Investigate A Data Set.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Data Analysis into Medical Appointment No-Shows 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",
    "In this project, I will be analysing a dataset of medical appointments to determine whether certain factors may be influencing whether a patient shows up for their scheduled appointment. This dataset contains over 100,000 medical appointments in Brazil from May 2016 (source: Kaggle). Python with Pandas, Numpy and Matplotlib libraries will be used for the data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions we are interested in:\n",
    "\n",
    "- **Question 1:** Does age affect whether a person is likely to no show?\n",
    "- **Question 2:** Which neighbourhood had the highest number of no show appointments?\n",
    "- **Question 3:** Are patients more likely to attend an appointment if they receive a SMS?\n",
    "- **Question 4:** Are patients eligible for a scholarship more likely to attend their appointment?\n",
    "- **Question 5:** Are males or females more likely to miss their appointment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "In this section, I will be loading the data, checking for cleanliness, then trimming and cleaning the dataset so it is ready for analysis.\n",
    "\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been downloaded as a .csv file from Kaggle. It will be loaded then some general properties of the data will be inspected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "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",
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       "      <td>No</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": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('noshowappointments_may_2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above shows that we have 110,527 entries, 14 columns of data (one is the index).\n",
    "Of the 13 columns containing useful data, the data they contain are as following:\n",
    "\n",
    "- PatientId - Identification of a patient\n",
    "- AppointmentID - Identification of every appointment\n",
    "- Gender - Male or female\n",
    "- ScheduledDay - When the appointment to see a doctor is scheduled\n",
    "- AppointmentDay - The day the appointment to see a doctor was made\n",
    "- Age - How old the patient is\n",
    "- Neighbourhood - Where the appointment will take place\n",
    "- Scholarship - Whether the patient is eligible for financial aid through Bolsa Família\n",
    "- Hipertension - Whether the patient suffers from hypertension\n",
    "- Diabetes - Whether the patient suffers from diabetes\n",
    "- Alcoholism - Whether the patient suffers from alcoholism\n",
    "- Handcap - Whether the patient is handicapped\n",
    "- SMS_received - Has at least 1 message been sent to the patient\n",
    "- No-Show - Did the patient no-show? (No indicates the patient did attend their appointment. Yes indicates a no show)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also shows us that we do not have any missing data as there are 110,527 entries for every column listed. Next, I will be looking at some summary statistics for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary table above shows that the average patient's age is 37 years old with the youngest being listed as -1 years old (will have to investigate how many patients have been listed with a negative age) and the oldest being 115 years old. The majority of patients are between 18 and 55 years old.\n",
    "\n",
    "It also shows us that for columns scholarship, hipertension, diabetes, alcoholism and SMS_received, the min and max values are 0 and 1 suggesting the has been recorded on a true or false basis where 1 = True and 0 = False.\n",
    "\n",
    "Despite handcap being listed as a true or false on the Kaggle website, the min and max values are listed between 0 and 4 suggesting the values indicate the 5 varying degrees of handicap.\n",
    "\n",
    "Less than 10% of the patients are eligible for scholarship. \n",
    "\n",
    "32% of patients received at least one SMS about their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, by examining the number of unique values in our dataset, we can begin to plan how the data should be cleaned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId          62299\n",
       "AppointmentID     110527\n",
       "Gender                 2\n",
       "ScheduledDay      103549\n",
       "AppointmentDay        27\n",
       "Age                  104\n",
       "Neighbourhood         81\n",
       "Scholarship            2\n",
       "Hipertension           2\n",
       "Diabetes               2\n",
       "Alcoholism             2\n",
       "Handcap                5\n",
       "SMS_received           2\n",
       "No-show                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that some patients have attended multiple appointments. AppointmentDay only has 27 values whilst ScheduledDay has 103,549. This suggests that when recording data about when an appointment was made, the time of the booking was not recorded whilst the time of the appointment was. \n",
    "\n",
    "Data has been collected from a total of 81 neighbourhoods.\n",
    "\n",
    "Scholarship, hipertension, diabetes, alcoholism, SMS_received and no-show all have just two values, this therefore corresponds with the table above that shows they can only contain the values 0 or 1.\n",
    "\n",
    "Handcap has 5 unique values. As the datatype for Handcap is int and the min and max values were 0 to 4. This indicates that Handcap is measured on a scale from 0-4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset now needs to be cleaned so that we can extract meaningful data from it to perform the data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping columns that are not useful**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will first be dropping the columns that are not useful for this investigation. PatientId and appointmentID are not useful as we are looking at the factors affecting no-shows rather than the characteristics of specific patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<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",
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       "    </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",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender          ScheduledDay        AppointmentDay  Age      Neighbourhood  \\\n",
       "0      F  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z   62    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z   62      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "0            0             1         0           0        0             0   \n",
       "1            0             0         0           0        0             0   \n",
       "2            0             0         0           0        0             0   \n",
       "3            0             0         0           0        0             0   \n",
       "4            0             1         1           0        0             0   \n",
       "\n",
       "  No-show  \n",
       "0      No  \n",
       "1      No  \n",
       "2      No  \n",
       "3      No  \n",
