Investigate_a_Dataset (No-show).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: No Show Appointment DATA\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "> **I would be investigating a dataset collected on medical appointments in Brazil.**.\n",
    "\n",
    "> **The column names of my dataset and their description are listed below:**\n",
    "\n",
    "    PatientID------- Patient's identity number\n",
    "\n",
    "    AppointmentID--- Patient's appointment number\n",
    "   \n",
    "    Gender---------- Patient's Gender (\"M\" for Male, \"F\" for Female)\n",
    "   \n",
    "    ScheduledDay---- The date and time patients booked their appointment\n",
    "   \n",
    "    AppointmentDay-- The date and time patients were to show up for their appointment\n",
    "   \n",
    "    Age------------- Patient's age\n",
    "   \n",
    "    Neighbourhood--- Location of the hospital\n",
    "    \n",
    "    Scholarship----- Whether or not a patient is enrolled in Brasilian welfare program (0= No, 1= Yes)\n",
    "   \n",
    "    Hipertension---- Whether or not the patient is hypertensive (0= No, 1=Yes)\n",
    "   \n",
    "    Diabetes-------- Whether or not the patient is diabetic (0= No, 1=Yes)\n",
    "   \n",
    "    Alcoholism------ Whether or not the patient consumes alcohol (0= No, 1=Yes)\n",
    "   \n",
    "    Handcap--------- Whether or not the patient is handicapped (0= No, 1=Yes)\n",
    "   \n",
    "    SMS_received---- Whether or not the patient recieved SMS notification (0= No, 1=Yes)\n",
    "   \n",
    "    No-show--------- Whether or not the patient showed up for their appointment (\"No\"= Showed Up, \"Yes\"= Did Not Show Up)\n",
    "       \n",
    "       \n",
    "### Question(s) for Analysis\n",
    "The following questions were addressed:\n",
    "1. What is the age distribution of patients?\n",
    "2. What is the distribution of patients by hospital location?\n",
    "3. What is the distribution of patients by the day they booked their appointment\n",
    "4. What is the distribution of patients by their appointment dates\n",
    "5. What is the distribution of patients that showed up and did not show up for appointments based on the following factors: age, whether or not they are diabetic, gender, whether or not they are enrolled in the welfare program, whether or not they are hypertensive, hospital location, whether or not they take alcohol, whether or not they recieved sms notifications\n",
    "6. Does waiting time between scheduling and appointment day predict whether a patient will come for appointment\n",
    "7. What day of the week has the highest and lowest appointment show ups and the highest and lowest failed appointment show ups\n",
    "8. Are there any patients who have both diabetes and hypertension? If yes, what is the age  distribution of patients that showed up and those that did not show up for their appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upgrade pandas to use dataframe.explode() function. \n",
    "#!pip install --upgrade pandas==0.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading csv file\n",
    "df=pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: Original dataset has 110527 rows, and 14 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting descriptive statistics of dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: 25% of patients are less than 18years, 50% are less than 37 years, 75% are less than 55 years, and the maximum age is 115. \n",
    "    Errors exist in some column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
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       "      <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",
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       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id  appointment_id gender         scheduled_day  \\\n",
       "0  2.987250e+13         5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14         5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12         5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11         5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12         5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "        appointment_day  age      neighbourhood  scholarship  hypertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handicap  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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fixing column name error\n",
    "df.columns=['patient_id', 'appointment_id', 'gender', 'scheduled_day', 'appointment_day', 'age', 'neighbourhood', 'scholarship', 'hypertension', 'diabetes', 'alcoholism', 'handicap','sms_received', 'no_show']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      "patient_id         110527 non-null float64\n",
      "appointment_id     110527 non-null int64\n",
      "gender             110527 non-null object\n",
      "scheduled_day      110527 non-null object\n",
      "appointment_day    110527 non-null object\n",
      "age                110527 non-null int64\n",
      "neighbourhood      110527 non-null object\n",
      "scholarship        110527 non-null int64\n",
      "hypertension       110527 non-null int64\n",
      "diabetes           110527 non-null int64\n",
      "alcoholism         110527 non-null int64\n",
      "handicap           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": [
    "#getting summary of the dataframe\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observations: patient_id data type should be int\n",
