Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The hospitals are evaluating efficiency of Insulin based treatment for patients Recommend if solo insulin treatments work well towards the above stated objective Should we recommend solo insulin or does it work better in conjunction with other drugs/ treatement ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as mn\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference Sites!!\n",
    "http://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008(# Project Discription)\n",
    "http://www.residentmar.io/2016/03/28/missingno.html (# missingno Package Description)\n",
    "https://medium.com/@kuldeepnpatel/chi-square-test-of-independence-bafd14028250 (# Chi-Square Test)\n",
    "https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-1-exploratory-data-analysis-with-pandas-de57880f1a68 (# Python Functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Discription:\n",
    "### 1.Patient identifiers (encounter_id,patient_nbr)\n",
    "#### Encounter_id is a unique identifier for each admission, and patient_nbr uniquely identifies each patient (encounter_id varies for every visit by the patient but patient_nbr going to be same)\n",
    "### 2.Patient Demographics\n",
    "#### race, age, gender, weight cover the basic demographic information associated with each patient.  \n",
    "#### Payer_code is an additional variable that identifies which health insurance (Medicare /Medicaid / Commercial) the patient holds.\n",
    "### 3.Admission and discharge details\n",
    "#### admission_source_id and admission_type_id identify who referred the patient to the hospital (e.g. physician vs. emergency dept.)\n",
    "#### and what type of admission this was (Emergency vs. Elective vs. Urgent). And \n",
    "#### discharge_disposition_id indicates where the patient was discharged to after treatment.\n",
    "### 4.Patient Medical History\n",
    "#### num_outpatient: number of outpatient visits by the patient in the year prior to the current encounter\n",
    "#### num_inpatient: number of inpatient visits by the patient in the year prior to the current encounter\n",
    "#### num_emergency: number of emergency visits by the patient in the year prior to the current encounter\n",
    "### 5.Patient admission details\n",
    "#### medical_specialty: the specialty of the physician admitting the patient\n",
    "#### diag_1, diag_2, diag_3: ICD9 codes for the primary, secondary and tertiary diagnoses of the patient. \n",
    "##### ICD9 are the universal codes that all physicians use to record diagnoses. There are various easy to use tools to lookup what individual codes mean (Wikipedia is pretty decent on its own)\n",
    "#### time_in_hospital: the patient's length of stay in the hospital (in days)\n",
    "#### number_diagnoses: Total no. of diagnosis entered for the patient\n",
    "#### num_lab_procedures: No. of lab procedures performed in the current encounter\n",
    "#### num_procedures: No. of non-lab procedures performed in the current encounter\n",
    "#### num_medications: No. of distinct medications prescribed in the current encounter\n",
    "### 6.Clinical Results\n",
    "#### max_glu_serum: indicates results of the glucose serum test\n",
    "#### A1Cresult: indicates results of the A1c test\n",
    "### 7.Medication Details\n",
    "#### diabetesMed: indicates if any diabetes medication was prescribed\n",
    "#### change: indicates if there was a change in diabetes medication\n",
    "#### 24 medication variables: indicate whether the dosage of the medicines was changed in any manner during the encounter\n",
    "### 8.Readmission indicator\n",
    "#####    Indicates whether a patient was readmitted after a particular admission. \n",
    "####    There are 3 levels for this variable: “NO” = no readmission, “< 30” = readmission within 30 days and \n",
    "####    “> 30” = readmission after more than 30 days. \n",
    "##### The 30 day distinction is of practical importance to hospitals because federal regulations penalize hospitals for an excessive proportion of such readmissions. \n",
    "##   The event of interest is readmitted within < 30 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetic = pd.read_csv('diabetic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data contains 50 columns and 101766 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>encounter_id</th>\n",
       "      <td>2278392</td>\n",
       "      <td>149190</td>\n",
       "      <td>64410</td>\n",
       "      <td>500364</td>\n",
       "      <td>16680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_nbr</th>\n",
       "      <td>8222157</td>\n",
       "      <td>55629189</td>\n",
       "      <td>86047875</td>\n",
       "      <td>82442376</td>\n",
       "      <td>42519267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>[0-10)</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>[40-50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_type_id</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_source_id</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payer_code</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical_specialty</th>\n",
       "      <td>Pediatrics-Endocrinology</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>41</td>\n",
       "      <td>59</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procedures</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_medications</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_outpatient</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_emergency</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_inpatient</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_1</th>\n",
       "      <td>250.83</td>\n",
       "      <td>276</td>\n",
       "      <td>648</td>\n",
       "      <td>8</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_2</th>\n",
       "      <td>?</td>\n",
       "      <td>250.01</td>\n",
       "      <td>250</td>\n",
       "      <td>250.43</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_3</th>\n",
       "      <td>?</td>\n",
       "      <td>255</td>\n",
       "      <td>V27</td>\n",
       "      <td>403</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_diagnoses</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_glu_serum</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Cresult</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repaglinide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nateglinide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorpropamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acetohexamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolbutamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pioglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rosiglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acarbose</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miglitol</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolazamide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>examide</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citoglipton</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Ch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetesMed</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readmitted</th>\n",
       "      <td>NO</td>\n",
       "      <td>&gt;30</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0          1  \\\n",
       "encounter_id                               2278392     149190   \n",
       "patient_nbr                                8222157   55629189   \n",
       "race                                     Caucasian  Caucasian   \n",
       "gender                                      Female     Female   \n",
       "age                                         [0-10)    [10-20)   \n",
       "weight                                           ?          ?   \n",
       "admission_type_id                                6          1   \n",
       "discharge_disposition_id                        25          1   \n",
       "admission_source_id                              1          7   \n",
       "time_in_hospital                                 1          3   \n",
       "payer_code                                       ?          ?   \n",
       "medical_specialty         Pediatrics-Endocrinology          ?   \n",
       "num_lab_procedures                              41         59   \n",
       "num_procedures                                   0          0   \n",
       "num_medications                                  1         18   \n",
       "number_outpatient                                0          0   \n",
       "number_emergency                                 0          0   \n",
       "number_inpatient                                 0          0   \n",
       "diag_1                                      250.83        276   \n",
       "diag_2                                           ?     250.01   \n",
       "diag_3                                           ?        255   \n",
       "number_diagnoses                                 1          9   \n",
       "max_glu_serum                                 None       None   \n",
       "A1Cresult                                     None       None   \n",
       "metformin                                       No         No   \n",
       "repaglinide                                     No         No   \n",
       "nateglinide                                     No         No   \n",
       "chlorpropamide                                  No         No   \n",
       "glimepiride                                     No         No   \n",
       "acetohexamide                                   No         No   \n",
       "glipizide                                       No         No   \n",
       "glyburide                                       No         No   \n",
       "tolbutamide                                     No         No   \n",
       "pioglitazone                                    No         No   \n",
