Medical_Data_Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4222116",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analysing the data from no show appointments using python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d439424a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da09ea7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid') #plot style\n",
    "#plot inline with notebook\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13ef5c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Wrangling - load, display, check and analyse for any duplicates and missing values in data set\n",
    "\n",
    "#Load the dataset and check the columns from the file\n",
    "\n",
    "df = pd.read_csv('noshowappointments.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a5c94be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(110527, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PatientId</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppointmentID</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScheduledDay</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppointmentDay</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hipertension</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcoholism</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handcap</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMS_received</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-show</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "PatientId       float64\n",
       "AppointmentID     int64\n",
       "Gender           object\n",
       "ScheduledDay     object\n",
       "AppointmentDay   object\n",
       "Age               int64\n",
       "Neighbourhood    object\n",
       "Scholarship       int64\n",
       "Hipertension      int64\n",
       "Diabetes          int64\n",
       "Alcoholism        int64\n",
       "Handcap           int64\n",
       "SMS_received      int64\n",
       "No-show          object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the shape of the data\n",
    "print(df.shape)\n",
    "pd.DataFrame(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9cb6d058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistics of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b6324e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PatientId         False\n",
       "AppointmentID     False\n",
       "Gender            False\n",
       "ScheduledDay      False\n",
       "AppointmentDay    False\n",
       "Age               False\n",
       "Neighbourhood     False\n",
       "Scholarship       False\n",
       "Hipertension      False\n",
       "Diabetes          False\n",
       "Alcoholism        False\n",
       "Handcap           False\n",
       "SMS_received      False\n",
       "No-show           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check any missing values\n",
    "df.info()\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf100a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "#can be concluded that there's no missing value from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b59a6c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates\n",
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2b910ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insights gained from data wrangling\n",
    "#1. Dataset doesn't have any duplicates or missing values\n",
    "#2. data types of some columns should be corrected (like date etc.,)\n",
    "#3. no-show (target column) can be converted to int (0 or 1) for better understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a78b5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data cleaning - drop/add columns according to relevancy and change data types "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae190f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender          ScheduledDay        AppointmentDay  Age      Neighbourhood  \\\n",
       "0      F  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z   62    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z   62      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "0            0             1         0           0        0             0   \n",
       "1            0             0         0           0        0             0   \n",
       "2            0             0         0           0        0             0   \n",
       "3            0             0         0           0        0             0   \n",
       "4            0             1         1           0        0             0   \n",
       "\n",
       "  No-show  \n",
       "0      No  \n",
       "1      No  \n",
       "2      No  \n",
       "3      No  \n",
       "4      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop irrelevant columns\n",
    "df.drop(['PatientId','AppointmentID'],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72f1a58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>scheduledday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>appointmentday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neighbourhood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>scholarship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hipertension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>alcoholism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>handcap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sms_received</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>no_show</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "0           gender\n",
       "1     scheduledday\n",
       "2   appointmentday\n",
       "3              age\n",
       "4    neighbourhood\n",
       "5      scholarship\n",
       "6     hipertension\n",
       "7         diabetes\n",
       "8       alcoholism\n",
       "9          handcap\n",
       "10    sms_received\n",
       "11         no_show"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change column names (to lower case -all), correct no-show column name\n",
    "df.columns=df.columns.str.lower().str.replace('-','_')\n",
    "pd.DataFrame(df.columns)\n",
    "#Also ocrrect spelling for hipertension --> hypertension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e45aa76",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change data types for date columns\n",
    "df['scheduledday']=pd.to_datetime(df['scheduledday'])\n",
    "df['appointmentday']=pd.to_datetime(df['appointmentday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c067c13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes']\n",
      "[1 0]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender              scheduledday            appointmentday  age  \\\n",
       "0      F 2016-04-29 18:38:08+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00    8   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "\n",
       "       neighbourhood  scholarship  hipertension  diabetes  alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   handcap  sms_received  show  \n",
       "0        0             0     1  \n",
       "1        0             0     1  \n",
       "2        0             0     1  \n",
       "3        0             0     1  \n",
       "4        0             0     1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change no-show column values to (1 or 0) and column name to show \n",
    "print(df.no_show.unique())\n",
    "df.no_show=df.no_show.map({'No':1,'Yes':0})\n",
    "df.rename(columns={'no_show':'show'},inplace=True)\n",
