Investigate_a_Dataset-Copy1..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project: Investigate a Dataset - [No_show_appointments]\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    ">This project focuses on analysis of the [No_show_appointments]( https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv.) dataset from kaggle. The dataset is a csv file that collects information from 110527 medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. This project aims at exploring and analyzing this dataset and describing how different characteristic feature of each patient affect their attendance of the medical appointment.\n",
    "\n",
    "\n",
    "### Question(s) for Analysis\n",
    "\n",
    "<ol>\n",
    "    <li>Which age group attended their appointment the most?</li>\n",
    "    <li>Among patients with hypertension, those with diabetes and those suffering from alcoholism, which group showed for their appointment the most?</li>\n",
    "    <li>Did sending a sms affect the attendance of the patient of their medical appointment?</li>\n",
    "</ol>\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this cell to set up import statements for all of the packages that you\n",
    "#   plan to use.\n",
    "\n",
    "# Remember to include a 'magic word' so that your visualizations are plotted\n",
    "#   inline with the notebook. See this page for more:\n",
    "#   http://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline\n",
    "\n",
    "sns.set_style('darkgrid')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "\n",
    "\n",
    "### General Properties\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load your data and print out a few lines. Perform operations to inspect data\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Surveying the dataset to note its features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "PatientId         110527 non-null float64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking data to see if it contains missing values, duplicate rows, and incorrect values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
      "PatientId         110527 non-null float64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#checking for missing values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">All columns in this dataset has are filled with 110527 values, this implies that no value is missing in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inspecting dataset for duplicate values\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The dataset is free from duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for incorrect data.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Here it can be noticed that the min value of the age column has negative values, which is not expected for an individual's age. This will be rectified in the next session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId          62299\n",
       "AppointmentID     110527\n",
       "Gender                 2\n",
       "ScheduledDay      103549\n",
       "AppointmentDay        27\n",
       "Age                  104\n",
       "Neighbourhood         81\n",
       "Scholarship            2\n",
       "Hipertension           2\n",
       "Diabetes               2\n",
       "Alcoholism             2\n",
       "Handcap                5\n",
       "SMS_received           2\n",
       "No-show                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Handcap.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PatientId [  2.98724998e+13   5.58997777e+14   4.26296230e+12 ...,   7.26331493e+13\n",
      "   9.96997666e+14   1.55766317e+13]\n",
      "\n",
      "\n",
      "AppointmentID [5642903 5642503 5642549 ..., 5630692 5630323 5629448]\n",
      "\n",
      "\n",
      "Gender ['F' 'M']\n",
      "\n",
      "\n",
      "ScheduledDay ['2016-04-29T18:38:08Z' '2016-04-29T16:08:27Z' '2016-04-29T16:19:04Z' ...,\n",
      " '2016-04-27T16:03:52Z' '2016-04-27T15:09:23Z' '2016-04-27T13:30:56Z']\n",
      "\n",
      "\n",
      "AppointmentDay ['2016-04-29T00:00:00Z' '2016-05-03T00:00:00Z' '2016-05-10T00:00:00Z'\n",
      " '2016-05-17T00:00:00Z' '2016-05-24T00:00:00Z' '2016-05-31T00:00:00Z'\n",
      " '2016-05-02T00:00:00Z' '2016-05-30T00:00:00Z' '2016-05-16T00:00:00Z'\n",
      " '2016-05-04T00:00:00Z' '2016-05-19T00:00:00Z' '2016-05-12T00:00:00Z'\n",
      " '2016-05-06T00:00:00Z' '2016-05-20T00:00:00Z' '2016-05-05T00:00:00Z'\n",
      " '2016-05-13T00:00:00Z' '2016-05-09T00:00:00Z' '2016-05-25T00:00:00Z'\n",
      " '2016-05-11T00:00:00Z' '2016-05-18T00:00:00Z' '2016-05-14T00:00:00Z'\n",
      " '2016-06-02T00:00:00Z' '2016-06-03T00:00:00Z' '2016-06-06T00:00:00Z'\n",
      " '2016-06-07T00:00:00Z' '2016-06-01T00:00:00Z' '2016-06-08T00:00:00Z']\n",
      "\n",
      "\n",
      "Age [ 62  56   8  76  23  39  21  19  30  29  22  28  54  15  50  40  46   4\n",
      "  13  65  45  51  32  12  61  38  79  18  63  64  85  59  55  71  49  78\n",
      "  31  58  27   6   2  11   7   0   3   1  69  68  60  67  36  10  35  20\n",
      "  26  34  33  16  42   5  47  17  41  44  37  24  66  77  81  70  53  75\n",
      "  73  52  74  43  89  57  14   9  48  83  72  25  80  87  88  84  82  90\n",
      "  94  86  91  98  92  96  93  95  97 102 115 100  99  -1]\n",
      "\n",
      "\n",
      "Neighbourhood ['JARDIM DA PENHA' 'MATA DA PRAIA' 'PONTAL DE CAMBURI' 'REPÚBLICA'\n",
      " 'GOIABEIRAS' 'ANDORINHAS' 'CONQUISTA' 'NOVA PALESTINA' 'DA PENHA'\n",