       "rosiglitazone                                   No         No   \n",
       "acarbose                                        No         No   \n",
       "miglitol                                        No         No   \n",
       "troglitazone                                    No         No   \n",
       "tolazamide                                      No         No   \n",
       "examide                                         No         No   \n",
       "citoglipton                                     No         No   \n",
       "insulin                                         No         Up   \n",
       "glyburide-metformin                             No         No   \n",
       "glipizide-metformin                             No         No   \n",
       "glimepiride-pioglitazone                        No         No   \n",
       "metformin-rosiglitazone                         No         No   \n",
       "metformin-pioglitazone                          No         No   \n",
       "change                                          No         Ch   \n",
       "diabetesMed                                     No        Yes   \n",
       "readmitted                                      NO        >30   \n",
       "\n",
       "                                        2          3          4  \n",
       "encounter_id                        64410     500364      16680  \n",
       "patient_nbr                      86047875   82442376   42519267  \n",
       "race                      AfricanAmerican  Caucasian  Caucasian  \n",
       "gender                             Female       Male       Male  \n",
       "age                               [20-30)    [30-40)    [40-50)  \n",
       "weight                                  ?          ?          ?  \n",
       "admission_type_id                       1          1          1  \n",
       "discharge_disposition_id                1          1          1  \n",
       "admission_source_id                     7          7          7  \n",
       "time_in_hospital                        2          2          1  \n",
       "payer_code                              ?          ?          ?  \n",
       "medical_specialty                       ?          ?          ?  \n",
       "num_lab_procedures                     11         44         51  \n",
       "num_procedures                          5          1          0  \n",
       "num_medications                        13         16          8  \n",
       "number_outpatient                       2          0          0  \n",
       "number_emergency                        0          0          0  \n",
       "number_inpatient                        1          0          0  \n",
       "diag_1                                648          8        197  \n",
       "diag_2                                250     250.43        157  \n",
       "diag_3                                V27        403        250  \n",
       "number_diagnoses                        6          7          5  \n",
       "max_glu_serum                        None       None       None  \n",
       "A1Cresult                            None       None       None  \n",
       "metformin                              No         No         No  \n",
       "repaglinide                            No         No         No  \n",
       "nateglinide                            No         No         No  \n",
       "chlorpropamide                         No         No         No  \n",
       "glimepiride                            No         No         No  \n",
       "acetohexamide                          No         No         No  \n",
       "glipizide                          Steady         No     Steady  \n",
       "glyburide                              No         No         No  \n",
       "tolbutamide                            No         No         No  \n",
       "pioglitazone                           No         No         No  \n",
       "rosiglitazone                          No         No         No  \n",
       "acarbose                               No         No         No  \n",
       "miglitol                               No         No         No  \n",
       "troglitazone                           No         No         No  \n",
       "tolazamide                             No         No         No  \n",
       "examide                                No         No         No  \n",
       "citoglipton                            No         No         No  \n",
       "insulin                                No         Up     Steady  \n",
       "glyburide-metformin                    No         No         No  \n",
       "glipizide-metformin                    No         No         No  \n",
       "glimepiride-pioglitazone               No         No         No  \n",
       "metformin-rosiglitazone                No         No         No  \n",
       "metformin-pioglitazone                 No         No         No  \n",
       "change                                 No         Ch         Ch  \n",
       "diabetesMed                           Yes        Yes        Yes  \n",
       "readmitted                             NO         NO         NO  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['encounter_id', 'patient_nbr', 'race', 'gender', 'age', 'weight',\n",
       "       'admission_type_id', 'discharge_disposition_id', 'admission_source_id',\n",
       "       'time_in_hospital', 'payer_code', 'medical_specialty',\n",
       "       'num_lab_procedures', 'num_procedures', 'num_medications',\n",
       "       'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1',\n",
       "       'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult',\n",
       "       'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',\n",
       "       'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
       "       'tolazamide', 'examide', 'citoglipton', 'insulin',\n",
       "       'glyburide-metformin', 'glipizide-metformin',\n",
       "       'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
       "       'metformin-pioglitazone', 'change', 'diabetesMed', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>encounter_id</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>1.652016e+08</td>\n",
       "      <td>1.026403e+08</td>\n",
       "      <td>12522.0</td>\n",
       "      <td>84961194.0</td>\n",
       "      <td>152388987.0</td>\n",
       "      <td>2.302709e+08</td>\n",
       "      <td>443867222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_nbr</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>5.433040e+07</td>\n",
       "      <td>3.869636e+07</td>\n",
       "      <td>135.0</td>\n",
       "      <td>23413221.0</td>\n",
       "      <td>45505143.0</td>\n",
       "      <td>8.754595e+07</td>\n",
       "      <td>189502619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_type_id</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>2.024006e+00</td>\n",
       "      <td>1.445403e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>3.715642e+00</td>\n",
       "      <td>5.280166e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_source_id</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>5.754437e+00</td>\n",
       "      <td>4.064081e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>4.395987e+00</td>\n",
       "      <td>2.985108e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>4.309564e+01</td>\n",
       "      <td>1.967436e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.700000e+01</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procedures</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>1.339730e+00</td>\n",
       "      <td>1.705807e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_medications</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>1.602184e+01</td>\n",
       "      <td>8.127566e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_outpatient</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>3.693572e-01</td>\n",
       "      <td>1.267265e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_emergency</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>1.978362e-01</td>\n",
       "      <td>9.304723e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>76.0</td>\n",
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       "    <tr>\n",
       "      <th>number_inpatient</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>6.355659e-01</td>\n",
       "      <td>1.262863e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>21.0</td>\n",
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       "      <th>number_diagnoses</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>7.422607e+00</td>\n",
       "      <td>1.933600e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>16.0</td>\n",
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      "text/plain": [
       "                             count          mean           std      min  \\\n",
       "encounter_id              101766.0  1.652016e+08  1.026403e+08  12522.0   \n",
       "patient_nbr               101766.0  5.433040e+07  3.869636e+07    135.0   \n",
       "admission_type_id         101766.0  2.024006e+00  1.445403e+00      1.0   \n",
       "discharge_disposition_id  101766.0  3.715642e+00  5.280166e+00      1.0   \n",
       "admission_source_id       101766.0  5.754437e+00  4.064081e+00      1.0   \n",
       "time_in_hospital          101766.0  4.395987e+00  2.985108e+00      1.0   \n",
       "num_lab_procedures        101766.0  4.309564e+01  1.967436e+01      1.0   \n",
       "num_procedures            101766.0  1.339730e+00  1.705807e+00      0.0   \n",
       "num_medications           101766.0  1.602184e+01  8.127566e+00      1.0   \n",
       "number_outpatient         101766.0  3.693572e-01  1.267265e+00      0.0   \n",
       "number_emergency          101766.0  1.978362e-01  9.304723e-01      0.0   \n",
       "number_inpatient          101766.0  6.355659e-01  1.262863e+00      0.0   \n",
       "number_diagnoses          101766.0  7.422607e+00  1.933600e+00      1.0   \n",
