n",
       "\n",
       "       DIAZEPAM_total_day_supply ALLOPURINOL_total_drug_cost  \\\n",
       "438548                       0.0                         0.0   \n",
       "207538                       0.0                         0.0   \n",
       "762303                       0.0                         0.0   \n",
       "245938                       0.0                         0.0   \n",
       "819406                       0.0                         0.0   \n",
       "...                          ...                         ...   \n",
       "481077                       0.0                         0.0   \n",
       "352034                       0.0                         0.0   \n",
       "583453                       0.0                         0.0   \n",
       "606940                       0.0                         0.0   \n",
       "869023                       0.0                         0.0   \n",
       "\n",
       "       ALLOPURINOL_total_day_supply OXYCONTIN_total_drug_cost  \\\n",
       "438548                          0.0                       0.0   \n",
       "207538                          0.0                       0.0   \n",
       "762303                          0.0                       0.0   \n",
       "245938                          0.0                       0.0   \n",
       "819406                          0.0                       0.0   \n",
       "...                             ...                       ...   \n",
       "481077                          0.0                       0.0   \n",
       "352034                          0.0                       0.0   \n",
       "583453                          0.0                       0.0   \n",
       "606940                          0.0                       0.0   \n",
       "869023                          0.0                       0.0   \n",
       "\n",
       "        OXYCONTIN_total_claim_count  OXYCONTIN_total_day_supply  drug_mean  \\\n",
       "438548                          0.0                         0.0   2.108091   \n",
       "207538                          0.0                         0.0   2.137465   \n",
       "762303                          0.0                         0.0   2.319593   \n",
       "245938                          0.0                         0.0   1.761301   \n",
       "819406                          0.0                         0.0   2.493950   \n",
       "...                             ...                         ...        ...   \n",
       "481077                          0.0                         0.0   2.652837   \n",
       "352034                          0.0                         0.0   2.591229   \n",
       "583453                          0.0                         0.0   1.999360   \n",
       "606940                          0.0                         0.0   2.367822   \n",
       "869023                          0.0                         0.0   2.317403   \n",
       "\n",
       "        drug_sum  drug_variance  \n",
       "438548  2.582949   26421.602800  \n",
       "207538  2.436910    5154.170450  \n",
       "762303  3.016896   64058.213780  \n",
       "245938  2.533136    3340.673667  \n",
       "819406  2.970142  124938.304533  \n",
       "...          ...            ...  \n",
       "481077  4.032122  280769.276020  \n",
       "352034  3.544481  170010.593494  \n",
       "583453  2.840714    5186.767024  \n",
       "606940  2.667920   49770.125000  \n",
       "869023  3.014697   44190.731280  \n",
       "\n",
       "[183603 rows x 168 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = FeaturesAll_pd1.ix[ix_train]\n",
    "df_valid = FeaturesAll_pd1.ix[ix_valid]\n",
    "\n",
    "df_train.fillna(0)\n",
    "df_valid.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['npi', 'total_drug_cost_sum', 'total_drug_cost_mean',\n",
       "       'total_drug_cost_max', 'total_claim_count_sum',\n",
       "       'total_claim_count_mean', 'total_claim_count_max',\n",
       "       'total_day_supply_sum', 'total_day_supply_mean', 'total_day_supply_max',\n",
       "       ...\n",
       "       'DIAZEPAM_total_claim_count', 'DIAZEPAM_total_day_supply',\n",
       "       'ALLOPURINOL_total_drug_cost', 'ALLOPURINOL_total_day_supply',\n",
       "       'OXYCONTIN_total_drug_cost', 'OXYCONTIN_total_claim_count',\n",
       "       'OXYCONTIN_total_day_supply', 'drug_mean', 'drug_sum', 'drug_variance'],\n",
       "      dtype='object', length=168)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the Specialty Weight\n",
    "spec_dict =[]\n",
    "spec_fraud_1 = df_train[df_train['is_fraud']==1]['Speciality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "counts = Counter(spec_fraud_1)\n",
    "spec_dict =  dict(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "FeaturesAll_pd1['Spec_Weight'] = FeaturesAll_pd1['Speciality'].map(lambda x: spec_dict.get(x, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df_train = FeaturesAll_pd1.ix[ix_train]\n",
    "df_valid = FeaturesAll_pd1.ix[ix_valid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "292"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train[df_train['is_fraud'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "npi                            object\n",
      "total_drug_cost_sum           float64\n",
      "total_drug_cost_mean          float64\n",
      "total_drug_cost_max           float64\n",
      "total_claim_count_sum         float64\n",
      "                               ...   \n",
      "OXYCONTIN_total_day_supply    float64\n",
      "drug_mean                     float64\n",
      "drug_sum                      float64\n",
      "drug_variance                 float64\n",
      "Spec_Weight                     int64\n",
      "Length: 169, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>...</th>\n",
