Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 1: Investigate a Dataset (No-show Appointments Dataset)\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#imports\">Imports</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>\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "# Introduction\n",
    "\n",
    "A person makes a doctor appointment, receives all the instructions and no-show. Who to blame?\n",
    "\n",
    "### No-show Appointments Dataset\n",
    "\n",
    "This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment or not. <br>\n",
    "This dataset contains some information about the patients like: \n",
    "<ul>\n",
    "<li>Gender</li>\n",
    "<li>Age</li>\n",
    "<li>whether or not the patient is enrolled in Brasilian welfare program \"Bolsa Família.\"</li>\n",
    "</ul>  \n",
    "Others about the appointment like: \n",
    "<ul>\n",
    "<li>Day of the appointment</li>\n",
    "<li>Day of the registeration for the appointment</li>\n",
    "<li>Where the appointment takes place</li>\n",
    "<li>SMS received, messages sent to the patients</li>\n",
    "</ul>\n",
    "And some diseases the patients have like: \n",
    "<ul>\n",
    "<li>Hipertension</li>\n",
    "<li>Diabetes</li>\n",
    "<li>Alcoholism</li>\n",
    "<li>Handcap</li></ul>\n",
    "<hr>\n",
    "\n",
    "### Questions\n",
    "In my Investigation of this dataset I will try to check and answer these questions:\n",
    "<ul>\n",
    "\n",
    "<li>Does the patient's \"Age/Gender\" affect their show to the appointments?</li>\n",
    "<li>On which days patients show more to their appointments?</li>\n",
    "<li>Can we found any correlations between \"the gap between the day of registration and the day of the appointments\" and \"the show-on the appointments\"?</li>\n",
    "<li>Are more messages sent to the patients increase their probabilities of showing up to the appointment? </li>\n",
    "<li>What about patients enrolled in the Brasilian welfare program are those more likely to show up?</li>\n",
    "<li>Does any deseases affect show/no-show to the appointments?</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<a id='imports'></a>\n",
    "\n",
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "# Data Wrangling\n",
    "\n",
    "In this section of the report, we will load in the data, look briefly at it, check for cleanliness, and then trim and clean the dataset for analysis.<hr>\n",
    "###  Dataset loading\n",
    "first we start with loading the dataset into dataframe format and show the first 5 rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks like PatientId and AppointmentID are to columns we can drop because they are unique for each patient.<br>\n",
    "- No-show have a Yes or No which we could change to 0 and 1.<br>\n",
    "\n",
    "But first, let's look at the shape of the data, data types, and some summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that ScheduledDay and AppointmentDay are of type string.\n",
    "Also, we can split them to day and time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
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       "    <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",
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      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "luckily there is no missing data, lets check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks good so far, we dont have much cleaning to do here.<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning (part 1)\n",
    "\n",
    "lets first drop PatientId and AppointmentID\n",
    "- because they are unieque for each pateint and will not be useful\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender          ScheduledDay        AppointmentDay  Age      Neighbourhood  \\\n",
       "0      F  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z   62    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z   62      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "0            0             1         0           0        0             0   \n",
       "1            0             0         0           0        0             0   \n",
       "2            0             0         0           0        0             0   \n",
       "3            0             0         0           0        0             0   \n",
       "4            0             1         1           0        0             0   \n",
       "\n",
       "  No-show  \n",
       "0      No  \n",
       "1      No  \n",
       "2      No  \n",
       "3      No  \n",
       "4      No  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PatientId','AppointmentID'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets check again for duplicates after dropping the 'PatientId' and 'AppointmentID' to be sure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "635"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so we found 635 duplicated we can drop them as it will not affect much our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning (part 2)\n",
    "\n",
    "now lets deal with the data and time for both ScheduledDay and AppointmentDay\n",
    "* step 1: first lets remove the Z at the end.\n",
    "* step 2: then split them into scheduled_date/scheduled_time and appointment_date/appointment_time\n",
    "* step 3: then there is a function in pandas df.dayofweek which return the day of the weak from the year, it can be useful in our analysis too and we can make it using appointment_date so we will make new column called appointment_dow (day of the weak)\n",
    "\n",
    "Ref: https://towardsdatascience.com/working-with-datetime-in-pandas-dataframe-663f7af6c587\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2016-04-29T18:38:08\n",
       "Name: ScheduledDay, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScheduledDay'] = df['ScheduledDay'].map(lambda x: str(x)[:-1])\n",
    "df['ScheduledDay'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2016-04-29T00:00:00\n",
       "Name: AppointmentDay, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AppointmentDay'] = df['AppointmentDay'].map(lambda x: str(x)[:-1])\n",
