Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigation of the No-show Appointments Dataset \n",
    "![no-show](./image/no-show.jpg)\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",
    "The **no-show appointments [dataset](https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv)** contains information on over one hundred thousand scheduled medical appointments in Brazil. The dataset has 14 variables made up of 13 independent variables and 1 dependent variable indicating whether the patient showed up for their appointment or not. \n",
    "\n",
    "The table below shows the various columns in the dataset and their descriptions.\n",
    "\n",
    "\n",
    "|No. |Column Name |Description|\n",
    "|-----|-----|-----|\n",
    "|1. |`PatientId` |Unique identifier of a patient.|\n",
    "|2. |`AppointmentID`|Unique identifier of the appointment.|\n",
    "|3. |`Gender` |Gender of the patient.|\n",
    "|4. |`ScheduledDay` |The day when the patient set up their appointment for i.e: The day that they were to visit the doctor.|\n",
    "|5. |`AppointmentDay` |The day of the actuall appointment, i.e: The day the patient visits the doctor.|\n",
    "|6. |`Age` |Age of the patient.|\n",
    "|7. |`Neighbourhood` |The location of the hospital where the appointment takes place.|\n",
    "|8. |`Scholarship` |Indicates whether or not the patient is enrolled in the Brazilian welfare program.|\n",
    "|9. |`Hipertension` |Whether or not the patient has hypertension.|\n",
    "|10.|`Diabetes` |Whether or not the patient has diabetes.|\n",
    "|11.|`Alcoholism` |Whether or not the patient suffers from alchoholism.|\n",
    "|12.|`SMS_received` |Whether or not an SMS message was sent to the patient.|\n",
    "|13.|`Handcap` |Whether or not the patient is handicaped.|\n",
    "|14.|`No-show` |Was the patient a no-show for their appointment.|\n",
    "\n",
    "\n",
    "\n",
    "### Question for Analysis\n",
    "\n",
    "The following are the questions that will be addressed in the analysis of the  no-show appointment dataset:\n",
    "\n",
    "1. Does the `Age` of the patient determine whether they will show up for their appointment or not?\n",
    "\n",
    "\n",
    "2. Are patients whom received an `SMS` more likely to show up for their appointment than those that were not sent?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upgrade pandas to use dataframe.explode() function. \n",
    "# !pip install --upgrade pandas==0.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the data\n",
    "# df = pd.read_csv('https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv')\n",
    "df = pd.read_csv('./Database_No_show_appointments/noshowappointments-kagglev2-may-2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show a sample of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find rows with missing values in the data\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are no rows with missing values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, SMS_received, No-show]\n",
       "Index: []"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find rows that are duplicated in the datas\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the above analysis, there are no dupliacted values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data types of the columns in the data\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The `ScheduledDay` and `AppointmentDay` columns are of  string datatype instead of datetime datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the numeric columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The `Age` column has a minimum age of -1 years and a maximum age of 115 years. This requires further analysis since those values may be outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. change the `ScheduledDay` and `AppointmentDay` to datetime datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ScheduledDay', 'AppointmentDay'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter the ScheduledDay and AppointmentDay columns \n",
    "date_cols = df.columns[3:5]\n",
    "date_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScheduledDay      object\n",
       "AppointmentDay    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the datatype before changing\n",
    "df[date_cols].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the columns to datetime datatype\n",
    "df[date_cols] = df[date_cols].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScheduledDay      datetime64[ns]\n",
       "AppointmentDay    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm the dattype after changing\n",
    "df[date_cols].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Clean up `Age` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function that takes in a column name from the datset and plots its distribution\n",
    "\n",
    "def plot_column_distribution(col_name):\n",
    "    fig, ax = plt.subplots(figsize =(8,8))\n",
    "    ax.hist(df[f'{col_name}'], alpha=0.5, label=f'{col_name}', bins=50)\n",
    "    ax.set_title(f'Distributions of the {col_name} of the patients')\n",
    "    ax.set_xlabel(f'{col_name}')\n",
    "    ax.set_ylabel('Count')\n",
    "    ax.legend(loc='upper right')\n",
    "    plt.show();\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe643ee4e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use the created function to plot distribution of the Age column in the dataset\n",
