investigate-a-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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",
    "<li><a href=\"#limitations\">Limitations</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> In this project i am going to be analyzing data of patient appointment, the main goal is to predict if patient will show up for their scheduled appointment or not and to determine how patients who show up for their appointment differs from patients who do not show up for their appointment. The dataset collects information from 100 thousands medical appointments in Brazil and is focused on whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row.Some of the vital columns i have to pay attention to when analyzing the data are, \n",
    "**ScheduledDay** which provides data on what day the patient set up their appointment.\n",
    "\n",
    ">**Neighborhood** which provides data on the location of the hospital.\n",
    "\n",
    ">**Scholarship** which provides data on whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.\n",
    "\n",
    ">**No-show** provides data of wether patient will show up for the appointment or not\n",
    "\n",
    ">**Neighborhood** provides data of where the patient live.\n",
    "\n",
    ">>My Independent variables here are **ScheduledDay**, **Scholarship** and **Handicap**. And my dependent variable is No-show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">All the the python modules needed for this project are been loaded below with their alias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load your data and print out a few lines.\n",
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below Get the Total rows and columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#code to show the number of rows and columns in the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below returns description of data in a dataframe or statistics of the data in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Povides statistical description of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code below shows a summary of the entire dataframe"
   ]
  },
  {
   "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",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Prints out concise summary of the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code below returns the datatype of each column in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prints out the datatype of all the columns of the dataset\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below check for Column with null values in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print out the sum of all null values of each column in the dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below checks if any of columns in the no-show appointment dataframe have null values -The expected result should be False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prints a boolean to check if there are any null values iin the dataset\n",
    "df.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below check for the number of duplicated values in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    ">(Here, i want to make sure the data is clean for proper analysis, some of the activities i will be carrying out here are, changing datatype, Renaming and replacing the data of the the No-Show column, Removing unconsistent data from the AppointmentDay, and the ScheduledDay columns and changing their datatypes )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code below change all Yes to 1 and all no to 0 in the No-Show column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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",
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       "      <td>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <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>0</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>0</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       0  \n",
       "1         0           0        0             0       0  \n",
       "2         0           0        0             0       0  \n",
       "3         0           0        0             0       0  \n",
       "4         1           0        0             0       0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace the words Yes wth 1 and No with 0\n",
    "df['No-show'] = df['No-show'].str.replace('Yes','1')\n",
    "df['No-show'] = df['No-show'].str.replace('No','0')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below change he datatype of the No-Show column to int64, i am converting this column so that i can easily exploy statistics and compare ith other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The code change the datatype of N-Show column to int64\n",
    "df['No-show'] = df['No-show'].astype('int64')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The codes below are used to remove the character Z for both columns ScheduledDay and AppointmentDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "  <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>2016-04-29T18:38:08</td>\n",
       "      <td>2016-04-29T00:00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27</td>\n",
       "      <td>2016-04-29T00:00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04</td>\n",
       "      <td>2016-04-29T00:00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2016-04-29T17:29:31</td>\n",
       "      <td>2016-04-29T00:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender         ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31   \n",
       "\n",
       "        AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00    8  PONTAL DE CAMBURI            0             0   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received  No-show  \n",
       "0         0           0        0             0        0  \n",
       "1         0           0        0             0        0  \n",
       "2         0           0        0             0        0  \n",
       "3         0           0        0             0        0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace the Letter Z with nothing in the tables of AppointmentDay and ScheduledDay\n",
    "df['AppointmentDay'] = df['AppointmentDay'].str.replace('Z','')\n",