    "df['AppointmentDay'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* then split them into scheduled_date/scheduled_time and appointment_date/appointment_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['scheduled_date'] = df['ScheduledDay'].apply(lambda x: x.split(\"T\")[0])\n",
    "df['scheduled_time'] = df['ScheduledDay'].apply(lambda x: x.split(\"T\")[1])\n",
    "df.drop(['ScheduledDay'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['appointment_date'] = df['AppointmentDay'].apply(lambda x: x.split(\"T\")[0])\n",
    "df['appointment_time'] = df['AppointmentDay'].apply(lambda x: x.split(\"T\")[1])\n",
    "df.drop(['AppointmentDay'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</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_date</th>\n",
       "      <th>scheduled_time</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>appointment_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</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>2016-04-29</td>\n",
       "      <td>18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age    Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "0      F   62  JARDIM DA PENHA            0             1         0   \n",
       "\n",
       "   Alcoholism  Handcap  SMS_received No-show scheduled_date scheduled_time  \\\n",
       "0           0        0             0      No     2016-04-29       18:38:08   \n",
       "\n",
       "  appointment_date appointment_time  \n",
       "0       2016-04-29         00:00:00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this is step 3, lets make the appointment_dow\n",
    "\n",
    "Ref: https://towardsdatascience.com/working-with-datetime-in-pandas-dataframe-663f7af6c587\n",
    "\n",
    "note: you should also change the type of the data from string to datetime<br>\n",
    "Ref: https://stackoverflow.com/questions/33365055/attributeerror-can-only-use-dt-accessor-with-datetimelike-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>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_date</th>\n",
       "      <th>scheduled_time</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>appointment_time</th>\n",
       "      <th>appointment_dow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</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>2016-04-29</td>\n",
       "      <td>18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age    Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "0      F   62  JARDIM DA PENHA            0             1         0   \n",
       "\n",
       "   Alcoholism  Handcap  SMS_received No-show scheduled_date scheduled_time  \\\n",
       "0           0        0             0      No     2016-04-29       18:38:08   \n",
       "\n",
       "  appointment_date appointment_time appointment_dow  \n",
       "0       2016-04-29         00:00:00          Friday  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dw_mapping={\n",
    "    0: 'Monday', \n",
    "    1: 'Tuesday', \n",
    "    2: 'Wednesday', \n",
    "    3: 'Thursday', \n",
    "    4: 'Friday',\n",
    "    5: 'Saturday', \n",
    "    6: 'Sunday'\n",
    "} \n",
    "df['appointment_date'] = pd.to_datetime(df['appointment_date'], errors='coerce')\n",
    "df['scheduled_date'] = pd.to_datetime(df['scheduled_date'], errors='coerce')\n",
    "\n",
    "df['appointment_dow'] = df['appointment_date'].dt.weekday.map(dw_mapping)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning (part 3)\n",
    "\n",
    "let's change the No-show value to 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(to_replace=['No', 'Yes'], value=[1, 0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning (part 4)\n",
    "\n",
    "and finaly lets add new column for the defferince between scheduled_date and appointment_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diff_bet_app_sch'] = df['appointment_date'] - df['scheduled_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<a id='eda'></a>\n",
    "# Exploratory Data Analysis\n",
    "\n",
    "Now that we've trimmed and cleaned our data, we're ready to move on to exploration.\n",
    "<br><br>\n",
    "lets look at the correlation matrix to show the relationship the No-show and all other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <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>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.092467</td>\n",
       "      <td>0.504550</td>\n",
       "      <td>0.292419</td>\n",
       "      <td>0.095696</td>\n",
       "      <td>0.077523</td>\n",
       "      <td>0.012672</td>\n",
       "      <td>0.060878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship</th>\n",
       "      <td>-0.092467</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019702</td>\n",
       "      <td>-0.024835</td>\n",
       "      <td>0.034404</td>\n",
       "      <td>-0.008937</td>\n",
       "      <td>0.001148</td>\n",
       "      <td>-0.029288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hipertension</th>\n",
       "      <td>0.504550</td>\n",
       "      <td>-0.019702</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.432938</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.080497</td>\n",
       "      <td>-0.006297</td>\n",
       "      <td>0.035990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes</th>\n",
       "      <td>0.292419</td>\n",
       "      <td>-0.024835</td>\n",
       "      <td>0.432938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018634</td>\n",
       "      <td>0.058124</td>\n",
       "      <td>-0.014509</td>\n",
       "      <td>0.014525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcoholism</th>\n",
       "      <td>0.095696</td>\n",
       "      <td>0.034404</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.018634</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>-0.026367</td>\n",
       "      <td>0.000587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handcap</th>\n",
       "      <td>0.077523</td>\n",
       "      <td>-0.008937</td>\n",
       "      <td>0.080497</td>\n",
       "      <td>0.058124</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023861</td>\n",
       "      <td>0.006216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMS_received</th>\n",
       "      <td>0.012672</td>\n",
       "      <td>0.001148</td>\n",
       "      <td>-0.006297</td>\n",
       "      <td>-0.014509</td>\n",
       "      <td>-0.026367</td>\n",