    "    scheduled_day and appointment_day data types should be datetime\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fixing data type of patient_id\n",
    "df['patient_id']=df['patient_id'].apply(np.int64)\n",
    "df['patient_id'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fixing data type of scheduled_day\n",
    "df['scheduled_day']=pd.to_datetime(df['scheduled_day']).dt.date.astype('datetime64[ns]')\n",
    "df['scheduled_day'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fixing data type of appointment_day\n",
    "df['appointment_day']=pd.to_datetime(df['appointment_day']).dt.date.astype('datetime64[ns]')\n",
    "df['appointment_day'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                  int64\n",
       "appointment_id              int64\n",
       "gender                     object\n",
       "scheduled_day      datetime64[ns]\n",
       "appointment_day    datetime64[ns]\n",
       "age                         int64\n",
       "neighbourhood              object\n",
       "scholarship                 int64\n",
       "hypertension                int64\n",
       "diabetes                    int64\n",
       "alcoholism                  int64\n",
       "handicap                    int64\n",
       "sms_received                int64\n",
       "no_show                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if all dtypes are correct\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: all data types are now correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: No duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id         0\n",
       "appointment_id     0\n",
       "gender             0\n",
       "scheduled_day      0\n",
       "appointment_day    0\n",
       "age                0\n",
       "neighbourhood      0\n",
       "scholarship        0\n",
       "hypertension       0\n",
       "diabetes           0\n",
       "alcoholism         0\n",
       "handicap           0\n",
       "sms_received       0\n",
       "no_show            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: No missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Investigating unique values and value counts of individual columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29872499824296, 558997776694438,   4262962299951, ...,\n",
       "        72633149253362, 996997666245785,  15576631729893])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "822145925426128    88\n",
       "99637671331        84\n",
       "26886125921145     70\n",
       "33534783483176     65\n",
       "6264198675331      62\n",
       "75797461494159     62\n",
       "258424392677       62\n",
       "871374938638855    62\n",
       "66844879846766     57\n",
       "872278549442       55\n",
       "89239687393655     54\n",
       "8435223536         51\n",
       "853439686798559    50\n",
       "14479974122994     46\n",
       "65433599726992     46\n",
       "81894521843749     42\n",
       "9452745294842      42\n",
       "188232341789524    40\n",
       "2271579924275      38\n",
       "9496196639835      38\n",
       "13364929297498     37\n",
       "1484143378533      35\n",
       "986162815579582    34\n",
       "88834999836575     34\n",
       "712458866975343    33\n",
       "6128878448536      30\n",
       "416755661551767    30\n",
       "81213966782532     29\n",
       "8634164126317      24\n",
       "36994987339512     23\n",
       "                   ..\n",
       "98683352133221      1\n",
       "5394313945329       1\n",
       "48689197872217      1\n",
       "9675119787546       1\n",
       "763619586595        1\n",
       "867726299814126     1\n",
       "77425139319169      1\n",
       "39423418767468      1\n",
       "961392519656997     1\n",
       "737858311826761     1\n",
       "271517596623238     1\n",
       "8249496395977       1\n",
       "137479426839        1\n",
       "6529316371746       1\n",
       "3212962263947       1\n",
       "198193457888        1\n",
       "1425822256863       1\n",
       "216133833234618     1\n",
       "37589497678822      1\n",
       "31638467315         1\n",
       "89279955685         1\n",
       "5883897911366       1\n",
       "56326578686847      1\n",
       "869587212288428     1\n",
       "735858598529        1\n",
       "2886912523138       1\n",
       "68129842443312      1\n",
       "99264711372         1\n",
       "2212945531847       1\n",
       "57863365759569      1\n",
       "Name: patient_id, Length: 62299, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of patient_id\n",
    "df['patient_id'].unique()\n",
    "df['patient_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: some patients booked multiple appointments over the period the data was collected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of gender\n",
    "df['gender'].unique()\n",
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: More females than males booked medical appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 62,  56,   8,  76,  23,  39,  21,  19,  30,  29,  22,  28,  54,\n",
       "        15,  50,  40,  46,   4,  13,  65,  45,  51,  32,  12,  61,  38,\n",
       "        79,  18,  63,  64,  85,  59,  55,  71,  49,  78,  31,  58,  27,\n",
       "         6,   2,  11,   7,   0,   3,   1,  69,  68,  60,  67,  36,  10,\n",
       "        35,  20,  26,  34,  33,  16,  42,   5,  47,  17,  41,  44,  37,\n",
       "        24,  66,  77,  81,  70,  53,  75,  73,  52,  74,  43,  89,  57,\n",
       "        14,   9,  48,  83,  72,  25,  80,  87,  88,  84,  82,  90,  94,\n",