      " 'TABUAZEIRO' 'BENTO FERREIRA' 'SÃO PEDRO' 'SANTA MARTHA' 'SÃO CRISTÓVÃO'\n",
      " 'MARUÍPE' 'GRANDE VITÓRIA' 'SÃO BENEDITO' 'ILHA DAS CAIEIRAS'\n",
      " 'SANTO ANDRÉ' 'SOLON BORGES' 'BONFIM' 'JARDIM CAMBURI' 'MARIA ORTIZ'\n",
      " 'JABOUR' 'ANTÔNIO HONÓRIO' 'RESISTÊNCIA' 'ILHA DE SANTA MARIA'\n",
      " 'JUCUTUQUARA' 'MONTE BELO' 'MÁRIO CYPRESTE' 'SANTO ANTÔNIO' 'BELA VISTA'\n",
      " 'PRAIA DO SUÁ' 'SANTA HELENA' 'ITARARÉ' 'INHANGUETÁ' 'UNIVERSITÁRIO'\n",
      " 'SÃO JOSÉ' 'REDENÇÃO' 'SANTA CLARA' 'CENTRO' 'PARQUE MOSCOSO' 'DO MOSCOSO'\n",
      " 'SANTOS DUMONT' 'CARATOÍRA' 'ARIOVALDO FAVALESSA' 'ILHA DO FRADE'\n",
      " 'GURIGICA' 'JOANA D´ARC' 'CONSOLAÇÃO' 'PRAIA DO CANTO' 'BOA VISTA'\n",
      " 'MORADA DE CAMBURI' 'SANTA LUÍZA' 'SANTA LÚCIA' 'BARRO VERMELHO'\n",
      " 'ESTRELINHA' 'FORTE SÃO JOÃO' 'FONTE GRANDE' 'ENSEADA DO SUÁ'\n",
      " 'SANTOS REIS' 'PIEDADE' 'JESUS DE NAZARETH' 'SANTA TEREZA' 'CRUZAMENTO'\n",
      " 'ILHA DO PRÍNCIPE' 'ROMÃO' 'COMDUSA' 'SANTA CECÍLIA' 'VILA RUBIM'\n",
      " 'DE LOURDES' 'DO QUADRO' 'DO CABRAL' 'HORTO' 'SEGURANÇA DO LAR'\n",
      " 'ILHA DO BOI' 'FRADINHOS' 'NAZARETH' 'AEROPORTO'\n",
      " 'ILHAS OCEÂNICAS DE TRINDADE' 'PARQUE INDUSTRIAL']\n",
      "\n",
      "\n",
      "Scholarship [0 1]\n",
      "\n",
      "\n",
      "Hipertension [1 0]\n",
      "\n",
      "\n",
      "Diabetes [0 1]\n",
      "\n",
      "\n",
      "Alcoholism [0 1]\n",
      "\n",
      "\n",
      "Handcap [0 1 2 3 4]\n",
      "\n",
      "\n",
      "SMS_received [0 1]\n",
      "\n",
      "\n",
      "No-show ['No' 'Yes']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for columns in df:\n",
    "    print (columns, df[columns].unique())\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Here it can be noticed that all other columns other than Age contains the right and relevant datatype. The column for age has an incorrect value of -1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After discussing the structure of the data and any problems that need to be\n",
    "#   cleaned, perform those cleaning steps in the second part of this section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The incorrect value in the Age column will be changed to the mean of the age column here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_mean = df.Age.mean().astype(int)\n",
    "age_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Age'] == -1, 'Age'] = age_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Renaming the columns to ensure uniformity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment-day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</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": [
       "     patient_id  appointment_id gender         scheduled_day  \\\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",
       "        appointment-day  age      neighbourhood  scholarship  hypertension  \\\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column_names = ['patient_id', 'appointment_id', 'gender', 'scheduled_day', 'appointment-day', 'age', 'neighbourhood', 'scholarship', 'hypertension', 'diabetes', 'alcoholism', 'handcap', 'sms_received', 'no_show']\n",
    "df.columns = new_column_names\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General dataset overview\n",
    "Here the statistics of each unique value in each column is being analyzed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</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>37.089218</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>23.109921</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>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>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": [
       "         patient_id  appointment_id            age    scholarship  \\\n",
       "count  1.105270e+05    1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14    5.675305e+06      37.089218       0.098266   \n",
       "std    2.560949e+14    7.129575e+04      23.109921       0.297675   \n",
       "min    3.921784e+04    5.030230e+06       0.000000       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",
       "        hypertension       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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a function to plot histograms\n",
    "def plot_hist(dataframe, x_label, y_label, title):\n",
    "    hist = plt.hist(dataframe, alpha = 0.75)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.title(plot_title)\n",
    "    print(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Use this, and more code cells, to explore your data. Don't forget to add\n",
    "#   Markdown cells to document your observations and findings.\n",
    "\n",
    "#Histograms to show the statistics of each column of the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.221459e+14    0.079619\n",
      "9.963767e+10    0.076000\n",
      "2.688613e+13    0.063333\n",
      "3.353478e+13    0.058809\n",
      "2.584244e+11    0.056095\n",
      "7.579746e+13    0.056095\n",
      "8.713749e+14    0.056095\n",
      "6.264199e+12    0.056095\n",
      "6.684488e+13    0.051571\n",
      "8.722785e+11    0.049762\n",
      "8.923969e+13    0.048857\n",
      "8.435224e+09    0.046143\n",
      "8.534397e+14    0.045238\n",
      "1.447997e+13    0.041619\n",
      "6.543360e+13    0.041619\n",
      "8.189452e+13    0.038000\n",
      "9.452745e+12    0.038000\n",
      "1.882323e+14    0.036190\n",