       "      <td>-0.023861</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-show</th>\n",
       "      <td>0.060878</td>\n",
       "      <td>-0.029288</td>\n",
       "      <td>0.035990</td>\n",
       "      <td>0.014525</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>-0.128622</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Age  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "Age           1.000000    -0.092467      0.504550  0.292419    0.095696   \n",
       "Scholarship  -0.092467     1.000000     -0.019702 -0.024835    0.034404   \n",
       "Hipertension  0.504550    -0.019702      1.000000  0.432938    0.087790   \n",
       "Diabetes      0.292419    -0.024835      0.432938  1.000000    0.018634   \n",
       "Alcoholism    0.095696     0.034404      0.087790  0.018634    1.000000   \n",
       "Handcap       0.077523    -0.008937      0.080497  0.058124    0.002962   \n",
       "SMS_received  0.012672     0.001148     -0.006297 -0.014509   -0.026367   \n",
       "No-show       0.060878    -0.029288      0.035990  0.014525    0.000587   \n",
       "\n",
       "               Handcap  SMS_received   No-show  \n",
       "Age           0.077523      0.012672  0.060878  \n",
       "Scholarship  -0.008937      0.001148 -0.029288  \n",
       "Hipertension  0.080497     -0.006297  0.035990  \n",
       "Diabetes      0.058124     -0.014509  0.014525  \n",
       "Alcoholism    0.002962     -0.026367  0.000587  \n",
       "Handcap       1.000000     -0.023861  0.006216  \n",
       "SMS_received -0.023861      1.000000 -0.128622  \n",
       "No-show       0.006216     -0.128622  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks like all relation positive/negative are very small, but there is a Negative correlation between SMS_received and the No-show data\n",
    "- we can assume that the absent patients get more SMSs, or it will not help as much to send them SMSs, but we can take a closer look later\n",
    "- also we can see  that Hypertension and Diabetes are correlated with the increase in the age which to be predicted\n",
    "\n",
    "<br>\n",
    "now we can look at each variable individually "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### Research Question 1: Does the patient's \"Age/Gender\" affect their show to the appointments?\n",
    "\n",
    "lets look at age first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "attend = df['No-show'] == 1\n",
    "absent = df['No-show'] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.7956807982\n",
      "34.2839170966\n"
     ]
    }
   ],
   "source": [
    "print(df['Age'][attend].mean())\n",
    "print(df['Age'][absent].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the mean look very close, lets try to visualize it \n",
    "<br>but first lets make a function to style the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_style(xl,yl,title):\n",
    "    ax.set_xlabel(xl)\n",
    "    ax.set_ylabel(yl)\n",
    "    ax.set_title(title)\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ded475e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['Age'][attend].hist(alpha = 0.5, label='showed')\n",
    "ax = df['Age'][absent].hist(alpha = 0.5, label='no show')\n",
    "plot_style(xl = \"Age\",yl=\"Count\",title=\"Ages of Attend and Absent Patients\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks like age doesn't affect their attendance, although very young ages(kids) and from 40-60 looked like they are more likely to attend the appointment.\n",
    "<br><br>\n",
    "lets look at Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    56962\n",
       "M    30832\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'][attend].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    14455\n",
       "M     7643\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'][absent].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's calculate the percentage of attendance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25376566834029701"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'][absent].value_counts()['F']/df['Gender'][attend].value_counts()['F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2478918007265179"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'][absent].value_counts()['M']/df['Gender'][attend].value_counts()['M']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and with the visualisation its the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de7e3ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby('Gender')['No-show'].mean().plot(kind='pie');\n",
    "ax.set_title=\"Mean Value of Attend Male Vs Female\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de7da2be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby('Gender')['No-show'].value_counts().plot(kind='pie');\n",
    "ax.set_title=\"count of Attend Male Vs Female\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very close too, so looks like Age and Gender don't affect the attendance that much\n",
    "<br> But there are more females than males in the dataset\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2: On which days patients show more to their appointments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de7e15710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['appointment_dow'][attend].hist(alpha = 0.5, label='attened')\n",
    "ax = df['appointment_dow'][absent].hist(alpha = 0.5, label='absent')\n",
    "plot_style(xl=\"Day\",yl=\"Count\",title=\"Attend and Absent count in each day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks the same each day, so days dont affect the attendace<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3: Can we found any correlations between \"the gap between the day of registration and the day of the appointments\" and \"the show-on the appointments\"?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      87794\n",
       "mean      8 days 17:38:09.641661\n",
       "std      14 days 13:08:02.004906\n",
       "min              0 days 00:00:00\n",
       "25%              0 days 00:00:00\n",
       "50%              2 days 00:00:00\n",
       "75%             12 days 00:00:00\n",
       "max            179 days 00:00:00\n",
       "Name: diff_bet_app_sch, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diff_bet_app_sch'][attend].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      22098\n",