       "4      No  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PatientId','AppointmentID'], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   Gender          110527 non-null  object\n",
      " 1   ScheduledDay    110527 non-null  object\n",
      " 2   AppointmentDay  110527 non-null  object\n",
      " 3   Age             110527 non-null  int64 \n",
      " 4   Neighbourhood   110527 non-null  object\n",
      " 5   Scholarship     110527 non-null  int64 \n",
      " 6   Hipertension    110527 non-null  int64 \n",
      " 7   Diabetes        110527 non-null  int64 \n",
      " 8   Alcoholism      110527 non-null  int64 \n",
      " 9   Handcap         110527 non-null  int64 \n",
      " 10  SMS_received    110527 non-null  int64 \n",
      " 11  No-show         110527 non-null  object\n",
      "dtypes: int64(7), object(5)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Renaming column names**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns will be renamed to make it easier. They will all be converted to lowercase and underscores instead of hyphens. I will also be renaming the hipertension and handcap columns to hypertension and handicap respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to lowercase, replace hyphens with underscores\n",
    "df.rename(columns = lambda x : x.strip().lower().replace(\"-\",\"_\"),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>F</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "    <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename columns\n",
    "df = df.rename(columns={'hipertension':'hypertension','handcap':'handicap'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <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  hypertension  diabetes  alcoholism  handicap  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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting scheduledday and appointmentday datatypes from string to datetime**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scheduledday and appointmentday currently both have the datatype string. These need to be converted to date time format instead to be more useful incase we want to perform analysis based on date and time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08</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>2016-04-29 16:08:27</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>2016-04-29 16:19:04</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>2016-04-29 17:29:31</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>2016-04-29 16:07:23</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 2016-04-29 18:38:08  2016-04-29T00:00:00Z   62    JARDIM DA PENHA   \n",
       "1      M 2016-04-29 16:08:27  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "2      F 2016-04-29 16:19:04  2016-04-29T00:00:00Z   62      MATA DA PRAIA   \n",
       "3      F 2016-04-29 17:29:31  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI   \n",
       "4      F 2016-04-29 16:07:23  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "\n",
       "   scholarship  hypertension  diabetes  alcoholism  handicap  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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scheduledday = df['scheduledday'].astype('datetime64[ns]')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "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",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   gender          110527 non-null  object        \n",
      " 1   scheduledday    110527 non-null  datetime64[ns]\n",
      " 2   appointmentday  110527 non-null  object        \n",
      " 3   age             110527 non-null  int64         \n",
      " 4   neighbourhood   110527 non-null  object        \n",
      " 5   scholarship     110527 non-null  int64         \n",
      " 6   hypertension    110527 non-null  int64         \n",
      " 7   diabetes        110527 non-null  int64         \n",
      " 8   alcoholism      110527 non-null  int64         \n",
      " 9   handicap        110527 non-null  int64         \n",
      " 10  sms_received    110527 non-null  int64         \n",
      " 11  no_show         110527 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(7), object(4)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert appointmentday to datetime datatype\n",
    "df.appointmentday = df['appointmentday'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender        scheduledday appointmentday  age      neighbourhood  \\\n",
       "0      F 2016-04-29 18:38:08     2016-04-29   62    JARDIM DA PENHA   \n",
       "1      M 2016-04-29 16:08:27     2016-04-29   56    JARDIM DA PENHA   \n",
       "2      F 2016-04-29 16:19:04     2016-04-29   62      MATA DA PRAIA   \n",
       "3      F 2016-04-29 17:29:31     2016-04-29    8  PONTAL DE CAMBURI   \n",
       "4      F 2016-04-29 16:07:23     2016-04-29   56    JARDIM DA PENHA   \n",
       "\n",
       "   scholarship  hypertension  diabetes  alcoholism  handicap  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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   gender          110527 non-null  object        \n",
      " 1   scheduledday    110527 non-null  datetime64[ns]\n",
      " 2   appointmentday  110527 non-null  datetime64[ns]\n",
      " 3   age             110527 non-null  int64         \n",
      " 4   neighbourhood   110527 non-null  object        \n",
      " 5   scholarship     110527 non-null  int64         \n",
      " 6   hypertension    110527 non-null  int64         \n",
      " 7   diabetes        110527 non-null  int64         \n",
      " 8   alcoholism      110527 non-null  int64         \n",
      " 9   handicap        110527 non-null  int64         \n",
      " 10  sms_received    110527 non-null  int64         \n",
      " 11  no_show         110527 non-null  object        \n",
      "dtypes: datetime64[ns](2), int64(7), object(3)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Handling negative age values**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen in the summary statistics before, the minimum age in the dataset was -1 years old. As it is impossible to have a negative age, I will be examining further what values have been entered into the age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0      3539\n",
       " 1      2273\n",
       " 52     1746\n",
       " 49     1652\n",
       " 53     1651\n",
       "        ... \n",
       " 115       5\n",
       " 100       4\n",
       " 102       2\n",
       " 99        1\n",
       "-1         1\n",
       "Name: age, Length: 104, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that there is just one entry of a negative age. This data will therefore be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop negative ages\n",
    "neg_age = df[df.age<0]\n",
    "df.drop(neg_age.index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3539\n",
       "1      2273\n",
       "52     1746\n",
       "49     1652\n",
       "53     1651\n",
       "       ... \n",
       "98        6\n",
       "115       5\n",
       "100       4\n",
       "102       2\n",
       "99        1\n",