       "\n",
       "                                 25%          50%           75%          max  \n",
       "encounter_id              84961194.0  152388987.0  2.302709e+08  443867222.0  \n",
       "patient_nbr               23413221.0   45505143.0  8.754595e+07  189502619.0  \n",
       "admission_type_id                1.0          1.0  3.000000e+00          8.0  \n",
       "discharge_disposition_id         1.0          1.0  4.000000e+00         28.0  \n",
       "admission_source_id              1.0          7.0  7.000000e+00         25.0  \n",
       "time_in_hospital                 2.0          4.0  6.000000e+00         14.0  \n",
       "num_lab_procedures              31.0         44.0  5.700000e+01        132.0  \n",
       "num_procedures                   0.0          1.0  2.000000e+00          6.0  \n",
       "num_medications                 10.0         15.0  2.000000e+01         81.0  \n",
       "number_outpatient                0.0          0.0  0.000000e+00         42.0  \n",
       "number_emergency                 0.0          0.0  0.000000e+00         76.0  \n",
       "number_inpatient                 0.0          0.0  1.000000e+00         21.0  \n",
       "number_diagnoses                 6.0          8.0  9.000000e+00         16.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      "encounter_id                101766 non-null int64\n",
      "patient_nbr                 101766 non-null int64\n",
      "race                        101766 non-null object\n",
      "gender                      101766 non-null object\n",
      "age                         101766 non-null object\n",
      "weight                      101766 non-null object\n",
      "admission_type_id           101766 non-null int64\n",
      "discharge_disposition_id    101766 non-null int64\n",
      "admission_source_id         101766 non-null int64\n",
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      "payer_code                  101766 non-null object\n",
      "medical_specialty           101766 non-null object\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101766 non-null object\n",
      "diag_2                      101766 non-null object\n",
      "diag_3                      101766 non-null object\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null object\n",
      "A1Cresult                   101766 non-null object\n",
      "metformin                   101766 non-null object\n",
      "repaglinide                 101766 non-null object\n",
      "nateglinide                 101766 non-null object\n",
      "chlorpropamide              101766 non-null object\n",
      "glimepiride                 101766 non-null object\n",
      "acetohexamide               101766 non-null object\n",
      "glipizide                   101766 non-null object\n",
      "glyburide                   101766 non-null object\n",
      "tolbutamide                 101766 non-null object\n",
      "pioglitazone                101766 non-null object\n",
      "rosiglitazone               101766 non-null object\n",
      "acarbose                    101766 non-null object\n",
      "miglitol                    101766 non-null object\n",
      "troglitazone                101766 non-null object\n",
      "tolazamide                  101766 non-null object\n",
      "examide                     101766 non-null object\n",
      "citoglipton                 101766 non-null object\n",
      "insulin                     101766 non-null object\n",
      "glyburide-metformin         101766 non-null object\n",
      "glipizide-metformin         101766 non-null object\n",
      "glimepiride-pioglitazone    101766 non-null object\n",
      "metformin-rosiglitazone     101766 non-null object\n",
      "metformin-pioglitazone      101766 non-null object\n",
      "change                      101766 non-null object\n",
      "diabetesMed                 101766 non-null object\n",
      "readmitted                  101766 non-null object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n"
     ]
    }
   ],
   "source": [
    "diabetic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.026511</td>\n",
       "      <td>-0.019116</td>\n",
       "      <td>-0.038161</td>\n",
       "      <td>-0.117126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <td>-0.132876</td>\n",
       "      <td>-0.136814</td>\n",
       "      <td>0.083483</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018193</td>\n",
       "      <td>0.162748</td>\n",
       "      <td>0.023415</td>\n",
       "      <td>0.015921</td>\n",
       "      <td>0.108753</td>\n",
       "      <td>-0.008715</td>\n",
       "      <td>-0.024471</td>\n",
       "      <td>0.020787</td>\n",
       "      <td>0.046891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_source_id</th>\n",
       "      <td>-0.112402</td>\n",
       "      <td>-0.032568</td>\n",
       "      <td>0.106654</td>\n",
       "      <td>0.018193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006965</td>\n",
       "      <td>0.048885</td>\n",
       "      <td>-0.135400</td>\n",
       "      <td>-0.054533</td>\n",
       "      <td>0.027244</td>\n",
       "      <td>0.059892</td>\n",
       "      <td>0.036314</td>\n",
       "      <td>0.072114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>-0.062221</td>\n",
       "      <td>-0.024092</td>\n",
       "      <td>-0.012500</td>\n",
       "      <td>0.162748</td>\n",
       "      <td>-0.006965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318450</td>\n",
       "      <td>0.191472</td>\n",
       "      <td>0.466135</td>\n",
       "      <td>-0.008916</td>\n",
       "      <td>-0.009681</td>\n",
       "      <td>0.073623</td>\n",
       "      <td>0.220186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>-0.026062</td>\n",
       "      <td>0.015946</td>\n",
       "      <td>-0.143713</td>\n",
       "      <td>0.023415</td>\n",
       "      <td>0.048885</td>\n",
       "      <td>0.318450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058066</td>\n",
       "      <td>0.268161</td>\n",
       "      <td>-0.007602</td>\n",
       "      <td>-0.002279</td>\n",
       "      <td>0.039231</td>\n",
       "      <td>0.152773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procedures</th>\n",
       "      <td>-0.014225</td>\n",
       "      <td>-0.015570</td>\n",
       "      <td>0.129888</td>\n",
       "      <td>0.015921</td>\n",
       "      <td>-0.135400</td>\n",
       "      <td>0.191472</td>\n",
       "      <td>0.058066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385767</td>\n",
       "      <td>-0.024819</td>\n",
       "      <td>-0.038179</td>\n",
       "      <td>-0.066236</td>\n",
       "      <td>0.073734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_medications</th>\n",
       "      <td>0.076113</td>\n",
       "      <td>0.020665</td>\n",
       "      <td>0.079535</td>\n",
       "      <td>0.108753</td>\n",
       "      <td>-0.054533</td>\n",
       "      <td>0.466135</td>\n",
       "      <td>0.268161</td>\n",
       "      <td>0.385767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045197</td>\n",
       "      <td>0.013180</td>\n",
       "      <td>0.064194</td>\n",
       "      <td>0.261526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_outpatient</th>\n",
       "      <td>0.103756</td>\n",
       "      <td>0.103379</td>\n",
       "      <td>0.026511</td>\n",
       "      <td>-0.008715</td>\n",
       "      <td>0.027244</td>\n",
       "      <td>-0.008916</td>\n",
       "      <td>-0.007602</td>\n",
       "      <td>-0.024819</td>\n",
       "      <td>0.045197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091459</td>\n",
       "      <td>0.107338</td>\n",
       "      <td>0.094152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_emergency</th>\n",
       "      <td>0.082803</td>\n",
       "      <td>0.062352</td>\n",
       "      <td>-0.019116</td>\n",
       "      <td>-0.024471</td>\n",
       "      <td>0.059892</td>\n",
       "      <td>-0.009681</td>\n",
       "      <td>-0.002279</td>\n",
       "      <td>-0.038179</td>\n",
       "      <td>0.013180</td>\n",
       "      <td>0.091459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266559</td>\n",
       "      <td>0.055539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_inpatient</th>\n",
       "      <td>0.030962</td>\n",
       "      <td>0.012480</td>\n",
       "      <td>-0.038161</td>\n",
       "      <td>0.020787</td>\n",
       "      <td>0.036314</td>\n",
       "      <td>0.073623</td>\n",
       "      <td>0.039231</td>\n",
       "      <td>-0.066236</td>\n",
       "      <td>0.064194</td>\n",
       "      <td>0.107338</td>\n",
       "      <td>0.266559</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.104710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_diagnoses</th>\n",
       "      <td>0.265149</td>\n",
       "      <td>0.226847</td>\n",
       "      <td>-0.117126</td>\n",
       "      <td>0.046891</td>\n",
       "      <td>0.072114</td>\n",
       "      <td>0.220186</td>\n",
       "      <td>0.152773</td>\n",
       "      <td>0.073734</td>\n",
       "      <td>0.261526</td>\n",
       "      <td>0.094152</td>\n",
       "      <td>0.055539</td>\n",
       "      <td>0.104710</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          encounter_id  patient_nbr  admission_type_id  \\\n",
       "encounter_id                  1.000000     0.512028          -0.158961   \n",
       "patient_nbr                   0.512028     1.000000          -0.011128   \n",
       "admission_type_id            -0.158961    -0.011128           1.000000   \n",
       "discharge_disposition_id     -0.132876    -0.136814           0.083483   \n",
       "admission_source_id          -0.112402    -0.032568           0.106654   \n",
       "time_in_hospital             -0.062221    -0.024092          -0.012500   \n",
       "num_lab_procedures           -0.026062     0.015946          -0.143713   \n",
