data-cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook part 1\n",
    "# Data Cleaning and EDA\n",
    "_By **Avi Patel**_\n",
    "\n",
    "## Overview\n",
    "This project seeks to create a model that classifies whether a patient is likely to be readmitted to the hospital within 30 days of discharge givin the health conditions. This information will help Hospitals to determine individual patients' risk of returning in the hospital in this time period.\n",
    "\n",
    "\n",
    "\n",
    "## Business Problems\n",
    "American hospitals spent over $41 billion on patients who got readmitted within 30 days of discharge. Being able to determine factors that lead to higher readmission in such patients, and correspondingly being able to predict which patients will get readmitted can help hospitals save millions of dollars while improving quality of care."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import missingno as msno\n",
    "import math\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)\n",
    "\n",
    "sns.set_palette(palette='icefire')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/diabetic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Unique Values Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe which contains unique values of all columns and it's count\n",
    "def uniqueValueDataFrame(df):\n",
    "    col_name = []\n",
    "    unique_value_list = []\n",
    "    total_unique_value = []\n",
    "    data_type = []\n",
    "    for col in df.columns:\n",
    "        col_name.append(col)\n",
    "        unique_value_list.append(df[col].unique())\n",
    "        total_unique_value.append(len(df[col].unique()))\n",
    "        data_type.append(df[col].dtype)\n",
    "    \n",
    "    data = pd.DataFrame({\"col\":col_name, \"unique values\":unique_value_list, \"unique count\":total_unique_value, \"data type\":data_type})\n",
    "    \n",
    "    data = data.set_index(\"col\")\n",
    "    return data.sort_values(\"unique count\",ascending=False)\n",
    "\n",
    "\n",
    "unique_data = uniqueValueDataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique values</th>\n",
       "      <th>unique count</th>\n",
       "      <th>data type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>col</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>encounter_id</th>\n",
       "      <td>[2278392, 149190, 64410, 500364, 16680, 35754, 55842, 63768, 12522, 15738, 28236, 36900, 40926, 42570, 62256, 73578, 77076, 84222, 89682, 148530, 150006, 150048, 182796, 183930, 216156, 221634, 23...</td>\n",
       "      <td>101766</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_nbr</th>\n",
       "      <td>[8222157, 55629189, 86047875, 82442376, 42519267, 82637451, 84259809, 114882984, 48330783, 63555939, 89869032, 77391171, 85504905, 77586282, 49726791, 86328819, 92519352, 108662661, 107389323, 694...</td>\n",
       "      <td>71518</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_3</th>\n",
       "      <td>[?, 255, V27, 403, 250, V45, 38, 486, 996, 197, 250.6, 427, 627, 414, 416, 714, 428, 582, V43, 250.01, 263, 250.42, 276, 482, 401, 250.41, 585, 781, 278, 998, 568, 682, 618, 250.02, 305, 707, 496,...</td>\n",
       "      <td>790</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_2</th>\n",
       "      <td>[?, 250.01, 250, 250.43, 157, 411, 492, 427, 198, 403, 288, 998, 507, 174, 425, 456, 401, 715, 496, 428, 585, 250.02, 410, 999, 996, 135, 244, 41, 571, 276, 997, 599, 424, 491, 553, 707, 286, 440,...</td>\n",
       "      <td>749</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diag_1</th>\n",
       "      <td>[250.83, 276, 648, 8, 197, 414, 428, 398, 434, 250.7, 157, 518, 999, 410, 682, 402, 737, 572, V57, 189, 786, 427, 996, 277, 584, 462, 473, 411, 174, 486, 998, 511, 432, 626, 295, 196, 250.6, 618, ...</td>\n",
       "      <td>717</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <td>[41, 59, 11, 44, 51, 31, 70, 73, 68, 33, 47, 62, 60, 55, 49, 75, 45, 29, 35, 42, 66, 36, 19, 64, 25, 53, 52, 87, 27, 37, 46, 28, 48, 72, 10, 2, 65, 67, 40, 54, 58, 57, 43, 32, 83, 34, 39, 69, 38, ...</td>\n",
       "      <td>118</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_medications</th>\n",
       "      <td>[1, 18, 13, 16, 8, 21, 12, 28, 17, 11, 15, 31, 2, 23, 19, 7, 20, 14, 10, 22, 9, 27, 25, 4, 32, 6, 30, 26, 24, 33, 5, 39, 3, 29, 61, 40, 46, 41, 36, 34, 35, 50, 43, 42, 37, 51, 38, 45, 54, 52, 49, ...</td>\n",
       "      <td>75</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical_specialty</th>\n",
       "      <td>[Pediatrics-Endocrinology, ?, InternalMedicine, Family/GeneralPractice, Cardiology, Surgery-General, Orthopedics, Gastroenterology, Surgery-Cardiovascular/Thoracic, Nephrology, Orthopedics-Reconst...</td>\n",
       "      <td>73</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_outpatient</th>\n",
       "      <td>[0, 2, 1, 5, 7, 9, 3, 8, 4, 12, 11, 6, 20, 15, 10, 13, 14, 16, 21, 35, 17, 29, 36, 18, 19, 27, 22, 24, 42, 39, 34, 26, 33, 25, 23, 28, 37, 38, 40]</td>\n",
       "      <td>39</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_emergency</th>\n",
       "      <td>[0, 1, 2, 4, 3, 9, 5, 7, 6, 8, 22, 25, 10, 13, 42, 16, 11, 28, 15, 14, 18, 12, 21, 20, 19, 46, 76, 37, 64, 63, 54, 24, 29]</td>\n",
       "      <td>33</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <td>[25, 1, 3, 6, 2, 5, 11, 7, 10, 4, 14, 18, 8, 13, 12, 16, 17, 22, 23, 9, 20, 15, 24, 28, 19, 27]</td>\n",
       "      <td>26</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_inpatient</th>\n",
       "      <td>[0, 1, 2, 3, 6, 5, 4, 7, 8, 9, 15, 10, 11, 14, 12, 13, 17, 16, 21, 18, 19]</td>\n",
       "      <td>21</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payer_code</th>\n",
       "      <td>[?, MC, MD, HM, UN, BC, SP, CP, SI, DM, CM, CH, PO, WC, OT, OG, MP, FR]</td>\n",
       "      <td>18</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_source_id</th>\n",
       "      <td>[1, 7, 2, 4, 5, 6, 20, 3, 17, 8, 9, 14, 10, 22, 11, 25, 13]</td>\n",