      "9.496197e+12    0.034381\n",
      "2.271580e+12    0.034381\n",
      "1.336493e+13    0.033476\n",
      "1.484143e+12    0.031666\n",
      "8.883500e+13    0.030762\n",
      "9.861628e+14    0.030762\n",
      "7.124589e+14    0.029857\n",
      "4.167557e+14    0.027143\n",
      "6.128878e+12    0.027143\n",
      "8.121397e+13    0.026238\n",
      "8.634164e+12    0.021714\n",
      "3.699499e+13    0.020809\n",
      "                  ...   \n",
      "6.375629e+12    0.000905\n",
      "9.369127e+12    0.000905\n",
      "5.375556e+14    0.000905\n",
      "1.662184e+11    0.000905\n",
      "7.234615e+13    0.000905\n",
      "9.649990e+12    0.000905\n",
      "6.912783e+10    0.000905\n",
      "1.954265e+13    0.000905\n",
      "2.736377e+10    0.000905\n",
      "5.532694e+11    0.000905\n",
      "7.149583e+12    0.000905\n",
      "8.676752e+13    0.000905\n",
      "7.838359e+13    0.000905\n",
      "5.962625e+11    0.000905\n",
      "4.919862e+13    0.000905\n",
      "3.477350e+14    0.000905\n",
      "1.626595e+13    0.000905\n",
      "7.794917e+12    0.000905\n",
      "1.161950e+13    0.000905\n",
      "5.615364e+14    0.000905\n",
      "4.355592e+11    0.000905\n",
      "1.321328e+12    0.000905\n",
      "1.751987e+13    0.000905\n",
      "4.262579e+13    0.000905\n",
      "3.115681e+13    0.000905\n",
      "1.222828e+13    0.000905\n",
      "6.821231e+11    0.000905\n",
      "7.163981e+14    0.000905\n",
      "9.798964e+14    0.000905\n",
      "2.724571e+11    0.000905\n",
      "Name: patient_id, Length: 62299, dtype: float64\n",
      "\n",
      "\n",
      "5769215    0.000905\n",
      "5731652    0.000905\n",
      "5707080    0.000905\n",
      "5702986    0.000905\n",
      "5715276    0.000905\n",
      "5717325    0.000905\n",
      "5711182    0.000905\n",
      "5758289    0.000905\n",
      "5762391    0.000905\n",
      "5741913    0.000905\n",
      "5483871    0.000905\n",
      "5660001    0.000905\n",
      "5653858    0.000905\n",
      "5666148    0.000905\n",
      "5668197    0.000905\n",
      "5641576    0.000905\n",
      "5639531    0.000905\n",
      "5649772    0.000905\n",
      "5645678    0.000905\n",
      "5647727    0.000905\n",
      "5692785    0.000905\n",
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      "5694838    0.000905\n",
      "5696887    0.000905\n",
      "5674360    0.000905\n",
      "5733701    0.000905\n",
      "5651786    0.000905\n",
      "5672315    0.000905\n",
      "5719362    0.000905\n",
      "5672187    0.000905\n",
      "             ...   \n",
      "5744033    0.000905\n",
      "5748131    0.000905\n",
      "5739943    0.000905\n",
      "5672324    0.000905\n",
      "5682563    0.000905\n",
      "5680512    0.000905\n",
      "5782866    0.000905\n",
      "5496110    0.000905\n",
      "5713200    0.000905\n",
      "5711153    0.000905\n",
      "5717298    0.000905\n",
      "5709110    0.000905\n",
      "5707063    0.000905\n",
      "5729592    0.000905\n",
      "5463358    0.000905\n",
      "5565768    0.000905\n",
      "5776721    0.000905\n",
      "5789023    0.000905\n",
      "5590396    0.000905\n",
      "5606756    0.000905\n",
      "5608807    0.000905\n",
      "5635434    0.000905\n",
      "5621101    0.000905\n",
      "5686470    0.000905\n",
      "5582192    0.000905\n",
      "5586290    0.000905\n",
      "5584243    0.000905\n",
      "5598584    0.000905\n",
      "5602682    0.000905\n",
      "5771266    0.000905\n",
      "Name: appointment_id, Length: 110527, dtype: float64\n",
      "\n",
      "\n",
      "F    64.997693\n",
      "M    35.002307\n",
      "Name: gender, dtype: float64\n",
      "\n",
      "\n",
      "2016-05-06T07:09:54Z    0.021714\n",
      "2016-05-06T07:09:53Z    0.020809\n",
      "2016-04-25T17:17:46Z    0.019905\n",
      "2016-04-25T17:18:27Z    0.019905\n",
      "2016-04-25T17:17:23Z    0.017190\n",
      "2016-05-31T09:54:52Z    0.013571\n",
      "2016-05-10T08:18:24Z    0.013571\n",
      "2016-04-25T17:19:03Z    0.013571\n",
      "2016-06-07T16:15:14Z    0.012667\n",
      "2016-05-03T07:52:31Z    0.012667\n",
      "2016-03-29T10:44:23Z    0.011762\n",
      "2016-05-09T08:07:11Z    0.011762\n",
      "2016-05-10T08:29:24Z    0.011762\n",
      "2016-05-25T09:36:45Z    0.011762\n",
      "2016-05-13T08:54:09Z    0.010857\n",
      "2016-05-25T16:34:32Z    0.010857\n",
      "2016-05-19T13:43:43Z    0.009952\n",
      "2016-05-06T08:04:41Z    0.009952\n",
      "2016-05-20T14:10:56Z    0.009952\n",
      "2016-04-25T16:24:42Z    0.009952\n",
      "2016-05-20T13:06:03Z    0.009952\n",
      "2016-06-03T08:51:28Z    0.009952\n",
      "2016-05-12T13:58:44Z    0.009952\n",
      "2016-04-18T15:40:28Z    0.009952\n",
      "2016-04-18T15:39:02Z    0.009952\n",
      "2016-03-29T10:43:41Z    0.009952\n",
      "2016-05-13T08:38:50Z    0.009952\n",
      "2016-05-04T08:51:42Z    0.009952\n",
      "2016-04-18T15:43:23Z    0.009952\n",
      "2016-05-10T17:35:47Z    0.009952\n",
      "                          ...   \n",
      "2016-05-02T09:07:20Z    0.000905\n",
      "2016-05-02T10:49:02Z    0.000905\n",
      "2016-05-16T09:21:26Z    0.000905\n",
      "2016-05-02T19:07:49Z    0.000905\n",
      "2016-06-01T08:41:48Z    0.000905\n",
      "2016-06-01T07:12:24Z    0.000905\n",
      "2016-04-27T07:09:24Z    0.000905\n",
      "2016-05-16T09:52:40Z    0.000905\n",
      "2016-05-25T07:14:18Z    0.000905\n",