       "num_procedures               -0.014225    -0.015570           0.129888   \n",
       "num_medications               0.076113     0.020665           0.079535   \n",
       "number_outpatient             0.103756     0.103379           0.026511   \n",
       "number_emergency              0.082803     0.062352          -0.019116   \n",
       "number_inpatient              0.030962     0.012480          -0.038161   \n",
       "number_diagnoses              0.265149     0.226847          -0.117126   \n",
       "\n",
       "                          discharge_disposition_id  admission_source_id  \\\n",
       "encounter_id                             -0.132876            -0.112402   \n",
       "patient_nbr                              -0.136814            -0.032568   \n",
       "admission_type_id                         0.083483             0.106654   \n",
       "discharge_disposition_id                  1.000000             0.018193   \n",
       "admission_source_id                       0.018193             1.000000   \n",
       "time_in_hospital                          0.162748            -0.006965   \n",
       "num_lab_procedures                        0.023415             0.048885   \n",
       "num_procedures                            0.015921            -0.135400   \n",
       "num_medications                           0.108753            -0.054533   \n",
       "number_outpatient                        -0.008715             0.027244   \n",
       "number_emergency                         -0.024471             0.059892   \n",
       "number_inpatient                          0.020787             0.036314   \n",
       "number_diagnoses                          0.046891             0.072114   \n",
       "\n",
       "                          time_in_hospital  num_lab_procedures  \\\n",
       "encounter_id                     -0.062221           -0.026062   \n",
       "patient_nbr                      -0.024092            0.015946   \n",
       "admission_type_id                -0.012500           -0.143713   \n",
       "discharge_disposition_id          0.162748            0.023415   \n",
       "admission_source_id              -0.006965            0.048885   \n",
       "time_in_hospital                  1.000000            0.318450   \n",
       "num_lab_procedures                0.318450            1.000000   \n",
       "num_procedures                    0.191472            0.058066   \n",
       "num_medications                   0.466135            0.268161   \n",
       "number_outpatient                -0.008916           -0.007602   \n",
       "number_emergency                 -0.009681           -0.002279   \n",
       "number_inpatient                  0.073623            0.039231   \n",
       "number_diagnoses                  0.220186            0.152773   \n",
       "\n",
       "                          num_procedures  num_medications  number_outpatient  \\\n",
       "encounter_id                   -0.014225         0.076113           0.103756   \n",
       "patient_nbr                    -0.015570         0.020665           0.103379   \n",
       "admission_type_id               0.129888         0.079535           0.026511   \n",
       "discharge_disposition_id        0.015921         0.108753          -0.008715   \n",
       "admission_source_id            -0.135400        -0.054533           0.027244   \n",
       "time_in_hospital                0.191472         0.466135          -0.008916   \n",
       "num_lab_procedures              0.058066         0.268161          -0.007602   \n",
       "num_procedures                  1.000000         0.385767          -0.024819   \n",
       "num_medications                 0.385767         1.000000           0.045197   \n",
       "number_outpatient              -0.024819         0.045197           1.000000   \n",
       "number_emergency               -0.038179         0.013180           0.091459   \n",
       "number_inpatient               -0.066236         0.064194           0.107338   \n",
       "number_diagnoses                0.073734         0.261526           0.094152   \n",
       "\n",
       "                          number_emergency  number_inpatient  number_diagnoses  \n",
       "encounter_id                      0.082803          0.030962          0.265149  \n",
       "patient_nbr                       0.062352          0.012480          0.226847  \n",
       "admission_type_id                -0.019116         -0.038161         -0.117126  \n",
       "discharge_disposition_id         -0.024471          0.020787          0.046891  \n",
       "admission_source_id               0.059892          0.036314          0.072114  \n",
       "time_in_hospital                 -0.009681          0.073623          0.220186  \n",
       "num_lab_procedures               -0.002279          0.039231          0.152773  \n",
       "num_procedures                   -0.038179         -0.066236          0.073734  \n",
       "num_medications                   0.013180          0.064194          0.261526  \n",
       "number_outpatient                 0.091459          0.107338          0.094152  \n",
       "number_emergency                  1.000000          0.266559          0.055539  \n",
       "number_inpatient                  0.266559          1.000000          0.104710  \n",
       "number_diagnoses                  0.055539          0.104710          1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caucasian          76099\n",
       "AfricanAmerican    19210\n",
       "?                   2273\n",
       "Hispanic            2037\n",
       "Other               1506\n",
       "Asian                641\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.race.value_counts()\n",
    "#Caucasian : The color adjectives used in 1779 are weiss \"white\" (Caucasian race), gelbbraun \"yellow-brown\" (Mongolian race),\n",
    "            #schwarz \"black\" (Aethiopian race), kupferroth \"copper-red\" (American race) and schwarzbraun \"black-brown\" (Malayan race).\n",
    "#AfricanAmerican : relating to black Americans   \n",
    "#Hispanic : People of Spanish speaking Origin(Include Spain and does not include Brail)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: quilt in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (2.9.14)\n",
      "Requirement already satisfied: six>=1.10.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (1.11.0)\n",
      "Requirement already satisfied: pandas>=0.21.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (0.23.0)\n",
      "Requirement already satisfied: numpy>=1.14.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (1.14.3)\n",
      "Requirement already satisfied: pyarrow>=0.9.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (0.11.1)\n",
      "Requirement already satisfied: pyyaml>=3.12 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (3.12)\n",
      "Requirement already satisfied: appdirs>=1.4.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (1.4.3)\n",
      "Requirement already satisfied: requests>=2.12.4 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (2.18.4)\n",
      "Requirement already satisfied: future>=0.16.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (0.17.1)\n",
      "Requirement already satisfied: packaging>=16.8 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (17.1)\n",
      "Requirement already satisfied: xlrd>=1.0.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (1.1.0)\n",
      "Requirement already satisfied: tqdm>=4.11.2 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from quilt) (4.28.1)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from pandas>=0.21.0->quilt) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from pandas>=0.21.0->quilt) (2018.4)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from requests>=2.12.4->quilt) (3.0.4)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from requests>=2.12.4->quilt) (2.6)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from requests>=2.12.4->quilt) (1.22)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from requests>=2.12.4->quilt) (2018.10.15)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\kovvu\\anaconda3\\lib\\site-packages (from packaging>=16.8->quilt) (2.2.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed 1.23.3 requires msgpack, which is not installed.\n",
      "keras 2.2.4 has requirement keras_applications>=1.0.6, but you'll have keras-applications 1.0.4 which is incompatible.\n",
      "keras 2.2.4 has requirement keras_preprocessing>=1.0.5, but you'll have keras-preprocessing 1.0.2 which is incompatible.\n",
      "You are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install quilt\n",
    "!quilt install ResidentMario/missingno_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install missingno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of variables:\n",
    "->The dataset used covers ~50 different variables to describe every hospital diabetes admission. \n",
    "  In this section we give an overview and brief description of the variables in this dataset.\n",
    "\n",
    "#Patient identifiers:\n",
    " Encounter_id is a unique identifier for each admission, and patient_nbr uniquely identifies each patient\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr       race  gender      age weight  \\\n",
       "0       2278392      8222157  Caucasian  Female   [0-10)      ?   \n",
       "1        149190     55629189  Caucasian  Female  [10-20)      ?   \n",
       "\n",
       "   admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                  6                        25                    1   \n",
       "1                  1                         1                    7   \n",
       "\n",
       "   time_in_hospital    ...     citoglipton insulin  glyburide-metformin  \\\n",
       "0                 1    ...              No      No                   No   \n",
       "1                 3    ...              No      Up                   No   \n",
       "\n",
