Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Discovering Medical No-Show Appointments in Brazil\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> We will examine a dataset with information from 100k medical appointments in Brazil. It focuses on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row. These characteristics, in particular \"Gender\", \"Neighbourhood\" and \"Scholarship\". I will investigate, if their is any association to the dependent variable \"No_show\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> **Tip**: In this section of the report, you will load in the data, check for cleanliness, and then trim and clean your dataset for analysis. Make sure that you document your steps carefully and justify your cleaning decisions.\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      "PatientId         110527 non-null float64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values in this dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229ab535c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(8, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see, that overall more people show up at their medical appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Since the columns PatientId and AppointmentID seem to be generated on a rolling basis, their values are probably random and thus have no value for us so that we can get rid of them for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PatientId', 'AppointmentID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirming the deleting of the two columns PatientId and AppointmentID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender          ScheduledDay        AppointmentDay  Age      Neighbourhood  \\\n",
       "0      F  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z   62    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z   62      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "0            0             1         0           0        0             0   \n",
       "1            0             0         0           0        0             0   \n",
       "2            0             0         0           0        0             0   \n",
       "3            0             0         0           0        0             0   \n",
       "4            0             1         1           0        0             0   \n",
       "\n",
       "  No-show  \n",
       "0      No  \n",
       "1      No  \n",
       "2      No  \n",
       "3      No  \n",
       "4      No  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since \"-\" doesn't work for variable or column names, I rename the \"No-show\" column to \"No_show\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"No-show\": \"No_show\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirming the renaming of the No_show column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </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",
       "\n",
       "   Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "0            0             1         0           0        0             0   \n",
       "\n",
       "  No_show  \n",
       "0      No  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting No_show from object to integer, so that I can better apply built-in math functions. No --> 0 (False), Yes --> 1 (True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'Yes': 1, 'No': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({'No_show': mapping}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking, if No_show was converted to an integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 12 columns):\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No_show           110527 non-null int64\n",
      "dtypes: int64(8), object(4)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "### Q1: Is \"Gender\" associated with medical no-show appointments?\n",
    "We are interested in the question, if a particular gender tends to more often not show (or show) up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before beginning: let's create two variables, that declare a \"show\" or \"no-show\".\n",
    "showed = df.No_show == False\n",
    "no_showed = df.No_show == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that a lot more female than male data is represented in the dataframe. Thus we later calculate the proportions, to get a clearer picture of the no-show rates of males and females. For now, let's take q quick look at the absolute statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229ba27ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Gender[showed].value_counts().plot(kind='bar', alpha=0.5, color='blue', label='showed')\n",
    "df.Gender[no_showed].value_counts().plot(kind='bar', alpha=0.5, color='orange', label='no_showed')\n",
    "plt.xlabel('Gender \\n F=Female, M=Male')\n",
    "plt.ylabel('No. of Appointments')\n",
    "plt.title('Genders and no. of medical appointments (showed vs. non-showed)')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the proportion of male and female no-show rates is equal, but we want to know this more accurate. Let's calculate the exact proportion for the show-up repectively no-show of females and males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229bba1d68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "showed_genders = df.Gender[showed].value_counts()\n",
    "no_showed_genders = df.Gender[no_showed].value_counts()\n",
    "all_genders = df.Gender.value_counts()\n",
    "\n",
    "prop_showed_genders = showed_genders / all_genders\n",
    "prop_no_showed_genders = no_showed_genders / all_genders\n",
    "\n",
    "prop_showed_genders.plot(kind='bar', alpha=0.5, color='blue', label='showed')\n",
    "prop_no_showed_genders.plot(kind='bar', alpha=0.5, color='orange', label='no_showed')\n",
    "plt.xlabel('Gender \\n F=Female, M=Male')\n",
    "plt.ylabel('Percentage of Appointments')\n",
    "plt.title('Genders and percentage of medical appointments (showed vs. non-showed)')\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion of question 1:\n",
    "Males & Females both roughly show up to 80% to their medical appointments (and respectively don't show up around 20%). Thus, there is no real difference between the genders and their no-show rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2: Is \"Neighbourhood\" associated with medical no-show appointments?\n",