      "2016-05-16T12:25:58Z    0.000905\n",
      "2016-06-06T11:18:24Z    0.000905\n",
      "2016-05-11T13:30:58Z    0.000905\n",
      "2016-05-30T15:09:22Z    0.000905\n",
      "2016-04-26T09:21:00Z    0.000905\n",
      "2016-05-16T15:20:31Z    0.000905\n",
      "2016-06-03T15:29:57Z    0.000905\n",
      "2016-05-24T16:03:22Z    0.000905\n",
      "2016-05-19T07:26:09Z    0.000905\n",
      "2016-06-06T11:41:43Z    0.000905\n",
      "2016-01-29T15:30:32Z    0.000905\n",
      "2016-05-11T07:10:42Z    0.000905\n",
      "2016-05-05T11:08:22Z    0.000905\n",
      "2016-05-16T09:03:19Z    0.000905\n",
      "2016-05-09T15:32:45Z    0.000905\n",
      "2016-05-13T10:04:07Z    0.000905\n",
      "2016-04-29T07:22:13Z    0.000905\n",
      "2016-06-07T10:56:36Z    0.000905\n",
      "2016-04-28T10:01:33Z    0.000905\n",
      "2016-04-18T14:24:30Z    0.000905\n",
      "2016-05-17T11:31:46Z    0.000905\n",
      "Name: scheduled_day, Length: 103549, dtype: float64\n",
      "\n",
      "\n",
      "2016-06-06T00:00:00Z    4.245117\n",
      "2016-05-16T00:00:00Z    4.173641\n",
      "2016-05-09T00:00:00Z    4.089498\n",
      "2016-05-30T00:00:00Z    4.084070\n",
      "2016-06-08T00:00:00Z    4.052403\n",
      "2016-05-11T00:00:00Z    4.047880\n",
      "2016-06-01T00:00:00Z    4.038832\n",
      "2016-06-07T00:00:00Z    3.995404\n",
      "2016-05-12T00:00:00Z    3.975499\n",
      "2016-05-02T00:00:00Z    3.959214\n",
      "2016-05-18T00:00:00Z    3.956499\n",
      "2016-05-17T00:00:00Z    3.955595\n",
      "2016-06-02T00:00:00Z    3.899500\n",
      "2016-05-10T00:00:00Z    3.897690\n",
      "2016-05-31T00:00:00Z    3.871452\n",
      "2016-05-05T00:00:00Z    3.866024\n",
      "2016-05-19T00:00:00Z    3.863309\n",
      "2016-05-03T00:00:00Z    3.850643\n",
      "2016-05-04T00:00:00Z    3.771024\n",
      "2016-06-03T00:00:00Z    3.700453\n",
      "2016-05-24T00:00:00Z    3.627168\n",
      "2016-05-13T00:00:00Z    3.607263\n",
      "2016-05-25T00:00:00Z    3.536692\n",
      "2016-05-06T00:00:00Z    3.509550\n",
      "2016-05-20T00:00:00Z    3.463407\n",
      "2016-04-29T00:00:00Z    2.926887\n",
      "2016-05-14T00:00:00Z    0.035285\n",
      "Name: appointment-day, dtype: float64\n",
      "\n",
      "\n",
      "0      3.201933\n",
      "1      2.056511\n",
      "52     1.579705\n",
      "49     1.494657\n",
      "53     1.493753\n",
      "56     1.479277\n",
      "38     1.473848\n",
      "59     1.469324\n",
      "2      1.463896\n",
      "50     1.459372\n",
      "57     1.450324\n",
      "36     1.429515\n",
      "51     1.417753\n",
      "19     1.397848\n",
      "39     1.389706\n",
      "37     1.387896\n",
      "54     1.384277\n",
      "34     1.380658\n",
      "33     1.378849\n",
      "30     1.376134\n",
      "6      1.376134\n",
      "3      1.368896\n",
      "17     1.365277\n",
      "32     1.361658\n",
      "5      1.347182\n",
      "44     1.345373\n",
      "18     1.345373\n",
      "58     1.329087\n",
      "46     1.320944\n",
      "45     1.314611\n",
      "         ...   \n",
      "72     0.556425\n",
      "74     0.544663\n",
      "76     0.516616\n",
      "75     0.492187\n",
      "78     0.489473\n",
      "77     0.476807\n",
      "80     0.462330\n",
      "81     0.392664\n",
      "82     0.354664\n",
      "79     0.352855\n",
      "84     0.281379\n",
      "83     0.253332\n",
      "85     0.248808\n",
      "86     0.235237\n",
      "87     0.166475\n",
      "89     0.156523\n",
      "88     0.113999\n",
      "90     0.098618\n",
      "92     0.077809\n",
      "91     0.059714\n",
      "93     0.047952\n",
      "94     0.029857\n",
      "95     0.021714\n",
      "96     0.015381\n",
      "97     0.009952\n",
      "98     0.005429\n",
      "115    0.004524\n",
      "100    0.003619\n",
      "102    0.001810\n",
      "99     0.000905\n",
      "Name: age, Length: 103, dtype: float64\n",
      "\n",
      "\n",
      "JARDIM CAMBURI                 6.982004\n",
      "MARIA ORTIZ                    5.252110\n",
      "RESISTÊNCIA                    4.008975\n",
      "JARDIM DA PENHA                3.507740\n",
      "ITARARÉ                        3.179314\n",
      "CENTRO                         3.016458\n",
      "TABUAZEIRO                     2.833697\n",
      "SANTA MARTHA                   2.832792\n",
      "JESUS DE NAZARETH              2.581270\n",
      "BONFIM                         2.508889\n",
      "SANTO ANTÔNIO                  2.484461\n",
      "SANTO ANDRÉ                    2.326128\n",
      "CARATOÍRA                      2.320700\n",
      "JABOUR                         2.270034\n",
      "SÃO PEDRO                      2.214843\n",
      "ILHA DO PRÍNCIPE               2.050178\n",
      "NOVA PALESTINA                 2.048368\n",
      "ANDORINHAS                     2.046559\n",
      "DA PENHA                       2.005845\n",
      "ROMÃO                          2.004035\n",
      "GURIGICA                       1.825798\n",
      "SÃO JOSÉ                       1.788703\n",
      "BELA VISTA                     1.725370\n",
      "MARUÍPE                        1.720846\n",
      "FORTE SÃO JOÃO                 1.709085\n",
      "ILHA DE SANTA MARIA            1.705466\n",
      "SÃO CRISTÓVÃO                  1.661133\n",
      "REDENÇÃO                       1.405087\n",
      "SÃO BENEDITO                   1.301944\n",
      "JOANA D´ARC                    1.291087\n",