       "        86,  91,  98,  92,  96,  93,  95,  97, 102, 115, 100,  99,  -1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       " 0      3539\n",
       " 1      2273\n",
       " 52     1746\n",
       " 49     1652\n",
       " 53     1651\n",
       " 56     1635\n",
       " 38     1629\n",
       " 59     1624\n",
       " 2      1618\n",
       " 50     1613\n",
       " 57     1603\n",
       " 36     1580\n",
       " 51     1567\n",
       " 19     1545\n",
       " 39     1536\n",
       " 37     1533\n",
       " 54     1530\n",
       " 34     1526\n",
       " 33     1524\n",
       " 30     1521\n",
       " 6      1521\n",
       " 3      1513\n",
       " 17     1509\n",
       " 32     1505\n",
       " 5      1489\n",
       " 44     1487\n",
       " 18     1487\n",
       " 58     1469\n",
       " 46     1460\n",
       " 45     1453\n",
       "        ... \n",
       " 74      602\n",
       " 76      571\n",
       " 75      544\n",
       " 78      541\n",
       " 77      527\n",
       " 80      511\n",
       " 81      434\n",
       " 82      392\n",
       " 79      390\n",
       " 84      311\n",
       " 83      280\n",
       " 85      275\n",
       " 86      260\n",
       " 87      184\n",
       " 89      173\n",
       " 88      126\n",
       " 90      109\n",
       " 92       86\n",
       " 91       66\n",
       " 93       53\n",
       " 94       33\n",
       " 95       24\n",
       " 96       17\n",
       " 97       11\n",
       " 98        6\n",
       " 115       5\n",
       " 100       4\n",
       " 102       2\n",
       " 99        1\n",
       "-1         1\n",
       "Name: age, Length: 104, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of age\n",
    "df['age'].unique()\n",
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: a -1 value has been reported for age. Having a -1 for age is not possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([99832], dtype='int64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identifying the row containing -1 as age\n",
    "wrong_age=df.query('age==-1').index\n",
    "wrong_age\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observation: The index (row) containing -1 as age is 99832"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping index 99832\n",
    "df=df.drop(labels=[99832], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    index successfully dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    99665\n",
       "1    10861\n",
       "Name: scholarship, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of scholarship\n",
    "df['scholarship'].unique()\n",
    "df['scholarship'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Most patients are not enrolled in the welfare program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    88725\n",
       "1    21801\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of hypertension\n",
    "df['hypertension'].unique()\n",
    "df['hypertension'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Majority of the records belong to patients who have not been diagnosed as hypertensive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    102583\n",
       "1      7943\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of diabetes\n",
    "df['diabetes'].unique()\n",
    "df['diabetes'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Majority of the records belong to patients who have not been diagnosed as diabetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    107166\n",
       "1      3360\n",
       "Name: alcoholism, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of alcoholism\n",
    "df['alcoholism'].unique()\n",
    "df['alcoholism'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the records belong to patients who do not consume alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: handicap, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of handicap\n",
    "df['handicap'].unique()\n",
    "df['handicap'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    The handicap column has 5 unique values (0, 1, 2, 3, 4). What each value denotes was not communicated in any of the resources given to complete this project, therefore, i would be dropping the handicap column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the handicap column\n",
    "df=df.drop(columns=['handicap'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    column successfully dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    75044\n",
       "1    35482\n",
       "Name: sms_received, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating unique values and counts of SMS_received\n",
    "df['sms_received'].unique()\n",
    "df['sms_received'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Majority of the records belong to patients who did not receive sms notification of their appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Showed Up          88207\n",
       "Did Not Show Up    22319\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reassigning the values of the no_show column to prevent confusion\n",
    "df['no_show']= df['no_show'].replace([\"No\"], \"Showed Up\")\n",
    "df['no_show']= df['no_show'].replace([\"Yes\"], \"Did Not Show Up\")\n",
    "#generating unique values and counts of no_show\n",
    "df['no_show'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   Majority of the records belong to patients who showed up for their appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a7906ff98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def histogram (x, xlabeli, ylabeli, titlei):\n",
    "    x.sort_values (ascending = True).plot(kind='hist');\n",