       "Name: age, Length: 103, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This check confirms that the negative age has been removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Neighbourhood check**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The neighbourhood column lists all the different locations that the appointment can take place. Below it shows that there were 81 locations and the locations are also listed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JARDIM CAMBURI                 7717\n",
       "MARIA ORTIZ                    5805\n",
       "RESISTÊNCIA                    4431\n",
       "JARDIM DA PENHA                3877\n",
       "ITARARÉ                        3514\n",
       "                               ... \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, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.neighbourhood.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['JARDIM DA PENHA', 'MATA DA PRAIA', 'PONTAL DE CAMBURI',\n",
       "       'REPÚBLICA', 'GOIABEIRAS', 'ANDORINHAS', 'CONQUISTA',\n",
       "       'NOVA PALESTINA', 'DA PENHA', 'TABUAZEIRO', 'BENTO FERREIRA',\n",
       "       'SÃO PEDRO', 'SANTA MARTHA', 'SÃO CRISTÓVÃO', 'MARUÍPE',\n",
       "       'GRANDE VITÓRIA', 'SÃO BENEDITO', 'ILHA DAS CAIEIRAS',\n",
       "       'SANTO ANDRÉ', 'SOLON BORGES', 'BONFIM', 'JARDIM CAMBURI',\n",
       "       'MARIA ORTIZ', 'JABOUR', 'ANTÔNIO HONÓRIO', 'RESISTÊNCIA',\n",
       "       'ILHA DE SANTA MARIA', 'JUCUTUQUARA', 'MONTE BELO',\n",
       "       'MÁRIO CYPRESTE', 'SANTO ANTÔNIO', 'BELA VISTA', 'PRAIA DO SUÁ',\n",
       "       'SANTA HELENA', 'ITARARÉ', 'INHANGUETÁ', 'UNIVERSITÁRIO',\n",
       "       'SÃO JOSÉ', 'REDENÇÃO', 'SANTA CLARA', 'CENTRO', 'PARQUE MOSCOSO',\n",
       "       'DO MOSCOSO', 'SANTOS DUMONT', 'CARATOÍRA', 'ARIOVALDO FAVALESSA',\n",
       "       'ILHA DO FRADE', 'GURIGICA', 'JOANA D´ARC', 'CONSOLAÇÃO',\n",
       "       'PRAIA DO CANTO', 'BOA VISTA', 'MORADA DE CAMBURI', 'SANTA LUÍZA',\n",
       "       'SANTA LÚCIA', 'BARRO VERMELHO', 'ESTRELINHA', 'FORTE SÃO JOÃO',\n",
       "       'FONTE GRANDE', 'ENSEADA DO SUÁ', 'SANTOS REIS', 'PIEDADE',\n",
       "       'JESUS DE NAZARETH', 'SANTA TEREZA', 'CRUZAMENTO',\n",
       "       'ILHA DO PRÍNCIPE', 'ROMÃO', 'COMDUSA', 'SANTA CECÍLIA',\n",
       "       'VILA RUBIM', 'DE LOURDES', 'DO QUADRO', 'DO CABRAL', 'HORTO',\n",
       "       'SEGURANÇA DO LAR', 'ILHA DO BOI', 'FRADINHOS', 'NAZARETH',\n",
       "       'AEROPORTO', 'ILHAS OCEÂNICAS DE TRINDADE', 'PARQUE INDUSTRIAL'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.neighbourhood.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**True or false columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next set of columns list values as 0 or 1 with 0 indicating false and 1 indicating true. I will be examing each of these columns to ensure that they only contain the value 0 or 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99665\n",
       "1    10861\n",
       "Name: scholarship, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88725\n",
       "1    21801\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hypertension.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    102583\n",
       "1      7943\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diabetes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    107166\n",
       "1      3360\n",
       "Name: alcoholism, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.alcoholism.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: handicap, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.handicap.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the source website, Kaggle, handicap is listed as true or false despite having 5 different values. As such, any value that is not 0 will be converted to 1 indicated the patient is handicap as we are not told how the numbers differ from each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting handicap values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2241\n",
       "Name: handicap, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert handicap values to 1 = yes, 0 = no\n",
    "df.loc[df.handicap > 0, 'handicap'] = 1\n",
    "df.handicap.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    75044\n",
       "1    35482\n",
       "Name: sms_received, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sms_received.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column for no_show is currently being recorded as Yes or No. It would be easier to store this data as a boolean just like the other columns for consistency. I will therefore be converting it to 0 and 1. As 'No' means they did show and 'Yes' means they didn't show, I will be making No = 0 and Yes = 1 so that a 1 in the no_show column indicates that the patient did not show up to their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting no_show values to 0 and 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender        scheduledday appointmentday  age      neighbourhood  \\\n",
       "0      F 2016-04-29 18:38:08     2016-04-29   62    JARDIM DA PENHA   \n",
       "1      M 2016-04-29 16:08:27     2016-04-29   56    JARDIM DA PENHA   \n",
       "2      F 2016-04-29 16:19:04     2016-04-29   62      MATA DA PRAIA   \n",
       "3      F 2016-04-29 17:29:31     2016-04-29    8  PONTAL DE CAMBURI   \n",
       "4      F 2016-04-29 16:07:23     2016-04-29   56    JARDIM DA PENHA   \n",
       "\n",
       "   scholarship  hypertension  diabetes  alcoholism  handicap  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        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert no_show values to 0=No and 1=Yes\n",
    "df['no_show'] = df['no_show'].replace({'No':0,'Yes':1})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.089219</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197248</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.020276</td>\n",
       "      <td>0.321029</td>\n",
       "      <td>0.201934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.110026</td>\n",
       "      <td>0.297676</td>\n",
       "      <td>0.397923</td>\n",
       "      <td>0.258266</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.140943</td>\n",
       "      <td>0.466874</td>\n",
       "      <td>0.401445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age    scholarship   hypertension       diabetes  \\\n",
       "count  110526.000000  110526.000000  110526.000000  110526.000000   \n",
       "mean       37.089219       0.098266       0.197248       0.071865   \n",
       "std        23.110026       0.297676       0.397923       0.258266   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        18.000000       0.000000       0.000000       0.000000   \n",
       "50%        37.000000       0.000000       0.000000       0.000000   \n",
       "75%        55.000000       0.000000       0.000000       0.000000   \n",
       "max       115.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "          alcoholism       handicap   sms_received        no_show  \n",
       "count  110526.000000  110526.000000  110526.000000  110526.000000  \n",
       "mean        0.030400       0.020276       0.321029       0.201934  \n",
       "std         0.171686       0.140943       0.466874       0.401445  \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       1.000000       0.000000  \n",