      "                                 ...   \n",
      "SANTOS REIS                    0.494902\n",
      "ESTRELINHA                     0.486759\n",
      "SANTA CLARA                    0.457807\n",
      "SOLON BORGES                   0.424331\n",
      "PIEDADE                        0.408950\n",
      "SANTA CECÍLIA                  0.405331\n",
      "SANTA LÚCIA                    0.396283\n",
      "SANTA LUÍZA                    0.387236\n",
      "BARRO VERMELHO                 0.382712\n",
      "DO MOSCOSO                     0.373664\n",
      "MÁRIO CYPRESTE                 0.335665\n",
      "BOA VISTA                      0.282284\n",
      "COMDUSA                        0.280474\n",
      "DE LOURDES                     0.275951\n",
      "ARIOVALDO FAVALESSA            0.255141\n",
      "ANTÔNIO HONÓRIO                0.245189\n",
      "FRADINHOS                      0.233427\n",
      "ENSEADA DO SUÁ                 0.212618\n",
      "SANTA HELENA                   0.161047\n",
      "HORTO                          0.158332\n",
      "UNIVERSITÁRIO                  0.137523\n",
      "SEGURANÇA DO LAR               0.131190\n",
      "NAZARETH                       0.122142\n",
      "MORADA DE CAMBURI              0.086857\n",
      "PONTAL DE CAMBURI              0.062428\n",
      "ILHA DO BOI                    0.031666\n",
      "ILHA DO FRADE                  0.009048\n",
      "AEROPORTO                      0.007238\n",
      "ILHAS OCEÂNICAS DE TRINDADE    0.001810\n",
      "PARQUE INDUSTRIAL              0.000905\n",
      "Name: neighbourhood, Length: 81, dtype: float64\n",
      "\n",
      "\n",
      "0    90.173442\n",
      "1     9.826558\n",
      "Name: scholarship, dtype: float64\n",
      "\n",
      "\n",
      "0    80.275408\n",
      "1    19.724592\n",
      "Name: hypertension, dtype: float64\n",
      "\n",
      "\n",
      "0    92.813521\n",
      "1     7.186479\n",
      "Name: diabetes, dtype: float64\n",
      "\n",
      "\n",
      "0    96.960019\n",
      "1     3.039981\n",
      "Name: alcoholism, dtype: float64\n",
      "\n",
      "\n",
      "0    97.972441\n",
      "1     1.847512\n",
      "2     0.165570\n",
      "3     0.011762\n",
      "4     0.002714\n",
      "Name: handcap, dtype: float64\n",
      "\n",
      "\n",
      "0    67.897437\n",
      "1    32.102563\n",
      "Name: sms_received, dtype: float64\n",
      "\n",
      "\n",
      "No     79.806744\n",
      "Yes    20.193256\n",
      "Name: no_show, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#checking the percentage of unique value in each column\n",
    "for col in df:\n",
    "    print(df[col].value_counts(normalize=True)*100)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**from this we can deduce the following statistics:**\n",
    "> <ul>\n",
    "    <li>80% of the patient attended their appointment</li>\n",
    "    <li>32% of the patient recieved an sms</li>\n",
    "    <li>3% had issues related to alcoholism</li>\n",
    "    <li>7% had diabetes</li>\n",
    "    <li>20% had hypertension</li>\n",
    "    <li>10% were on scholarship</li>\n",
    "    <li>65% were women</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 1 ( Which age group attended their appointment the most?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         senior_adults\n",
       "1                adults\n",
       "2         senior_adults\n",
       "3              children\n",
       "4                adults\n",
       "5         senior_adults\n",
       "6          young_adults\n",
       "7          young_adults\n",
       "8          young_adults\n",
       "9             teenagers\n",
       "10         young_adults\n",
       "11         young_adults\n",
       "12         young_adults\n",
       "13         young_adults\n",
       "14               adults\n",
       "15            teenagers\n",
       "16               adults\n",
       "17               adults\n",
       "18         young_adults\n",
       "19               adults\n",
       "20         young_adults\n",
       "21             children\n",
       "22            teenagers\n",
       "23               adults\n",
       "24        senior_adults\n",
       "25               adults\n",
       "26               adults\n",
       "27             children\n",
       "28               adults\n",
       "29         young_adults\n",
       "              ...      \n",
       "110497    senior_adults\n",
       "110498           adults\n",
       "110499    senior_adults\n",
       "110500           adults\n",
       "110501           adults\n",
       "110502     young_adults\n",
       "110503    senior_adults\n",
       "110504         children\n",
       "110505           adults\n",
       "110506         children\n",
       "110507         children\n",
       "110508           adults\n",
       "110509     young_adults\n",
       "110510    senior_adults\n",
       "110511        teenagers\n",
       "110512           adults\n",
       "110513         children\n",
       "110514           adults\n",
       "110515     young_adults\n",
       "110516     young_adults\n",
       "110517        teenagers\n",
       "110518           adults\n",
       "110519     young_adults\n",
       "110520           adults\n",
       "110521           adults\n",
       "110522           adults\n",
       "110523           adults\n",
       "110524     young_adults\n",
       "110525     young_adults\n",