    "    plt.xlabel(xlabeli, size= 15)\n",
    "    plt.ylabel(ylabeli, size= 15)\n",
    "    plt.title(titlei, size= 20)\n",
    "    a=x.sort_values(ascending= True)\n",
    "    y=np.arange(0, 3000, 5000)\n",
    "    for aa,yy in zip(a,y):\n",
    "        plt.text(aa+1, yy,aa,va= 'center')\n",
    "histogram(df.age, 'age', 'frequency', 'age distribution of patients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a78fa4b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df.alcoholism, '0=no, 1=yes', 'frequency', 'distribution of patients by alcohol consumption')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a761d2198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df.hypertension, '0=no, 1=yes', 'frequency', 'distribution of patients by whether they are hypertensive or not')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a76190c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df.diabetes, '0=no, 1=yes', 'frequency', 'distribution of patients by whether they are diabetic or not')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a760a49e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df.scholarship, '0=no, 1=yes', 'frequency', 'distribution of patients by whether they are enrolled in the welfare program or not')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a75ff2240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def barchart (x, xlabeli, ylabeli, titlei):\n",
    "    x.value_counts().sort_values (ascending = True).plot(kind='bar',  figsize=(20,8));\n",
    "    plt.xlabel(xlabeli, size= 15)\n",
    "    plt.ylabel(ylabeli, size= 15)\n",
    "    plt.title(titlei, size= 20)\n",
    "    a=x.value_counts().sort_values(ascending= True)\n",
    "    y=np.arange(0, 3000, 5000)\n",
    "    for aa,yy in zip(a,y):\n",
    "        plt.text(aa+1, yy,aa,va= 'center')\n",
    "barchart(df.age, 'age', 'number of patients', 'age distribution of patients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a7604b860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barchart(df.neighbourhood, 'hospital location', 'number of patients', 'distribution of patients by hospital location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a75dab208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart showing the proportion of patients that showed up to those that did not show up\n",
    "df['no_show'].value_counts().plot(kind='pie', figsize=(6,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a75deb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart showing the proportion of males to females\n",
    "df['gender'].value_counts().plot(kind='pie', figsize=(6,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a75de52b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of patients by the day they booked their appointment\n",
    "plt.figure(figsize=(25,8))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=df['scheduled_day']);\n",
    "ax.set_title(\"Distribution of patients by the day they booked their appointment\");\n",
    "plt.xlabel('scheduling date');\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a75daeeb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of patients by their appointment dates\n",
    "plt.figure(figsize=(25,8))\n",
    "plt.xticks(rotation=80)\n",
    "ax = sns.countplot(x=df['appointment_day']);\n",
    "ax.set_title(\"Distribution of patients by their appointment dates\");\n",
    "plt.xlabel('appointment date');\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Additional Observations:\n",
    "    1. 3rd May, 2016 had the highest number of patients booking appointment\n",
    "    2. 6th June, 2016 was the highest booked date for appointment\n",
    "    3. Most of the patients had their hospital located in JARDIM CAMBURI and the least appointed hospital location was PARQUE INDUSTRIAL\n",
    "    4. Patients aged 0 had the highest number of appointments, while patients aged 99 had the lowest number of appointments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 1 (What is the distribution of patients that showed up and did not show up for appointments based on the following factors: age, whether or not they are diabetic, gender, whether or not they are enrolled in the welfare program, whether or not they are hypertensive, hospital location, whether or not they take alcohol, whether or not they recieved sms notifications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "show=df.no_show==\"Showed Up\"\n",
    "nshow=df.no_show==\"Did Not Show Up\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a761b9da0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a743ed0f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a7413f908>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a740b35c0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a740d37b8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a741f7390>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a76138f98>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a78f8d438>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a75f94240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouping = ['age', 'diabetes','gender','scholarship', 'hypertension', 'neighbourhood', 'alcoholism', 'sms_received']\n",
    "\n",
    "fig = plt.figure(figsize=(66, 8));\n",
    "for i, num in enumerate(grouping):\n",
    "    ax = fig.add_subplot(3, 3, i+1);\n",
    "    df.groupby([num, 'no_show'])[num].count().unstack('no_show').plot(ax=ax, kind='bar', stacked=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  (Does waiting time between scheduling and appointment day predict whether a patient will come for appointment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "5          2\n",