    "plot_column_distribution('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The above Age distribution reveals that patients below one year (infants) are the major age group in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe643e27400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box plot of the Age column\n",
    "age = df['Age']\n",
    "ax = sns.boxplot(x=age)\n",
    "plt.title('Box Plot of Age of the patients');\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the box plot of age, there seems to be an outlier that is above 110 years. We can drop that row together with the -1 year in order to clean up the `Age` column"
   ]
  },
  {
   "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>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>63912</th>\n",
       "      <td>3.196321e+13</td>\n",
       "      <td>5700278</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-16 09:17:44</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>115</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63915</th>\n",
       "      <td>3.196321e+13</td>\n",
       "      <td>5700279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-16 09:17:44</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>115</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68127</th>\n",
       "      <td>3.196321e+13</td>\n",
       "      <td>5562812</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-08 14:29:17</td>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>115</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76284</th>\n",
       "      <td>3.196321e+13</td>\n",
       "      <td>5744037</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-30 09:44:51</td>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>115</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97666</th>\n",
       "      <td>7.482346e+14</td>\n",
       "      <td>5717451</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-19 07:57:56</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>115</td>\n",
       "      <td>SÃO JOSÉ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06 08:58:13</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID Gender        ScheduledDay AppointmentDay  \\\n",
       "63912  3.196321e+13        5700278      F 2016-05-16 09:17:44     2016-05-19   \n",
       "63915  3.196321e+13        5700279      F 2016-05-16 09:17:44     2016-05-19   \n",
       "68127  3.196321e+13        5562812      F 2016-04-08 14:29:17     2016-05-16   \n",
       "76284  3.196321e+13        5744037      F 2016-05-30 09:44:51     2016-05-30   \n",
       "97666  7.482346e+14        5717451      F 2016-05-19 07:57:56     2016-06-03   \n",
       "99832  4.659432e+14        5775010      F 2016-06-06 08:58:13     2016-06-06   \n",
       "\n",
       "       Age Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "63912  115    ANDORINHAS            0             0         0           0   \n",
       "63915  115    ANDORINHAS            0             0         0           0   \n",
       "68127  115    ANDORINHAS            0             0         0           0   \n",
       "76284  115    ANDORINHAS            0             0         0           0   \n",
       "97666  115      SÃO JOSÉ            0             1         0           0   \n",
       "99832   -1         ROMÃO            0             0         0           0   \n",
       "\n",
       "       Handcap  SMS_received No-show  \n",
       "63912        1             0     Yes  \n",
       "63915        1             0     Yes  \n",
       "68127        1             0     Yes  \n",
       "76284        1             0      No  \n",
       "97666        0             1      No  \n",
       "99832        0             0      No  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the age column for the two outlying figures \n",
    "age_outliers = df[(df['Age'] > 110) | (df['Age'] == -1)]\n",
    "age_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the age outliers from the datset\n",
    "df.drop(age_outliers.index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110521, 14)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the final datset now has 110,521 rows\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Add `scheduled_weekday` and `appointment_weekday` column to the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can add the day of the week when the patients had schedulled the appointment and the day of the week when they went for their appointment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new scheduled_weekday column from the ScheduledDay column\n",
    "df['scheduled_weekday'] = df['ScheduledDay'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new appointment_weekday column from AppointmentDay column\n",
    "df['appointment_weekday'] = df['AppointmentDay'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "      <th>scheduled_weekday</th>\n",
       "      <th>appointment_weekday</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>2016-04-29 18:38:08</td>\n",
       "      <td>2016-04-29</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",
       "      <td>Friday</td>\n",
       "      <td>Friday</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>2016-04-29 16:08:27</td>\n",
       "      <td>2016-04-29</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",
       "      <td>Friday</td>\n",