       "      <th>DIAZEPAM_total_day_supply</th>\n",
       "      <th>ALLOPURINOL_total_drug_cost</th>\n",
       "      <th>ALLOPURINOL_total_day_supply</th>\n",
       "      <th>OXYCONTIN_total_drug_cost</th>\n",
       "      <th>OXYCONTIN_total_claim_count</th>\n",
       "      <th>OXYCONTIN_total_day_supply</th>\n",
       "      <th>drug_mean</th>\n",
       "      <th>drug_sum</th>\n",
       "      <th>drug_variance</th>\n",
       "      <th>Spec_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>593816</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>493584</td>\n",
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       "      <td>9.146782e+07</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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       "      <td>761450</td>\n",
       "      <td>1003000167</td>\n",
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       "      <td>1.301030</td>\n",
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       "    <tr>\n",
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       "      <td>1.008120e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125004</td>\n",
       "      <td>1790986933</td>\n",
       "      <td>3.026864</td>\n",
       "      <td>2.329524</td>\n",
       "      <td>2.544490</td>\n",
       "      <td>2.311754</td>\n",
       "      <td>1.621176</td>\n",
       "      <td>1.903090</td>\n",
       "      <td>3.147985</td>\n",
       "      <td>2.450249</td>\n",
       "      <td>2.734800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.319373</td>\n",
       "      <td>3.396642</td>\n",
       "      <td>2.910963e+04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54061</td>\n",
       "      <td>1790986941</td>\n",
       "      <td>2.279667</td>\n",
       "      <td>1.980912</td>\n",
       "      <td>2.016824</td>\n",
       "      <td>1.643453</td>\n",
       "      <td>1.352183</td>\n",
       "      <td>1.361728</td>\n",
       "      <td>2.451786</td>\n",
       "      <td>2.152288</td>\n",
       "      <td>2.152288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.997736</td>\n",
       "      <td>2.693199</td>\n",
       "      <td>2.445938e+03</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>864274</td>\n",
       "      <td>1790986966</td>\n",
       "      <td>2.682452</td>\n",
       "      <td>2.207131</td>\n",
       "      <td>2.256694</td>\n",
       "      <td>1.838849</td>\n",
       "      <td>1.374137</td>\n",
       "      <td>1.477121</td>\n",
       "      <td>3.098990</td>\n",
       "      <td>2.622559</td>\n",
       "      <td>2.968950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.032648</td>\n",
       "      <td>2.874279</td>\n",
       "      <td>7.066535e+03</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>886982</td>\n",
       "      <td>1790986974</td>\n",
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       "      <td>4.444690</td>\n",
       "      <td>5.086008</td>\n",
       "      <td>1.963788</td>\n",
       "      <td>1.146128</td>\n",
       "      <td>1.278754</td>\n",
       "      <td>3.482731</td>\n",
       "      <td>2.638489</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>734409 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "593816  1003000126             4.092566              2.888972   \n",
       "493584  1003000142             5.056408              3.714066   \n",
       "761450  1003000167             2.183981              1.885785   \n",
       "160806  1003000282             2.820195              2.519821   \n",
       "178678  1003000407             4.866301              3.081325   \n",
       "...            ...                  ...                   ...   \n",
       "220752  1790986925             5.026466              3.880391   \n",
       "125004  1790986933             3.026864              2.329524   \n",
       "54061   1790986941             2.279667              1.980912   \n",
       "864274  1790986966             2.682452              2.207131   \n",
       "886982  1790986974             5.289774              4.444690   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "593816             3.969747               2.586587                1.399024   \n",
       "493584             4.718552               3.266467                1.928954   \n",
       "761450             1.958086               1.591065                1.301030   \n",
       "160806             2.584467               1.740363                1.447158   \n",
       "178678             4.090744               3.359266                1.585183   \n",
       "...                     ...                    ...                     ...   \n",
       "220752             4.925252               2.545307                1.414973   \n",
       "125004             2.544490               2.311754                1.621176   \n",
       "54061              2.016824               1.643453                1.352183   \n",
       "864274             2.256694               1.838849                1.374137   \n",
       "886982             5.086008               1.963788                1.146128   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "593816               1.724276              3.906012               2.702700   \n",
       "493584               2.530200              4.744840               3.402582   \n",
       "761450               1.397940              2.198657               1.900367   \n",
       "160806               1.518514              3.168203               2.867467   \n",
       "178678               2.383815              4.622235               2.837526   \n",
       "...                       ...                   ...                    ...   \n",
       "220752               1.826075              4.053194               2.907565   \n",
       "125004               1.903090              3.147985               2.450249   \n",