    "df['ScheduledDay'] = df['ScheduledDay'].str.replace('Z','')\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code below change the datatype of both AppointmentDay and ScheduledDay to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId                float64\n",
       "AppointmentID              int64\n",
       "Gender                    object\n",
       "ScheduledDay      datetime64[ns]\n",
       "AppointmentDay    datetime64[ns]\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                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The code change the datatype of AppointmentDay and ScheduledDay to datetime\n",
    "df['AppointmentDay'] = pd.to_datetime(df['AppointmentDay'])\n",
    "df['ScheduledDay'] = pd.to_datetime(df['ScheduledDay'])\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code below split the column ScheduledDay into two, ScheduledDay itself for the date and ScheduledTime for the time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>2016-04-29</td>\n",
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       "      <td>0</td>\n",
       "      <td>17:29:31</td>\n",
       "    </tr>\n",
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       "      <td>2016-04-29</td>\n",
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       "      <td>16:07:23</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0  2.987250e+13        5642903      F   2016-04-29     2016-04-29   62   \n",
       "1  5.589978e+14        5642503      M   2016-04-29     2016-04-29   56   \n",
       "2  4.262962e+12        5642549      F   2016-04-29     2016-04-29   62   \n",
       "3  8.679512e+11        5642828      F   2016-04-29     2016-04-29    8   \n",
       "4  8.841186e+12        5642494      F   2016-04-29     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 ScheduledTime  \n",
       "0        0             0        0      18:38:08  \n",
       "1        0             0        0      16:08:27  \n",
       "2        0             0        0      16:19:04  \n",
       "3        0             0        0      17:29:31  \n",
       "4        0             0        0      16:07:23  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code to extract date and time value of the ScheduledDay column and assign them to different variables\n",
    "df['ScheduledTime'] = df['ScheduledDay'].dt.time\n",
    "df['ScheduledDay'] = df['ScheduledDay'].dt.date\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  The code below list all the columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension',\n",
       "       'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show',\n",
       "       'ScheduledTime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List all the columns in the dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below rename the No-Show column to be consistent with the naming format of all the othe columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension',\n",
       "       'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'NoShow',\n",
       "       'ScheduledTime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename No-show to NoShow\n",
    "df.rename(columns = {'No-show':'NoShow'}, inplace = True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "### Research Question 1: will patient show up for appointment if they are scheduled for a specific date or time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Below i want to create a masks for patients who do not show up for their scheduled appointment and patients who showed up for their scheduled appointment, I want to also explore the mean of patients who did not show up for a specific day . Dates with highest mean indicate dates which  patients won't show up for their appointment and plot represent the ScheduledDay data in a horizontal bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Create a mask variables of those who showed up and did not showed p for their scheduled appointment\n",
    "NotShowedUp = df.NoShow ==True\n",
    "ShowedUp = df.NoShow == False\n",
    "#groupby ScheduledDay and calculate the mean of the relationship between ScheduledDay and NoShow\n",
    "Scheduleday= df.groupby('ScheduledDay').NoShow.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Below i want to define functions which i will reuse throughout the code to plot my bar charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to plot horizontal barcharts\n",
    "def horizontalbarChart(Dataframe, x_label, y_label, title, size):\n",
    "    Dataframe.plot.barh(figsize=size)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.show()\n",
    "# Define a function to plot Vertical barcharts\n",
    "def verticalbarChart(Dataframe, x_label, y_label, title, size):\n",
    "    Dataframe.plot.bar(figsize=size)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">the graph below shows the relationship between Noshow and ScheduleDay, we can clearly see that on some dayes, all the patients will respect their scheduled appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "horizontalbarChart(Scheduleday,'NoShow','ScheduleDay','Mean of Noshow vs Scheduleday', (10,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2: will patients with scholarship show up for appointment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The below code cell and graph, i want to explore the association between scholarship and NoShow Columns. from the bar chart below, patients with scholarship are likely not to show up for their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Groupby Scholarship find the mean of the relationship between Scholarship and NoShow\n",
    "# And represent the infomation in a bar chart\n",
    "scholarship = df.groupby('Scholarship').NoShow.mean()\n",
    "verticalbarChart(scholarship,'Scholarship','NoShow','NoShow Vs Scholarship', (10,8) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The below code is to explore if patient with scholarship will show up for their appointment or not, from the the below graph, we can clearly see that most patients with scholarship would show up for their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Scholarship[ShowedUp].hist(alpha=0.5, bins=20, label='ShowedUp'),\n",
    "df.Scholarship[NotShowedUp].hist(alpha=0.5, bins=20,label='NotShowedUp') \n",
    "plt.title(\"NoShow Vs Scholarship\")\n",
    "plt.xlabel(\"Scholarship\")\n",