       "      <td>17</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_diagnoses</th>\n",
       "      <td>[1, 9, 6, 7, 5, 8, 3, 4, 2, 16, 12, 13, 15, 10, 11, 14]</td>\n",
       "      <td>16</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_in_hospital</th>\n",
       "      <td>[1, 3, 2, 4, 5, 13, 12, 9, 7, 10, 6, 11, 8, 14]</td>\n",
       "      <td>14</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>[[0-10), [10-20), [20-30), [30-40), [40-50), [50-60), [60-70), [70-80), [80-90), [90-100)]</td>\n",
       "      <td>10</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>[?, [75-100), [50-75), [0-25), [100-125), [25-50), [125-150), [175-200), [150-175), &gt;200]</td>\n",
       "      <td>10</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission_type_id</th>\n",
       "      <td>[6, 1, 2, 3, 4, 5, 8, 7]</td>\n",
       "      <td>8</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procedures</th>\n",
       "      <td>[0, 5, 1, 6, 2, 3, 4]</td>\n",
       "      <td>7</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>[Caucasian, AfricanAmerican, ?, Other, Asian, Hispanic]</td>\n",
       "      <td>6</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide</th>\n",
       "      <td>[No, Steady, Up, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <td>[No, Steady, Down, Up]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>[No, Up, Steady, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miglitol</th>\n",
       "      <td>[No, Steady, Down, Up]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acarbose</th>\n",
       "      <td>[No, Steady, Up, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rosiglitazone</th>\n",
       "      <td>[No, Steady, Up, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pioglitazone</th>\n",
       "      <td>[No, Steady, Up, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glyburide</th>\n",
       "      <td>[No, Steady, Up, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repaglinide</th>\n",
       "      <td>[No, Up, Steady, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorpropamide</th>\n",
       "      <td>[No, Steady, Down, Up]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nateglinide</th>\n",
       "      <td>[No, Steady, Down, Up]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin</th>\n",
       "      <td>[No, Steady, Up, Down]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1Cresult</th>\n",
       "      <td>[None, &gt;7, &gt;8, Norm]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride</th>\n",
       "      <td>[No, Steady, Down, Up]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_glu_serum</th>\n",
       "      <td>[None, &gt;300, Norm, &gt;200]</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readmitted</th>\n",
       "      <td>[NO, &gt;30, &lt;30]</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolazamide</th>\n",
       "      <td>[No, Steady, Up]</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>[Female, Male, Unknown/Invalid]</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetesMed</th>\n",
       "      <td>[No, Yes]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>[No, Ch]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acetohexamide</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troglitazone</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolbutamide</th>\n",
       "      <td>[No, Steady]</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>examide</th>\n",
       "      <td>[No]</td>\n",
       "      <td>1</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citoglipton</th>\n",
       "      <td>[No]</td>\n",
       "      <td>1</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                    unique values  \\\n",
       "col                                                                                                                                                                                                                                 \n",
       "encounter_id              [2278392, 149190, 64410, 500364, 16680, 35754, 55842, 63768, 12522, 15738, 28236, 36900, 40926, 42570, 62256, 73578, 77076, 84222, 89682, 148530, 150006, 150048, 182796, 183930, 216156, 221634, 23...   \n",
       "patient_nbr               [8222157, 55629189, 86047875, 82442376, 42519267, 82637451, 84259809, 114882984, 48330783, 63555939, 89869032, 77391171, 85504905, 77586282, 49726791, 86328819, 92519352, 108662661, 107389323, 694...   \n",
       "diag_3                    [?, 255, V27, 403, 250, V45, 38, 486, 996, 197, 250.6, 427, 627, 414, 416, 714, 428, 582, V43, 250.01, 263, 250.42, 276, 482, 401, 250.41, 585, 781, 278, 998, 568, 682, 618, 250.02, 305, 707, 496,...   \n",
       "diag_2                    [?, 250.01, 250, 250.43, 157, 411, 492, 427, 198, 403, 288, 998, 507, 174, 425, 456, 401, 715, 496, 428, 585, 250.02, 410, 999, 996, 135, 244, 41, 571, 276, 997, 599, 424, 491, 553, 707, 286, 440,...   \n",
       "diag_1                    [250.83, 276, 648, 8, 197, 414, 428, 398, 434, 250.7, 157, 518, 999, 410, 682, 402, 737, 572, V57, 189, 786, 427, 996, 277, 584, 462, 473, 411, 174, 486, 998, 511, 432, 626, 295, 196, 250.6, 618, ...   \n",
       "num_lab_procedures        [41, 59, 11, 44, 51, 31, 70, 73, 68, 33, 47, 62, 60, 55, 49, 75, 45, 29, 35, 42, 66, 36, 19, 64, 25, 53, 52, 87, 27, 37, 46, 28, 48, 72, 10, 2, 65, 67, 40, 54, 58, 57, 43, 32, 83, 34, 39, 69, 38, ...   \n",
       "num_medications           [1, 18, 13, 16, 8, 21, 12, 28, 17, 11, 15, 31, 2, 23, 19, 7, 20, 14, 10, 22, 9, 27, 25, 4, 32, 6, 30, 26, 24, 33, 5, 39, 3, 29, 61, 40, 46, 41, 36, 34, 35, 50, 43, 42, 37, 51, 38, 45, 54, 52, 49, ...   \n",
       "medical_specialty         [Pediatrics-Endocrinology, ?, InternalMedicine, Family/GeneralPractice, Cardiology, Surgery-General, Orthopedics, Gastroenterology, Surgery-Cardiovascular/Thoracic, Nephrology, Orthopedics-Reconst...   \n",
       "number_outpatient                                                              [0, 2, 1, 5, 7, 9, 3, 8, 4, 12, 11, 6, 20, 15, 10, 13, 14, 16, 21, 35, 17, 29, 36, 18, 19, 27, 22, 24, 42, 39, 34, 26, 33, 25, 23, 28, 37, 38, 40]   \n",
