investigate-a-dataset-project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Project: Analysis of Missed Medical Appointments in Brazil \n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "# Introduction\n",
    "\n",
    "> With life expectancies and the cost of medical treatments growing worldwide as countries develop, healthcare is a major issues facing every nation. While the United States may have some of the highest healthcare costs in the world and its own unique challenges, these issues don't exist in a bubble, and valuable information can be learned by studying the healthcare systems of other countries. In this project, data concerning doctors visits in Brazil, provided by Kaggle, will be used to see what factors affect the chances that someone will make or miss their appointment. By gaining insight into what can lead to a reduction in missed appointments, it's possible to find solutions which can lead to  a system that provides better access to care at lowers costs for any country. \n",
    "\n",
    "> This project will include an exploratory data analysis into the dataset, and then focus on the three independent variables of patient age, patient disability status, and whether the patient received a text message reminder for the appointment. These factors will be analyzed in how they affect the dependent variable of the rate of appointment attendence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements for all packages\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "# Data Wrangling\n",
    "\n",
    "## General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading data from Kaggle and performing shape, info(), head(), and describe() to gain a general understanding of it\n",
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      "PatientId         110527 non-null float64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
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       "      <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",
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       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
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       "  </thead>\n",
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       "    <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",
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      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirming there are no null values\n",
    "sum(df.isnull().any(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The columns in this data set are: <br/>\n",
    "PatientId - A unique identification number for each patient <br/>\n",
    "AppointmentID - A unique identification number for each appointment made <br/>\n",
    "Gender - A M-F value showing the patient's gender <br/>\n",
    "ScheduledDay - A date-time value for the day the appointment was scheduled <br/>\n",
    "AppointmentDay - A date-time value for the day of the scheduled appointment <br/>\n",
    "Age - The age of the patient <br/>\n",
    "Neighbourhood - The name of the neighborhood where the appointment takes place <br/>\n",
    "Scholarship - A 0-1 value showing whether the patient is receiving government assistance through the Bolsa Familia program <br/>\n",
    "Hipertension - A 0-1 value showing whether the patient has high blood pressure <br/>\n",
    "Diabetes - A 0-1 value showing whether the patient has diabetes <br/>\n",
    "Alcoholism - A 0-1 value showing whether the patient has alcoholism <br/>\n",
    "Handcap - A value showing whether the patient has a disability <br/>\n",
    "SMS_received - A 0-1 value showing whether the patient received a text message for the appointment  <br/>\n",
    "No-show - A No-Yes value with 'No' indicating the patient made the appointment, and 'Yes'  indicating the patient missed the appointment <br/>\n",
    "\n",
    "> From the above functions it can be determined that the dataset is fairly clean, and actually has no null values that need to be addressed. Some of the values such as the '-1' minimum value of age, and the '4' maximum value for 'Handcap' are potentially erroneous, but will be explored further to investigate their cause. The column titles will be edited to improve readability. The columns PatientId, AppointmentID, ScheduledDay, and Neighbourhood will be removed as they either have no clear potential relationship with the dependent variable of whether a patient missed their appointment or not, or are outside the scope of this investigation. The column titles will also be edited for ease of use. An additional column showing a zero or one value for whether a patient attended their appointment or missed it, respectively, will also be added to make calculating percentages of various groups of patients easier in the analysis portion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For the purposes of this project the PatientId, AppointmentID, ScheduledDay, and Neighbourhood columns will