       "   glipizide-metformin  glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "0                   No                        No                       No   \n",
       "1                   No                        No                       No   \n",
       "\n",
       "   metformin-pioglitazone  change diabetesMed readmitted  \n",
       "0                      No      No          No         NO  \n",
       "1                      No      Ch         Yes        >30  \n",
       "\n",
       "[2 rows x 50 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetic.replace('?' , np.NaN, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr             race  gender      age weight  \\\n",
       "0       2278392      8222157        Caucasian  Female   [0-10)    NaN   \n",
       "1        149190     55629189        Caucasian  Female  [10-20)    NaN   \n",
       "2         64410     86047875  AfricanAmerican  Female  [20-30)    NaN   \n",
       "3        500364     82442376        Caucasian    Male  [30-40)    NaN   \n",
       "4         16680     42519267        Caucasian    Male  [40-50)    NaN   \n",
       "\n",
       "   admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                  6                        25                    1   \n",
       "1                  1                         1                    7   \n",
       "2                  1                         1                    7   \n",
       "3                  1                         1                    7   \n",
       "4                  1                         1                    7   \n",
       "\n",
       "   time_in_hospital    ...     citoglipton insulin  glyburide-metformin  \\\n",
       "0                 1    ...              No      No                   No   \n",
       "1                 3    ...              No      Up                   No   \n",
       "2                 2    ...              No      No                   No   \n",
       "3                 2    ...              No      Up                   No   \n",
       "4                 1    ...              No  Steady                   No   \n",
       "\n",
       "   glipizide-metformin  glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "0                   No                        No                       No   \n",
       "1                   No                        No                       No   \n",
       "2                   No                        No                       No   \n",
       "3                   No                        No                       No   \n",
       "4                   No                        No                       No   \n",
       "\n",
       "   metformin-pioglitazone  change diabetesMed readmitted  \n",
       "0                      No      No          No         NO  \n",
       "1                      No      Ch         Yes        >30  \n",
       "2                      No      No         Yes         NO  \n",
       "3                      No      Ch         Yes         NO  \n",
       "4                      No      Ch         Yes         NO  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      "encounter_id                101766 non-null int64\n",
      "patient_nbr                 101766 non-null int64\n",
      "race                        99493 non-null object\n",
      "gender                      101766 non-null object\n",
      "age                         101766 non-null object\n",
      "weight                      3197 non-null object\n",
      "admission_type_id           101766 non-null int64\n",
      "discharge_disposition_id    101766 non-null int64\n",
      "admission_source_id         101766 non-null int64\n",
      "time_in_hospital            101766 non-null int64\n",
      "payer_code                  61510 non-null object\n",
      "medical_specialty           51817 non-null object\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101745 non-null object\n",
      "diag_2                      101408 non-null object\n",
      "diag_3                      100343 non-null object\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null object\n",
      "A1Cresult                   101766 non-null object\n",
      "metformin                   101766 non-null object\n",
      "repaglinide                 101766 non-null object\n",
      "nateglinide                 101766 non-null object\n",
      "chlorpropamide              101766 non-null object\n",
      "glimepiride                 101766 non-null object\n",
      "acetohexamide               101766 non-null object\n",
      "glipizide                   101766 non-null object\n",
      "glyburide                   101766 non-null object\n",
      "tolbutamide                 101766 non-null object\n",
      "pioglitazone                101766 non-null object\n",
      "rosiglitazone               101766 non-null object\n",
      "acarbose                    101766 non-null object\n",
      "miglitol                    101766 non-null object\n",
      "troglitazone                101766 non-null object\n",
      "tolazamide                  101766 non-null object\n",
      "examide                     101766 non-null object\n",
      "citoglipton                 101766 non-null object\n",
      "insulin                     101766 non-null object\n",
      "glyburide-metformin         101766 non-null object\n",
      "glipizide-metformin         101766 non-null object\n",
      "glimepiride-pioglitazone    101766 non-null object\n",
      "metformin-rosiglitazone     101766 non-null object\n",
      "metformin-pioglitazone      101766 non-null object\n",
      "change                      101766 non-null object\n",
      "diabetesMed                 101766 non-null object\n",
      "readmitted                  101766 non-null object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n"
     ]
    }
   ],
   "source": [
    "diabetic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "428       6862\n",
       "414       6581\n",
       "786       4016\n",
       "410       3614\n",
       "486       3508\n",
       "427       2766\n",
       "491       2275\n",
       "715       2151\n",
       "682       2042\n",
       "434       2028\n",
       "780       2019\n",
       "996       1967\n",
       "276       1889\n",
       "38        1688\n",
       "250.8     1680\n",
       "599       1595\n",
       "584       1520\n",
       "V57       1207\n",
       "250.6     1183\n",
       "518       1115\n",
       "820       1082\n",
       "577       1057\n",
       "493       1056\n",
       "435       1016\n",
       "562        989\n",
       "574        965\n",
       "296        896\n",
       "560        876\n",
       "250.7      871\n",
       "250.13     851\n",
       "          ... \n",
       "V60          1\n",
       "994          1\n",
       "543          1\n",
       "837          1\n",
       "366          1\n",
       "250.51       1\n",
       "817          1\n",
       "V51          1\n",
       "363          1\n",
       "10           1\n",
       "957          1\n",
       "391          1\n",
       "V07          1\n",
       "988          1\n",
       "649          1\n",
       "684          1\n",
       "834          1\n",
       "V67          1\n",
       "97           1\n",
       "V70          1\n",
       "870          1\n",
       "E909         1\n",
       "143          1\n",
       "915          1\n",
       "216          1\n",
       "691          1\n",
       "207          1\n",
       "375          1\n",
       "671          1\n",
       "832          1\n",
       "Name: diag_1, Length: 716, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.diag_1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in diabetic.columns:\n",
    "    if diabetic[column].dtype == 'object':\n",
    "        diabetic[column] = diabetic[column].astype('category') \n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      "encounter_id                101766 non-null int64\n",
      "patient_nbr                 101766 non-null int64\n",
      "race                        99493 non-null category\n",
      "gender                      101766 non-null category\n",
      "age                         101766 non-null category\n",
      "weight                      3197 non-null category\n",
      "admission_type_id           101766 non-null int64\n",
      "discharge_disposition_id    101766 non-null int64\n",
      "admission_source_id         101766 non-null int64\n",
      "time_in_hospital            101766 non-null int64\n",
      "payer_code                  61510 non-null category\n",
      "medical_specialty           51817 non-null category\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101745 non-null category\n",
      "diag_2                      101408 non-null category\n",
      "diag_3                      100343 non-null category\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null category\n",
      "A1Cresult                   101766 non-null category\n",
      "metformin                   101766 non-null category\n",
      "repaglinide                 101766 non-null category\n",
      "nateglinide                 101766 non-null category\n",
      "chlorpropamide              101766 non-null category\n",
      "glimepiride                 101766 non-null category\n",
      "acetohexamide               101766 non-null category\n",
      "glipizide                   101766 non-null category\n",
      "glyburide                   101766 non-null category\n",
      "tolbutamide                 101766 non-null category\n",
      "pioglitazone                101766 non-null category\n",
      "rosiglitazone               101766 non-null category\n",
      "acarbose                    101766 non-null category\n",
      "miglitol                    101766 non-null category\n",
      "troglitazone                101766 non-null category\n",
      "tolazamide                  101766 non-null category\n",
      "examide                     101766 non-null category\n",
      "citoglipton                 101766 non-null category\n",
      "insulin                     101766 non-null category\n",