       "number_emergency                                                                                       [0, 1, 2, 4, 3, 9, 5, 7, 6, 8, 22, 25, 10, 13, 42, 16, 11, 28, 15, 14, 18, 12, 21, 20, 19, 46, 76, 37, 64, 63, 54, 24, 29]   \n",
       "discharge_disposition_id                                                                                                          [25, 1, 3, 6, 2, 5, 11, 7, 10, 4, 14, 18, 8, 13, 12, 16, 17, 22, 23, 9, 20, 15, 24, 28, 19, 27]   \n",
       "number_inpatient                                                                                                                                       [0, 1, 2, 3, 6, 5, 4, 7, 8, 9, 15, 10, 11, 14, 12, 13, 17, 16, 21, 18, 19]   \n",
       "payer_code                                                                                                                                                [?, MC, MD, HM, UN, BC, SP, CP, SI, DM, CM, CH, PO, WC, OT, OG, MP, FR]   \n",
       "admission_source_id                                                                                                                                                   [1, 7, 2, 4, 5, 6, 20, 3, 17, 8, 9, 14, 10, 22, 11, 25, 13]   \n",
       "number_diagnoses                                                                                                                                                          [1, 9, 6, 7, 5, 8, 3, 4, 2, 16, 12, 13, 15, 10, 11, 14]   \n",
       "time_in_hospital                                                                                                                                                                  [1, 3, 2, 4, 5, 13, 12, 9, 7, 10, 6, 11, 8, 14]   \n",
       "age                                                                                                                                    [[0-10), [10-20), [20-30), [30-40), [40-50), [50-60), [60-70), [70-80), [80-90), [90-100)]   \n",
       "weight                                                                                                                                  [?, [75-100), [50-75), [0-25), [100-125), [25-50), [125-150), [175-200), [150-175), >200]   \n",
       "admission_type_id                                                                                                                                                                                        [6, 1, 2, 3, 4, 5, 8, 7]   \n",
       "num_procedures                                                                                                                                                                                              [0, 5, 1, 6, 2, 3, 4]   \n",
       "race                                                                                                                                                                      [Caucasian, AfricanAmerican, ?, Other, Asian, Hispanic]   \n",
       "glipizide                                                                                                                                                                                                  [No, Steady, Up, Down]   \n",
       "glyburide-metformin                                                                                                                                                                                        [No, Steady, Down, Up]   \n",
       "insulin                                                                                                                                                                                                    [No, Up, Steady, Down]   \n",
       "miglitol                                                                                                                                                                                                   [No, Steady, Down, Up]   \n",
       "acarbose                                                                                                                                                                                                   [No, Steady, Up, Down]   \n",
       "rosiglitazone                                                                                                                                                                                              [No, Steady, Up, Down]   \n",
       "pioglitazone                                                                                                                                                                                               [No, Steady, Up, Down]   \n",
       "glyburide                                                                                                                                                                                                  [No, Steady, Up, Down]   \n",
       "repaglinide                                                                                                                                                                                                [No, Up, Steady, Down]   \n",
       "chlorpropamide                                                                                                                                                                                             [No, Steady, Down, Up]   \n",
       "nateglinide                                                                                                                                                                                                [No, Steady, Down, Up]   \n",
       "metformin                                                                                                                                                                                                  [No, Steady, Up, Down]   \n",
       "A1Cresult                                                                                                                                                                                                    [None, >7, >8, Norm]   \n",