       "54061                1.361728              2.451786               2.152288   \n",
       "864274               1.477121              3.098990               2.622559   \n",
       "886982               1.278754              3.482731               2.638489   \n",
       "\n",
       "        total_day_supply_max  ... DIAZEPAM_total_day_supply  \\\n",
       "593816              3.184975  ...                       0.0   \n",
       "493584              4.073205  ...                       0.0   \n",
       "761450              2.089905  ...                       0.0   \n",
       "160806              2.930949  ...                       0.0   \n",
       "178678              3.374748  ...                       0.0   \n",
       "...                      ...  ...                       ...   \n",
       "220752              3.416807  ...                       0.0   \n",
       "125004              2.734800  ...                       0.0   \n",
       "54061               2.152288  ...                       0.0   \n",
       "864274              2.968950  ...                       0.0   \n",
       "886982              2.909021  ...                       0.0   \n",
       "\n",
       "       ALLOPURINOL_total_drug_cost ALLOPURINOL_total_day_supply  \\\n",
       "593816                        0.00                          0.0   \n",
       "493584                        0.00                          0.0   \n",
       "761450                        0.00                          0.0   \n",
       "160806                      276.87                        852.0   \n",
       "178678                        0.00                          0.0   \n",
       "...                            ...                          ...   \n",
       "220752                        0.00                          0.0   \n",
       "125004                        0.00                          0.0   \n",
       "54061                         0.00                          0.0   \n",
       "864274                        0.00                          0.0   \n",
       "886982                        0.00                          0.0   \n",
       "\n",
       "       OXYCONTIN_total_drug_cost OXYCONTIN_total_claim_count  \\\n",
       "593816                       0.0                         0.0   \n",
       "493584                       0.0                         0.0   \n",
       "761450                       0.0                         0.0   \n",
       "160806                       0.0                         0.0   \n",
       "178678                       0.0                         0.0   \n",
       "...                          ...                         ...   \n",
       "220752                       0.0                         0.0   \n",
       "125004                       0.0                         0.0   \n",
       "54061                        0.0                         0.0   \n",
       "864274                       0.0                         0.0   \n",
       "886982                       0.0                         0.0   \n",
       "\n",
       "        OXYCONTIN_total_day_supply  drug_mean  drug_sum  drug_variance  \\\n",
       "593816                         0.0   1.783389  2.476600   1.853793e+03   \n",
       "493584                         0.0   3.622318  5.099339   9.146782e+07   \n",
       "761450                         0.0   1.816573  2.510210   1.843332e+03   \n",
       "160806                         0.0   2.752383  3.053028   1.653873e+05   \n",
       "178678                         0.0   2.671752  4.085835   3.488178e+05   \n",
       "...                            ...        ...       ...            ...   \n",
       "220752                         0.0   2.676065  3.754406   1.008120e+06   \n",
       "125004                         0.0   2.319373  3.396642   2.910963e+04   \n",
       "54061                          0.0   1.997736  2.693199   2.445938e+03   \n",
       "864274                         0.0   2.032648  2.874279   7.066535e+03   \n",
       "886982                         0.0   0.000000  0.000000   0.000000e+00   \n",
       "\n",
       "        Spec_Weight  \n",
       "593816           55  \n",
       "493584            4  \n",
       "761450           22  \n",
       "160806           21  \n",
       "178678           60  \n",
       "...             ...  \n",
       "220752            0  \n",
       "125004            1  \n",
       "54061            22  \n",
       "864274            5  \n",
       "886982            2  \n",
       "\n",
       "[734409 rows x 169 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>total_drug_cost_sum</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>606940</td>\n",
       "      <td>1992999833</td>\n",
       "      <td>2.486870</td>\n",
       "      <td>2.012570</td>\n",
       "      <td>2.093036</td>\n",
       "      <td>1.556303</td>\n",
       "      <td>1.102662</td>\n",
       "      <td>1.146128</td>\n",
       "      <td>3.021603</td>\n",
       "      <td>2.545307</td>\n",
       "      <td>2.592177</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>2.367822</td>\n",
       "      <td>2.667920</td>\n",
       "      <td>49770.125000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>869023</td>\n",
       "      <td>1992999882</td>\n",
       "      <td>2.793036</td>\n",
       "      <td>2.018368</td>\n",
       "      <td>2.103940</td>\n",
       "      <td>1.963788</td>\n",
       "      <td>1.208620</td>\n",
       "      <td>1.301030</td>\n",
       "      <td>3.422097</td>\n",
       "      <td>2.644767</td>\n",
       "      <td>2.756636</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>2.317403</td>\n",
       "      <td>3.014697</td>\n",
       "      <td>44190.731280</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183603 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "438548  1790986990             3.717641              3.019004   \n",