       "      <td>Friday</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>2016-04-29 16:19:04</td>\n",
       "      <td>2016-04-29</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",
       "      <td>Friday</td>\n",
       "      <td>Friday</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>2016-04-29 17:29:31</td>\n",
       "      <td>2016-04-29</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",
       "      <td>Friday</td>\n",
       "      <td>Friday</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>2016-04-29 16:07:23</td>\n",
       "      <td>2016-04-29</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",
       "      <td>Friday</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender        ScheduledDay AppointmentDay  Age  \\\n",
       "0  2.987250e+13        5642903      F 2016-04-29 18:38:08     2016-04-29   62   \n",
       "1  5.589978e+14        5642503      M 2016-04-29 16:08:27     2016-04-29   56   \n",
       "2  4.262962e+12        5642549      F 2016-04-29 16:19:04     2016-04-29   62   \n",
       "3  8.679512e+11        5642828      F 2016-04-29 17:29:31     2016-04-29    8   \n",
       "4  8.841186e+12        5642494      F 2016-04-29 16:07:23     2016-04-29   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   Handcap  SMS_received No-show scheduled_weekday appointment_weekday  \n",
       "0        0             0      No            Friday              Friday  \n",
       "1        0             0      No            Friday              Friday  \n",
       "2        0             0      No            Friday              Friday  \n",
       "3        0             0      No            Friday              Friday  \n",
       "4        0             0      No            Friday              Friday  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inspect datset to confirm creation of the new columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wednesday    23.404602\n",
       "Tuesday      23.199211\n",
       "Monday       20.549941\n",
       "Friday       17.207590\n",
       "Thursday     15.603369\n",
       "Saturday      0.035287\n",
       "Name: appointment_weekday, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# proportion of days when patients that came in for their appointments\n",
    "df['appointment_weekday'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function to plot the relationship between appointment schedule or attendance day and showing up.\n",
    "def plot_appointment_weekday(weekday_type):\n",
    "    sns.countplot(data=df, x=f'{weekday_type}', hue='No-show')\n",
    "    plt.title(f'Relationship between {weekday_type} and showing up or not')\n",
    "    plt.legend(['Yes','No'], title='No-show')\n",
    "    plt.xlabel(f'{weekday_type}')\n",
    "    plt.ylabel('number of patients')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe643d41780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_appointment_weekday('appointment_weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe63fc4e9b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_appointment_weekday('scheduled_weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6400ff240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot showing the relationship between day of appointment and showing up or not \n",
    "sns.countplot(data=df, x='appointment_weekday', hue='No-show');\n",
    "plt.title('Relationship between day of appointment and showing up or not')\n",
    "plt.legend(['Yes','No'], title='No-show');\n",
    "plt.xlabel('day of appointment')\n",
    "plt.ylabel('number of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the above analysis, it can be seen that majority of patients turned up for their appointments from Monday to Friday. Saturday was the least favoured day for appointments with Sunday having no appointments at all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tuesday      23.676948\n",
       "Wednesday    21.952389\n",
       "Monday       20.883814\n",
       "Friday       17.113490\n",
       "Thursday     16.351644\n",
       "Saturday      0.021715\n",
       "Name: scheduled_weekday, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#proportion of days of the week when appointments were scheduled\n",
    "df['scheduled_weekday'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe643ee4c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot showing relationship between day of schedulling appointment and showing up or not\n",
    "sns.countplot(data=df, x='scheduled_weekday', hue='No-show');\n",
    "plt.title('Relationship between day of scheduling appointment and showing up or not')\n",
    "plt.legend(['Yes','No'], title='No-show');\n",
    "plt.xlabel('day appointment scheduled')\n",
    "plt.ylabel('number of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analysis Of the day of scheduling appointmentsalso shows that majority of patients set their appointments during weekdays with Saturday being the least favoured day and Sunday having no appointments schedulled at all\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "\n",
    "### Does the `Age` of the patient determine whether they will show up for their appointment or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    110521.000000\n",
      "mean         37.085694\n",
      "std          23.104606\n",
      "min           0.000000\n",
      "25%          18.000000\n",
      "50%          37.000000\n",
      "75%          55.000000\n",