       "110526           adults\n",
       "Name: age_groups, Length: 110527, dtype: category\n",
       "Categories (5, object): [children < teenagers < young_adults < adults < senior_adults]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a new column is created that classifies age into age groups.\n",
    "age_edges = [-1, 12, 19, 39, 60, 120]\n",
    "age_group_names = ['children', 'teenagers', 'young_adults', 'adults', 'senior_adults']\n",
    "\n",
    "df['age_groups'] = pd.cut(df['age'], age_edges, labels=age_group_names)\n",
    "df['age_groups']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 15 columns):\n",
      "patient_id         110527 non-null float64\n",
      "appointment_id     110527 non-null int64\n",
      "gender             110527 non-null object\n",
      "scheduled_day      110527 non-null object\n",
      "appointment-day    110527 non-null object\n",
      "age                110527 non-null int64\n",
      "neighbourhood      110527 non-null object\n",
      "scholarship        110527 non-null int64\n",
      "hypertension       110527 non-null int64\n",
      "diabetes           110527 non-null int64\n",
      "alcoholism         110527 non-null int64\n",
      "handcap            110527 non-null int64\n",
      "sms_received       110527 non-null int64\n",
      "no_show            110527 non-null object\n",
      "age_groups         110527 non-null category\n",
      "dtypes: category(1), float64(1), int64(8), object(5)\n",
      "memory usage: 11.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_groups     no_show\n",
       "children       No         16729\n",
       "               Yes         4307\n",
       "teenagers      No          6941\n",
       "               Yes         2434\n",
       "young_adults   No         22191\n",
       "               Yes         6680\n",
       "adults         No         25591\n",
       "               Yes         5892\n",
       "senior_adults  No         16756\n",
       "               Yes         3006\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counts for the each age_groups and no_show\n",
    "age_groups_attendance_count_1 = df.groupby(['age_groups','no_show']).size()\n",
    "age_groups_attendance_count_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233bcbdc88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar plot to show yes and no values for each age_groups\n",
    "age_groups_attendance_count= age_groups_attendance_count_1.unstack()\n",
    "age_groups_attendance_count.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A better visualisation of the percentage of attendance of each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_groups\n",
       "children         21036\n",
       "teenagers         9375\n",
       "young_adults     28871\n",
       "adults           31483\n",
       "senior_adults    19762\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#age_group total counts\n",
    "age_groups_total_counts= df.groupby('age_groups').count()['age']\n",
    "age_groups_total_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### getting the percentage  of each age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     79.525575\n",
       "Yes    20.474425\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children_percentage = age_groups_attendance_count_1['children']*100/age_groups_total_counts['children']\n",
    "children_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     74.037333\n",
       "Yes    25.962667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teenagers_percentage = age_groups_attendance_count_1['teenagers']*100/age_groups_total_counts['teenagers']\n",
    "teenagers_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     76.862596\n",
       "Yes    23.137404\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "young_adults_percentage = age_groups_attendance_count_1['young_adults']*100/age_groups_total_counts['young_adults']\n",
    "young_adults_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     81.285138\n",
       "Yes    18.714862\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults_percentage = age_groups_attendance_count_1['adults']*100/age_groups_total_counts['adults']\n",
    "adults_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     84.788989\n",
       "Yes    15.211011\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senior_adults_percentage = age_groups_attendance_count_1['senior_adults']*100/age_groups_total_counts['senior_adults']\n",
    "senior_adults_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can deduce the following stastics:\n",
    "<ul>\n",
    "<li>80% of patients in the children age group attended their appointments\n",
    "<li>74% of patients in the teenagers age group attended their appointments\n",
    "<li>77% of patients in the young adult age group attended their appointments\n",
    "<li>82% of patients in the adult age group attended their appointments\n",
    "<li>85% of patients in the senior adult age group attended their appointments\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233b6e0898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a barchart for the percentage of attendance in each age_group\n",
    "frames = [children_percentage, teenagers_percentage, young_adults_percentage, adults_percentage, senior_adults_percentage]\n",
    "result = pd.concat(frames, keys=['children', 'teenagers', 'young_adults', 'adults', 'senior_adults'])\n",