       "207538  1790987055             1.936865              1.936865   \n",
       "762303  1790987063             2.798975              2.102756   \n",
       "245938  1790987089             2.409984              1.936229   \n",
       "819406  1790987105             2.854646              2.854646   \n",
       "...            ...                  ...                   ...   \n",
       "481077  1992999759             4.200928              2.739295   \n",
       "352034  1992999791             3.134601              2.658116   \n",
       "583453  1992999825             3.528849              2.626657   \n",
       "606940  1992999833             2.486870              2.012570   \n",
       "869023  1992999882             2.793036              2.018368   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "438548             3.691204               1.826075                1.152288   \n",
       "207538             1.936865               1.342423                1.342423   \n",
       "762303             2.349608               2.206826                1.518514   \n",
       "245938             2.264180               1.690196                1.230449   \n",
       "819406             2.854646               1.763428                1.763428   \n",
       "...                     ...                    ...                     ...   \n",
       "481077             3.957589               2.758912                1.317198   \n",
       "352034             2.893911               1.851258                1.386202   \n",
       "583453             3.305646               2.225309                1.339948   \n",
       "606940             2.093036               1.556303                1.102662   \n",
       "869023             2.103940               1.963788                1.208620   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "438548               1.322219              3.228400               2.530456   \n",
       "207538               1.342423              2.274158               2.274158   \n",
       "762303               1.812913              3.162863               2.465085   \n",
       "245938               1.322219              2.629410               2.154323   \n",
       "819406               1.763428              2.209515               2.209515   \n",
       "...                       ...                   ...                    ...   \n",
       "481077               1.591065              4.407799               2.945876   \n",
       "352034               1.633468              3.316180               2.839478   \n",
       "583453               1.662758              3.573684               2.671404   \n",
       "606940               1.146128              3.021603               2.545307   \n",
       "869023               1.301030              3.422097               2.644767   \n",
       "\n",
       "        total_day_supply_max  ... DIAZEPAM_total_day_supply  \\\n",
       "438548              2.782473  ...                       0.0   \n",
       "207538              2.274158  ...                       0.0   \n",
       "762303              2.806858  ...                       0.0   \n",
       "245938              2.271842  ...                       0.0   \n",
       "819406              2.209515  ...                       0.0   \n",
       "...                      ...  ...                       ...   \n",
       "481077              3.401573  ...                       0.0   \n",
       "352034              3.090258  ...                       0.0   \n",
       "583453              3.104487  ...                       0.0   \n",
       "606940              2.592177  ...                       0.0   \n",
       "869023              2.756636  ...                       0.0   \n",
       "\n",
       "       ALLOPURINOL_total_drug_cost ALLOPURINOL_total_day_supply  \\\n",
       "438548                         0.0                          0.0   \n",
       "207538                         0.0                          0.0   \n",
       "762303                         0.0                          0.0   \n",
       "245938                         0.0                          0.0   \n",
       "819406                         0.0                          0.0   \n",
       "...                            ...                          ...   \n",
       "481077                         0.0                          0.0   \n",
       "352034                         0.0                          0.0   \n",
       "583453                         0.0                          0.0   \n",
       "606940                         0.0                          0.0   \n",
       "869023                         0.0                          0.0   \n",
       "\n",
       "       OXYCONTIN_total_drug_cost OXYCONTIN_total_claim_count  \\\n",
       "438548                       0.0                         0.0   \n",
       "207538                       0.0                         0.0   \n",
       "762303                       0.0                         0.0   \n",
       "245938                       0.0                         0.0   \n",
       "819406                       0.0                         0.0   \n",
       "...                          ...                         ...   \n",
       "481077                       0.0                         0.0   \n",
       "352034                       0.0                         0.0   \n",
       "583453                       0.0                         0.0   \n",
       "606940                       0.0                         0.0   \n",
       "869023                       0.0                         0.0   \n",
       "\n",
       "        OXYCONTIN_total_day_supply  drug_mean  drug_sum  drug_variance  \\\n",
       "438548                         0.0   2.108091  2.582949   26421.602800   \n",