       "max         1.000000       1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can identify out of 110,527 how many were no show appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88207\n",
       "1    22319\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.no_show.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.193438647919944"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.no_show.mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Patients attended 88,207 appointments but 22,319 were no shows. Therefore, 20% of appointments result in no shows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Duplicate entries**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the values in the dataset have been checked, the dataset should be checked for duplicate entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "635"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 635 duplucate entries, these will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 109891 entries, 0 to 110526\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   gender          109891 non-null  object        \n",
      " 1   scheduledday    109891 non-null  datetime64[ns]\n",
      " 2   appointmentday  109891 non-null  datetime64[ns]\n",
      " 3   age             109891 non-null  int64         \n",
      " 4   neighbourhood   109891 non-null  object        \n",
      " 5   scholarship     109891 non-null  int64         \n",
      " 6   hypertension    109891 non-null  int64         \n",
      " 7   diabetes        109891 non-null  int64         \n",
      " 8   alcoholism      109891 non-null  int64         \n",
      " 9   handicap        109891 non-null  int64         \n",
      " 10  sms_received    109891 non-null  int64         \n",
      " 11  no_show         109891 non-null  int64         \n",
      "dtypes: datetime64[ns](2), int64(8), object(2)\n",
      "memory usage: 10.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that the duplicated rows have now been dropped as there were 110,526 entries and now there are 109,891."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, I am creating a histogram of the entire dataframe. This allows us to quickly visualise our data before a more detailed analysis is performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(12,12));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The above histograms initially show us that:\n",
    ">- The majority of patients are below 60 years old.\n",
    ">- The majority of patients are not eligible for a scholarship.\n",
    ">- The majority of patients do not suffer from hypertension, diabetes, alcoholism or handicap.\n",
    ">- The majority of patients have not received a SMS.\n",
    ">- The majority of patients do attend their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 1: Does age affect whether a person is likely to no show?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a mask of the people that did show to their appointment and those who didn't.\n",
    "show = df.no_show == 0\n",
    "no_show = df.no_show == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I will see the average age of patient who didn't attend their appointment vs the ones who do attend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.28391709656982"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age[no_show].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.796122697709386"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age[show].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean age of patients that no_show are 34 years old whilst the mean age of those who show is 37 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Graph to show the number of no show and show appointments by age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bars\n",
    "df.age[no_show].hist(alpha = 0.5, bins=20,color = 'green', label = 'no show', figsize=(6,6))\n",
    "df.age[show].hist(alpha = 0.3 , bins=20, label = 'show')\n",
    "\n",
    "# Labels and titles\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.title('Number of no show vs show appointments by age')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The graph shows us that there are a lot of younger patients as the graph is skewed to the right. For the patients aged around 0-5, 9,500 appointments were attended whilst 2,200 were missed. This means around 19% of appointments were no shows which is slightly under the mean which is 21%.\n",
    "It also shows that patients aged around 20 were more likely to no show. This is beacuse 1750 were no shows and 5,500 were shows. This results in over 24% of appointments being no shows. \n",
    "When looking at the older patients, for example those around 80, 500 were no shows whilst 2,500 were shows. This means that only 16% of appointments were no shows for those around 80 years old. \n",
    "Therefore, this graph suggests that age is a factor that influences whether an appointment is missed. Teenagers are more likely to miss their appointment whilst the older patients are more likely to attend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2: Which neighbourhood had the highest number of no show appointments?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Identifying which appointments were show and which were no show**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "AEROPORTO                 8\n",
       "ANDORINHAS             2194\n",
       "ANTÔNIO HONÓRIO         271\n",
       "ARIOVALDO FAVALESSA     280\n",
       "BARRO VERMELHO          422\n",
       "                       ... \n",
       "SÃO JOSÉ               1963\n",
       "SÃO PEDRO              2431\n",
       "TABUAZEIRO             3123\n",
       "UNIVERSITÁRIO           152\n",
       "VILA RUBIM              851\n",
       "Name: no_show, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = df.groupby('neighbourhood')['no_show'].count()\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are 81 neighbourhoods listed, it would be much easier to compare the neighbourhoods if the values were sorted in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "JARDIM CAMBURI                 1429\n",
       "MARIA ORTIZ                    1219\n",
       "ITARARÉ                         902\n",
       "RESISTÊNCIA                     885\n",
       "JESUS DE NAZARETH               695\n",
       "                               ... \n",
       "PONTAL DE CAMBURI                12\n",
       "ILHA DO BOI                       3\n",
       "ILHAS OCEÂNICAS DE TRINDADE       2\n",
       "ILHA DO FRADE                     2\n",
       "AEROPORTO                         1\n",
       "Name: no_show, Length: 80, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_count = df[no_show].groupby('neighbourhood')['no_show'].count()\n",
    "no_show_count.sort_values(ascending=False, inplace=True)\n",
    "no_show_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_show_count.plot(kind='bar', figsize=(20,8))\n",
    "plt.xlabel('Neighbourhood')\n",
    "plt.ylabel('Number of no show appointments')\n",