    "result.unstack().plot(kind='bar', figsize=(10,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  (Among patients with hypertension, those with diabetes and those suffering from alcoholism, which group showed for their appointment the most?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</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>37.089218</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>23.109921</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>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>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": [
       "         patient_id  appointment_id            age    scholarship  \\\n",
       "count  1.105270e+05    1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14    5.675305e+06      37.089218       0.098266   \n",
       "std    2.560949e+14    7.129575e+04      23.109921       0.297675   \n",
       "min    3.921784e+04    5.030230e+06       0.000000       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",
       "        hypertension       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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here the counts of appointments and those attended for each medical issue are computed and a barchart is plotted to compare the percentage with each issue that attended their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exploratory Data Analysis for hypertensive patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hypertension  no_show\n",
       "0             No         70179\n",
       "              Yes        18547\n",
       "1             No         18029\n",
       "              Yes         3772\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#attendace statistics for patients with hypertension\n",
    "hypertension_attendance_count = df.groupby(['hypertension','no_show']).size()\n",
    "hypertension_attendance_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21801"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of patients with hypertension \n",
    "hypertension_attendance_total = hypertension_attendance_count[1].sum()\n",
    "hypertension_attendance_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233a8e9908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#piechart to compare the number of hypertensive patients who showed up and didn't show up for their appointment\n",
    "hypertension_attendance_count.unstack().iloc[1].plot(kind = 'pie',figsize=(4,4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     82.698041\n",
       "Yes    17.301959\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of hypertensive patient who attended and didn't attend their appointment\n",
    "hypertension_attendance_percentage = hypertension_attendance_count[1]*100/hypertension_attendance_total\n",
    "hypertension_attendance_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exploratory Data Analysis for diabetic patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes  no_show\n",
       "0         No         81695\n",
       "          Yes        20889\n",
       "1         No          6513\n",
       "          Yes         1430\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#attendace statistics for patients with diabetes\n",
    "diabetes_attendance_count = df.groupby(['diabetes','no_show']).size()\n",
    "diabetes_attendance_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7943"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of patients with diabetes\n",
    "diabetes_attendance_total = diabetes_attendance_count[1].sum()\n",
    "diabetes_attendance_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233b7df320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#piechart to compare the number of diabetic patients who showed up and didn't show up for their appointment\n",
    "diabetes_attendance_count.unstack().iloc[1].plot(kind = 'pie',figsize=(4,4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     81.996727\n",
       "Yes    18.003273\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of diabetic patient who attended and didn't attend their appointment\n",
    "diabetes_attendance_percentage = diabetes_attendance_count[1]*100/diabetes_attendance_total\n",
    "diabetes_attendance_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exploratory Data Analysis for alcoholic patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcoholism  no_show\n",
       "0           No         85525\n",
       "            Yes        21642\n",
       "1           No          2683\n",
       "            Yes          677\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcoholism_attendance_count = df.groupby(['alcoholism','no_show']).size()\n",
    "alcoholism_attendance_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3360"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of patients with alcoholism\n",
    "alcoholism_attendance_total = alcoholism_attendance_count[1].sum()\n",
    "alcoholism_attendance_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233b72ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#piechart to compare the number of alcoholic patients who showed up and didn't show up for their appointment\n",
    "alcoholism_attendance_count.unstack().iloc[1].plot(kind = 'pie',figsize=(4,4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     79.85119\n",
       "Yes    20.14881\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of alcoholic patient who attended and didn't attend their appointment\n",