       "207538                         0.0   2.137465  2.436910    5154.170450   \n",
       "762303                         0.0   2.319593  3.016896   64058.213780   \n",
       "245938                         0.0   1.761301  2.533136    3340.673667   \n",
       "819406                         0.0   2.493950  2.970142  124938.304533   \n",
       "...                            ...        ...       ...            ...   \n",
       "481077                         0.0   2.652837  4.032122  280769.276020   \n",
       "352034                         0.0   2.591229  3.544481  170010.593494   \n",
       "583453                         0.0   1.999360  2.840714    5186.767024   \n",
       "606940                         0.0   2.367822  2.667920   49770.125000   \n",
       "869023                         0.0   2.317403  3.014697   44190.731280   \n",
       "\n",
       "        Spec_Weight  \n",
       "438548           55  \n",
       "207538           22  \n",
       "762303            6  \n",
       "245938           22  \n",
       "819406            1  \n",
       "...             ...  \n",
       "481077           21  \n",
       "352034            0  \n",
       "583453            1  \n",
       "606940            0  \n",
       "869023            6  \n",
       "\n",
       "[183603 rows x 169 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_features1 = numerical_features + ['drug_sum','Spec_Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total_drug_cost_sum',\n",
       " 'total_drug_cost_mean',\n",
       " 'Total_Payment_Sum',\n",
       " 'total_drug_cost_max',\n",
       " 'total_claim_count_sum',\n",
       " 'total_claim_count_mean',\n",
       " 'total_claim_count_max',\n",
       " 'total_day_supply_sum',\n",
       " 'total_day_supply_mean',\n",
       " 'total_day_supply_max',\n",
       " 'claim_max-mean',\n",
       " 'supply_max-mean',\n",
       " 'drug_max-mean',\n",
       " 'drug_sum',\n",
       " 'Spec_Weight']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "292"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positives=len(df_train[df_train['is_fraud'] == 1])\n",
    "positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "734409"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_size=len(df_train)\n",
    "dataset_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03975986133067542"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_ones=(float(positives)/float(dataset_size))*100\n",
    "per_ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2514.099315068493"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negatives=float(dataset_size-positives)\n",
    "t=negatives/positives\n",
    "t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00039759861330675416"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BalancingRatio= positives/dataset_size\n",
    "BalancingRatio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00039759861330675416"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BalancingRatio= positives/dataset_size\n",
    "BalancingRatio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:1544: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 3.\n",
      "  \" = {}.\".format(effective_n_jobs(self.n_jobs)))\n"
     ]
    }
   ],
   "source": [
    "X= df_train[numerical_features1].values\n",
    "Y = df_train['is_fraud'].values\n",
    "clf =  LogisticRegression(C=1e5, class_weight={0:1, 1:4000}, n_jobs=3)\n",
    "clf.fit(X,Y)\n",
    "y_p=clf.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_0 = {'n_estimators': 100, 'max_depth': 8, 'min_samples_split': 3, 'learning_rate': 0.01}\n",
    "params_1 = {'n_estimators': 500, 'max_depth': 10, 'min_samples_split': 5, 'class_weight' : {0:1, 1:2514}, 'n_jobs':5}\n",
    "\n",
    "scaler = StandardScaler()\n",
    "    \n",
    "clfs = [\n",
    "    LogisticRegression(C=1e5,class_weight= {0:1, 1:2514}, n_jobs=5),\n",
    "    \n",
    "    GaussianNB(),\n",
    "\n",
    "    ensemble.RandomForestClassifier(**params_1),\n",
    "\n",
    "    ensemble.ExtraTreesClassifier(**params_1),\n",
    "    \n",
    "    ensemble.GradientBoostingClassifier(**params_0)\n",
    "    \n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train[numerical_features1].values\n",
    "\n",
    "y_train = df_train['is_fraud'].values\n",
    "    \n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_valid = df_valid[numerical_features1].values\n",
    "y_valid = df_valid['is_fraud'].values\n",
    "X_valid_x= scaler.transform(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\debpr\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:1544: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 5.\n",
      "  \" = {}.\".format(effective_n_jobs(self.n_jobs)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.22671\n",
      "\tPrecision: 0.00073\n",
      "\tRecall: 0.65789\n",
      "\tF1: 0.00145\n",
      "\tauc: 0.67744\n",
      "\tAccuracy: 0.62555\n",
      "\n",
      "GaussianNB:\n",
      "\tBrier: 0.00548\n",
      "\tPrecision: 0.00115\n",
      "\tRecall: 0.01316\n",
      "\tF1: 0.00212\n",
      "\tauc: 0.61570\n",
      "\tAccuracy: 0.99488\n",
      "\n",
      "RandomForestClassifier:\n",
      "\tBrier: 0.04947\n",
      "\tPrecision: 0.00200\n",
      "\tRecall: 0.07895\n",
      "\tF1: 0.00391\n",
      "\tauc: 0.71774\n",
      "\tAccuracy: 0.98333\n",
      "\n",
      "ExtraTreesClassifier:\n",
      "\tBrier: 0.15010\n",
      "\tPrecision: 0.00099\n",
      "\tRecall: 0.39474\n",
      "\tF1: 0.00198\n",
      "\tauc: 0.69883\n",
      "\tAccuracy: 0.83511\n",
      "\n",
      "GradientBoostingClassifier:\n",
      "\tBrier: 0.00080\n",
      "\tPrecision: 0.01333\n",
      "\tRecall: 0.01316\n",
      "\tF1: 0.01325\n",
      "\tauc: 0.67603\n",