    "Are there people or patients from certain neighbourhoods that more often don't show up at their medical appointment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I would like to examine the average no_show rate of Neighbourhoods of the top 5 and last 5 from all Neighborhoods. I restict my view to these 10 rows for a better overview, since we have over 80 neighbourhoods in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighbourhood\n",
       "ILHAS OCEÂNICAS DE TRINDADE    1.000000\n",
       "SANTOS DUMONT                  0.289185\n",
       "SANTA CECÍLIA                  0.274554\n",
       "SANTA CLARA                    0.264822\n",
       "ITARARÉ                        0.262664\n",
       "SOLON BORGES                   0.147122\n",
       "MÁRIO CYPRESTE                 0.145553\n",
       "AEROPORTO                      0.125000\n",
       "ILHA DO BOI                    0.085714\n",
       "PARQUE INDUSTRIAL              0.000000\n",
       "Name: No_show, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Neighbourhood').No_show.mean().sort_values(ascending=False).iloc[np.r_[0:5, -5:0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I plot my results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229b685c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Neighbourhood').No_show.mean().sort_values(ascending=False).iloc[np.r_[0:5, -5:0]].plot(kind='bar', label='');\n",
    "plt.xlabel('Neighbourhoods')\n",
    "plt.ylabel('Percentage of no-showed Appointments')\n",
    "plt.title('No-show distribution in Neighbourhoods (all participants)| Top5 and Last5 Neighbourhoods')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the values ILHAS OCEÂNICAS DE TRINDADE = 1 (100% no-show rate) and PARQUE INDUSTRIAL = 0 seem a little bit to strong in each respective dimension I would like to check their \"amount of participants\" in our sample. Since they just have 2 and 1 participants I decide get rid of these \"low\" values and just will examine neighbourhoods with more than 100 participants in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JARDIM CAMBURI                 7717\n",
       "MARIA ORTIZ                    5805\n",
       "RESISTÊNCIA                    4431\n",
       "JARDIM DA PENHA                3877\n",
       "ITARARÉ                        3514\n",
       "ILHA DO BOI                      35\n",
       "ILHA DO FRADE                    10\n",
       "AEROPORTO                         8\n",
       "ILHAS OCEÂNICAS DE TRINDADE       2\n",
       "PARQUE INDUSTRIAL                 1\n",
       "Name: Neighbourhood, dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Neighbourhood'].value_counts().iloc[np.r_[0:5, -5:0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = df['Neighbourhood'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbourhoods_with_more_100_people = df[df.Neighbourhood.isin(v.index[v.gt(99)])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighbourhood\n",
       "SANTOS DUMONT        0.289185\n",
       "SANTA CECÍLIA        0.274554\n",
       "SANTA CLARA          0.264822\n",
       "ITARARÉ              0.262664\n",
       "JESUS DE NAZARETH    0.243954\n",
       "SANTA MARTHA         0.158416\n",
       "DO CABRAL            0.157143\n",
       "DE LOURDES           0.154098\n",
       "SOLON BORGES         0.147122\n",
       "MÁRIO CYPRESTE       0.145553\n",
       "Name: No_show, dtype: float64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbourhoods_with_more_100_people.groupby('Neighbourhood').No_show.mean().sort_values(ascending=False).iloc[np.r_[0:5, -5:0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are significant differences between the Neighborhoods and their no-show rates. While for example in MÁRIO CYPRESTE the no-show rate is around 14.5%, the no-show rate from SANTOS DUMONT participants is almost 29%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I plot again the new top5 and last5 Neighbourhoods with over 100 people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229bbdb5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Neighbourhoods_with_more_100_people.groupby('Neighbourhood').No_show.mean().sort_values(ascending=False).iloc[np.r_[0:5, -5:0]].plot(kind='bar', label='')\n",
    "plt.xlabel('Neighbourhoods')\n",
    "plt.ylabel('Percentage of no-showed Appointments')\n",
    "plt.title('Top5 and Last5 Neighbourhoods and their no-show rates')\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion of question 2:\n",
    "There is an association between Neighbourhoods and no-show rate - in some regions no-show rates are almost 100% higher than in regions where no-show rates are low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3 Is \"Scholarship\" associated with medical no-show appointments?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‘Scholarship’ indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família. Bolsa Família is a social welfare program of the Government of Brazil. Bolsa Família provides financial aid to poor Brazilian families. Let's see if one of the groups has a higher percentage of no-show rates than the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Scholarship\n",
       "0    0.198072\n",
       "1    0.237363\n",
       "Name: No_show, dtype: float64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Scholarship').No_show.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229b62e7b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Scholarship').No_show.mean().plot(kind='bar', label= \"\")\n",
    "plt.xlabel('Scholarships \\n 0=No Scholarship, 1=Scholarship')\n",
    "plt.ylabel('Percentage of no-showed Appointments')\n",
    "plt.title('No-show distribution of Scholarship vs. Non-Scholarship Receivers')\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion of question 3:\n",
    "It seems like that the poorer families (those who receive the scholarship), more often (23.73%) have no-shows at their medical appointments than the Non-Scholarship receivers (19.81%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "<li> We found that there is no particular difference between the no-show rates from males and females, both are around 20%.\n",
    "<li> We found that there is a difference between neighbourhoods and no-show rates. Neighbourhoods (where the sample is n>=100) and their no-showrates vary between 14.55% and 28.91%.\n",
    "<li> It seems like Scholarship reveivers (i.e. poorer families) have a higher no-show rate (23.73%) than non-Scholarship receivers (19.81%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}