be dropped from the dataset. While it is possible that using PatientId to find individuals who had multiple appointments and tracking their rates of missed appointments could be useful, it's outside of the scope of this project. Additionally, with the information present, AppointmentID doesn't provide any unique information which could influence rates of missed appointments. It's not clear that the day an appointment is scheduled would have any effect on whether it would be missed, so for the scope of this project it won't be included. Finally, though Neighbourhood has the potential to play a large role in the number of missed appointments, without additional information such as income level and population density providing a greater sense of the causes for differences between neighborhoods, any analysis would be lacking in depth, and therefore will be left outside the scope of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>AppointmentDay</th>\n",
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       "      <th>Scholarship</th>\n",
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       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</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        AppointmentDay  Age  Scholarship  Hipertension  Diabetes  \\\n",
       "0      F  2016-04-29T00:00:00Z   62            0             1         0   \n",
       "1      M  2016-04-29T00:00:00Z   56            0             0         0   \n",
       "2      F  2016-04-29T00:00:00Z   62            0             0         0   \n",
       "3      F  2016-04-29T00:00:00Z    8            0             0         0   \n",
       "4      F  2016-04-29T00:00:00Z   56            0             1         1   \n",
       "\n",
       "   Alcoholism  Handcap  SMS_received No-show  \n",
       "0           0        0             0      No  \n",
       "1           0        0             0      No  \n",
       "2           0        0             0      No  \n",
       "3           0        0             0      No  \n",
       "4           0        0             0      No  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping PatientId, AppointmentID, ScheduledDay, and Neighbourhood columns which won't be necessary for further analysis\n",
    "df.drop(['PatientId','AppointmentID','ScheduledDay','Neighbourhood'], axis=1, inplace=True)\n",
    "# confirming change\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>appt_day</th>\n",
       "      <th>age</th>\n",
       "      <th>govt_assist</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>disabled</th>\n",
       "      <th>sms_rcvd</th>\n",
       "      <th>appt_missed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</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>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</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              appt_day  age  govt_assist  hypertension  diabetes  \\\n",
       "0      F  2016-04-29T00:00:00Z   62            0             1         0   \n",
       "1      M  2016-04-29T00:00:00Z   56            0             0         0   \n",
       "2      F  2016-04-29T00:00:00Z   62            0             0         0   \n",
       "3      F  2016-04-29T00:00:00Z    8            0             0         0   \n",
       "4      F  2016-04-29T00:00:00Z   56            0             1         1   \n",
       "\n",
       "   alcoholism  disabled  sms_rcvd appt_missed  \n",
       "0           0         0         0          No  \n",
       "1           0         0         0          No  \n",
       "2           0         0         0          No  \n",
       "3           0         0         0          No  \n",
       "4           0         0         0          No  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# editing column titles for readability\n",
    "df.rename(columns={'Gender':'gender','AppointmentDay':'appt_day','Age':'age','Scholarship':'govt_assist','Hipertension':'hypertension','Diabetes':'diabetes','Alcoholism':'alcoholism','Handcap':'disabled','SMS_received':'sms_rcvd','No-show':'appt_missed'}, inplace=True)\n",
    "# confirming change\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-04-29T00:00:00Z\n",
      "2016-06-08T00:00:00Z\n"
     ]
    }
   ],
   "source": [
    "# checking range of dates for both appt_day\n",
    "print(df['appt_day'].min())\n",
    "print(df['appt_day'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The AppointmentDay column doesn't seem to have any time values, only dates. Converting this into day of the week values could be useful, as it's possible that more appointments are missed on certain days of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
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       "      <th>govt_assist</th>\n",
       "      <th>hypertension</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</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</td>\n",