      "\tAccuracy: 0.99919\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prob_result = []\n",
    "df_m = []\n",
    "clfs_fited = []\n",
    "for clf in clfs:\n",
    "    print(\"%s:\" %  clf.__class__.__name__)\n",
    "    clf.fit(X_train,y_train)\n",
    "    clfs_fited.append(clf)\n",
    "    y_pred = clf.predict(X_valid_x)\n",
    "    prob_pos  = clf.predict_proba(X_valid_x)[:, 1]\n",
    "    prob_result.append(prob_pos)\n",
    "    m = confusion_matrix(y_valid, y_pred)\n",
    "    clf_score = brier_score_loss(y_valid, prob_pos, pos_label=y_valid.max())\n",
    "    print(\"\\tBrier: %1.5f\" % (clf_score))\n",
    "    print(\"\\tPrecision: %1.5f\" % precision_score(y_valid, y_pred))\n",
    "    print(\"\\tRecall: %1.5f\" % recall_score(y_valid, y_pred))\n",
    "    print(\"\\tF1: %1.5f\" % f1_score(y_valid, y_pred))\n",
    "    print(\"\\tauc: %1.5f\" % roc_auc_score(y_valid, prob_pos))\n",
    "    print(\"\\tAccuracy: %1.5f\\n\" % accuracy_score(y_valid, y_pred))\n",
    "    df_m.append(\n",
    "        pd.DataFrame(m, index=['True Negative', 'True Positive'], columns=['Pred. Negative', 'Pred. Positive'])\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_valid, prob_result[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_valid, prob_result[2])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, lw=1, label='ROC (area = %0.2f)' % roc_auc)\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[183453,     74],\n",
       "       [    75,      1]], dtype=int64)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.11853889,  0.15953108,  0.59242696,  0.20967059, -0.69654097,\n",
       "       -1.24766823, -0.96141074, -0.5083302 , -0.57096415, -0.57829914,\n",
       "       -0.63166835, -0.50143326,  0.25524295, -0.23454024,  1.62109627])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid_x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>total_drug_cost_sum</th>\n",
       "      <th>total_drug_cost_mean</th>\n",
       "      <th>total_drug_cost_max</th>\n",
       "      <th>total_claim_count_sum</th>\n",
       "      <th>total_claim_count_mean</th>\n",
       "      <th>total_claim_count_max</th>\n",
       "      <th>total_day_supply_sum</th>\n",
       "      <th>total_day_supply_mean</th>\n",
       "      <th>total_day_supply_max</th>\n",
       "      <th>...</th>\n",
       "      <th>DIAZEPAM_total_day_supply</th>\n",
       "      <th>ALLOPURINOL_total_drug_cost</th>\n",
       "      <th>ALLOPURINOL_total_day_supply</th>\n",
       "      <th>OXYCONTIN_total_drug_cost</th>\n",
       "      <th>OXYCONTIN_total_claim_count</th>\n",
       "      <th>OXYCONTIN_total_day_supply</th>\n",
       "      <th>drug_mean</th>\n",
       "      <th>drug_sum</th>\n",
       "      <th>drug_variance</th>\n",
       "      <th>Spec_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>593816</td>\n",
       "      <td>1003000126</td>\n",
       "      <td>4.092566</td>\n",
       "      <td>2.888972</td>\n",
       "      <td>3.969747</td>\n",
       "      <td>2.586587</td>\n",
       "      <td>1.399024</td>\n",
       "      <td>1.724276</td>\n",
       "      <td>3.906012</td>\n",
       "      <td>2.702700</td>\n",
       "      <td>3.184975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.783389</td>\n",
       "      <td>2.476600</td>\n",
       "      <td>1.853793e+03</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493584</td>\n",
       "      <td>1003000142</td>\n",
       "      <td>5.056408</td>\n",
       "      <td>3.714066</td>\n",
       "      <td>4.718552</td>\n",
       "      <td>3.266467</td>\n",
       "      <td>1.928954</td>\n",
       "      <td>2.530200</td>\n",
       "      <td>4.744840</td>\n",
       "      <td>3.402582</td>\n",
       "      <td>4.073205</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.622318</td>\n",
       "      <td>5.099339</td>\n",
       "      <td>9.146782e+07</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>761450</td>\n",
       "      <td>1003000167</td>\n",
       "      <td>2.183981</td>\n",
       "      <td>1.885785</td>\n",
       "      <td>1.958086</td>\n",
       "      <td>1.591065</td>\n",
       "      <td>1.301030</td>\n",
       "      <td>1.397940</td>\n",
       "      <td>2.198657</td>\n",
       "      <td>1.900367</td>\n",
       "      <td>2.089905</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.816573</td>\n",
       "      <td>2.510210</td>\n",
       "      <td>1.843332e+03</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160806</td>\n",
       "      <td>1003000282</td>\n",
       "      <td>2.820195</td>\n",
       "      <td>2.519821</td>\n",
       "      <td>2.584467</td>\n",
       "      <td>1.740363</td>\n",
       "      <td>1.447158</td>\n",
       "      <td>1.518514</td>\n",
       "      <td>3.168203</td>\n",
       "      <td>2.867467</td>\n",
       "      <td>2.930949</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276.87</td>\n",
       "      <td>852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.752383</td>\n",
       "      <td>3.053028</td>\n",
       "      <td>1.653873e+05</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178678</td>\n",
       "      <td>1003000407</td>\n",
       "      <td>4.866301</td>\n",
       "      <td>3.081325</td>\n",
       "      <td>4.090744</td>\n",
       "      <td>3.359266</td>\n",
       "      <td>1.585183</td>\n",
       "      <td>2.383815</td>\n",
       "      <td>4.622235</td>\n",
       "      <td>2.837526</td>\n",
       "      <td>3.374748</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.671752</td>\n",
       "      <td>4.085835</td>\n",
       "      <td>3.488178e+05</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220752</td>\n",
       "      <td>1790986925</td>\n",
       "      <td>5.026466</td>\n",
       "      <td>3.880391</td>\n",
       "      <td>4.925252</td>\n",