    "plt.title('Number of no show appointments by neighbourhood')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar chart shows us that the neighbourhood with the most no shows is Jardim Camburi whilst the one with the least no shows is Aeroporto. This however does not show us how many total appointments were made in each neighbourhood. It could be that the neighbourhoods with the lowest number of no shows are actually due to fewer appointments being made. It would therefore be more useful to find out the proportion of appointments that resulted in no shows. This would allow us to compare neighbourhoods with different numbers of appointments more easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Proportion of no show appointments by neighbourhood**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The proportion is calculated by dividing the number of no show appointments by the total number of appointments in each neighbourhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "AEROPORTO              0.125000\n",
       "ANDORINHAS             0.226527\n",
       "ANTÔNIO HONÓRIO        0.184502\n",
       "ARIOVALDO FAVALESSA    0.221429\n",
       "BARRO VERMELHO         0.215640\n",
       "                         ...   \n",
       "SÃO JOSÉ               0.217524\n",
       "SÃO PEDRO              0.209379\n",
       "TABUAZEIRO             0.181876\n",
       "UNIVERSITÁRIO          0.210526\n",
       "VILA RUBIM             0.165687\n",
       "Name: no_show, Length: 81, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_proportions = no_show_count / total\n",
    "no_show_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "ILHAS OCEÂNICAS DE TRINDADE    1.000000\n",
       "SANTOS DUMONT                  0.287736\n",
       "SANTA CECÍLIA                  0.274554\n",
       "SANTA CLARA                    0.263473\n",
       "ITARARÉ                        0.259942\n",
       "                                 ...   \n",
       "SOLON BORGES                   0.147122\n",
       "MÁRIO CYPRESTE                 0.141689\n",
       "AEROPORTO                      0.125000\n",
       "ILHA DO BOI                    0.085714\n",
       "PARQUE INDUSTRIAL                   NaN\n",
       "Name: no_show, Length: 81, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_proportions.sort_values(ascending=False, inplace=True)\n",
    "no_show_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot proportion of no show appointments by neighbourhood\n",
    "no_show_proportions.plot(kind='bar', figsize=(20,6))\n",
    "\n",
    "# labels\n",
    "plt.xlabel('Neighbourhood')\n",
    "plt.ylabel('Proportion of no show appointments')\n",
    "plt.title('Proportion of no show appointments by neighbourhood')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.751851341797483"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_show_proportions.mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean no show proportion is 21%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart above shows that Ilhas Oceânicas de Trindade had 100% no show appointments. This appears to be an outlier as well as Parque Industrial that has no no show appointments. The majority of neighbourhoods had around 20% no show appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Identifying the number of appointments made in outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48754</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-14 12:25:43</td>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>51</td>\n",
       "      <td>ILHAS OCEÂNICAS DE TRINDADE</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48765</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-14 12:26:13</td>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>58</td>\n",
       "      <td>ILHAS OCEÂNICAS DE TRINDADE</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender        scheduledday appointmentday  age  \\\n",
       "48754      F 2016-04-14 12:25:43     2016-05-13   51   \n",
       "48765      F 2016-04-14 12:26:13     2016-05-13   58   \n",
       "\n",
       "                     neighbourhood  scholarship  hypertension  diabetes  \\\n",
       "48754  ILHAS OCEÂNICAS DE TRINDADE            0             0         0   \n",
       "48765  ILHAS OCEÂNICAS DE TRINDADE            0             0         0   \n",
       "\n",
       "       alcoholism  handicap  sms_received  no_show  \n",
       "48754           0         0             0        1  \n",
       "48765           0         0             0        1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('neighbourhood == \"ILHAS OCEÂNICAS DE TRINDADE\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This query shows us that there were only two appointments at Ilhas Oceânicas de Trindade and both resulted in no show appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75199</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05 10:48:59</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>17</td>\n",
       "      <td>PARQUE INDUSTRIAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender        scheduledday appointmentday  age      neighbourhood  \\\n",
       "75199      F 2016-05-05 10:48:59     2016-05-05   17  PARQUE INDUSTRIAL   \n",
       "\n",
       "       scholarship  hypertension  diabetes  alcoholism  handicap  \\\n",
       "75199            0             0         0           0         0   \n",
       "\n",
       "       sms_received  no_show  \n",
       "75199             0        0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('neighbourhood == \"PARQUE INDUSTRIAL\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This query shows us that there was only one appointment at Parque Industrial and this was not a no show appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> When comparing the number of no show appointments in each neighbourhood, Jardim Camburi had the most. However, as not all neighbourhoods had the same number of appointments, the proportion of no show appointments were compared for each neighbourhood. The mean proportion of no shows was 21% and the majority of the neighbourhoods were around the 15-25% region. There were some outliers however. All Ilhas Oceânicas de Trindade appointments were no shows and all Parque Industrial were shows. When examined more carefully, it was found that this was due to the very small sample size of 2 and 1 respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3: Are patients more likely to attend an appointment if they receive a SMS?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    74409\n",
       "1    35482\n",
       "Name: sms_received, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sms_received.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.288358464296444"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sms_received.mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Above shows the total number of patients not receiving a SMS and patients receiving a SMS. Also only 32% of patients receive a SMS before their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating show and no show appointments for sms received vs not received**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "sms_yes = df.query('sms_received ==\"1\"')\n",
    "sms_no = df.query('sms_received ==\"0\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sms_received  no_show\n",