       "6          2\n",
       "7          2\n",
       "8          0\n",
       "9          2\n",
       "10         2\n",
       "11         3\n",
       "12         1\n",
       "13         1\n",
       "14         1\n",
       "15         3\n",
       "16         1\n",
       "17         1\n",
       "18         3\n",
       "19         0\n",
       "20         2\n",
       "21         2\n",
       "22         4\n",
       "23         1\n",
       "24         0\n",
       "25         3\n",
       "26         0\n",
       "27         2\n",
       "28         0\n",
       "29         0\n",
       "          ..\n",
       "110497     0\n",
       "110498     0\n",
       "110499     0\n",
       "110500     0\n",
       "110501     0\n",
       "110502     0\n",
       "110503     0\n",
       "110504     0\n",
       "110505     0\n",
       "110506     0\n",
       "110507     0\n",
       "110508     0\n",
       "110509     0\n",
       "110510     0\n",
       "110511     0\n",
       "110512     0\n",
       "110513     0\n",
       "110514     0\n",
       "110515     2\n",
       "110516     1\n",
       "110517     0\n",
       "110518    41\n",
       "110519    41\n",
       "110520    35\n",
       "110521    35\n",
       "110522    35\n",
       "110523    35\n",
       "110524    41\n",
       "110525    41\n",
       "110526    41\n",
       "Name: waiting_period, Length: 110526, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['waiting_period'] =  (df.appointment_day - df.scheduled_day).dt.days\n",
    "df['waiting_period']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a7427af28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.waiting_period[show].hist();\n",
    "plt.xlabel('Waiting Period in Days Between Scheduling and Appointment');\n",
    "plt.ylabel('Frequency');\n",
    "plt.title('Waiting Period of Patients Who Showed Up to Appointment');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of patients that showed up to their appointment reduced as waiting period increased. Therefore, a patient would most likely show up if the waiting period is short."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  (What day of the week has the highest and lowest appointment show ups and the highest and lowest failed appointment show ups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['appointment_dayofweek']=df['appointment_day'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a6facdf98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.appointment_dayofweek[show].hist();\n",
    "plt.xlabel('Day');\n",
    "plt.ylabel('Frequency');\n",
    "plt.title('Appointment Days of Patients Who Showed Up to their Appointment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest number of appointment showups was observed among patients that had their appointments on a Wednesday, the lowest number of showups was observed among patients that had their appointments on a Thursday. No appointments were scheduled for a Saturday or Sunday. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a6fad8518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.appointment_dayofweek[nshow].hist();\n",
    "plt.xlabel('Day');\n",
    "plt.ylabel('Frequency');\n",
    "plt.title('Appointment Days of Patients Who Did Not Show Up to their Appointment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest number of failed appointment showups was observed among patients that had their appointments on a Tuesday, the lowest number of failed showups was observed among patients that had their appointments on a Thursday. No appointments were scheduled for a Saturday or Sunday. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4  (Are they any patients who have both diabetes and hypertension? If yes, what is the age  distribution of patients that showed up and those that did not show up for appointment?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>waiting_period</th>\n",
       "      <th>appointment_dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Showed Up</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>72984587621439</td>\n",
       "      <td>5637975</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>63</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Showed Up</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1578131861739</td>\n",
       "      <td>5637986</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>64</td>\n",
       "      <td>TABUAZEIRO</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Showed Up</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>589458459955</td>\n",
       "      <td>5633116</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>39</td>\n",
       "      <td>MARUÍPE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Showed Up</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>45421316129453</td>\n",
       "      <td>5552934</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-06</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>68</td>\n",
       "      <td>REPÚBLICA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Showed Up</td>\n",
       "      <td>23</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        patient_id  appointment_id gender scheduled_day appointment_day  age  \\\n",
       "4    8841186448183         5642494      F    2016-04-29      2016-04-29   56   \n",
       "36  72984587621439         5637975      F    2016-04-29      2016-04-29   63   \n",
       "37   1578131861739         5637986      F    2016-04-29      2016-04-29   64   \n",