       "      <td>2.545307</td>\n",
       "      <td>1.414973</td>\n",
       "      <td>1.826075</td>\n",
       "      <td>4.053194</td>\n",
       "      <td>2.907565</td>\n",
       "      <td>3.416807</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.676065</td>\n",
       "      <td>3.754406</td>\n",
       "      <td>1.008120e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125004</td>\n",
       "      <td>1790986933</td>\n",
       "      <td>3.026864</td>\n",
       "      <td>2.329524</td>\n",
       "      <td>2.544490</td>\n",
       "      <td>2.311754</td>\n",
       "      <td>1.621176</td>\n",
       "      <td>1.903090</td>\n",
       "      <td>3.147985</td>\n",
       "      <td>2.450249</td>\n",
       "      <td>2.734800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.319373</td>\n",
       "      <td>3.396642</td>\n",
       "      <td>2.910963e+04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54061</td>\n",
       "      <td>1790986941</td>\n",
       "      <td>2.279667</td>\n",
       "      <td>1.980912</td>\n",
       "      <td>2.016824</td>\n",
       "      <td>1.643453</td>\n",
       "      <td>1.352183</td>\n",
       "      <td>1.361728</td>\n",
       "      <td>2.451786</td>\n",
       "      <td>2.152288</td>\n",
       "      <td>2.152288</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.997736</td>\n",
       "      <td>2.693199</td>\n",
       "      <td>2.445938e+03</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>864274</td>\n",
       "      <td>1790986966</td>\n",
       "      <td>2.682452</td>\n",
       "      <td>2.207131</td>\n",
       "      <td>2.256694</td>\n",
       "      <td>1.838849</td>\n",
       "      <td>1.374137</td>\n",
       "      <td>1.477121</td>\n",
       "      <td>3.098990</td>\n",
       "      <td>2.622559</td>\n",
       "      <td>2.968950</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.032648</td>\n",
       "      <td>2.874279</td>\n",
       "      <td>7.066535e+03</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886982</td>\n",
       "      <td>1790986974</td>\n",
       "      <td>5.289774</td>\n",
       "      <td>4.444690</td>\n",
       "      <td>5.086008</td>\n",
       "      <td>1.963788</td>\n",
       "      <td>1.146128</td>\n",
       "      <td>1.278754</td>\n",
       "      <td>3.482731</td>\n",
       "      <td>2.638489</td>\n",
       "      <td>2.909021</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>734409 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  total_drug_cost_sum  total_drug_cost_mean  \\\n",
       "593816  1003000126             4.092566              2.888972   \n",
       "493584  1003000142             5.056408              3.714066   \n",
       "761450  1003000167             2.183981              1.885785   \n",
       "160806  1003000282             2.820195              2.519821   \n",
       "178678  1003000407             4.866301              3.081325   \n",
       "...            ...                  ...                   ...   \n",
       "220752  1790986925             5.026466              3.880391   \n",
       "125004  1790986933             3.026864              2.329524   \n",
       "54061   1790986941             2.279667              1.980912   \n",
       "864274  1790986966             2.682452              2.207131   \n",
       "886982  1790986974             5.289774              4.444690   \n",
       "\n",
       "        total_drug_cost_max  total_claim_count_sum  total_claim_count_mean  \\\n",
       "593816             3.969747               2.586587                1.399024   \n",
       "493584             4.718552               3.266467                1.928954   \n",
       "761450             1.958086               1.591065                1.301030   \n",
       "160806             2.584467               1.740363                1.447158   \n",
       "178678             4.090744               3.359266                1.585183   \n",
       "...                     ...                    ...                     ...   \n",
       "220752             4.925252               2.545307                1.414973   \n",
       "125004             2.544490               2.311754                1.621176   \n",
       "54061              2.016824               1.643453                1.352183   \n",
       "864274             2.256694               1.838849                1.374137   \n",
       "886982             5.086008               1.963788                1.146128   \n",
       "\n",
       "        total_claim_count_max  total_day_supply_sum  total_day_supply_mean  \\\n",
       "593816               1.724276              3.906012               2.702700   \n",
       "493584               2.530200              4.744840               3.402582   \n",
       "761450               1.397940              2.198657               1.900367   \n",
       "160806               1.518514              3.168203               2.867467   \n",
       "178678               2.383815              4.622235               2.837526   \n",
       "...                       ...                   ...                    ...   \n",
       "220752               1.826075              4.053194               2.907565   \n",
       "125004               1.903090              3.147985               2.450249   \n",
       "54061                1.361728              2.451786               2.152288   \n",
       "864274               1.477121              3.098990               2.622559   \n",
       "886982               1.278754              3.482731               2.638489   \n",
       "\n",
       "        total_day_supply_max  ... DIAZEPAM_total_day_supply  \\\n",
       "593816              3.184975  ...                       NaN   \n",
       "493584              4.073205  ...                       NaN   \n",