    "plt.ylabel(\"Number of patients\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3: Is Gender Associated with NoSHow?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code below will compute the mean of those who did not showed up for appointment for the different gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    0.203146\n",
       "M    0.199679\n",
       "Name: NoShow, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Gender')['NoShow'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Below we can se the differences in the mean through the bar chat, we can see that the differences between the number of females who did not show up for the appointment and the number of men who did not show up for the appointment is insignificant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender = df.groupby('Gender')['NoShow'].mean()\n",
    "verticalbarChart(gender,'Gender', 'NoShow', 'Relationship between NoShow and Gender', (8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The above graph is not accurate because i don't know how many males and females are involve, i want to check the number of males and female sheduled for appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the above statistics, we can see that there are more females than males, so we cannot conclude that a particular gender will not respect appointmen most often"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Gender[ShowedUp].hist(alpha=0.5, bins=20, label='ShowedUp'),\n",
    "df.Gender[NotShowedUp].hist(alpha=0.5, bins=20,label='NotShowedUp')\n",
    "plt.title(\"Relationship Between Gender and NoShow\")\n",
    "plt.xlabel(\"x-axis: Gender\")\n",
    "plt.ylabel(\"y-axis: Num of patient\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4: Will Handicap patients show up for their Appointments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">To start with, the below code will exploy all the unique values of the Handicap column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Handcap'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I would like to explore the relationship between Handicap and NoShow as well, so from the graph below, it is obvious that those with more critical handicap situations would likely not show up for their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between Handcap and NoShow')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here i am going o groupby Handicap, find the relationship betweeen Handicap and NoShow then compute the mean \n",
    "df.groupby('Handcap')['NoShow'].mean().plot(kind=\"pie\", figsize=(6,6))\n",
    "plt.title('Relationship between Handcap and NoShow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5: Will Hipertension patients  show up for their Scheduled appointment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The code below , i want to represent the relationship between Hipertension and NoShow in a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Hipertension[ShowedUp].hist(alpha=0.5, bins=20, label='ShowedUp', figsize =(10,8)),\n",
    "df.Hipertension[NotShowedUp].hist(alpha=0.5, bins=20,label='NotShowedUp');\n",
    "plt.title(\"Hipertension\")\n",
    "plt.xlabel(\"Hipertension\")\n",
    "plt.ylabel(\"Num of patient\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The goal of the below code cell is to calculate the mean of those with Hypertension and will not show up for their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hipertension\n",
       "0    0.209037\n",
       "1    0.173020\n",
       "Name: NoShow, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Hipertension').NoShow.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 6: Will patients who receive SMS show up for their Scheduled appointment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">In the cells bellow i want to explore if patients who receive sms will show up for their scheduled appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SMS_received[ShowedUp].hist(alpha=0.5, bins=20, label='ShowedUp', figsize = (10,8)),\n",
    "df.SMS_received[NotShowedUp].hist(alpha=0.5, bins=20,label='NotShowedUp');\n",
    "plt.title(\"Relationship between SMS received and NoShow\")\n",
    "plt.xlabel(\"SMS_received\")\n",
    "plt.ylabel(\"number of patients\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The goal of the below code cell is to calculate the mean of those who received SMS and will or will not show up for their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMS_received\n",
       "0    0.167033\n",
       "1    0.275745\n",
       "Name: NoShow, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SMS_received').NoShow.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> From the above analysis, i found out that NoShow is affected by the scheduleday, whether the patient is handicap or not, that is patients with crucial handicap situations would likely not show up for their appointments, Scholarship and SMS_received are also independent variables which NoShow depends on. The effect of Hypertension and gender to NoShow colum is very minimal.\n",
    "\n",
    ">To conclude, for a health center to improve in the number of patients who respect their appointappointments, they need to take into consideration ScheduledDay, Scholarship and if the patient is HAndicap. Also they have to consider sending reminder SMS to their patients concerning their scheduled appointments. from the above analysis, i can't really conclude on the effect of gender on noshow because the number of females are significantly greater than the number of males, again Health centers can consider providing means for Handicap patients so that they can start showing up for their appointments or they can consider remote appointments.Appointment date has a sif\n",
    "\n",
    ">The Udacity class room has been my guide throughout this project with the help my course mate who gave me a head up on how to create a resusable function to plot charts\n",
    "\n",
    ">stackoverflow.com .\n",
    ">>str.replace() function, \n",
    ">>dt.time, dt.date\n",
    ">>pd.to_datetime() and df.replace() function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='limitations'></a>\n",
    "## Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">During this project, i had limitations in doing advanced analysis and creating functions that i could reuse due to limited knowledge. \n",
    ">Again, i could not effectively use matplotlib library to create advanced visualisations due to limited knowledge"
   ]
  }
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