       "glimepiride                                                                                                                                                                                                [No, Steady, Down, Up]   \n",
       "max_glu_serum                                                                                                                                                                                            [None, >300, Norm, >200]   \n",
       "readmitted                                                                                                                                                                                                         [NO, >30, <30]   \n",
       "tolazamide                                                                                                                                                                                                       [No, Steady, Up]   \n",
       "gender                                                                                                                                                                                            [Female, Male, Unknown/Invalid]   \n",
       "glimepiride-pioglitazone                                                                                                                                                                                             [No, Steady]   \n",
       "diabetesMed                                                                                                                                                                                                             [No, Yes]   \n",
       "change                                                                                                                                                                                                                   [No, Ch]   \n",
       "metformin-pioglitazone                                                                                                                                                                                               [No, Steady]   \n",
       "metformin-rosiglitazone                                                                                                                                                                                              [No, Steady]   \n",
       "acetohexamide                                                                                                                                                                                                        [No, Steady]   \n",
       "glipizide-metformin                                                                                                                                                                                                  [No, Steady]   \n",
       "troglitazone                                                                                                                                                                                                         [No, Steady]   \n",
       "tolbutamide                                                                                                                                                                                                          [No, Steady]   \n",
       "examide                                                                                                                                                                                                                      [No]   \n",
       "citoglipton                                                                                                                                                                                                                  [No]   \n",
       "\n",
       "                          unique count data type  \n",
       "col                                               \n",
       "encounter_id                    101766     int64  \n",
       "patient_nbr                      71518     int64  \n",
       "diag_3                             790    object  \n",
       "diag_2                             749    object  \n",
       "diag_1                             717    object  \n",
       "num_lab_procedures                 118     int64  \n",
       "num_medications                     75     int64  \n",
       "medical_specialty                   73    object  \n",
       "number_outpatient                   39     int64  \n",
       "number_emergency                    33     int64  \n",
       "discharge_disposition_id            26     int64  \n",
       "number_inpatient                    21     int64  \n",
       "payer_code                          18    object  \n",
       "admission_source_id                 17     int64  \n",
       "number_diagnoses                    16     int64  \n",
       "time_in_hospital                    14     int64  \n",
       "age                                 10    object  \n",
       "weight                              10    object  \n",
       "admission_type_id                    8     int64  \n",
       "num_procedures                       7     int64  \n",
       "race                                 6    object  \n",
       "glipizide                            4    object  \n",
       "glyburide-metformin                  4    object  \n",
       "insulin                              4    object  \n",
       "miglitol                             4    object  \n",
       "acarbose                             4    object  \n",
       "rosiglitazone                        4    object  \n",
       "pioglitazone                         4    object  \n",
       "glyburide                            4    object  \n",
       "repaglinide                          4    object  \n",
       "chlorpropamide                       4    object  \n",
       "nateglinide                          4    object  \n",
       "metformin                            4    object  \n",