       "761450              2.089905  ...                       NaN   \n",
       "160806              2.930949  ...                       NaN   \n",
       "178678              3.374748  ...                       NaN   \n",
       "...                      ...  ...                       ...   \n",
       "220752              3.416807  ...                       NaN   \n",
       "125004              2.734800  ...                       NaN   \n",
       "54061               2.152288  ...                       NaN   \n",
       "864274              2.968950  ...                       NaN   \n",
       "886982              2.909021  ...                       NaN   \n",
       "\n",
       "       ALLOPURINOL_total_drug_cost ALLOPURINOL_total_day_supply  \\\n",
       "593816                         NaN                          NaN   \n",
       "493584                         NaN                          NaN   \n",
       "761450                         NaN                          NaN   \n",
       "160806                      276.87                        852.0   \n",
       "178678                         NaN                          NaN   \n",
       "...                            ...                          ...   \n",
       "220752                         NaN                          NaN   \n",
       "125004                         NaN                          NaN   \n",
       "54061                          NaN                          NaN   \n",
       "864274                         NaN                          NaN   \n",
       "886982                         NaN                          NaN   \n",
       "\n",
       "       OXYCONTIN_total_drug_cost OXYCONTIN_total_claim_count  \\\n",
       "593816                       NaN                         NaN   \n",
       "493584                       NaN                         NaN   \n",
       "761450                       NaN                         NaN   \n",
       "160806                       NaN                         NaN   \n",
       "178678                       NaN                         NaN   \n",
       "...                          ...                         ...   \n",
       "220752                       NaN                         NaN   \n",
       "125004                       NaN                         NaN   \n",
       "54061                        NaN                         NaN   \n",
       "864274                       NaN                         NaN   \n",
       "886982                       NaN                         NaN   \n",
       "\n",
       "        OXYCONTIN_total_day_supply  drug_mean  drug_sum  drug_variance  \\\n",
       "593816                         NaN   1.783389  2.476600   1.853793e+03   \n",
       "493584                         NaN   3.622318  5.099339   9.146782e+07   \n",
       "761450                         NaN   1.816573  2.510210   1.843332e+03   \n",
       "160806                         NaN   2.752383  3.053028   1.653873e+05   \n",
       "178678                         NaN   2.671752  4.085835   3.488178e+05   \n",
       "...                            ...        ...       ...            ...   \n",
       "220752                         NaN   2.676065  3.754406   1.008120e+06   \n",
       "125004                         NaN   2.319373  3.396642   2.910963e+04   \n",
       "54061                          NaN   1.997736  2.693199   2.445938e+03   \n",
       "864274                         NaN   2.032648  2.874279   7.066535e+03   \n",
       "886982                         NaN        NaN  0.000000            NaN   \n",
       "\n",
       "        Spec_Weight  \n",
       "593816           55  \n",
       "493584            4  \n",
       "761450           22  \n",
       "160806           21  \n",
       "178678           60  \n",
       "...             ...  \n",
       "220752            0  \n",
       "125004            1  \n",
       "54061            22  \n",
       "864274            5  \n",
       "886982            2  \n",
       "\n",
       "[734409 rows x 169 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.51659971, -1.2952079 ,  0.36949694, -1.40529175, -1.26771914,\n",
       "       -0.47171971, -0.92246538, -1.37743858, -1.07968694, -1.23435305,\n",
       "       -1.18777193, -1.28920355, -1.31745957, -0.32395815,  0.0772416 ])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid_x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_valid_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.17580909, -0.01527296, -0.77052688,  0.46608395,  0.20160072,\n",
       "       -0.24072507, -0.1863803 ,  0.108827  , -0.22907934, -0.05881123,\n",
       "       -0.12337776,  0.21832414,  1.21115731, -0.29965648,  1.62109627])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = clfs_fited[2].feature_importances_\n",
    "# make importances relative to max importance\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 44.229221  ,  46.06615724,  46.32629575,  47.46613081,\n",
       "        47.57418307,  50.14372325,  56.21753059,  57.81815599,\n",
       "        58.63888811,  58.81457006,  61.99445656,  72.88707111,\n",
       "        91.60169541,  92.11134126, 100.        ])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance[sorted_idx]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [numerical_features1[ix] for ix in sorted_idx]\n",
    "bardata = {\"name\":features[::-1], \"importance percent\":feature_importance[sorted_idx][::-1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure()\n",
    "\n",
    "# Create plot title\n",
    "plt.title(\"Feature Importance\")\n",
    "\n",
    "# Add bars\n",
    "plt.bar(range(X.shape[1]), feature_importance[sorted_idx])\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(X.shape[1]), features, rotation=90)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}