    "print(df.show.unique())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ba80e0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new column for days difference between scheduling and appointment\n",
    "diff=(df.appointmentday.dt.date-df.scheduledday.dt.date)\n",
    "day_diff=(pd.to_timedelta(diff)).dt.days\n",
    "df.insert(3,'day_diff',day_diff)\n",
    "df.day_diff.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f5f7dfdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype              \n",
      "---  ------          --------------   -----              \n",
      " 0   gender          110527 non-null  object             \n",
      " 1   scheduledday    110527 non-null  datetime64[ns, UTC]\n",
      " 2   appointmentday  110527 non-null  datetime64[ns, UTC]\n",
      " 3   day_diff        110527 non-null  int64              \n",
      " 4   age             110527 non-null  int64              \n",
      " 5   neighbourhood   110527 non-null  object             \n",
      " 6   scholarship     110527 non-null  int64              \n",
      " 7   hipertension    110527 non-null  int64              \n",
      " 8   diabetes        110527 non-null  int64              \n",
      " 9   alcoholism      110527 non-null  int64              \n",
      " 10  handcap         110527 non-null  int64              \n",
      " 11  sms_received    110527 non-null  int64              \n",
      " 12  show            110527 non-null  int64              \n",
      "dtypes: datetime64[ns, UTC](2), int64(9), object(2)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#check the data after cleaning\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "87804dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#After cleaning the data, analysis can be a smoother process to check the correlation between different columns and show column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b8aa50c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d437f50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to check the ratio of show in each category\n",
    "def plot_rat(x):\n",
    "    df.groupby(x).show.mean().plot(kind='bar',\n",
    "                                    edgecolor='black',\n",
    "                                    figsize=(14,8)).set_ylabel('Ratio of show');\n",
    "    display(df.groupby(x)[['show']].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1ef4af5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_diff</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.183702</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "      <td>0.798067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.254996</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "      <td>0.401444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            day_diff            age    scholarship   hipertension  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean       10.183702      37.088874       0.098266       0.197246   \n",
       "std        15.254996      23.110205       0.297675       0.397921   \n",
       "min        -6.000000      -1.000000       0.000000       0.000000   \n",
       "25%         0.000000      18.000000       0.000000       0.000000   \n",
       "50%         4.000000      37.000000       0.000000       0.000000   \n",
       "75%        15.000000      55.000000       0.000000       0.000000   \n",
       "max       179.000000     115.000000       1.000000       1.000000   \n",
       "\n",
       "            diabetes     alcoholism        handcap   sms_received  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.071865       0.030400       0.022248       0.321026   \n",
       "std         0.258265       0.171686       0.161543       0.466873   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       1.000000   \n",
       "max         1.000000       1.000000       4.000000       1.000000   \n",
       "\n",
       "                show  \n",
       "count  110527.000000  \n",
       "mean        0.798067  \n",
       "std         0.401444  \n",
       "min         0.000000  \n",
       "25%         1.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistics of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "32f18041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of patients who didn't show up for their appointment is 20.19 %\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>show</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       show\n",
       "show       \n",
       "0     22319\n",
       "1     88208"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the percentage of show and no show\n",
    "print(f\"Percentage of patients who didn't show up for their appointment is { round((1-df.show.mean())*100,2) } %\" )\n",
    "no_show=len(df[df.show==0])/len(df.show)\n",
    "show=len(df[df.show==1])/len(df.show)\n",
    "plt.bar(['show','no show'],[show*100,no_show*100],color=['g','r']);\n",
    "plt.title('Percentage of patients showing up or missing their appointment ');\n",
    "plt.ylabel('Percentage');\n",
    "plt.xlabel('show or no-show');\n",
    "display(df.groupby('show')[['show']].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "151d1772",
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysing factors that influence the patients for show/no show\n",
    "show=(df.show == 1)\n",
    "no_show=(df.show == 0)\n",
    "total_miss=len(df[no_show])\n",
    "total=len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "47a549cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of Females and Males who missed their appointment:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>13.204013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>6.989242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "gender           \n",
       "F       13.204013\n",
       "M        6.989242"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreive analytics based on gender\n",
    "no_show_gender=df[no_show]['gender'].value_counts()\n",
    "no_show_gender.plot(kind='pie');\n",
    "plt.title('patients who missed their appointment by gender');\n",
    "print('percentage of Females and Males who missed their appointment:')\n",
    "pd.DataFrame(no_show_gender*100/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ad1c53ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of patients')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['gender','show']).size().unstack('gender').plot(kind='bar').set_ylabel('number of patients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "746c2e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insights from this analysis\n",
    "#Females tend to miss the appoitment more when compared to males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b2568153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarship</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.801928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.762637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 show\n",
       "scholarship          \n",
       "0            0.801928\n",
       "1            0.762637"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreiving analytics based on scholarship\n",
    "plot_rat(df.scholarship)\n",