      "glyburide-metformin         101766 non-null category\n",
      "glipizide-metformin         101766 non-null category\n",
      "glimepiride-pioglitazone    101766 non-null category\n",
      "metformin-rosiglitazone     101766 non-null category\n",
      "metformin-pioglitazone      101766 non-null category\n",
      "change                      101766 non-null category\n",
      "diabetesMed                 101766 non-null category\n",
      "readmitted                  101766 non-null category\n",
      "dtypes: category(37), int64(13)\n",
      "memory usage: 14.1 MB\n"
     ]
    }
   ],
   "source": [
    "diabetic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetic.admission_type_id = diabetic.admission_type_id.astype('category')\n",
    "diabetic.discharge_disposition_id = diabetic.discharge_disposition_id.astype('category')\n",
    "diabetic.admission_source_id = diabetic.admission_source_id.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      "encounter_id                101766 non-null int64\n",
      "patient_nbr                 101766 non-null int64\n",
      "race                        99493 non-null category\n",
      "gender                      101766 non-null category\n",
      "age                         101766 non-null category\n",
      "weight                      3197 non-null category\n",
      "admission_type_id           101766 non-null category\n",
      "discharge_disposition_id    101766 non-null category\n",
      "admission_source_id         101766 non-null category\n",
      "time_in_hospital            101766 non-null int64\n",
      "payer_code                  61510 non-null category\n",
      "medical_specialty           51817 non-null category\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101745 non-null category\n",
      "diag_2                      101408 non-null category\n",
      "diag_3                      100343 non-null category\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null category\n",
      "A1Cresult                   101766 non-null category\n",
      "metformin                   101766 non-null category\n",
      "repaglinide                 101766 non-null category\n",
      "nateglinide                 101766 non-null category\n",
      "chlorpropamide              101766 non-null category\n",
      "glimepiride                 101766 non-null category\n",
      "acetohexamide               101766 non-null category\n",
      "glipizide                   101766 non-null category\n",
      "glyburide                   101766 non-null category\n",
      "tolbutamide                 101766 non-null category\n",
      "pioglitazone                101766 non-null category\n",
      "rosiglitazone               101766 non-null category\n",
      "acarbose                    101766 non-null category\n",
      "miglitol                    101766 non-null category\n",
      "troglitazone                101766 non-null category\n",
      "tolazamide                  101766 non-null category\n",
      "examide                     101766 non-null category\n",
      "citoglipton                 101766 non-null category\n",
      "insulin                     101766 non-null category\n",
      "glyburide-metformin         101766 non-null category\n",
      "glipizide-metformin         101766 non-null category\n",
      "glimepiride-pioglitazone    101766 non-null category\n",
      "metformin-rosiglitazone     101766 non-null category\n",
      "metformin-pioglitazone      101766 non-null category\n",
      "change                      101766 non-null category\n",
      "diabetesMed                 101766 non-null category\n",
      "readmitted                  101766 non-null category\n",
      "dtypes: category(40), int64(10)\n",
      "memory usage: 12.0 MB\n"
     ]
    }
   ],
   "source": [
    "diabetic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>4.395987</td>\n",
       "      <td>2.985108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>43.095641</td>\n",
       "      <td>19.674362</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procedures</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>1.339730</td>\n",
       "      <td>1.705807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_medications</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>16.021844</td>\n",
       "      <td>8.127566</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_outpatient</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>0.369357</td>\n",
       "      <td>1.267265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_emergency</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>0.197836</td>\n",
       "      <td>0.930472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_inpatient</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>0.635566</td>\n",
       "      <td>1.262863</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_diagnoses</th>\n",
       "      <td>101766.0</td>\n",
       "      <td>7.422607</td>\n",
       "      <td>1.933600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std  min   25%   50%   75%  \\\n",
       "time_in_hospital    101766.0   4.395987   2.985108  1.0   2.0   4.0   6.0   \n",
       "num_lab_procedures  101766.0  43.095641  19.674362  1.0  31.0  44.0  57.0   \n",
       "num_procedures      101766.0   1.339730   1.705807  0.0   0.0   1.0   2.0   \n",
       "num_medications     101766.0  16.021844   8.127566  1.0  10.0  15.0  20.0   \n",
       "number_outpatient   101766.0   0.369357   1.267265  0.0   0.0   0.0   0.0   \n",
       "number_emergency    101766.0   0.197836   0.930472  0.0   0.0   0.0   0.0   \n",
       "number_inpatient    101766.0   0.635566   1.262863  0.0   0.0   0.0   1.0   \n",
       "number_diagnoses    101766.0   7.422607   1.933600  1.0   6.0   8.0   9.0   \n",
       "\n",
       "                      max  \n",
       "time_in_hospital     14.0  \n",
       "num_lab_procedures  132.0  \n",
       "num_procedures        6.0  \n",
       "num_medications      81.0  \n",
       "number_outpatient    42.0  \n",
       "number_emergency     76.0  \n",
       "number_inpatient     21.0  \n",
       "number_diagnoses     16.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 point summary stats\n",
    "id_columns = ['encounter_id', 'patient_nbr','admission_type_id','discharge_disposition_id','admission_source_id']\n",
    "#The above columns are droped because these are only certain numbers for identification\n",
    "diabetic.drop(columns = id_columns).describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inference from the summary Statistics:\n",
    "#   - The min time spent in hospital is 1 day while max is 14 days. Median being 4\n",
    "#   - Few patients had to go through a number of lab procedures, as high as 132\n",
    "#   - Medications for few patients are as high as 81\n",
    "#   - Patients have visited the hospitals as outpatient for as much as 42\n",
    "#   - Emergency visits are also high for some cases - 76"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising and Analysing Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23821d04400>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn.matrix(diabetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The mn.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.\n",
    "##### This visualization will comfortably accommodate up to 50 labelled variables. Past that range labels begin to overlap or become unreadable, and by default large displays omit them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns which contains missing values are race, weight, payer_code, medical_specialty, diag_1, diag_2, diag_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x238221abeb8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mn.bar is a simple visualization of nullity by column\n",
    "mn.bar(diabetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2381dad46a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn.heatmap(diabetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The missingno correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.\n",
    "##### Nullity correlation ranges from -1 (if one variable appears the other definitely does not) to 0 (variables appearing or not appearing have no effect on one another) to 1 (if one variable appears the other definitely also does).\n",
    "##### Variables that are always full or always empty have no meaningful correlation, and so are silently removed from the visualization—in this case for instance gender,age and patient_nbr columns, which are completely filled, are not included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Any columns that are 100% populated do not show up in this matrix, which is why we see the 7 columns that we do. \n",
    "\n",
    "# We see the correlation value of -0.1 between the Payer_code and Medical_Specialty columns. A correlation of -1 means if one \n",
    "# column has a value, the other NEVER has a value. A correlation of 1 means if one column has a value the other \n",
    "# ALWAYS has a value. The value we see between Medical_specialty and Payer_code means roughly: more often than not, patients\n",
    "# that have a diag_2 have an diag_3.\n",
    "\n",
    "# If there are shaded boxes in the heatmap with no numbers, that means the correlation is very close to (but not exactly) 0, \n",
    "# meaning there is no relationship at all between the presense of the variables. In this case, and based upon the fact \n",
    "# we didn't see missing values for these columns in the matrix, we can assume there are a small number of missing values\n",
    "# for these columns and they're randomly distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dendrogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x238220e5d30>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn.dendrogram(diabetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Percentage of data is missing in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caucasian          76099\n",