       "47    589458459955         5633116      F    2016-04-28      2016-04-29   39   \n",
       "73  45421316129453         5552934      F    2016-04-06      2016-04-29   68   \n",
       "\n",
       "      neighbourhood  scholarship  hypertension  diabetes  alcoholism  \\\n",
       "4   JARDIM DA PENHA            0             1         1           0   \n",
       "36    SÃO CRISTÓVÃO            0             1         1           0   \n",
       "37       TABUAZEIRO            1             1         1           0   \n",
       "47          MARUÍPE            0             1         1           0   \n",
       "73        REPÚBLICA            0             1         1           0   \n",
       "\n",
       "    sms_received    no_show  waiting_period appointment_dayofweek  \n",
       "4              0  Showed Up               0                Friday  \n",
       "36             0  Showed Up               0                Friday  \n",
       "37             0  Showed Up               0                Friday  \n",
       "47             0  Showed Up               1                Friday  \n",
       "73             1  Showed Up              23                Friday  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(5345, 15)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new=df.query('diabetes==1 and hypertension==1 and no_show==\"Showed Up\"')\n",
    "df_new.head()\n",
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a6f748668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age distribution of patients with diabetes and hypertension that showedup for appointment\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.xticks(rotation=75)\n",
    "ax = sns.countplot(x=df_new.age)\n",
    "ax.set_title(\"age distribution of patients with diabetes and hypertension that showed up\");\n",
    "plt.xlabel('age');\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>waiting_period</th>\n",
       "      <th>appointment_dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>274164858852</td>\n",
       "      <td>5635414</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>78</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Did Not Show Up</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>426698361512188</td>\n",
       "      <td>5642059</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>SANTOS DUMONT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Did Not Show Up</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>522886422</td>\n",
       "      <td>5633872</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>66</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Did Not Show Up</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>42726588167966</td>\n",
       "      <td>5634178</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>67</td>\n",
       "      <td>PRAIA DO CANTO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Did Not Show Up</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>3969538272968</td>\n",
       "      <td>5636130</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>69</td>\n",
       "      <td>CENTRO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Did Not Show Up</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id  appointment_id gender scheduled_day appointment_day  \\\n",
       "44      274164858852         5635414      F    2016-04-28      2016-04-29   \n",
       "212  426698361512188         5642059      M    2016-04-29      2016-04-29   \n",
       "463        522886422         5633872      F    2016-04-28      2016-04-29   \n",
       "619   42726588167966         5634178      F    2016-04-28      2016-04-29   \n",
       "729    3969538272968         5636130      F    2016-04-28      2016-04-29   \n",
       "\n",
       "     age   neighbourhood  scholarship  hypertension  diabetes  alcoholism  \\\n",
       "44    78   SÃO CRISTÓVÃO            0             1         1           0   \n",
       "212   62   SANTOS DUMONT            0             1         1           0   \n",
       "463   66   SÃO CRISTÓVÃO            0             1         1           0   \n",
       "619   67  PRAIA DO CANTO            0             1         1           0   \n",
       "729   69          CENTRO            0             1         1           0   \n",
       "\n",
       "     sms_received          no_show  waiting_period appointment_dayofweek  \n",
       "44              0  Did Not Show Up               1                Friday  \n",
       "212             0  Did Not Show Up               0                Friday  \n",
       "463             0  Did Not Show Up               1                Friday  \n",
       "619             0  Did Not Show Up               1                Friday  \n",
       "729             0  Did Not Show Up               1                Friday  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1141, 15)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nonew=df.query('diabetes==1 and hypertension== 1 and no_show==\"Did Not Show Up\"')\n",
    "df_nonew.head()\n",
    "df_nonew.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a6f4c9710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age distribution of patients with diabetes and hypertension that did not show up for appointment\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.xticks(rotation=75)\n",
    "ax = sns.countplot(x=df_nonew.age)\n",
    "ax.set_title(\"age distribution of patients with diabetes and hypertension that did not show up\");\n",
    "plt.xlabel('age');\n",
    "plt.ylabel('frequency ');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "I have successfully cleaned and analyzed the no show medical appointment data. \n",