       "A1Cresult                            4    object  \n",
       "glimepiride                          4    object  \n",
       "max_glu_serum                        4    object  \n",
       "readmitted                           3    object  \n",
       "tolazamide                           3    object  \n",
       "gender                               3    object  \n",
       "glimepiride-pioglitazone             2    object  \n",
       "diabetesMed                          2    object  \n",
       "change                               2    object  \n",
       "metformin-pioglitazone               2    object  \n",
       "metformin-rosiglitazone              2    object  \n",
       "acetohexamide                        2    object  \n",
       "glipizide-metformin                  2    object  \n",
       "troglitazone                         2    object  \n",
       "tolbutamide                          2    object  \n",
       "examide                              1    object  \n",
       "citoglipton                          1    object  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas import option_context\n",
    "\n",
    "with option_context('display.max_colwidth', 200):\n",
    "    display(unique_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations and next steps\n",
    "___\n",
    "    - citoglipton, examide are Categorical columns with just one category 'No' so we will drop these columuns as they wont be adding any value to the prediction.\n",
    "    - encounter_id and patient_nbr are just ID's of the record and patient respectively so we will drop those as well.\n",
    "    - gender has 3 category which needs to be fixed.\n",
    "    - target variable readmitted has 3 values which need to be converted into 0 and 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "___\n",
    "    - In the dataset missing values are represented as '?' sign and not as 'NaN' beacause of which it says that we dont have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Let's check for the missing values_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace('?', np.nan , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df,sort='descending',color='#66a9bc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation and next steps\n",
    "___\n",
    "    - We cannot just update missing values with mean or median which has almost more than half of the values missing.\n",
    "    - Need to drop all the unnecessary columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_na_list = ['examide' ,'citoglipton','weight','encounter_id','patient_nbr','payer_code','medical_specialty']  \n",
    "df.drop(columns=drop_na_list, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating and Visualizing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Target Distribution_**\n",
    "\n",
    "_as we are checking if the patient was readmitted within 30 days we will update values with >30 as 0 as the patient was not admitted before 30 days_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     54864\n",
       ">30    35545\n",
       "<30    11357\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.readmitted.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.readmitted = [1 if each=='<30' else 0 for each in df.readmitted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='readmitted', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='readmitted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88026\n",
       "1    11314\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.readmitted.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_There is clearly a class imbalace problem._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Fixing 3 categories in gender_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female             54708\n",
       "Male               47055\n",
       "Unknown/Invalid        3\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.gender.replace('Unknown/Invalid', np.nan , inplace=True)\n",
    "df.dropna(subset=['gender'], how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5),dpi=100)\n",
    "sns.countplot(df['gender'], hue=df.readmitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].replace(['Female','Male'],[0,1]).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Dealing with Race variable_**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caucasian          76099\n",
       "AfricanAmerican    19210\n",
       "Hispanic            2037\n",
       "Other               1505\n",
       "Asian                641\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.race.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='race', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5),dpi=100)\n",
    "sns.countplot(df['race'], hue=df.readmitted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Vs Readmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5),dpi=100)\n",
    "sns.countplot(df['age'], hue=df.readmitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5),dpi=100)\n",
    "age_prob = sns.catplot(x = \"age\", y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "age_prob.set_ylabels(\"Readmitted Probability\")\n",
    "age_prob.set_xlabels(\"Age Group\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Dealing With Diagnosis 1/2/3_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformCategory(value):\n",
    "    \n",
    "    if ('V' in value) or ('E' in value):\n",
    "        category = 'Other'\n",
    "    else:\n",
    "        value = float(value)\n",
    "        if (value >= 390) and (value <= 459) or (value == 785):\n",
    "            category = 'Circulatory'\n",
    "        elif (value >= 460) and (value <= 519) or (value == 786):\n",
    "            category = 'Respiratory'\n",
    "        elif (value >= 520) and (value <= 579) or (value == 787):\n",
    "            category = 'Digestive'\n",
    "        elif (value >= 250) and (value< 251):\n",
    "            category = 'Injury' \n",
    "        elif (value >= 800) and (value <= 999):\n",
    "            category = 'Musculoskeletal'\n",