       "mean     15 days 19:28:43.269074\n",
       "std      16 days 14:42:13.852774\n",
       "min            -6 days +00:00:00\n",
       "25%              4 days 00:00:00\n",
       "50%             11 days 00:00:00\n",
       "75%             23 days 00:00:00\n",
       "max            179 days 00:00:00\n",
       "Name: diff_bet_app_sch, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diff_bet_app_sch'][absent].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- the mean of the attended patients is 8 days, on the other hand, it's 15 days with the absent patients<br>\n",
    "\n",
    "So we see here that it's more likely for the patient to absent if there is a big gap between the day of registration and the day of the appointments<br>\n",
    "also -6 days in absent min means there is an outlier in the data set \n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4: Are more messages sent to the patients increase their probabilities of showing up to the appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de7d0d048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['SMS_received'][attend].hist(alpha = 0.5, label='attended')\n",
    "ax = df['SMS_received'][absent].hist(alpha = 0.5, label='absent')\n",
    "plot_style(xl=\"SMS Sent\",yl=\"Count\",title=\"Attend and Absent vs recevied SMS or not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29270792992687428"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SMS_received'][attend].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44275500045252963"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SMS_received'][absent].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks like there are more absent get SMS but they did't show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5: What about patients enrolled in the Brasilian welfare program are those more likely to show up?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de7c787f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['Scholarship'][attend].hist(alpha = 0.5, label='attended')\n",
    "ax = df['Scholarship'][absent].hist(alpha = 0.5, label='absent')\n",
    "plot_style(xl=\"Scholarship\",yl=\"Count\",title=\"Attend and Absent vs under the program or not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.093913023669043444"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Scholarship'][attend].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11566657616073853"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Scholarship'][absent].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "look like the same ratio however, 800k of the attendance are enrolled in the Scholarship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 6: Does any disease affect show/no-show to the appointments?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>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_date</th>\n",
       "      <th>scheduled_time</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>appointment_time</th>\n",
       "      <th>appointment_dow</th>\n",
       "      <th>diff_bet_app_sch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age    Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "0      F   62  JARDIM DA PENHA            0             1         0   \n",
       "\n",
       "   Alcoholism  Handcap  SMS_received  No-show scheduled_date scheduled_time  \\\n",
       "0           0        0             0        1     2016-04-29       18:38:08   \n",
       "\n",
       "  appointment_date appointment_time appointment_dow diff_bet_app_sch  \n",
       "0       2016-04-29         00:00:00          Friday           0 days  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.204433104768\n",
      "0.16870305005\n"
     ]
    }
   ],
   "source": [
    "print(df['Hipertension'][attend].mean())\n",
    "print(df['Hipertension'][absent].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0737066314327\n",
      "0.0643497149063\n"
     ]
    }
   ],
   "source": [
    "print(df['Diabetes'][attend].mean())\n",
    "print(df['Diabetes'][absent].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0304804428549\n",
      "0.0302289799982\n"
     ]
    }
   ],
   "source": [
    "print(df['Alcoholism'][attend].mean())\n",
    "print(df['Alcoholism'][absent].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0226325261407\n",
      "0.0201375690108\n"
     ]
    }
   ],
   "source": [
    "print(df['Handcap'][attend].mean())\n",
    "print(df['Handcap'][absent].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "only Hipertension affect attendance by small amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8de7cad470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['Hipertension'][attend].hist(alpha = 0.5, label='attended')\n",
    "ax = df['Hipertension'][absent].hist(alpha = 0.5, label='absent')\n",
    "plot_style(xl=\"have Hipertension\",yl=\"Count\",title=\"Attend and Absent vs if the patient have Hipertension or not\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "1. Age doesn't affect No-show as much, but it appears that kids and elders are more likely to show on the appointment\n",
    "2. Gender does not have any effect, but I found that more females are going to this appointment than males\n",
    "3. It's more likely for the patient to No-show if there is a big gap between the day of registration and the day of the appointments\n",
    "4. more SMS messages sent to absent patients doesn't seem to affect their attendance that much\n",
    "5. Scholarship enrollment Doesn't affect the No-show phenomena but it appears that most of the dataset are covered by it\n",
    "6. Having a disease doesn't appear to be the reason for the No-show phenomena\n",
    "\n",
    "#### Limitations\n",
    "\n",
    "1. the dataset was not very easy to search into, all correlations were very small, and all the differences were the same for each case (absent vs attend) except for the Age and the gap between the day of registration and the day of the appointments\n",
    "2. the dataset was very clean, although I found some outliers here and there but very small numbers as we saw -6 days and the 115-year-old guy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}