       "      <td>56</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   appt_day  age  govt_assist  hypertension  diabetes  alcoholism  \\\n",
       "0      F 2016-04-29   62            0             1         0           0   \n",
       "1      M 2016-04-29   56            0             0         0           0   \n",
       "2      F 2016-04-29   62            0             0         0           0   \n",
       "3      F 2016-04-29    8            0             0         0           0   \n",
       "4      F 2016-04-29   56            0             1         1           0   \n",
       "\n",
       "   disabled  sms_rcvd appt_missed  \n",
       "0         0         0          No  \n",
       "1         0         0          No  \n",
       "2         0         0          No  \n",
       "3         0         0          No  \n",
       "4         0         0          No  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting appt_day column into datetime format\n",
    "df['appt_day'] = pd.to_datetime(df['appt_day'])\n",
    "# confirming change\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>appt_day</th>\n",
       "      <th>age</th>\n",
       "      <th>govt_assist</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>disabled</th>\n",
       "      <th>sms_rcvd</th>\n",
       "      <th>appt_missed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>62</td>\n",
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       "      <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>Friday</td>\n",
       "      <td>56</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>62</td>\n",
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       "      <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>Friday</td>\n",
       "      <td>8</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>Friday</td>\n",
       "      <td>56</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 appt_day  age  govt_assist  hypertension  diabetes  alcoholism  \\\n",
       "0      F   Friday   62            0             1         0           0   \n",
       "1      M   Friday   56            0             0         0           0   \n",
       "2      F   Friday   62            0             0         0           0   \n",
       "3      F   Friday    8            0             0         0           0   \n",
       "4      F   Friday   56            0             1         1           0   \n",
       "\n",
       "   disabled  sms_rcvd appt_missed  \n",
       "0         0         0          No  \n",
       "1         0         0          No  \n",
       "2         0         0          No  \n",
       "3         0         0          No  \n",
       "4         0         0          No  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting appt_day column from datetime to showing day of the week value\n",
    "df['appt_day'] = df['appt_day'].dt.weekday_name\n",
    "# confirming change\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0      3539\n",
       " 1      2273\n",
       " 52     1746\n",
       " 49     1652\n",
       " 53     1651\n",
       " 56     1635\n",
       " 38     1629\n",
       " 59     1624\n",
       " 2      1618\n",
       " 50     1613\n",
       " 57     1603\n",
       " 36     1580\n",
       " 51     1567\n",
       " 19     1545\n",
       " 39     1536\n",
       " 37     1533\n",
       " 54     1530\n",
       " 34     1526\n",
       " 33     1524\n",
       " 30     1521\n",
       " 6      1521\n",
       " 3      1513\n",
       " 17     1509\n",
       " 32     1505\n",
       " 5      1489\n",
       " 44     1487\n",
       " 18     1487\n",
       " 58     1469\n",
       " 46     1460\n",
       " 45     1453\n",
       "        ... \n",
       " 74      602\n",
       " 76      571\n",
       " 75      544\n",
       " 78      541\n",
       " 77      527\n",
       " 80      511\n",
       " 81      434\n",
       " 82      392\n",
       " 79      390\n",
       " 84      311\n",
       " 83      280\n",
       " 85      275\n",
       " 86      260\n",
       " 87      184\n",
       " 89      173\n",
       " 88      126\n",
       " 90      109\n",
       " 92       86\n",
       " 91       66\n",
       " 93       53\n",
       " 94       33\n",
       " 95       24\n",
       " 96       17\n",
       " 97       11\n",
       " 98        6\n",
       " 115       5\n",
       " 100       4\n",
       " 102       2\n",
       " 99        1\n",
       "-1         1\n",
       "Name: age, Length: 104, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check value counts of Age and check if '-1' min is an error\n",
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It doesn't seem that the '-1' Age is an error, but more likely is referring to a pregnancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108286\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: disabled, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check value counts of Disabled to see if '4' max is an error\n",