       "AfricanAmerican    19210\n",
       "Race-Missing        2273\n",
       "Hispanic            2037\n",
       "Other               1506\n",
       "Asian                641\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.race.replace(np.NaN, 'Race-Missing').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize = True will give in terms of Percentages of nullity data missing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caucasian          0.747784\n",
       "AfricanAmerican    0.188766\n",
       "Race-Missing       0.022336\n",
       "Hispanic           0.020017\n",
       "Other              0.014799\n",
       "Asian              0.006299\n",
       "Name: race, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.race.replace(np.NaN, 'Race-Missing').value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight-Missing    0.968585\n",
       "[75-100)          0.013128\n",
       "[50-75)           0.008814\n",
       "[100-125)         0.006142\n",
       "[125-150)         0.001425\n",
       "[25-50)           0.000953\n",
       "[0-25)            0.000472\n",
       "[150-175)         0.000344\n",
       "[175-200)         0.000108\n",
       ">200              0.000029\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.weight.replace(np.NaN, 'Weight-Missing').value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Missing    0.395574\n",
       "MC         0.318761\n",
       "HM         0.061651\n",
       "SP         0.049201\n",
       "BC         0.045742\n",
       "MD         0.034707\n",
       "CP         0.024890\n",
       "UN         0.024055\n",
       "CM         0.019034\n",
       "OG         0.010151\n",
       "PO         0.005817\n",
       "DM         0.005395\n",
       "CH         0.001435\n",
       "WC         0.001327\n",
       "OT         0.000934\n",
       "MP         0.000776\n",
       "SI         0.000540\n",
       "FR         0.000010\n",
       "Name: payer_code, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.payer_code.replace(np.NaN, 'Missing').value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Missing                              0.490822\n",
       "InternalMedicine                     0.143810\n",
       "Emergency/Trauma                     0.074337\n",
       "Family/GeneralPractice               0.073109\n",
       "Cardiology                           0.052591\n",
       "Surgery-General                      0.030452\n",
       "Nephrology                           0.015850\n",
       "Orthopedics                          0.013757\n",
       "Orthopedics-Reconstructive           0.012116\n",
       "Radiologist                          0.011202\n",
       "Pulmonology                          0.008559\n",
       "Psychiatry                           0.008392\n",
       "Urology                              0.006731\n",
       "ObstetricsandGynecology              0.006594\n",
       "Surgery-Cardiovascular/Thoracic      0.006407\n",
       "Gastroenterology                     0.005542\n",
       "Surgery-Vascular                     0.005238\n",
       "Surgery-Neuro                        0.004599\n",
       "PhysicalMedicineandRehabilitation    0.003842\n",
       "Oncology                             0.003420\n",
       "Pediatrics                           0.002496\n",
       "Hematology/Oncology                  0.002034\n",
       "Neurology                            0.001995\n",
       "Pediatrics-Endocrinology             0.001562\n",
       "Otolaryngology                       0.001228\n",
       "Endocrinology                        0.001179\n",
       "Surgery-Thoracic                     0.001071\n",
       "Psychology                           0.000992\n",
       "Podiatry                             0.000983\n",
       "Surgery-Cardiovascular               0.000963\n",
       "                                       ...   \n",
       "Obstetrics                           0.000187\n",
       "Anesthesiology-Pediatric             0.000187\n",
       "Rheumatology                         0.000167\n",
       "Pathology                            0.000167\n",
       "OutreachServices                     0.000118\n",
       "Anesthesiology                       0.000118\n",
       "PhysicianNotFound                    0.000108\n",
       "Surgery-Colon&Rectal                 0.000108\n",
       "Surgery-Maxillofacial                0.000108\n",
       "Pediatrics-Neurology                 0.000098\n",
       "Endocrinology-Metabolism             0.000079\n",
       "Surgery-Pediatric                    0.000079\n",
       "AllergyandImmunology                 0.000069\n",
       "Psychiatry-Child/Adolescent          0.000069\n",
       "Cardiology-Pediatric                 0.000069\n",
       "DCPTEAM                              0.000059\n",
       "Pediatrics-Hematology-Oncology       0.000039\n",
       "Dentistry                            0.000039\n",
       "Pediatrics-EmergencyMedicine         0.000029\n",
       "Pediatrics-AllergyandImmunology      0.000029\n",
       "Resident                             0.000020\n",
       "Surgery-PlasticwithinHeadandNeck     0.000010\n",
       "Dermatology                          0.000010\n",
       "Neurophysiology                      0.000010\n",
       "Psychiatry-Addictive                 0.000010\n",
       "Speech                               0.000010\n",
       "SportsMedicine                       0.000010\n",
       "Proctology                           0.000010\n",
       "Pediatrics-InfectiousDiseases        0.000010\n",
       "Perinatology                         0.000010\n",
       "Name: medical_specialty, Length: 73, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetic.medical_specialty.replace(np.NaN, 'Missing').value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     0.539119\n",
       ">30    0.349282\n",
       "<30    0.111599\n",
       "Name: readmitted, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the target class proportion\n",
    "diabetic.readmitted.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     54864\n",
       ">30    35545\n",
       "<30    11357\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the target class\n",
    "diabetic.readmitted.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting Numerical and Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101766, 40)\n",
      "(101766, 10)\n"
     ]
    }
   ],
   "source": [
    "categorical_data = diabetic.select_dtypes(include='category')\n",
    "numerical_data = diabetic.select_dtypes(include='int64')\n",
    "print(categorical_data.shape)\n",
    "print(numerical_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>payer_code</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pediatrics-Endocrinology</td>\n",
       "      <td>250.83</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              race  gender      age weight admission_type_id  \\\n",
       "0        Caucasian  Female   [0-10)    NaN                 6   \n",
       "1        Caucasian  Female  [10-20)    NaN                 1   \n",
       "2  AfricanAmerican  Female  [20-30)    NaN                 1   \n",
       "3        Caucasian    Male  [30-40)    NaN                 1   \n",
       "4        Caucasian    Male  [40-50)    NaN                 1   \n",
       "\n",
       "  discharge_disposition_id admission_source_id payer_code  \\\n",
       "0                       25                   1        NaN   \n",
       "1                        1                   7        NaN   \n",
       "2                        1                   7        NaN   \n",
       "3                        1                   7        NaN   \n",
       "4                        1                   7        NaN   \n",
       "\n",
       "          medical_specialty  diag_1    ...     citoglipton insulin  \\\n",
       "0  Pediatrics-Endocrinology  250.83    ...              No      No   \n",
       "1                       NaN     276    ...              No      Up   \n",
       "2                       NaN     648    ...              No      No   \n",
       "3                       NaN       8    ...              No      Up   \n",
       "4                       NaN     197    ...              No  Steady   \n",
       "\n",
       "  glyburide-metformin glipizide-metformin glimepiride-pioglitazone  \\\n",
       "0                  No                  No                       No   \n",
       "1                  No                  No                       No   \n",
       "2                  No                  No                       No   \n",
       "3                  No                  No                       No   \n",
       "4                  No                  No                       No   \n",
       "\n",
       "  metformin-rosiglitazone metformin-pioglitazone change diabetesMed readmitted  \n",
       "0                      No                     No     No          No         NO  \n",
       "1                      No                     No     Ch         Yes        >30  \n",
       "2                      No                     No     No         Yes         NO  \n",
       "3                      No                     No     Ch         Yes         NO  \n",
       "4                      No                     No     Ch         Yes         NO  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>number_diagnoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr  time_in_hospital  num_lab_procedures  \\\n",
       "0       2278392      8222157                 1                  41   \n",
       "1        149190     55629189                 3                  59   \n",
       "2         64410     86047875                 2                  11   \n",
       "3        500364     82442376                 2                  44   \n",
       "4         16680     42519267                 1                  51   \n",
       "\n",
       "   num_procedures  num_medications  number_outpatient  number_emergency  \\\n",
       "0               0                1                  0                 0   \n",