       "1             0          25698\n",
       "              1           9784\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms_yes_count = sms_yes.groupby('sms_received').no_show.value_counts()\n",
    "sms_yes_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sms_received  no_show\n",
       "0             0          62095\n",
       "              1          12314\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms_no_count = sms_no.groupby('sms_received').no_show.value_counts()\n",
    "sms_no_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = np.arange(len(sms_yes_count))\n",
    "width = 0.35 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bars\n",
    "sms_yes_bars = plt.bar(ind, sms_yes_count, width, color='teal', alpha=.7, label='sms received')\n",
    "sms_no_bars = plt.bar(ind + width, sms_no_count, width, color='crimson', alpha=.7, label='no sms received')\n",
    "\n",
    "\n",
    "#title and labels\n",
    "plt.ylabel('Number of appointments')\n",
    "plt.xlabel('Appointment status')\n",
    "plt.title('Number of no show vs show appointments by sms received')\n",
    "locations = ind + width/2 \n",
    "labels = ['show','no show']\n",
    "plt.xticks(locations,labels)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows us that a significant number of patients show up to their appointment despite not receiving a SMS. However, as only 32% of patients received a sms,  I will be looking at the proportion of appointments attended by patients instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sms_received\n",
       "0    74409\n",
       "1    35482\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms_total = df.groupby('sms_received')['no_show'].count()\n",
    "sms_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sms_yes_total = sms_yes_count / sms_total\n",
    "sms_no_total = sms_no_count / sms_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sms_received  no_show\n",
       "1             0          0.724255\n",
       "              1          0.275745\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms_yes_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sms_received  no_show\n",
       "0             0          0.834509\n",
       "              1          0.165491\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms_no_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cua9w/vkTufvu3zB//ruce+63OOigQ5gx436uuWYqa69dx/33r36nzXfffYebbrqdQYNqOf/8b3PIIV9m5MgdefrpvzBlyjUcfviRPPXUbKZOvZmGhgamTLmG1177BzNnPsTkyTdQUVHBhAkns8MOIz4qc//9x/LAA/exzTbbcf/997H//gfx+9//hkGDavn2t89n0aKFnHLK8dx++y+ZPPkqrr/+FgYOHMS3vpX4zq6pUPIXkW5nzJgDueOOWzjzzNPo338AJ510KgAbbfQZqqqqqKmpYf31N6C6upqamoGsWBFuvXvBBRdz3XXX8P777zNixBdWK3fQoFoGDaoF4LXX5nHbbT/njjtuAaCqqoo33nidzTffkkwmQyaTYcKEs5g58yHeeedtTj/9JACWLFnCv//974/K3GGHkUye/FMWL17Eiy8+x4QJZ3HllZfz4ovP8fLLLwHhorn//e99+vfv/9H6t9pq61S2XVJK/iLS7Tz22KMMG7YNRx99PA89NJ3bb7+Zvfbaj7bOaFyxYgUPPzyTCy64hObmZr761cPYY4+9GDJk5YVPlZUrz27/1Kc25PDDj+Sznx3G66//i+eee4ahQzfkN7/5NU1NTTQ1NXHWWd/glFMmsOGGGzFp0lVUVFRw9913sNFGG/Pww3/8qMxdd92DH//4R+y00y5kMhmGDt2QddZZh6997Wjq65dzyy03UVMzkKVLl7FgwQIGDx7M3Lkvs84666a2DYtR8heRotaozHDdAavde/5jlUcbZ45uttkWfP/73yWTyVBZWcmECWeyePGSNsvs06cPAwcO5KijxlFTU8P2249g3XWHtDr/KaeczqRJP2LFihXU1y/n9NPPYpNNjB12GMlJJx1DU1MTBx10CJtssinDh2/PyScfw4oVDWy++ZbU1a06RP5++x3AYYcdyF13TQPgwAO/xKWXXsSppx7PsmVLOeigQ6murubcc8/nzDNPpaZmEFVVXZt+U7mHr5lVApOBYUA9cKy7z8ubfgRwJuHjv8ndf9ZWeQ0N2WaN5d1+tSs+5K0Jl3Z1GF1i3UnfYsma/bs6jB7r7bdfZ8iQoV0dxkc0vENxhT6zurqaZ4DhheZP6wrfsUBfdx8JTAQmtZj+Y2APYEfgTDMbnFIcIiJSQFr7HaOA6QDu/qSZtfzleREYBDQCFUCbux+ZTAW1tf3SiLN3m7+cqqpMV0fRJTKZStWZj+GddyrIZLrX6C/dLZ7upqKifXkyreQ/EFiU9zxrZlXu3hifvwQ8AywD7nX3hW0Vpts4lqa2uZnGxo67JL8nyWabWKI6U7Lm5uZu1c2ibp/imptXz5N1dTWtzp/WT+liIH+tlbnEb2ZbA/sBnwY2BNYxs0NTikNERApIK/k/DuwLYGYjgDl50xYBHwIfunsWeBdQn7+ISCdKq9tnGjDazGYT+vTHm9k4YIC7TzWz64DHzGwF8A/g5pTiEJEO0L+pgcr6jrurl8Ze6nqpJH93bwJObPHy3LzpU4ApaaxbRDpeZf2KDh0nqpzHXuouo4HqIi8RkU7UXUYDVfIXkW7n/vt/zxNPPE59/XL+859/89WvHsXee4/h1VfncuWVl5PJZOjTpw9nn30eQ4asvIq35Wig5513IW+++YZGAy1AyV9EuqVly5ZyxRXX8Oabb3DOOWew995juPTSi5k48Tw22cSYNesRrrnmCi666LKPltFooMkp+YtIt7TxxpsCsM466340aud7781nk00MgGHDtmXKlGtWWablaKAnnHAKoNFAC1HyF5FuqdBNyddeu4558/7OxhtvwvPPP8snP/mpVaa3HA30jjtuYe+9NRpoIUr+IlJU0xp9whk6HVheKc455ztceeVlNDc3k8lkmDjxu6tMbzka6GmnncGyZUvbLLNcRwNNZVTPjqZRPUujUT01qmepNKpnz9NdRvUUEZFuTMlfRKQMKfmLSEE9oUtYglI+KyV/EVlNVVUfli1brB+AHqC5uZllyxZTVdW+g+g620dEVjN4cB0LFsxn6dKFXR0KEE771A9R66qq+jB4cF3xGfOXSSkWEenBMpkq1l57veIzdpLa2n66oVMH6/XJv7G6mfqm8rybVU3bd8cUkTLW65N/fVOWE353X1eH0SWm7TG6q0MQkW5KB3xFRMpQKi1/M6sEJgPDgHrgWHefF6cNAe7Km/1zwMR4gxcREekEaXX7jAX6uvvIeA/fScCBAO7+NrALgJmNBC4Grk8pDhERKSCtbp9RwHQAd3+SAmNLmFkFcDVwUryRu4iIdJK0Wv4DgUV5z7NmVuXujXmv7Q/8zd29WGGZTAW1tf1KCmT5B0upqs6UtGxvUFVVnu89k6ksuc5I96PPs+OllfwXAzV5zytbJH6AI4GfJiksm20u+RzfbKaJxoby3bFobCzP957NNrFE54X3GjrPvzR1dTWtTkur2+dxYF+A2Oc/p8A82wGzU1q/iIi0Ia2W/zRgtJnNBiqA8WY2Dhjg7lPNrA5Y4u66CklEpAukkvzdvQk4scXLc/Omzyec4ikiIl1AF3mJiJShoi1/M+sPDAYagOOBW9399bQDExGR9CRp+d9BODh7OeEHYGqqEYmISOqSJP/BwO+ADdz9R8Aa6YYkIiJpS5L8+wBnAs+a2RbAgHRDEhGRtCVJ/mcC6xDG4NkVOCnViEREJHVJTvXczd3Pjo+vNbMfAn9NMSYREUlZq8nfzI4BjgU2N7N948uVhG6gb3dCbCIikpK2Wv63AzOBcwldPgBNwLtpByUiIulqtc/f3evd/V+EK3XXBYYCnwZ26JzQREQkLUn6/O8hHPB9Mz5vBv6cWkQiIpK6JMl/iLt/IfVIRESk0yQ51XOuma2feiQiItJpkrT8RwFvmNl7hC6fZnfXj4GISA9WNPm7+6adEYiIiHSeJKN6bglMAWoJg7y95O73pRyXiIikKEmf/1XAeOA94EbggjQDEhGR9CW6k5e7zzOzZnefb2ZLis1vZpXAZGAYUA8c6+7z8qZvD1xBuMXj28CR7r68lDcgIiLtl6Tl/z8zOwHob2ZfARYmWGYs0NfdRwITgUm5CWZWAVwPjHf3UcB0wgVkIiLSSZIk/2MIV/a+BwyPz4vJJXXc/cm4XM6mwPvABDN7FFjL3b09QYuIyMeTpNtnGXAn0Dc+3xj4S5FlBgKL8p5nzazK3RuBtYEvAKcBfwfuM7Nn3H1ma4VlMhXU1vZLEOrqln+wlKrqTEnL9gZVVeX53jOZypLrjHQ/+jw7XpLkfz9hJM8FhD76ZuBLRZZZDNTkPa+MiR9Cq3+eu78MYGbTCbeJbDX5Z7PNLFz4QYJQCyybaaKxIVvSsr1BY2N5vvdstoklJdYZ6X5qa/uVnAPKWV1dTavTkiT/vu6+czvX+TiwP/BLMxsBzMmb9howwMw2jgeBdyKcRSQiIp0kSfL/s5ntBbySe8Hd3yiyzDRgtJnNJuwtjDezccAAd58a7xVwZzz4O9vd/1Bi/CIiUoIkyX9d4CesPMunmdBn3yp3byIMBZ1vbt70PwGfTxqkiIh0rCTJ39x989QjERGRTpMk+c+J/fbPEVr9uPuKVKMSEZFUJUn+XwT2y3veDGyUTjgiItIZkiT/Y9z9r7knZrZLeuGIiEhnaDX5m9lOwBbAN83sivhyJXAqsFUnxCYiIilpq+W/ABgCrAGsF19rAs5OOygREUlXq8nf3V8CXjKz6939rU6MSUREUpakz38PM/s2YQ+ggnAbRx3wFRHpwZIk/3MIQzW8mXIsIiLSSZIk/9fyb8QiIiI9X5Lk/4GZPQA8z8qLvM5NMygREUlX0iGdRUSkF2n1Tl5mlrv71n8L/ImISA/WVst/d+Bp4PD4vJmVN3N5MOW4REQkRW2d539p/D/ezLYiXO37qrs/30mxiYhISorewN3MTgOuJ4zhP9XMzko9KhERSVXR5A+MA3Zy9wnAjsCXU41IRERSl+Rsn4rczdfdvcHMGootYGaVwGRgGFAPHJt/rYCZnQEcA8yPL53g7t7e4EVEpDRJkv9jZnYPMItws/XHEywzlnDj95HxRjCTgAPzpm8LfM3dn2lnvCIi0gGKdvu4+1nAzwk/FDe5+7cSlDsKmB6XfxIY3mL6dsC3zeyxOG6QiIh0oqItfzNbB9gTMGA9M3vC3RcUWWwgsCjvedbMqnLdR8BdwLXAYmCamY1x9/taKyyTqaC2tl+xUAta/sFSqqozJS3bG1RVled7z2QqS64z0v3o8+x4Sbp97o5/NxEO+N4GjCmyzGKgJu95ZS7xm1kF8BN3XxSf/wHYBmg1+WezzSxc+EGCUAssm2misSFb0rK9QWNjeb73bLaJJSXWGel+amv7lZwDylldXU2r05Ikf9x9Snz4gpkdlmCRxwkjgf4y9vnPyZs2kHCfgM2BZcBuhB8WERHpJEmS/1wzOwJ4mNBX/76ZbQrg7q+2ssw0YLSZzSZcFTzezMYBA9x9qpmdG8urB2a6u8YPEhHpREmS/2bx7xhCIge4jjDMw26FFnD3JuDEFi/PzZt+G6H7SEREukDR5O/uu5pZHfAZwvAO/0s/LBERSVOS4R1OIvThnwM8YWZHph6ViIikKsnwDscDW7v7QYSzck5PNyQREUlbkuT/DpA7P/9D4P30whERkc6Q5IBvJfB8PHNnG6DazO4EcPdxaQYnIiLpSJL8L857fEdagYiISOdJkvznAHsB1YRTPdd39x+mGpWIiKQqSfK/B3gV+CywHNA11iIiPVySA764+4mAA6OBwalGJCIiqUuU/M2sL9CfcFXvgFQjEhGR1CVJ/tcCE4AHgTfJG6ZBRER6piTDO/w699jMfuXui9MNSURE0pao2ydHiV9EpHdoV/IXEZHeodXkb2bT4//vdV44IiLSGdrq8x9oZr8CdjIzy5+gYR1ERHq2tpL/PoQLuzYGprDyRi4iItLDtZr84w3WHzOzzxMu7tqScDOX3xYr1MwqgcnAMMKtGo9193kF5psK/M/dJ5YYv4iIlCDJAd/vE27h2AB83cwmJVhmLNDX3UcCE4HVljGzEwh7FiIi0smSJP8vuvvB7v4T4GBgVIJlRgHTAdz9SWB4/kQzGwmMINwLWEREOlmSgd2qzawy3pS9gjDEQzEDgUV5z7NmVuXujWa2HnABcBBwWJIgM5kKamv7JZl1Ncs/WEpVdaakZXuDqqryfO+ZTGXJdUa6H32eHS9J8r8beNzMngR2AO5KsMxioCbveaW75+4GdiiwNnA/MAToZ2Zz3f3m1grLZptZuLC0wUSzmSYaG7IlLdsbNDaW53vPZptYUmKdke6ntrZfyTmgnNXV1bQ6LcnwDpPMbAawGXCju7+UYJ2PA/sDvzSzEYR7AuTKuwq4CsDMjgI2ayvxi4hIx0vS8icm/CRJP2caMDre+rECGG9m44AB7j61/WGKiEhHSpT82yseHzixxcurjQaqFr+ISNfQ2D4iImWoaMvfzEYDZwBr5F5z993SDEpERNKVpNvnSsLNXN5MNxQREeksSZL/G+7+x9QjERGRTpMk+b9rZlOA54gXeOmMHRGRni1J8v9n/D8kzUBERKTzFD3bx90vBJ4GPgSej89FRKQHK5r8zeyHwHhgBWFUzx+nHpWIiKQqSbfPF919RwAz+ynwZLohiYhI2pJc5FUdb84CyUf1FBGRbqyUUT3vTjckERFJW1qjeoqISDfWarePmR0b//8QGAdsC4wzs0s6KTYREUlJWy3/3HAOLUfjVJ+/iEgP12ryd/cZ8eH27n5q7nUzuxW4Ne3AREQkPa0mfzM7BTgPWMvMvhRfrgBe7ozAREQkPW21/K8FrjWzc91d/fwiIr1IklM99wXalfzjdQGTgWFAPXCsu8/Lm34wMJFw/GCqu9/QnvJFROTjSZL8/2dmpwMONAG4+4NFlhkL9HX3kfEG7pOAAwHMLAP8CBgOLAVeNrPfuPt7pb0FERFpryTJ/33gc/EPQmu9WPIfBUwHcPcnzWx4boK7Z81sc3dvNLN1CMcRlrZVWCZTQW1tvwShrm75B0upqs6UtGxvUFVVnu89k6ksuc5I96PPs+MluchrvJltBWwBvOruzycodyCwKO951syq3L0xltkYDyJfC/wBaGirsGy2mYULP0iw2gLLZppobMiWtGxv0NhYnu89m21iSYl1Rrqf2tp+JeeAclZXV9PqtCSjep4GXA98AZhqZmclWOdiIH+tlbnEn+Pu9wIbAH2AryUoU0REOkiSgd3GATu5+wRgR+DLCZZ5nHCgmNjnPyc3wcwGmtmjZraGuzcBy4jHEkREpHMkSf4Ved01DRTpoommAcvNbDbhBvDfNLNxZna8uy8G7gD+bGaPEY4h3F5a+CIiUookB3wfM7N7gFnAToRWfZtii/7EFi/PzZs+FdB9gEVEukiSA75nmdl+hFE9b3L3+9MPS0Raaqxupr6pPA/gV6yo7+oQep2iyT+ejrknYMB6ZvaEuy9IPTIRWUV9U5YTfndfV4fRJW446AD6JuqllqSSbM27gVeAc4DXgNtSjUhERFKXpM8fd58SH75gZoelGI+IiHSCJMl/rpkdATwMbAe8b2abArj7q2kGJyIi6UiS/DeLf8cQhmIAuI5wiuZuKcUlIiIpSnK2z65m9gngM8BrGoBNRKTnSzK8w6HAbOBc4EkzOzL1qEREJFVJzvY5A9jO3ccC2wCnpxqRiIikLknyb3L3pQDuvgRYnm5IIiKStiQHfP9hZpOAPwNfBP6RbkgiIpK2JC3/YwkXd42O/49LNSIREUldkpb/fe6+Z+qRiIhIp0mS/Bea2QHAq6y8h68u7hIR6cGSJP864Jt5z3Vxl4hID9dm8jezgcB+7q6bZ4qI9CKtHvA1s1OBFwiDue3VeSGJiEja2mr5jyOM4T+QMIzzjKSFmlklMBkYBtQDx7r7vLzphwMTgCzwInByvPuXiIh0grZO9Vzu7iviWD592lnuWKCvu48EJgKTchPMbE3gImBXd/8CMAgY087yRUTkY0g0nj8rR/NMahQwHcDdnzSz4XnT6oEv5B1HqKLIVcOZTAW1tf3aGUKw/IOlVFVnSlq2N6iqKs/3nslUllxnuqtyrssVlJ4DpLC2kv+WZnYnIfHnHgPg7uOKlDsQWJT3PGtmVe7eGLt33gEws9OAAcBDbRWWzTazcGFpx5yzmSYaG8rzvqcAjY3l+d6z2SaWlFhnuqtyrsvNlJ4DylldXU2r09pK/vl37JrS6lyFLQby11rp7o25J/GYwGXApsDB7t7czvJFRORjaDX5u/ujH6Pcx4H9gV+a2QhgTovp1xG6f8bqQK+ISOdL2uffXtOA0WY2m9BtNN7MxhG6eJ4m3BVsFvAnMwP4qbtPSykWERFpIZXkH1vzJ7Z4eW7e4yQDyomISEqUhEVEypCSv4hIGVLyFxEpQ2kd8BUR6TCDVzRS9WFDV4fRJZrW6MOyyuoOL1fJX0S6vcr6Bt458/KuDqNLrDvpW7Bmxyd/dfuIiJQhJX8RkTKk5C8iUoaU/EVEypCSv4hIGVLyFxEpQ0r+IiJlSMlfRKQMKfmLiJQhJX8RkTKk5C8iUoZSGdsn3qN3MjCMcLvGY919Xot5+hFu3H6Mu89dvRQREUlLWi3/sUBfdx8JTAQm5U80s+HAn4HPpLR+ERFpQ1rJfxQwHcDdnwSGt5i+BnAQq97aUUREOklaQzoPBBblPc+aWZW7NwK4++MA8ebtRWUyFdTW9ispkOUfLKWqOlPSsr1BVVV5vvdMprLkOtNdqS6X53tPqy6nlfwXAzV5zytzib8U2WwzCxd+UNqymSYaG7KlrrrHa2wsz/eezTaxpMQ6012pLpfne/84dbmurqbVaWl1+zwO7AtgZiOAOSmtR0RESpBWy38aMNrMZgMVwHgzGwcMcPepKa1TREQSSiX5u3sTcGKLl1c7uOvuu6SxfhERaZsu8hIRKUNK/iIiZUjJX0SkDCn5i4iUISV/EZEypOQvIlKGlPxFRMqQkr+ISBlS8hcRKUNK/iIiZUjJX0SkDCn5i4iUISV/EZEypOQvIlKGlPxFRMqQkr+ISBlS8hcRKUOp3MnLzCqBycAwoB441t3n5U3fHzgfaARucvfr04hDREQKS6vlPxbo6+4jgYnApNwEM6sGrgT2BHYGjjezISnFISIiBaSV/EcB0wHc/UlgeN60zYF57r7A3VcAjwE7pRSHiIgUkEq3DzAQWJT3PGtmVe7eWGDaEmBQW4VVV2feq6ureb2UQOqAGUd9tZRFe4Whv5pUfKZeqm9XB9DBVJdVl0swtLUJaSX/xUBN3vPKmPgLTasBFhYpr67jQhMRkbS6fR4H9gUwsxHAnLxprwCbmNlaZtYH+CLwREpxiIhIARXNzc0dXmje2T5bAxXAeGBbYIC7T80726eScLbPtR0ehIiItCqV5C8iIt2bLvISESlDSv4iImVIyV9EpAwp+fdwZraLmd3V1XGIpEn1vOMp+YuIlCGd7dPDmNmmwM1AA3FgPOAM4F1gHeD37n6BmW0DXA1kgeXAcXG+x9z9HjObAUx39yvN7AbCKbezO/0NSa9lZkcRrvfpB3wGuNTdby5UN939jbzlWtbxrwGbEMYIUz3vIGr59zyjgWeAPYCLgcGEq7/HEsZIOjXOdz1wqrvvTLjm4grgXmAfM1sTqAX2MLMKwjUYutBO0jDI3ccABxAGeYTCdTNfoToOqucdSsm/57kReI8wcN6phJbRS+5e7+4fxOcA67v78/Hxn4EtCYPobQvsCvyaMGzGTsAT7q5dQEnD8/H/m6wcoqZQ3cxXqI6D6nmHUvLveQ4EZrn77sCvgHOAQhX6LTPbOj7eGXjV3ZuAp4GzgQcJX5LLCC0lkTQkqpstpheq44nLUj1PJq2B3SQ9TwO3m1kj0ETo7/x8gfmOA66Ju7uNwDHx9XsJ/akvADOArwOPphyzSL7W6mZOyzr+TcJowO0pS/W8CB3wFREpQ+r2EREpQ0r+IiJlSMlfRKQMKfmLiJQhJX8RkTKk5C/dmpmdY2b/NbMOuR+7mQ0xs8lF5jm1rekfY91fzDsnvdi8x5tZdRvTPxXviCdSEiV/6e6OAO4CvtIRhbn72+5+cpHZzuuIdRVwNLB+wnnPBTJtTN8N2PFjRyRlS+f5S7dlZrsApxPGhLnd3bc3s0eAucBmhPtDfzk+/g7hgqAhwFR3v7aVQb8qgbvcfYSZvUi48GdrwtWjBxKGE/gecAPwF2B/YE1gPeCncZ6tgLPc/bdmdihhILEsYTCxiWZ2AfBpwgBkQwkXKb0H/IEwMNmY3EBmZlYH3B3jqgZOJFy0dy1heIODgeuATwKfAB4ALgD+Rhgw7dS4/hPdfa6ZnRi3wY+AXwKDYvxnu/sjJX4U0gup5S/d2bHADe7uQL2Z7RBfn+3uuxCS5rnxtQ0Ig4eNAL5pZutQfACxgcAv4vT/APu4+8XA//L2DmrcfV/gUuAk4EvA8cB4M1sLuBDY3d1HARuY2ei4XL2770P48fqmuz9DSOZn549gSUj0i4B9gG8AA939RuBtwt7OJ4En3X0vYBRwkrtnCcn9Tnf/XSvb7jOEH4H9gXGEHwqRjyj5S7dkZoMJwwGfbmbTCS3YXF/8n+L/2YDlHsdBvz4EXiIkv2IDiAE8F//nDzxWaPpC4JU4MNiCOO/GhEHD7o97JFsAGyUsN+cBwt7Hb4HvE/Ze8v0P2N7M7gCuBNZooywIe0O4+98Iew+/IPzw6bsuq1CFkO7qSOBGd9/T3fcGdgD2JCTb7eI8OxK6PwA+Z2YZM+tHSPJ/p/gAYlB4sLCKItNz/klI7qPjnsjVwFNtLNfE6t+5XYD/uvuewEXAJS3mPQpY6O5HEMaz7xfHsckvazmhWwrCaJaY2WcJey37Eca1ubqN9yFlSMlfuqtjgdtyT+Iwvr8m3NTjKDN7FNiPMN47hP7yB4BZwEXu/h4rB/2aRex+Sbjul83s9mIzuft8QlfSo2b2FKHrptAPTM5TwI/MbPO8114AjjOzJ4DLgR/G12cB9wMzgX3NbDbwM8KP2vrAHOBAM/sKcBVwbbxxSe4g8d+BXczsL4SRMc8v/ralnOiAr/QosXvlRHefm/faLvG1DjkjSKQcqOUvIlKG1PIXESlDavmLiJQhJX8RkTKk5C8iUoaU/EVEypCSv4hIGfp/vvirFHVG/5UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bars\n",
    "sms_yes_bars = plt.bar(ind, sms_yes_total, width, color='teal', alpha=.7, label='sms received')\n",
    "sms_no_bars = plt.bar(ind + width, sms_no_total, width, color='crimson', alpha=.7, label='no sms received')\n",
    "\n",
    "\n",
    "#title and labels\n",
    "plt.ylabel('Proportion of appointments')\n",
    "plt.xlabel('Appointment status')\n",
    "plt.title('Proportion of No show vs show appointments by sms received')\n",
    "locations = ind + width/2 \n",
    "labels = ['show','no show']\n",
    "plt.xticks(locations,labels)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Therefore, if a patient doesn't receive a sms, it doesn't appear to influence whether they will attend the appointment as in fact, a higher proportion of patients attended their appointment despite not receiving a sms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4: Are patients eligible for a scholarship more likely to attend their appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.82883038647387"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9.8% of patients were eligible for a scholarship. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating the proportion of show and no show appointments depending on scholarship eligibility**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99090\n",
       "1    10801\n",
       "Name: scholarship, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scholarship.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "eligible = df.query('scholarship == \"1\"')\n",
    "not_eligible = df.query('scholarship == \"0\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scholarship  no_show\n",
       "1            0          8245\n",
       "             1          2556\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eligible_count = eligible.groupby('scholarship').no_show.value_counts()\n",
    "eligible_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scholarship  no_show\n",
       "0            0          79548\n",