    "df['disabled'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The max of '4' for the Disabled column is not in error and is a classification of disability, though the Kaggle website lists this data as being in 0-1 format only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88208\n",
       "1    22319\n",
       "Name: appt_missed_num, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it may be useful to include an additional column of 0 and 1 values to show appt_missed\n",
    "# 0 for attended the appointment, currently 'No'\n",
    "# 1 for missed appointment, currently 'Yes'\n",
    "df['appt_missed_num'] = df['appt_missed']\n",
    "df.replace({'appt_missed_num': {'No':0, 'Yes': 1}}, inplace=True)\n",
    "# confirming change\n",
    "df['appt_missed_num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>appt_day</th>\n",
       "      <th>age</th>\n",
       "      <th>govt_assist</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>disabled</th>\n",
       "      <th>sms_rcvd</th>\n",
       "      <th>appt_missed</th>\n",
       "      <th>appt_missed_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>Friday</td>\n",
       "      <td>56</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>62</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>8</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>56</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender appt_day  age  govt_assist  hypertension  diabetes  alcoholism  \\\n",
       "0      F   Friday   62            0             1         0           0   \n",
       "1      M   Friday   56            0             0         0           0   \n",
       "2      F   Friday   62            0             0         0           0   \n",
       "3      F   Friday    8            0             0         0           0   \n",
       "4      F   Friday   56            0             1         1           0   \n",
       "\n",
       "   disabled  sms_rcvd appt_missed  appt_missed_num  \n",
       "0         0         0          No                0  \n",
       "1         0         0          No                0  \n",
       "2         0         0          No                0  \n",
       "3         0         0          No                0  \n",
       "4         0         0          No                0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirming cleaned dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f022c703cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# general histograms of numeric variables\n",
    "ax = df.hist(figsize=(10,10));\n",
    "plt.suptitle('Exploratory Histograms', ha='center', fontsize='xx-large')\n",
    "plt.ylabel('Patients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">From this we can tell the age ranges skews right, with the majority of patients being 60 and under. Also the majority of patients are nonalcoholic, nondiabetic, not disabled, not receiving government assistance, don't have hypertension, and about a third of them received SMS messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients who attended their appointments and patients who missed their appointments\n",
    "attended = df.appt_missed == 'No'\n",
    "missed = df.appt_missed == 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients receiving government assistance to compare their appointment attendance\n",
    "yes_govt_assist = df.govt_assist == 1\n",
    "no_govt_assist = df.govt_assist == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23736304207715681"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[yes_govt_assist].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19807155900708365"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[no_govt_assist].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Patients receiving government assistance have a higher rate of missed appointments compared to those not receiving government assistance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients with hypertension from those without to compare their appointment attendance\n",
    "high_pressure = df.hypertension == 1\n",
    "avg_pressure = df.hypertension == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17301958625751113"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[high_pressure].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20903680995424115"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[avg_pressure].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Patients without hypertension have a higher rate of missing their appointments compared to those with hypertension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients with diabetes from those without to compare their appointment attendance\n",
    "diabetic = df.diabetes == 1\n",