    "        elif (value >= 710) and (value <= 739):\n",
    "            category = 'Genitourinary'\n",
    "        elif (value >= 580) and (value <= 629) or (value == 788):\n",
    "            category = 'Neoplasms'    \n",
    "        elif (value >= 140) and (value <= 239) :\n",
    "            category = 'Diabetes'\n",
    "        else :\n",
    "            category = 'Other'\n",
    "\n",
    "    return category\n",
    "\n",
    "# {1:'Other', 2:'Circulatory', 3:'Respiratory', 4:'Digestive', 5:'Injury', 6:'Musculoskeletal', 7:'Genitourinary', 8:'Neoplasms', 9:'Diabetes'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag = ['diag_1','diag_2','diag_3']\n",
    "for d in diag:\n",
    "    df[diag] = df[diag].fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in diag:\n",
    "    df[col] = df[col].apply(transformCategory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x228af7a4d90>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'diag_1', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "sns.catplot(x = 'diag_2', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "sns.catplot(x = 'diag_3', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Dealing with Change variable_**\n",
    "\n",
    "_Indicates if there was a change in diabetic medications (either dosage or generic name). Values: “Ch” and “No”_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['change'] = df['change'].replace('Ch', 1)\n",
    "df['change'] = df['change'].replace('No', 0)\n",
    "df.rename(columns={\"change\": \"medication_change\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x228afc14b20>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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frKo/yt98adPlYxfogdZtz1IpOju2deZ9J7l4H2s/mGTTPPkAAAAAAFbDvLfkJ8nDSe5L8kCSr6+q500TCQAAAABgMebaYVpVP5rk0ix9KdMHk5yd5L1JvnOyZAAAAAAAh9i8O0wvTfKPk3y8u5+f5IwkeydLBQAAAACwAPMWpp/t7s8mSVX9re7+aJJnHWANAAAAAMARZa5b8pPsrqoTkvx+kj+uqgeS3DtVKAAAAACARZirMO3uF4+3r62qdyV5epLrJ0sFAAAAALAA8+4wTVV9a5IN3f2bVbUuyfokH5ssGQAAAADAITbXM0yr6jVJfjrJz4yhpyb5ralCAQAAAAAswrxf+vTiJC9K8tdJ0t33Jjl+qlAAAAAAAIswb2H6SHd3kk6SqvrK6SIBAAAAACzGAQvTqqokf1hVv5rkhKr6sSTvSPJrU4cDAAAAADiUDvilT93dVfW9WXqG6aeSPCvJv+vuP544GwAAAADAIXXAwnR4b5L/293/ZsowAAAAAACLNG9h+vwkP15VH8/44qck6e7TJ0kFAAAAALAA8xam50yaAgAAAADgMDBXYdrdH586CAAAAADAoj1l0QEAAAAAAA4XClMAAAAAgEFhCgAAAAAwKEwBAAAAAAaFKQAAAADAoDAFAAAAABgUpgAAAAAAg8IUAAAAAGBQmAIAAAAADApTAAAAAIBBYQoAAAAAMChMAQAAAAAGhSkAAAAAwKAwBQAAAAAYFKYAAAAAAIPCFAAAAABgUJgCAAAAAAwKUwAAAACAQWEKAAAAADAoTAEAAAAABoUpAAAAAMCgMAUAAAAAGBSmAAAAAACDwhQAAAAAYFCYAgAAAAAMClMAAAAAgGHSwrSqNlfVnVW1q6ouX+F8VdUbxvnbqurMZefXVNUHquoPp8wJAAAAAJBMWJhW1ZokVyY5J8nGJOdX1cZl085JsmG8Lkxy1bLzlya5Y6qMAAAAAACzptxhelaSXd19V3c/kuTaJFuWzdmS5E295H1JTqiqk5Kkqk5O8s+S/PqEGQEAAAAAvmDKwnR9krtnjnePsXnn/JcklyV5bH8fUlUXVtUtVXXL3r17v6zAAAAAAMCT25SFaa0w1vPMqarvTvKJ7n7/gT6ku6/u7k3dvWndunUHkxMAAAAAIMm0henuJKfMHJ+c5N455zw3yYuq6i+zdCv/d1bVb00XFQAAAABg2sJ0R5INVXVaVR2T5Lwk25bN2ZbkglpydpIHu/u+7v6Z7j65u08d697Z3T80YVYAAAAAgKyd6sLd/WhVXZLkhiRrklzT3Tur6qJxfmuS7UnOTbIryWeSvGyqPAAAAAAABzJZYZok3b09S6Xo7NjWmfed5OIDXOPGJDdOEA8AAAAA4ItMeUs+AAAAAMARRWEKAAAAADAoTAEAAAAABoUpAAAAAMCgMAUAAAAAGBSmAAAAAACDwhQAAAAAYFCYAgAAAAAMClMAAAAAgEFhCgAAAAAwKEwBAAAAAAaFKQAAAADAoDAFAAAAABgUpgAAAAAAg8IUAAAAAGBQmAIAAAAADApTAAAAAIBBYQoAAAAAMChMAQAAAAAGhSkAAAAAwKAwBQAAAAAYFKYAAAAAAIPCFAAAAABgUJgCAAAAAAwKUwAAAACAQWEKAAAAADAoTAEAAAAABoUpAAAAAMCgMAUAAAAAGBSmAAAAAACDwhQAAAAAYFCYAgAAAAAMClMAAAAAgEFhCgAAAAAwKEwBAAAAAAaFKQAAAADAoDAFAAAAABgUpgAAAAAAg8IUAAAAAGBQmAIAAAAADApTAAAAAIBh0sK0qjZX1Z1VtauqLl/hfFXVG8b526rqzDF+SlW9q6ruqKqdVXXplDkBAAAAAJIJC9OqWpPkyiTnJNmY5Pyq2rhs2jlJNozXhUmuGuOPJvmp7v7GJGcnuXiFtQAAAAAAq2rKHaZnJdnV3Xd19yNJrk2yZdmcLUne1Evel+SEqjqpu+/r7luTpLsfSnJHkvUTZgUAAAAAmLQwXZ/k7pnj3fnS0vOAc6rq1CRnJLl5pQ+pqgur6paqumXv3r1fbmYAAAAA4ElsysK0VhjrJzKnqo5L8ntJXtndn1rpQ7r76u7e1N2b1q1bd9BhAQAAAACmLEx3Jzll5vjkJPfOO6eqnpqlsvTN3X3dhDkBAAAAAJJMW5juSLKhqk6rqmOSnJdk27I525JcUEvOTvJgd99XVZXkN5Lc0d2vnzAjAAAAAMAXrJ3qwt39aFVdkuSGJGuSXNPdO6vqonF+a5LtSc5NsivJZ5K8bCx/bpKXJLm9qj44xl7d3dunygsAAAAAMFlhmiSj4Ny+bGzrzPtOcvEK696dlZ9vCgAAAAAwmSlvyQcAAAAAOKIoTAEAAAAABoUpAAAAAMCgMAUAAAAAGBSmAAAAAACDwhQAAAAAYFCYAgAAAAAMClMAAAAAgEFhCgAAAAAwKEwBAAAAAAaFKQAAAADAoDAFAAAAABgUpgAAAAAAg8IUAAAAAGBQmAIAAAAADApTAAAAAIBBYQoAAAAAMChMAQAAAAAGhSkAAAAAwKAwBQAAAAAYFKYAAAAAAIPCFAAAAABgUJgCAAAAAAwKUwAAAACAQWEKAAAAADAoTAEAAAAABoUpAAAAAMCgMAUAAAAAGBSmAAAAAACDwhQAAAAAYFCYAgAAAAAMClMAAAAAgEFhCgAAAAAwKEwBAAAAAAaFKQAAAADAoDAFAAAAABgUpgAAAAAAg8IUAAAAAGBQmAIAAAAADApTAAAAAIBBYQoAAAAAMChMAQAAAACGSQvTqtpcVXdW1a6qunyF81VVbxjnb6uqM+ddCwAAAACw2iYrTKtqTZIrk5yTZGOS86tq47Jp5yTZMF4XJrnqCawFAAAAAFhVU+4wPSvJru6+q7sfSXJtki3L5mxJ8qZe8r4kJ1TVSXOuBQAAAABYVWsnvPb6JHfPHO9O8pw55qyfc22SpKouzNLu1CT5dFXd+WVkBjhaPCPJ/YsOASxe/fQPLToCAIcXfycCSfydOFzf3ZuXD05ZmNYKYz3nnHnWLg12X53k6icWDeDoVlW3dPemRecAAODw4u9EgAObsjDdneSUmeOTk9w755xj5lgLAAAAALCqpnyG6Y4kG6rqtKo6Jsl5SbYtm7MtyQW15OwkD3b3fXOuBQAAAABYVZPtMO3uR6vqkiQ3JFmT5Jru3llVF43zW5NsT3Jukl1JPpPkZftbO1VWgKOQR5UAALASfycCHEB1r/hoUAAAAACAJ50pb8kHAAAAADiiKEwBAAAAAAaFKcBRpqo2V9WdVbWrqi5fdB4AABavqq6pqk9U1YcXnQXgcKcwBTiKVNWaJFcmOSfJxiTnV9XGxaYCAOAw8N+SbF50CIAjgcIU4OhyVpJd3X1Xdz+S5NokWxacCQCABevuP03yyUXnADgSKEwBji7rk9w9c7x7jAEAAABzUJgCHF1qhbE+5CkAAADgCKUwBTi67E5yyszxyUnuXVAWAAAAOOIoTAGOLjuSbKiq06rqmCTnJdm24EwAAABwxFCYAhxFuvvRJJckuSHJHUn+R3fvXGwqAAAWrap+J8l7kzyrqnZX1Y8sOhPA4aq6PdoOAAAAACCxwxQAAAAA4AsUpgAAAAAAg8IUAAAAAGBQmAIAAAAADApTAAAAAIBBYQoAAAAAMChMAQA4ZKrqxqraNN5vr6oTDuIa31FV3zJzfFFVXbCKMff1uX9ZVc+Y+nMAAFistYsOAADAk1N3n3uQS78jyaeTvGdcZ+tqZQIAADtMAQDYr6o6tao+WlW/XlUfrqo3V9ULqurPquovquqsqvrKqrqmqnZU1QeqastYe2xVXVtVt1XV7yY5dua6X9ixWVUXjDkfqqr/Psa+p6puHtd7R1X9nao6NclFSX6yqj5YVd9WVa+tqleNNc+uqveNa72tqr56jN9YVb9UVX9eVf+rqr5tP7/vmqp6XVXdPq7z8pnTL6+qW8e5bxjzz6qq94yc76mqZ43xl1bVdVV1/fjvdMXMZ/zIyHFjVf1aVf3XMb6uqn5v/HfcUVXPXYX/hQAAPAF2mAIAMI9/kORfJLkwyY4kP5DkW5O8KMmrk3wkyTu7+1+N2+z/vKrekeTHk3ymu0+vqtOT3Lr8wlX1D5P8bJLndvf9VfU149S7k5zd3V1VP5rksu7+qaramuTT3f26sf67Zi73piQv7+6bqurnkrwmySvHubXdfVZVnTvGX7CP3/XCJKclOaO7H53JkyT3d/eZVfUTSV6V5EeTfDTJ88bcFyT5hSTfP+Y/O8kZSR5OcmdV/UqSzyf5t0nOTPJQkncm+dCY/8tJ/nN3v7uqnpnkhiTfuI+cAABMQGEKAMA8PtbdtydJVe1M8iejyLw9yalJTk7yosd3eib5iiTPTPK8JG9Iku6+rapuW+Ha35nkrd19/5j3yTF+cpLfraqTkhyT5GP7C1hVT09yQnffNIbemOQtM1OuGz/fPzLvywuSbO3uR5flWX6N7xvvn57kjVW1IUkneerM/D/p7gdHvo8k+btJnpHkpsevW1VvSfL1M5+9saoeX/9VVXV8dz+0n7wAAKwihSkAAPN4eOb9YzPHj2Xpb8rPJ/n+7r5zdtEo/voA1659zPmVJK/v7m1V9R1JXvtEQy/zeObPZ/9/B+8rz76u8fNJ3tXdLx6PDLhxhfmzayr79pQk39zd/28/cwAAmJBnmAIAsBpuyNLzPStJquqMMf6nSX5wjH1TktNXWPsnSf5lVf3tMe/xW+CfnuSe8f6HZ+Y/lOT45RcZOzkfmHk+6UuS3LR83hzenuSiqlq7LM++zOZ86RzX//Mk315VXz0+4/tnzr09ySWPH1TVs+fMDADAKlGYAgCwGn4+S7ei31ZVHx7HSXJVkuPGrfiXZaks/CLdvTPJv09yU1V9KMnrx6nXJnlLVf3PJPfPLPmDJC9+/Eufll3uh5P8x/F5z07ycwfxu/x6kr8av8uHsvS81v25IskvVtWfJVlzoIt39z1Zes7pzUnekaXnvz44Tr8iyabxZVMfydIXXAEAcAhV94HukAIAAFZTVR3X3Z8eO0zfluSa7n7bonMBAGCHKQAALMJrq+qDST6cpS+z+v2FpgEA4AvsMAUA4Empqv5pkl9aNvyx7n7xIvIAAHB4UJgCAAAAAAxuyQcAAAAAGBSmAAAAAACDwhQAAAAAYFCYAgAAAAAM/x/kLCIJVnTIPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'medication_change', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Dealing with Features having [No, Up, Steady, Down] and [No, Steady] as values_**\n",
    "\n",
    "_As [No] tell us that there was no change so will update it to 0 and if there was a change [Up, Down, steady] we will make it 1_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "nosteadycols = list()\n",
    "for i, d in unique_data.iterrows():\n",
    "    if d['unique count'] < 5:\n",
    "        val = d['unique values'].tolist()\n",
    "        if ('No' in val) and ('Steady' in val) :\n",
    "            nosteadycols.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cat(nrows, ncols, columns):\n",
    "    fig, axs = plt.subplots(nrows=nrows,ncols=ncols, figsize=(20,15))\n",
    "    axs = axs.flatten()\n",
    "    \n",
    "    # delete unused plots\n",
    "    total_plots = len(axs)\n",
    "    unused = len(axs) - len(columns)\n",
    "    if unused > 0:\n",
    "        for a in range(len(columns), total_plots):\n",
    "            fig.delaxes(axs[a])\n",
    "    \n",
    "    i = 0\n",
    "    for col in columns:\n",
    "        sns.barplot(x = col, y = \"readmitted\", data = df, ax=axs[i])\n",
    "        i += 1\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cat(7,3,nosteadycols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in nosteadycols:\n",
    "    df[col] = df[col].replace(['No','Steady','Up','Down'],[0,1,1,1]).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Converting diabetesMed to 0 and 1 from Yes and NO_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x228b024fa30>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qbk8NAQAAAAAk7jAFAAAAAPgKwRQAAAAAYBBMAQAAAAAGwRQAAAAAYBBMAQAAAAAGwRQAAAAAYBBMAQAAAACG/w/G9yTPZmIT2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'diabetesMed', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diabetesMed'] = df['diabetesMed'].replace(['No','Yes'],[0,1]).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Dealing With A1Cresult_**\n",
    "\n",
    "_The normal A1C range is 4-6%. For people with diabetes, the lower the A1C value, the better the diabetes control and the lower the risk of developing complications. so we will convert it to 0 if none or normal else 1_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_df = df[['diag_1', 'diag_2', 'diag_3','A1Cresult','readmitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diag_1       diag_2       diag_3       readmitted\n",
       "Circulatory  Circulatory  Circulatory  0             6151\n",
       "                                       1              691\n",
       "                          Diabetes     0               94\n",
       "                                       1                9\n",
       "                          Digestive    0              168\n",
       "                                                     ... \n",
       "Respiratory  Respiratory  Neoplasms    1               17\n",
       "                          Other        0              687\n",
       "                                       1               78\n",
       "                          Respiratory  0              469\n",
       "                                       1               48\n",
       "Name: A1Cresult, Length: 1283, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_analysis_df = analysis_df.groupby(['diag_1', 'diag_2', 'diag_3', 'readmitted']).count()['A1Cresult']\n",
    "g_analysis_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag_1 = analysis_df.diag_1.unique().tolist()\n",
    "diag_2 = analysis_df.diag_2.unique().tolist()\n",
    "diag_3 = analysis_df.diag_3.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "re_prob = dict()\n",
    "\n",
    "for one in diag_1:\n",
    "    for two in diag_2:\n",
    "        for three in diag_3:\n",
    "            readmission = analysis_df[(analysis_df['diag_1'] == one) & (analysis_df['diag_2'] == two) & (analysis_df['diag_3'] == three)]\n",
    "            yes = readmission[readmission.readmitted == 1].shape[0]\n",
    "            no = readmission[readmission.readmitted == 0].shape[0]\n",
    "            typ = one+' - '+two+' - '+three\n",
    "            prob = 0\n",
    "            if (yes + no) > 0:\n",
    "                prob = yes / (yes + no) * 100\n",
    "            re_prob[typ] = prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "re_prob = sorted(re_prob.items(), key=lambda kv: kv[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Other - Genitourinary - Diabetes': 100.0,\n",
       " 'Circulatory - Musculoskeletal - Diabetes': 100.0,\n",
       " 'Musculoskeletal - Musculoskeletal - Diabetes': 100.0,\n",
       " 'Genitourinary - Diabetes - Digestive': 100.0,\n",
       " 'Digestive - Musculoskeletal - Genitourinary': 100.0,\n",
       " 'Neoplasms - Genitourinary - Diabetes': 100.0,\n",
       " 'Injury - Injury - Respiratory': 50.0,\n",
       " 'Circulatory - Genitourinary - Musculoskeletal': 50.0,\n",
       " 'Circulatory - Genitourinary - Diabetes': 50.0,\n",
       " 'Respiratory - Musculoskeletal - Injury': 50.0,\n",
       " 'Respiratory - Musculoskeletal - Diabetes': 50.0,\n",