    "plt.title('Ratio of show or no-show by scholarship');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "60b63b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insight -- patients with scholarship tend to miss more when to compared to others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "789d65eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hipertension</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.790963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.826980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  show\n",
       "hipertension          \n",
       "0             0.790963\n",
       "1             0.826980"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ratio of show or no-show by scholarship')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreiving analytics based on hypertension\n",
    "plot_rat(df.hipertension)\n",
    "plt.title('Ratio of show or no-show by scholarship')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f5cbafdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insight --> people with hypertension likely show up than other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "981aca56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sms_received</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.832967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  show\n",
       "sms_received          \n",
       "0             0.832967\n",
       "1             0.724255"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ratio of show or no-show by sms_received')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreiving analytics based on sms\n",
    "plot_rat(df.sms_received)\n",
    "plt.title('Ratio of show or no-show by sms_received')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b7bf0cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insight -- people who did not receive sms are more likely to miss their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "234e77d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the propotion of different time difference for patients who missed their appiontments:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_diff2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>more_than_15</th>\n",
       "      <td>38.460505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>more_than_4</th>\n",
       "      <td>32.922622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fewdays</th>\n",
       "      <td>20.565438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameday</th>\n",
       "      <td>8.029034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count\n",
       "day_diff2              \n",
       "more_than_15  38.460505\n",
       "more_than_4   32.922622\n",
       "fewdays       20.565438\n",
       "sameday        8.029034"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreving analytics based on time difference between schedule day and appointment day\n",
    "\n",
    "#filter for positive day difference\n",
    "df1=df[df.day_diff>=0]\n",
    "#turn day diff into categorical column Day_diff2\n",
    "bin_edges=[-1,0,4,15,179]\n",
    "names=['sameday','fewdays','more_than_4','more_than_15']\n",
    "df['day_diff2']=pd.cut(df1.day_diff,bin_edges,labels=names)\n",
    "\n",
    "#filter for no-show records and count values for each category of day_diff2\n",
    "no_show_day_diff=df[no_show].day_diff2.value_counts()/len(df[no_show])*100\n",
    "no_show_day_diff.reindex(names).plot(kind='bar');\n",
    "plt.title('propotion of time difference for no_show appointments');\n",
    "plt.xlabel('days difference between scheduling and appointment');\n",
    "plt.ylabel('Ratio of no_show');\n",
    "print('the propotion of different time difference for patients who missed their appiontments:')\n",
    "pd.DataFrame(no_show_day_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "630b3132",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insight --> the longer the wait time is more likely patient wont show up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "0c5e5540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.317667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.965941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age\n",
       "count  22319.000000\n",
       "mean      34.317667\n",
       "std       21.965941\n",
       "min        0.000000\n",
       "25%       16.000000\n",
       "50%       33.000000\n",
       "75%       51.000000\n",
       "max      115.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#retreiving analytics based on age\n",
    "\n",
    "df[show].age.hist(alpha=0.5,label='show')\n",
    "df[no_show].age.hist(alpha=0.5,label='no-show')\n",
    "plt.legend()\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('ratio')\n",
    "plt.title('Histogram of age values for patients who showed up or missed their appointment')\n",
    "df[no_show][['age']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "911c22eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insight -->exact correlation cannot be estimated between age and no show, \n",
    "#but younger pateints tend to miss their appointments more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b9af312d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ILHAS OCEÂNICAS DE TRINDADE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANTOS DUMONT</th>\n",
       "      <td>0.289185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANTA CECÍLIA</th>\n",
       "      <td>0.274554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANTA CLARA</th>\n",
       "      <td>0.264822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITARARÉ</th>\n",
       "      <td>0.262664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLON BORGES</th>\n",
       "      <td>0.147122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÁRIO CYPRESTE</th>\n",
       "      <td>0.145553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AEROPORTO</th>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILHA DO BOI</th>\n",
       "      <td>0.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARQUE INDUSTRIAL</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                count\n",
       "neighbourhood                        \n",
       "ILHAS OCEÂNICAS DE TRINDADE  1.000000\n",
       "SANTOS DUMONT                0.289185\n",
       "SANTA CECÍLIA                0.274554\n",
       "SANTA CLARA                  0.264822\n",
       "ITARARÉ                      0.262664\n",
       "...                               ...\n",
       "SOLON BORGES                 0.147122\n",
       "MÁRIO CYPRESTE               0.145553\n",
       "AEROPORTO                    0.125000\n",
       "ILHA DO BOI                  0.085714\n",
       "PARQUE INDUSTRIAL                 NaN\n",
       "\n",
       "[81 rows x 1 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#retreving analytics based on neighbourhood\n",
    "rec_neigh=df['neighbourhood'].value_counts()\n",
    "rec_neigh_no_show=df[no_show].neighbourhood.value_counts()\n",
    "rec_neigh_no_show_percentage=rec_neigh_no_show/rec_neigh\n",
    "pd.DataFrame(rec_neigh_no_show_percentage.sort_values(axis=0, ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d3aed8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#With the above analysis (based on different columns), we can conclude key insights as mentioned under each analysis\n",
    "#dataset can be analysed and valuable insights can be drawn after overcoming certain data limitations \n",
    "# like -- negative date differences,additional relevant columns (vacation, time in exact hours, etc.,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3298a492",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}