       "             1          19542\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_eligible_count = not_eligible.groupby('scholarship').no_show.value_counts()\n",
    "not_eligible_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scholarship\n",
       "0    99090\n",
       "1    10801\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scholarship_total = df.groupby('scholarship')['no_show'].count()\n",
    "scholarship_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scholarship  no_show\n",
       "1            0          0.763355\n",
       "             1          0.236645\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eligible_proportion = eligible_count/scholarship_total\n",
    "eligible_proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scholarship  no_show\n",
       "0            0          0.802785\n",
       "             1          0.197215\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_eligible_proportion = not_eligible_count/scholarship_total\n",
    "not_eligible_proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bars\n",
    "eligible_bars = plt.bar(ind, eligible_proportion, width, color='darkgreen', alpha=.7, label='Scholarship')\n",
    "not_eligible_bars = plt.bar(ind + width, not_eligible_proportion, width, color='red', alpha=.7, label='No Scholarship')\n",
    "\n",
    "\n",
    "# Title and labels\n",
    "plt.ylabel('Proportion of appointments')\n",
    "plt.xlabel('Appointment status')\n",
    "plt.title('Proportion of No show vs show appointments by sms received')\n",
    "locations = ind + width/2 \n",
    "labels = ['show','no show']\n",
    "plt.xticks(locations,labels)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the graph above, it shows that those with a scholarship were slightly morelikely to no show than those without a scholarship. However, there is not a signficant difference in the proportion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5: Are males or females more likely to miss their appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71416\n",
       "M    38475\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "female = df.query('gender==\"F\"')\n",
    "male = df.query('gender==\"M\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "F    71416\n",
       "M    38475\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_count = female.groupby('gender').no_show.value_counts()\n",
    "male_count = male.groupby('gender').no_show.value_counts()\n",
    "total = df.groupby('gender')['no_show'].count()\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  no_show\n",
       "F       0          56961\n",
       "        1          14455\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  no_show\n",
       "F       0          0.797594\n",
       "        1          0.202406\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_prop = female_count/total\n",
    "female_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  no_show\n",
       "M       0          0.801352\n",
       "        1          0.198648\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_prop = male_count/total\n",
    "male_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels= ['show','no show']\n",
    "plt.pie(male_prop,labels=labels,autopct = '%1.1f%%',startangle=90)\n",
    "plt.title('Proportion of no shows vs shows for males')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels= ['show','no show']\n",
    "plt.pie(female_prop,labels=labels, autopct = '%1.1f%%',startangle=90)\n",
    "plt.title('Proportion of no shows vs shows for females')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The pie charts above show that gender does not seem be correlated with more missed appointments. 19.9% of males missed their appointments whilst 20.2% of females missed their appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "In conclusion, after analysing a dataset containing over 100,000 appointments in Brazil, over 20% resulted in no show appointments. \n",
    "\n",
    "When examining whether age influences whether the appointment was a no show, it was found that the youngest patients had around the average rate of no shows whilst those around 20 had a relatively high level of no shows. The older patients around 80 years old had a high attendance to appointments, only 16% were no shows.\n",
    "\n",
    "When examining the number of no show appointments based on neighbourhood, it was found that Jardim Camburi had the highest number of no show appointments. The proportion of no show appointments was then calculated and it was found that most neighbourhoods had a proportion of 15-25% but there were a few outliers. These outliers were abnormally high or low because of the small data size. \n",
    "\n",
    "SMS did not appear to have an effect on whether a patient was going to miss an appointment. \n",
    "\n",
    "Patients eligible for a scholarship were slightly more likely to miss their appointment compared to those without.\n",
    "\n",
    "Gender does not seem to have an effect on the number of no show appointments. Both males and females had around 20% of patients missing their appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Limitations\n",
    "\n",
    "There are some limitations with the dataset. Firstly, the data only contains information about appointments made for the month of May 2016. Despite there being over 100,000 rows of data, if the dataset included data from the whole year, it may be easier to identify trends, especially for the proportion of no shows by neighbourhood as some neighbourhoods had very few appointments that month. More data is required in neighbourhods with a small sample size to enable us to more clearly identify whether there is a significant increase or decrease in the proportion of no show appointments. With a larger dataset, statistical analysis could be performed to examine the correlations observed further.\n",
    "\n",
    "There are more health factors that could be recorded for example some conditions may require more urgent medical attention and therefore those suffering from that condition may be more likely to attend their appointment compared to those with less urgent issues.\n",
    "\n",
    "There may also be other factors affecting whether a patient no shows such as have they missed their appointment because they were late. Perhaps those using public transport were more likely to miss their appointment compared to those who drove. Perhaps patients living further away were more likely to no show. Weather may also have an impact. Additional variables could also be tracked to see whether they could be affecting the number of no show appointments.\n",
    "\n",
    "The dataset only records whether a patient received at least one SMS. It does not tell us when they received it. It could be that the SMS was sent as a confirmation as a booking or it could be a reminder message for the patient. It also doesn't say how many SMS were sent, if the number of SMS sent was recorded, this could be investigated further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**\n",
    "\n",
    "Kaggle datasource : https://www.kaggle.com/joniarroba/noshowappointments"
   ]
  }
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