       " 'Respiratory - Digestive - Musculoskeletal': 50.0,\n",
       " 'Musculoskeletal - Diabetes - Digestive': 50.0,\n",
       " 'Genitourinary - Other - Diabetes': 50.0,\n",
       " 'Genitourinary - Diabetes - Genitourinary': 50.0,\n",
       " 'Neoplasms - Respiratory - Genitourinary': 50.0,\n",
       " 'Genitourinary - Neoplasms - Musculoskeletal': 40.0,\n",
       " 'Other - Diabetes - Injury': 38.372093023255815,\n",
       " 'Diabetes - Diabetes - Genitourinary': 37.5,\n",
       " 'Circulatory - Digestive - Neoplasms': 37.5,\n",
       " 'Injury - Injury - Digestive': 35.0,\n",
       " 'Injury - Diabetes - Digestive': 33.33333333333333,\n",
       " 'Injury - Respiratory - Diabetes': 33.33333333333333,\n",
       " 'Injury - Musculoskeletal - Neoplasms': 33.33333333333333,\n",
       " 'Other - Musculoskeletal - Genitourinary': 33.33333333333333}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_prob = re_prob[:25]\n",
    "re_prob = dict(re_prob)\n",
    "re_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = re_prob.keys()\n",
    "values = re_prob.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10),dpi=100)\n",
    "plt.barh(list(keys), values)\n",
    "plt.xlabel('Percent of patient readmitted out of total patient')\n",
    "plt.ylabel('Primary - Secondary - Additional Diagnosis')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/diag.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x228af0f9df0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'A1Cresult', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['A1Cresult'] = df['A1Cresult'].replace(['None', 'Norm', '>7', '>8'],[-99,0,1,1]).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Dealing With max_glu_serum_**\n",
    "\n",
    "_If the glucose level is below 100 it is normal and if it goes beyond 300 that indicates diabetes and values above 100 may also indicate pre-diabetes, level depends what you ate before the test_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x228af66a760>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'max_glu_serum', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['max_glu_serum'] = df['max_glu_serum'].replace(['None', 'Norm', '>300', '>200'],[-99,0,1,1]).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CATEGORY ENCODING "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_one hot encoding on IDs category_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~df.discharge_disposition_id.isin([11,13,14,19,20,21])] # as people who died are not going to readmitt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As discharge ID NaN are for those who died we must remove all those record as it is obvious that they won't be returning/readmitting\n",
    "df['discharge_disposition_id'] = df['discharge_disposition_id'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot Encoding Race and Id's \n",
    "one_hot_data = pd.get_dummies(df, columns=['race'])\n",
    "\n",
    "columns_ids = ['admission_type_id', 'discharge_disposition_id', 'admission_source_id']\n",
    "\n",
    "one_hot_data[columns_ids] = one_hot_data[columns_ids].astype('str')\n",
    "one_hot_data = pd.get_dummies(one_hot_data, columns=columns_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = one_hot_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_ordinal encoding on features where order is important_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# age values = [[0-10), [10-20), [20-30), [30-40), [40-50), [50-60), [60-70), [70-80), [80-90), [90-100)]\n",
    "age_group = {'[0-10)':1, '[10-20)':2, '[20-30)':3, '[30-40)':4, '[40-50)':5,  '[50-60)':6, '[60-70)':7, '[70-80)':8, '[80-90)':9, '[90-100)':10}\n",
    "df['age'] = df.age.map(age_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for col in diag:\n",
    "    le = LabelEncoder()\n",
    "    df[col] = le.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_acetohexamide is left with just one category so we will drop the feature_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='acetohexamide', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88026\n",
       "1    11314\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.readmitted.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_inpatient = sns.catplot(x = 'number_inpatient', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "number_inpatient.set_ylabels(\"Readmission Probability\")\n",
    "number_inpatient.set_xlabels(\"Number Inpatient\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/number_inpatient.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_in_hospital = sns.catplot(x = 'time_in_hospital', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "time_in_hospital.set_ylabels(\"Readmission Probability\")\n",
    "time_in_hospital.set_xlabels(\"Time In Hospital (days)\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/time_in_hospital.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_medications = sns.catplot(x = 'num_medications', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "num_medications.set_ylabels(\"Readmission Probability\")\n",
    "num_medications.set_xlabels(\"Number Of Medications\")\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1350x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_lab_procedures = sns.catplot(x = 'num_lab_procedures', y = \"readmitted\", data = df, kind = \"bar\", height=5, aspect=30/8,palette=['#2492b6'])\n",
    "num_lab_procedures.set_ylabels(\"Readmission Probability\")\n",
    "num_lab_procedures.set_xlabels(\"Number Of Lab Procedures\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99340, 89)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Save the cleaned Dataframe_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('datasets/readmission_cleaned.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Step\n",
    "\n",
    "It's time to see how our model performs using diffrent modeling techniques and find out the best model.\n",
    "\n",
    "The above step can be found in `data-modeling.ipynb` file."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}