       "1               0               18                  0                 0   \n",
       "2               5               13                  2                 0   \n",
       "3               1               16                  0                 0   \n",
       "4               0                8                  0                 0   \n",
       "\n",
       "   number_inpatient  number_diagnoses  \n",
       "0                 0                 1  \n",
       "1                 0                 9  \n",
       "2                 1                 6  \n",
       "3                 0                 7  \n",
       "4                 0                 5  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Framing of Hypothesis and Testing : (Categorical Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chisquare,chi2_contingency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chi-square test is one of the most common ways to examine relationships between two or more categorical variables. Not surprisingly, It involves calculating a number, called the chi-square statistic - χ2. Which follows a chi-square distribution. The chi-square test relies on the difference between observed and expected values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Goodness of Fit Test between 2 categorical variables\n",
    "\n",
    "### H0: There is no relationship between 2 categorical Variables\n",
    "#####      i.e.,The two categorical variables are independent of each other\n",
    "### Ha: There is a relationship between 2 categorical Variables\n",
    "#####   i.e., The two categorical variables are dependent of each other\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goodness of Fit Test between 2 categorical variables Gender and Weight\n",
    "\n",
    "# H0: The two categorical variables Gender and Weight are independent\n",
    "# Ha: The two categorical variables are dependent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight-Missing    98569\n",
       "[75-100)           1336\n",
       "[50-75)             897\n",
       "[100-125)           625\n",
       "[125-150)           145\n",
       "[25-50)              97\n",
       "[0-25)               48\n",
       "[150-175)            35\n",
       "[175-200)            11\n",
       ">200                  3\n",
       "Name: weight, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a new category for missing weight\n",
    "diabetic.weight = diabetic.weight.replace(np.NaN, 'Weight-Missing')\n",
    "diabetic.weight.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating contingency table\n",
    "contingency = pd.crosstab(diabetic.gender,diabetic.weight,margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>weight</th>\n",
       "      <th>&gt;200</th>\n",
       "      <th>Weight-Missing</th>\n",
       "      <th>[0-25)</th>\n",
       "      <th>[100-125)</th>\n",
       "      <th>[125-150)</th>\n",
       "      <th>[150-175)</th>\n",
       "      <th>[175-200)</th>\n",
       "      <th>[25-50)</th>\n",
       "      <th>[50-75)</th>\n",
       "      <th>[75-100)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2</td>\n",
       "      <td>53060</td>\n",
       "      <td>22</td>\n",
       "      <td>242</td>\n",
       "      <td>60</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>71</td>\n",
       "      <td>595</td>\n",
       "      <td>633</td>\n",
       "      <td>54708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1</td>\n",
       "      <td>45506</td>\n",
       "      <td>26</td>\n",
       "      <td>383</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>302</td>\n",
       "      <td>703</td>\n",
       "      <td>47055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown/Invalid</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3</td>\n",
       "      <td>98569</td>\n",
       "      <td>48</td>\n",
       "      <td>625</td>\n",
       "      <td>145</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>97</td>\n",
       "      <td>897</td>\n",
       "      <td>1336</td>\n",
       "      <td>101766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weight           >200  Weight-Missing  [0-25)  [100-125)  [125-150)  \\\n",
       "gender                                                                \n",
       "Female              2           53060      22        242         60   \n",
       "Male                1           45506      26        383         85   \n",
       "Unknown/Invalid     0               3       0          0          0   \n",
       "All                 3           98569      48        625        145   \n",
       "\n",
       "weight           [150-175)  [175-200)  [25-50)  [50-75)  [75-100)     All  \n",
       "gender                                                                     \n",
       "Female                  18          5       71      595       633   54708  \n",
       "Male                    17          6       26      302       703   47055  \n",
       "Unknown/Invalid          0          0        0        0         0       3  \n",
       "All                     35         11       97      897      1336  101766  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi2, p, dof, ex = chi2_contingency(contingency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value is :  5.752331250046188e-20\n"
     ]
    }
   ],
   "source": [
    "print('P-Value is : ' , p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Considering 5% as the significance level, as the p value is less than 0.05, we reject the null hypothesis that the \n",
    "### weight category is independent of Gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goodness of Fit Test between 2 categorical variables Race and Weight\n",
    "\n",
    "# H0: The two categorical variables Race and Weight are independent\n",
    "# Ha: The two categorical variables are dependent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caucasian          0.747784\n",
       "AfricanAmerican    0.188766\n",
       "Race-Missing       0.022336\n",
       "Hispanic           0.020017\n",
       "Other              0.014799\n",
       "Asian              0.006299\n",
       "Name: race, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a new category for the missing race\n",
    "diabetic.race = diabetic.race.replace(np.NaN, 'Race-Missing')\n",
    "diabetic.race.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "contingency = pd.crosstab(diabetic.race,diabetic.weight,margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>weight</th>\n",
       "      <th>&gt;200</th>\n",
       "      <th>Weight-Missing</th>\n",
       "      <th>[0-25)</th>\n",
       "      <th>[100-125)</th>\n",
       "      <th>[125-150)</th>\n",
       "      <th>[150-175)</th>\n",
       "      <th>[175-200)</th>\n",
       "      <th>[25-50)</th>\n",
       "      <th>[50-75)</th>\n",
       "      <th>[75-100)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AfricanAmerican</th>\n",
       "      <td>1</td>\n",
       "      <td>19097</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>19210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caucasian</th>\n",
       "      <td>2</td>\n",
       "      <td>73192</td>\n",
       "      <td>40</td>\n",
       "      <td>567</td>\n",
       "      <td>129</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>88</td>\n",
       "      <td>816</td>\n",
       "      <td>1223</td>\n",
       "      <td>76099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>0</td>\n",
       "      <td>2037</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race-Missing</th>\n",
       "      <td>0</td>\n",
       "      <td>2135</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>57</td>\n",
       "      <td>2273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3</td>\n",
       "      <td>98569</td>\n",
       "      <td>48</td>\n",
       "      <td>625</td>\n",
       "      <td>145</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>97</td>\n",
       "      <td>897</td>\n",
       "      <td>1336</td>\n",
       "      <td>101766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weight           >200  Weight-Missing  [0-25)  [100-125)  [125-150)  \\\n",
       "race                                                                  \n",
       "AfricanAmerican     1           19097       6         27          5   \n",
       "Asian               0             632       0          0          0   \n",
       "Caucasian           2           73192      40        567        129   \n",
       "Hispanic            0            2037       0          0          0   \n",
       "Other               0            1476       1          2          2   \n",
       "Race-Missing        0            2135       1         29          9   \n",
       "All                 3           98569      48        625        145   \n",
       "\n",
       "weight           [150-175)  [175-200)  [25-50)  [50-75)  [75-100)     All  \n",
       "race                                                                       \n",
       "AfricanAmerican          1          1        4       25        43   19210  \n",
       "Asian                    0          0        0        8         1     641  \n",
       "Caucasian               33          9       88      816      1223   76099  \n",
       "Hispanic                 0          0        0        0         0    2037  \n",
       "Other                    0          0        1       12        12    1506  \n",
       "Race-Missing             1          1        4       36        57    2273  \n",
       "All                     35         11       97      897      1336  101766  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi2, p, dof, ex = chi2_contingency(contingency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value is :  3.3763874559071715e-108\n"
     ]
    }
   ],
   "source": [
    "print('P-Value is : ' , p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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