    "non_diabetic = df.diabetes == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18003273322422259"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[diabetic].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20362824612025268"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[non_diabetic].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Patients without diabetes have a higher rate of missing their appointments compared to those with diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients with alcoholism from those without to compare their appointment attendance\n",
    "alcoholic = df.alcoholism == 1\n",
    "non_alcoholic = df.alcoholism == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20148809523809524"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[alcoholic].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20194649472318904"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[non_alcoholic].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Patients with and without alcoholism have similar rates of appointment attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate male and female patients to compare their appointment attendance\n",
    "women = df.gender == 'F'\n",
    "men = df.gender == 'M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20314587973273943"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[women].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19967947889471915"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[men].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Male and female patients have similar rates of appointment attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wednesday    25867\n",
       "Tuesday      25640\n",
       "Monday       22715\n",
       "Friday       19019\n",
       "Thursday     17247\n",
       "Saturday        39\n",
       "Name: appt_day, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking total numbers of appointments per day of the week\n",
    "df['appt_day'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It appears that the vast majority of appointments occur on weekdays while an extremely small proportion occur on Saturdays and none take place on Sundays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients by the day of the week of their appointments to compare their attendance\n",
    "monday = df.appt_day == 'Monday'\n",
    "tuesday = df.appt_day == 'Tuesday'\n",
    "wednesday = df.appt_day == 'Wednesday'\n",
    "thursday = df.appt_day == 'Thursday'\n",
    "friday = df.appt_day == 'Friday'\n",
    "saturday = df.appt_day == 'Saturday'\n",
    "sunday = df.appt_day == 'Sunday'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20647149460708783"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[monday].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20093603744149766"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[tuesday].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19689179263153825"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[wednesday].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19354090566475329"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[thursday].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21226142278773857"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[friday].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23076923076923078"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[saturday].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">While there is some fluctuation across days of the week, with the few Saturday appointments having the highest rate of being missed, they are relatively consistent around 20%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does Patient Age Affect Rates of Missed Appointments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0228914240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram showing age breakdown of patients who attended their appointment and those who missed their appointment\n",
    "df.age[attended].hist(label='attended')\n",
    "df.age[missed].hist(label='missed')\n",
    "plt.legend();\n",
    "plt.suptitle('Patient Age vs Attending or Missing Appointment', size='x-large');\n",
    "plt.xlabel('Age');\n",
    "plt.ylabel('Patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Both groups skew right, though there appears to be proportionally more people who missed their appointments in the 20-40 age range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.790064393252315"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing the ages of patients who attended their appointments to those who missed theirs\n",
    "df.age[attended].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.317666562121957"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age[missed].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">While the mean age of those who attended their appointment is around 38, the mean age of those who missed their appointment is 34."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients who are underage, adults, and above the minimum retirement age to compare their attendance\n",