      "max         102.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#get the min, 25%, median, 75%, and max values of the Age column\n",
    "print(df['Age'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create bins of ages based on the min, 25%, median, 75%, and max values of the Age column\n",
    "bin_edges = [ 0, 18, 37, 55, 102]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create bin labels based on the age categories of the patients\n",
    "bin_labels = ['children', 'young_adults', 'adults', 'elderly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column with Age categories based on the Age of the patients\n",
    "df['Age_category'] = pd.cut(df['Age'], bin_edges, labels=bin_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe63fda2128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a plot showing the relationship between the Age of the patient and showing up or not for their appointment \n",
    "\n",
    "ax = df.groupby('Age_category')['No-show'].value_counts().unstack()\n",
    "ax.plot(kind = 'barh',figsize= (10,8))\n",
    "\n",
    "plt.title('Relationship between Age of patient and showing up for appointment')\n",
    "plt.ylabel('Age Category of patient')\n",
    "plt.xlabel('Number of patients')\n",
    "plt.legend(['Showed-up','No-show'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on the above analysis, it can be observed that more elderly people showed up fro their appointment than anyother age category\n",
    "\n",
    "- it can also be noted that the older the person, the more likely they are to shoe up for their appointment "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are patients whom were sent an SMS more likely to show up for their appointment than those that were not sent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    67.896599\n",
       "1    32.103401\n",
       "Name: SMS_received, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of patients who received sms\n",
    "df['SMS_received'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe640042a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot percentages of SMS_reveived column\n",
    "x = df['SMS_received'].value_counts(normalize=True)*100\n",
    "x.plot(kind='pie', autopct = '%1.0f%%', figsize=(8,8))\n",
    "plt.title('percentage of patients who received SMS')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the above summary 68% of patients did not receive an sms while 32% received\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename the SMS_received values to Yes and No for better interpretation\n",
    "df['SMS_received'] = df['SMS_received'].map({1: 'Yes', 0: 'No'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMS_received</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>62508</td>\n",
       "      <td>12532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>25697</td>\n",
       "      <td>9784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show          No    Yes\n",
       "SMS_received              \n",
       "No            62508  12532\n",
       "Yes           25697   9784"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabulate showing up and receiving sms by use of groupby\n",
    "df.groupby('SMS_received')['No-show'].value_counts().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe640042518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a plot showing the realtionship between receiving an sms and showing up for appointment\n",
    "ax = df.groupby('SMS_received')['No-show'].value_counts(normalize=True).unstack()*100\n",
    "ax.plot(kind = 'bar',figsize= (10,8))\n",
    "plt.title('Relationship between sending SMS to patients and showing up for appointment')\n",
    "plt.xlabel('SMS received')\n",
    "plt.ylabel('Percentage of patients')\n",
    "plt.legend(['Showed-up','No-show']);\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A larger percentage of patients showed up that had not received an SMS than those that had received. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "The following are the summaries that can be made from the analysis carried out of the dataset:\n",
    "\n",
    "1. The Age of a patient seems to determine whether they will show up for their appointment or not. Elderly patients more likely to show up for their appointents as comapred to younger patients. \n",
    "\n",
    "\n",
    "2. Patients who were sent SMS were less likely to show up fo their appointmetn than those that were not sent an SMS. Sending SMS does not seem to have an effect on patients showing up or not.\n",
    "\n",
    "\n",
    "3. Patients do not prefeer setting up or attending appointments on Saturday. It also appears that the hospitals are closed on Sunday as no appointments are schedulled or attended on this day.\n",
    "\n",
    "\n",
    "- A limitation of the analysis is that the binnig of the `Age_category` column was done based on the mininum, maximum, 25th, 50th and 75th percentile values. A more robust and statistical approach would be recommended so as to provide concrete statistical conclusions.\n",
    "\n",
    "\n",
    "- Further analysis on the dataset can be done to be able to determine possible predictors of not showing up for appointments. Additionally a machine learnig model can be trained on the data so as to predict whether or not a patient will show up for an appointment.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}