    "\n",
    "My data wrangling and cleaning process began with fixing errors in column names, then I proceeded to change data types of patient_id and schedule and appointment days to int and datetime respectively.\n",
    "\n",
    "The original data did not have any duplicate rows or missing values. I observed that some patients booked multiple hospital appointment during the period the data was collected. I observed that an entry recorded a -1 for age, so I dropped the row using the drop() function. \n",
    "\n",
    "The handicap column had 5 unique values (0, 1, 2, 3, 4). What each value denotes was not communicated in any of the resources given to complete this project, therefore, i dropped the columns using the drop () function. \n",
    "\n",
    "During my analysis, I made the following observations:\n",
    "\n",
    "1. 25% of patients are less than 18 years, 50% are less than 37 years, 75% are less than 55 years, and the maximum age is 115. \n",
    "\n",
    "2. Toddlers showed up the most for their appointment, followed by middle-aged patients, then the number of show ups declined afterwards as age increased from 58 upwards.\n",
    "\n",
    "3. Number of patients that did not show up generally declined as age increased.\n",
    "\n",
    "4. Generally, number of medical appointments reduced as age increased (from 59 upwards), and younger people have higher number of medical appointments.\n",
    "\n",
    "5. Patients whose hospital is located in Jardim Camburi showed up the most to their appointment, followed by Patients whose hospital is located in Maria Ortiz. \n",
    "\n",
    "6. Number of patients that showed up to their appointment reduced as waiting period increased. Therefore, a patient would most likely show up if the waiting period is short.\n",
    "\n",
    "7. The highest number of appointment showups was observed among patients that had their appointments on a Wednesday, the lowest number of showups was observed among patients that had their appointments on a Thursday. No appointments were scheduled for a Saturday or Sunday. \n",
    "\n",
    "8. The highest number of failed appointment showups was observed among patients that had their appointments on a Tuesday, the lowest number of failed showups was observed among patients that had their appointments on a Thursday. No appointments were scheduled for a Saturday or Sunday. \n",
    "\n",
    "9. 3rd May, 2016 had the highest number of patients booking appointments\n",
    "\n",
    "10. 6th June, 2016 was the highest booked date for appointment\n",
    "\n",
    "11. Most of the patients had their hospital located in JARDIM CAMBURI and the least appointed hospital location was PARQUE INDUSTRIAL\n",
    "\n",
    "12. Patients aged 0 had the highest number of appointments, while patients aged 99 had the lowest number of appointments\n",
    "\n",
    "13. Appointment scheduling started 10th, November 2015 and ended 8thJune, 2016\n",
    "\n",
    "14. Medical appointment started on 29th April 2016, and ended 8th June 2016\n",
    "\n",
    "15. 5345 patients had both diabetes and hypertension, and showed up to their appointment\n",
    "\n",
    "16. 1141 patients had both diabetes and hypertension, but did not show up to their appointment\n",
    "\n",
    "17. There are more female than male patients\n",
    "\n",
    "18. Majority of the patients are not enrolled in the welfare program, and did not recieve sms notification\n",
    "\n",
    "19. Majority of the patients do not have diabetes and hypertension\n",
    "\n",
    "20. Majority of the patients donot consume alcohol\n",
    "\n",
    "21. Histogram charts of gender, diabetes, scholarship, hypertension, alcohol, sms is not sufficient to predict their influence on whether ot not patients showed up for their appointment \n",
    "\n",
    "\n",
    "LIMITATION:\n",
    "1. I could not analyze and make inferences on the handicap data because the meaning of each of its values were not clear.\n",
    "2. The fact that some patients had multiple appointments, while others had fewer or just 1 appointment made it difficult to draw analyze and draw inferences on the distribution of patients.\n",
    "\n",
    "References: https://www.shanelynn.ie/pandas-drop-delete-dataframe-rows-columns/\n",
    "https://stackoverflow.com/questions/36786722/how-to-display-full-output-in-jupyter-not-only-last-result\n",
    "## Submitting your Project \n",
    "\n",
    "> **Tip**: Before you submit your project, you need to create a .html or .pdf version of this notebook in the workspace here. To do that, run the code cell below. If it worked correctly, you should get a return code of 0, and you should see the generated .html file in the workspace directory (click on the orange Jupyter icon in the upper left).\n",
    "\n",
    "> **Tip**: Alternatively, you can download this report as .html via the **File** > **Download as** submenu, and then manually upload it into the workspace directory by clicking on the orange Jupyter icon in the upper left, then using the Upload button.\n",
    "\n",
    "> **Tip**: Once you've done this, you can submit your project by clicking on the \"Submit Project\" button in the lower right here. This will create and submit a zip file with this .ipynb doc and the .html or .pdf version you created. Congratulations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 49,
     "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"
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  "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
}