    "alcoholism_attendance_percentage = alcoholism_attendance_count[1]*100/alcoholism_attendance_total\n",
    "alcoholism_attendance_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the following analysis we can deduce the following statistics:\n",
    "<ul>\n",
    "    <li>There are 21801 patients with hypertension and 83% of the showed up for their appointment</li>\n",
    "    <li>There are 7943 patients with diabetes and 82% of the showed up for their appointment</li>\n",
    "    <li>There are 3360 patients with issues relating to alcoholism and 80% of the showed up for their appointment</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233a8efb00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frames_2 = [hypertension_attendance_percentage, diabetes_attendance_percentage, alcoholism_attendance_percentage]\n",
    "result = pd.concat(frames_2, keys=['hypertension', 'diabetes', 'alcoholism'])\n",
    "result.unstack().plot(kind='bar', figsize=(10,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research question 3 (Did sending a sms affect the attendance of the patient of their medical appointment?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</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>37.089218</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>23.109921</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>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>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": [
       "         patient_id  appointment_id            age    scholarship  \\\n",
       "count  1.105270e+05    1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14    5.675305e+06      37.089218       0.098266   \n",
       "std    2.560949e+14    7.129575e+04      23.109921       0.297675   \n",
       "min    3.921784e+04    5.030230e+06       0.000000       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",
       "        hypertension       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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sms_received  no_show\n",
       "0             No         62510\n",
       "              Yes        12535\n",
       "1             No         25698\n",
       "              Yes         9784\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recieved_sms_count = df.groupby(['sms_received','no_show']).size()\n",
    "recieved_sms_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35482"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of patients that recieved a message\n",
    "recieved_sms_total = recieved_sms_count[1].sum()\n",
    "recieved_sms_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75045"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of patients that didn't recieved a message\n",
    "recieved_no_sms_total = recieved_sms_count[0].sum()\n",
    "recieved_no_sms_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233a7afdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#piechart to compare the number of patients that recieved a message who showed up and didn't show up for their appointment\n",
    "recieved_sms_count.unstack().iloc[1].plot(kind = 'pie',figsize=(4,4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233a805080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#piechart to compare the number of patients that didn't recieved a message who showed up and didn't show up for their appointment\n",
    "recieved_sms_count.unstack().iloc[0].plot(kind = 'pie',figsize=(4,4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     72.425455\n",
       "Yes    27.574545\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of (patient that recieved a message) who attended and didn't attend their appointment\n",
    "recieved_sms_percentage =  recieved_sms_count[1]*100/ recieved_sms_total\n",
    "recieved_sms_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     83.296689\n",
       "Yes    16.703311\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of alcoholic patient who attended and didn't attend their appointment\n",
    "recieved_no_sms_percentage = recieved_sms_count[0]*100/recieved_no_sms_total\n",
    "recieved_no_sms_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from this analysis we can dedude that:\n",
    "35482 patients recieved a message and 72% of them were in attendance\n",
    "75045 patients didn't recieved a message and 83% of them were in attendance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f233a766c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frames_3 = [recieved_sms_percentage, recieved_no_sms_percentage]\n",
    "result = pd.concat(frames_3, keys=['Recieved sms', 'Didn\\'trecieved sms'])\n",
    "result.unstack().plot(kind='bar', figsize=(10,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "<ul>\n",
    "<li> Patients in the the senior adult age group (60+) showed up the most for their appointment, while teenagers (13 - 19) showed up the least.\n",
    "<li> Hypertensive patients showed up the most.\n",
    "<li> Receiving a message did not increase the attendance of patients to their appointment.\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations \n",
    "\n",
    "<ul>\n",
    "    <li>Having to replace the incorrect value in the age column with the column's mean value without investigating what the real value is.</li>\n",
    "    <li> Not knowing what the meaning of every columns heading eg the handcap column.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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