    "children = df.age <= 17\n",
    "adults = df.query('age >= 18 & age <= 64')['age']\n",
    "seniors = df.age >= 65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21902848794740687"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[children].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26396101534657068"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[adults].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15504791001249826"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[seniors].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The grouping with the highest rate of missed appointments is adults at 26%. Second are those 17 and under at 21% and the lowest are seniors at 15%. It's possible that this could be due to adults having to fit appointments in during the workweek, while seniors and children may have less obligations or more flexible schedules during that time, though at this point the information to prove that definitively isn't available. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does Having a Disability Affect Rates of Missed Appointments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients by the various disability classifications to compare their appointment attendance \n",
    "disability_0 = df.disabled == 0\n",
    "disability_1 = df.disabled == 1\n",
    "disability_2 = df.disabled == 2\n",
    "disability_3 = df.disabled == 3\n",
    "disability_4 = df.disabled == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20235302809227415"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[disability_0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1792360430950049"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[disability_1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20218579234972678"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[disability_2].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23076923076923078"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[disability_3].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33333333333333331"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[disability_4].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Rates of Missed Appointments For Disability Classifications')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M2kDSryVdVy8hjYiIJVQ3b8veX8eaF9Q8bJ8KnCrpQEov8EMot0EYb/tBSdtSbi+xeVtNBkmHAocCrLjiittusskmREREc9OmTXvA9tqdzqebyWQ2C/eEHqoH7IWU3p7YfobSOQjb02rNZSNgausEts8AzgCYOHGip05daHBERAxB0lC3UGqkm6e5pgATJG1QeyLvT+nUM5+kCS0fd6d01kHS2rUBH5UnlU2gdGSLiIglUNdqJrbnSjqC0lt2DHCW7dslnQBMtT0ZOELliXTPUXpa9vWi3pHSo30upQv/YQ16h0dExCh50fSAz2muiIjhkzTNdtMbYg4oPeAjIqJjSSYREdGxJJOIiOhYkklERHQsySQiIjrWzU6LEREvWqcedvVohwDA4ae/ZbRDAFIziYiIxSDJJCIiOpZkEhERHUsyiYiIjiWZREREx5JMIiKiY0kmERHRsSSTiIjoWJJJRER0LMkkIiI6lmQSEREdSzKJiIiOJZlERETHkkwiIqJjQyYTSSc1KYuIiKVXk5rJLv2U7ba4A4mIiN41YDKR9AFJtwEbS7q15XUXcGuTmUuaJGm6pBmSju5n+GGSbpN0i6RfSNqsZdgn63TTJb1tUb5cRESMjMGetHgB8BPg80BrInjc9kNDzVjSGOBUSs1mNjBF0mTbd7Quw/bpdfy9gK8Ak2pS2R/YHFgH+B9JG9me1/yrRUTESBmwZmL7Udt32z6AkgyeAwysJGl8g3lvB8ywPdP2s8CFwN5ty3is5eOKdf7U8S60/Yztu4AZdX4REbEEGvIZ8JKOAI4D/gI8X4sNbDnEpGOBWS2fZwPb9zP/w4EPA8sBfQ8zHgvc2Dbt2KFijYiI0TFkMgGOAja2/eAw561+yvyCAvtU4FRJBwKfBg5pOq2kQ4FDAcaPb1JZioiIbmhyNdcs4NFFmPdsYN2Wz+OAOYOMfyGwz3CmtX2G7Ym2J6699tqLEGJERCwOTWomM4FrJV0OPNNXaPsrQ0w3BZggaQPgz5QG9QNbR5A0wfYf6sfdgb73k4ELJH2F0gA/AbipQawRETEKmiSTe+prufpqxPbc2t5yJTAGOMv27ZJOAKbangwcIWlnSuP+w5RTXNTxvg/cAcwFDs+VXBERS64hk4nt4wEkrWj7b8OZue0rgCvayo5pef+hQab9HPC54SwvIiJGR5Pbqbxe0h3AnfXzVpJO63pkERHRM5o0wH8NeBvwIIDt3wA7djOoiIjoLY3uGmx7VltR2i8iImK+Jg3wsyTtAFjScsCR1FNeERER0KxmchhwOKUH+mxg6/o5IiICaHY11wPAQSMQS0RE9Kgm9+baAPg3YP3W8W3v1b2wIiKilzRpM7kU+BbwYxbc6DEiImK+Jsnkadv/1fVIIiKiZzVJJidLOhb4GQvfm+vmrkUVERE9pUky2QJ4F+VZI63PM3nLgFNERMRSpUkyeTuwYX1aYkRExAs06WfyG2C1bgcSERG9q0nN5OXA7yRNYeE2k1waHBERQLNkcmzXo4iIiJ7WJJn8g+1PtBZIOgm4rjshRUREr2nSZrJLP2W7Le5AIiKidw1YM5H0AeCDwIaSbm0ZtDJwQ7cDi4iI3jHYaa4LgJ8AnweObil/3PZDXY0qIiJ6yoDJxPajwKPAAZLGUK7qWgZYSdJKtu8ZoRgjImIJ1+SuwUcAxwF/YeEe8Ft2L6yIiOglTa7mOgrY2PaD3Q4mIiJ6U5OruWZRTncNm6RJkqZLmiHp6H6Gf1jSHZJulfRzSeu1DJsn6Zb6mrwoy4+IiJHRpGYyE7hW0uUs3AP+K4NNVNtZTqVcWjwbmCJpsu07Wkb7NTDR9pP16rEvAPvVYU/Z3rr5V4mIiNHSpGZyD3AVsBzlsuC+11C2A2bYnllvEnkhsHfrCLavsf1k/XgjMK5p4BERseRo8gz44xdx3mMpp8j6zAa2H2T891IuRe6zvKSpwFzgRNuXLmIcERHRZYN1Wvya7aMk/Zhy9dZCGtzoUf2UvWA+dVn/DEwE3tRSPN72HEkbAldLus32H9umOxQ4FGD8+PFDhBMREd0yWM3kvPr3S4s479nAui2fxwFz2keStDPwKeBNtlvbZObUvzMlXQtsAyyUTGyfAZwBMHHixH4TVUREdN9gnRan1b/XSVoO2KgOmm77uQbzngJMkLQB8Gdgf+DA1hEkbQN8A5hk+68t5asDT9p+RtJawN9TGucjImIJ1KTT4k7AOcDdlFNX60o6xPb1g01ne27t8HglMAY4y/btkk4AptqeDHwRWAm4SBLAPfX02abANyQ9T7lI4MS2q8AiImIJ0uTS4C8Du9qeDiBpI+C7wLZDTWj7CuCKtrJjWt7vPMB0N1CePR8RET2gyaXBy/YlEgDbvweW7V5IERHRa5rUTKZK+hYLGuQPAqZ1L6SIiOg1TZLJB4DDgSMpbSbXA6d1M6iIiOgtTTotPiPpFODnlLsGT6892iMiIoBmV3PtDpxO6eMhYANJ77f9k8GnjIiIpUXTq7nebHsGgKRXAZez8K1PIiJiKdbkaq6/9iWSaibw14FGjoiIpU+Tmsntkq4Avk+5t9Y7KLeT/0cA2z/sYnwRsQS5c5NNRzsEADb93Z2jHUK0aZJMlqc8srfvJoz3A2sAe1KSS5JJRMRSrsnVXO8ZiUAiIqJ3DdlmImlDST+WdL+kv0r6Ub15Y0REBNCsAf4CSnvJK4F1gIsoT02MiIgAmiUT2T7P9tz6+g4DPOQqIiKWTk0a4K+RdDSlNmJgP+BySWsA2H6oi/FFREQPaJJM9qt/399W/i+U5LLhYo0oIiJ6TpOrudLYHhERg2pSM0HSa4DNKH1OALB9breCioiI3tLkRo/HAjtRkskVwG7AL4Akk4iIAJpdzbUv8FbgvtqBcSvgpV2NKiIiekqTZPKU7eeBuZJWodzkMY3uERExX9PH9q4GfJPyuN4ngJu6GlVERPSUJldzfbC+PV3ST4FVbN/a3bAiIqKXNDnNNZ/tu4eTSCRNkjRd0oza8bF9+Icl3SHpVkk/l7Rey7BDJP2hvg4ZTpwRETGyhpVMhkPSGOBUytVfmwEHSNqsbbRfAxNtbwlcDHyhTrsGcCywPbAdcKyk1bsVa0REdKZryYSSBGbYnmn7WcrtWPZuHcH2NbafrB9vBMbV928DrrL9kO2HgauASV2MNSIiOjBgm0nfvbcG0uCeXGOBWS2fZ1NqGgN5LwueK9/ftGOHWF7H1j/68m4vopG7T9x9tEOIFlucs8VohwDAbYfcNtohRAxosAb4aZR7bwkYDzxc368G3AMMdZsV9VPW792GJf0zMJEFT3NsNK2kQ4FDAcaPHz9EOBER0S0DnuayvYHtDYErgT1tr2V7TWAPmj2qdzawbsvnccCc9pEk7Qx8CtjL9jPDmdb2GbYn2p649tprNwgpIiK6oUmbyWttX9H3wfZPWFCDGMwUYIKkDSQtB+wPTG4dQdI2wDcoieSvLYOuBHaVtHpteN+1lkVExBKoSafFByR9Guh7KNY/Aw8ONZHtuZKOoCSBMcBZtm+XdAIw1fZk4IvASsBFkgDusb2X7Yck/SclIQGckOemREQsuZokkwMol+leQkkm19eyIdUazRVtZce0vN95kGnPAs5qspyIiBhdTXrAPwR8SNJKtp8YgZgiIqLHDNlmImkHSXcAd9TPW0k6reuRRUREz2jSAP9VSifCBwFs/wbYsZtBRUREb2nUA972rLaieV2IJSIielSTBvhZknYAXC/xPRK4s7thRUREL2lSMzkMOJxyO5PZwNb1c0REBNDsaq4HgINGIJaIiOhRTa7m+oKkVSQtW5858kC9l1ZERATQrM1kV9sfl/R2ymmudwDXUHrEx4vVcauOdgTFcY+OdgQR0UCTNpNl699/AL6b25pERES7JjWTH0v6HfAU8EFJawNPdzesiIjoJUPWTGwfDbye8njd54C/0fbExIiIWLo1aYB/BzDX9ryWuwev0/XIIiKiZzRpM/mM7cclvYFyW5VzgP/ublgREdFLmiSTvlun7A78t+0fAct1L6SIiOg1TZLJnyV9A3gncIWklzacLiIilhJNksI7KU9LnGT7EWAN4GNdjSoiInrKgJcGS1rF9mPA8sC1tWwN4Blg6ohEFxERPWGwfiYXAHsA0yiP61XLMAMbdjGuiIjoIQMmE9t71L8bjFw4ERHRi5r0gEfSlsD6rePb/mGXYoqIiB4zZDKRdBawJXA78HwtNpBkEhERQLOayetsb7YoM5c0CTgZGAOcafvEtuE7Al+jJKv9bV/cMmwecFv9eI/tvRYlhoiI6L4myeT/JG1m+47hzFjSGOBUYBfKreunSJrcNp97gHcDH+1nFk/Z3no4y4yIiNHRJJmcQ0ko91EuCxZg21sOMd12wAzbMwEkXUi5QeT8ZGL77jrs+f5mEBERvaFJMjkLeBfllNNwDvpjgVktn2cD2w9j+uUlTQXmAifavnQY00ZExAhqkkzusT15Eeatfso8jOnH254jaUPgakm32f7jQguQDgUOBRg/fvwihBgREYtDk2TyO0kXAD+mnOYCGl0aPBtYt+XzOGBO08Bsz6l/Z0q6FtgG+GPbOGcAZwBMnDhxOIkqIiIWoybJZAVKEtm1pazJpcFTgAmSNgD+DOwPHNgkKEmrA0/afkbSWsDfA19oMm1ERIy8IZOJ7fcsyoxtz5V0BOUmkWOAs2zfLukEYKrtyZJeC1wCrA7sKel425sDmwLfqA3zL6G0mQzrarKIiBg5jXrALyrbVwBXtJUd0/J+CuX0V/t0NwBbdDO2iIhYfPJckoiI6NiAyUTSh+rfvx+5cCIiohcNVjPpayv5+kgEEhERvWuwNpM7Jd0NrC3p1pbypj3gIyJiKTHY80wOkPQKytVYucliREQMaNCruWzfB2wlaTlgo1o83fZzXY8sIiJ6RpPnmbwJOBe4m3KKa11Jh9i+vsuxRUREj2jSz+QrwK62pwNI2gj4LrBtNwOLiIje0aSfybJ9iQTA9u+BZbsXUkRE9JomNZOpkr4FnFc/HwRM615IERHRa5okkw8AhwNHUtpMrgdO62ZQERHRW5rc6PEZSrvJV7ofTkRE9KLcmysiIjqWZBIRER1rnEwkrdjNQCIioncNmUwk7SDpDuDO+nkrSWmAj4iI+ZrUTL4KvA14EMD2b4AduxlURET0lkanuWzPaiua14VYIiKiRzXpZzJL0g6A6w0fj6Se8oqIiIBmNZPDKJ0WxwKzga2BD3YzqIiI6C1NaiYb2z6otaA+yveX3QkpIiJ6TZOaSX+P7c2jfCMiYr4BayaSXg/sQHls74dbBq0CjOl2YBER0TsGq5ksB6xESTgrt7weA/ZtMnNJkyRNlzRD0tH9DN9R0s2S5krat23YIZL+UF+HNP1CEREx8gZ7Bvx1wHWSzrb9p+HOWNIY4FRgF0rD/RRJk23f0TLaPcC7gY+2TbsGcCwwETAwrU778HDjiIiI7mvSAP+kpC8CmwPL9xXafssQ020HzLA9E0DShcDewPxkYvvuOuz5tmnfBlxl+6E6/CpgEuUJjxERsYRp0gB/PvA7YAPgeMqz4Kc0mG4s0NrZcXYta6LRtJIOlTRV0tT777+/4awjImJxa5JM1rT9LeA529fZ/hfgdQ2mUz9lbhhXo2ltn2F7ou2Ja6+9dsNZR0TE4tYkmTxX/94raXdJ2wDjGkw3G1i35fM4YE7DuDqZNiIiRliTZPJZSasCH6E0lJ8J/HuD6aYAEyRtUG/Dsj8wuWFcVwK7Slpd0urArrUsIiKWQIM2wNcrsibYvgx4FHhz0xnbnivpCEoSGAOcZft2SScAU21PlvRa4BJgdWBPScfb3tz2Q5L+kwVtMyf0NcZHRMSSZ9BkYnuepL0ot6EfNttXAFe0lR3T8n4KA5wys30WcNaiLDciIkZWk0uDb5B0CvA94G99hbZv7lpUERHRU5okkx3q3xNaygwM1c8kIiKWEkMmE9uN20kiImLp1OhJixEREYNJMomIiI4lmURERMeaNMBTnwG/fuv4ts/tUkwREdFjhkwmks4DXgXcAsyrxQaSTCIiAmhWM5kIbGa76U0aIyJiKdOkzeS3wCu6HUhERPSuJjWTtYA7JN0EPNNXaHvjgQhrAAALx0lEQVSvrkUVERE9pUkyOa7bQURERG9r0gP+upEIJCIieteQbSaSXidpiqQnJD0raZ6kx0YiuIiI6A1NGuBPAQ4A/gCsALyvlkVERAANOy3aniFpjO15wLcl3dDluCIiooc0SSZP1sfu3iLpC8C9wIrdDSsiInpJk9Nc76rjHUF5ONa6wD91M6iIiOgtTa7m+pOkFYBX2j5+BGKKiIge0+Rqrj0p9+X6af28taTJ3Q4sIiJ6R5PTXMcB2wGPANi+hXIH4YiICKBZMplr+9GuRxIRET2r0Y0eJR0IjJE0QdLXgUaXBkuaJGm6pBmSju5n+Eslfa8O/5Wk9Wv5+pKeknRLfZ0+jO8UEREjrEky+Tdgc8pNHr8LPAYcNdREksYApwK7AZsBB0jarG209wIP23418FXgpJZhf7S9dX0d1iDOiIgYJU2u5noS+FR9Dcd2wAzbMwEkXQjsDdzRMs7eLLiR5MXAKZI0zOVERMQoa3I110RJP5R0s6Rb+14N5j0WmNXyeXYt63cc23OBR4E167ANJP1a0nWS3jhAbIdKmipp6v33398gpIiI6IYmPeDPBz4G3AY8P4x591fDaH9a40Dj3AuMt/2gpG2BSyVtbnuhG0zaPgM4A2DixIl5EmRExChpkkzut70o/UpmU3rL9xkHzBlgnNmSlgFWBR6qjwh+BsD2NEl/BDYCpi5CHBER0WVNksmxks4Efs7CT1r84RDTTQEmSNoA+DOwP3Bg2ziTgUOA/wP2Ba62bUlrU5LKPEkbAhOAmU2+UEREjLwmyeQ9wCbAsiw4zWVg0GRie66kI4ArgTHAWbZvl3QCMLXWdr4FnCdpBvAQJeEA7AicIGkuMA84zPZDw/tqERExUpokk61sb7EoM7d9BXBFW9kxLe+fBt7Rz3Q/AH6wKMuMiIiR16SfyY399A+JiIiYr0nN5A3AIZLuorSZCLDtLbsaWURE9IwmyWRS16OIiIie1uh5JiMRSERE9K4mbSYRERGDSjKJiIiOJZlERETHkkwiIqJjSSYREdGxJJOIiOhYkklERHQsySQiIjqWZBIRER1LMomIiI4lmURERMeSTCIiomNJJhER0bEkk4iI6FiSSUREdCzJJCIiOpZkEhERHUsyiYiIjnU1mUiaJGm6pBmSju5n+Eslfa8O/5Wk9VuGfbKWT5f0tm7GGRERnelaMpE0BjgV2A3YDDhA0mZto70XeNj2q4GvAifVaTcD9gc2ByYBp9X5RUTEEqibNZPtgBm2Z9p+FrgQ2LttnL2Bc+r7i4G3SlItv9D2M7bvAmbU+UVExBJomS7Oeywwq+XzbGD7gcaxPVfSo8CatfzGtmnHti9A0qHAofXjE5KmL57QO7IW8EAnM9BJiymS0dfxuuB4LZ5IRl/n28W7sy7mU9ZFnyO+0XEM63U8B7qbTPr7b7vhOE2mxfYZwBnDD617JE21PXG041gSZF0skHWxQNbFAi+mddHN01yzgXVbPo8D5gw0jqRlgFWBhxpOGxERS4huJpMpwARJG0hajtKgPrltnMnAIfX9vsDVtl3L969Xe20ATABu6mKsERHRga6d5qptIEcAVwJjgLNs3y7pBGCq7cnAt4DzJM2g1Ej2r9PeLun7wB3AXOBw2/O6FetitkSddhtlWRcLZF0skHWxwItmXahUBCIiIhZdesBHRETHkkwiIqJjSSYREdGxbvYzWSpI2oTSY38spS/MHGCy7TtHNbAYVXW7GAv8yvYTLeWTbP909CIbeZK2A2x7Sr1V0iTgd7avGOXQRpWkc20fPNpxLC5pgO+ApE8AB1BuFTO7Fo+jXJV2oe0TRyu2JYmk99j+9mjHMVIkHQkcDtwJbA18yPaP6rCbbf/daMY3kiQdS7k/3zLAVZS7YFwL7AxcaftzoxfdyJHU3i1CwJuBqwFs7zXiQS1mSSYdkPR7YHPbz7WVLwfcbnvC6ES2ZJF0j+3xox3HSJF0G/B620/UO2FfDJxn+2RJv7a9zagGOILqutgaeClwHzDO9mOSVqDU2rYc1QBHiKSbKV0dzmTBXT6+y4LuENeNXnSLR05zdeZ5YB3gT23lr6zDlhqSbh1oEPDykYxlCTCm79SW7bsl7QRcLGk9+r9V0IvZ3NpH7ElJf7T9GIDtpyQtTfvIROBDwKeAj9m+RdJTL4Yk0ifJpDNHAT+X9AcW3NRyPPBq4IhRi2p0vBx4G/BwW7mAG0Y+nFF1n6Stbd8CUGsoewBnAVuMbmgj7llJL7P9JLBtX6GkVVmKfnDZfh74qqSL6t+/8CI7/r6ovsxIs/1TSRtRbo8/lnLgnA1M6aEe+4vLZcBKfQfQVpKuHflwRtXBlDs3zGd7LnCwpM7v8dpbdrT9DMw/oPZZlgW3Ulpq2J4NvEPS7sBjox3P4pQ2k4iI6Fj6mURERMeSTCIiomNJJrHEkXScpI/W9ydI2rmTebSVryPp4vp+J0mX1fd7STq6vt+ndq7riKT1Jf220/m0zG/+upD0Rkm3S7pF0ti+77QI83y3pHVaPp+5OL57LH3SAB9LNNvHLOb5zaE8O6e9fDILnrezD+WCgjsW57I71bYuDgK+1NIZ9AXfqaF3A7+lPnzO9vsWOcBYqqVmEksESZ+SNF3S/wAbt5SfLWnf+v5ESXdIulXSl2rZnpJ+JenXkv5HUmuflq0kXS3pD5L+tY7fb22h/kI/RdIOwF7AF+uv/lfVDmd9402QNK2f6V9dl/8bSTdLelXb8PUl/W8ddnNdDpJeKen6uqzf1hrHmPq9fyvpNkn/3rouJL0PeCdwjKTzW79TnfZLdbpbJf1bLT9G0pQ6zzNU7Evp/3B+Xf4Kkq6VNLFOc0Cdz28lndTyXZ6Q9Ln6XW9sW+exlErNJEadpG0pPYG3oWyTNwPT2sZZA3g7sIltS1qtDvoF8Lpa9j7g48BH6rAtgdcBKwK/lnT5ULHYvkHl1heX2e47HfZoS7+R9wBn9zPp+cCJti+RtDzlh9r/1zL8r8Autp+WNIHS+3kicCD1tiKSxgAvo/QYH2v7NXX5q7UuyPaZkt7QF6NKL/s+hwIbANvUB9StUctPsX1Cnd95wB512iOAj9qeWodR/64DnETpG/Iw8DNJ+9i+tK7PG21/StIXgH8FPjvUuo0Xt9RMYknwRuAS20/WHtLt9zGCck3+08CZkv4ReLKWjwOuVLltx8eAzVum+ZHtp2w/AFxD6Q+0KM4E3lMP9vsBF7QOlLQy5eB/CYDtp2snvVbLAt+scV4E9LVLTKnzPg7YwvbjwExgQ0lflzSJ4fVH2Bk4vfZrwfZDtfzNtQZ3G/AWFl5P/XktcK3t++u8zgd2rMOepZwGhJL01x9GfPEilWQSS4pBOzzVA9p2wA8obRp9d979OuVX9xbA+4HlB5nnonaq+gHlZoV7ANNsP9g2vMktUv4d+AuwFaVGshyA7espB+k/Ux5hfbDth+t411JuGHnmMGIVbd+z1pROA/at6+mbLLyeBprPQJ7zgg5q88gZjiDJJJYM1wNvr+fsVwb2bB9B0krAqvW25UdRTgUBrEo5EMMLe1TvLWl5SWsCO1FqAU08Dqzc98H208CVwH8DL7j7ca1NzZa0T431pZJe1jbaqsC9tRf4u4Axddz1gL/a/ibwLeDvJK0FvMT2D4DPAMO5y/DPgMMkLVPnvwYLEscDdT22NtYv9F1b/Ap4k6S1ao3sAOBFcx+pWPySTGLU2b4Z+B5wC6UW8L/9jLYycJnKDSWvo/zSBzgOuEjS/wIPtE1zE3A5cCPwn/VKriYuBD5WG/X7GtLPp/zi/9kA07wLOLLGdwPwirbhpwGHSLoR2Aj4Wy3fCbhF0q+BfwJOptya51pJt1DaZz7ZMG4otZh7gFsl/QY40PYjlNrIbcClLJxUzwZO72uA7yu0fW9d7jXAb4Cb+26jH9Gf3E4logGVPiur2v7MaMcSsSTKuc6IIUi6BHgVpeE6IvqRmklERHQsbSYREdGxJJOIiOhYkklERHQsySQiIjqWZBIRER1LMomIiI79P7RF15jPT2SMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0228ba8940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart visually showing mean rates of missed appointments for the disability classifications\n",
    "ax3 = df.groupby('disabled').appt_missed_num.mean().plot(kind='bar');\n",
    "ax3.set_xlabel('disability classification')\n",
    "ax3.set_ylabel('mean rate of missed appointment');\n",
    "ax3.set_title('Rates of Missed Appointments For Disability Classifications')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">People without disabilities have a rate of missed appointments at 20%, and while those with the disability label '1' have a lower rate at 18%, that rate then increases for each following disability label with '2' at 20%, '3' at 23%, and '4' at 33%. This is a concerning finding because it's likely that a person with a higher classification of disability would require a higher amount of medical services, and yet they are the most likely to miss an appointment. It's not possible to prove the causation in the project, but a possible cause could be a lack of proper assistance programs such as at-home care or transportation to medical facilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does Receiving a Text Message Affect Rates of Missed Appointments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating masks to separate patients by whether or not they received a text message\n",
    "yes_txt = df.sms_rcvd == 1\n",
    "no_txt = df.sms_rcvd == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27574544839637"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[yes_txt].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16703311346525418"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.appt_missed_num[no_txt].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Text Reminders to Rates of Missed Appointments')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0228c25ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart visually showing mean rates of missed appointments for the disability classifications\n",
    "ax4 = df.groupby('sms_rcvd').appt_missed_num.mean().plot(kind='bar');\n",
    "ax4.set_xticklabels(['No', 'Yes'])\n",
    "ax4.set_xlabel('received text reminder')\n",
    "ax4.set_ylabel('mean rate of missed appointment');\n",
    "ax4.set_title('Text Reminders to Rates of Missed Appointments')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Surprisingly, patients who did receive a text message reminder for their appointment were actually more likely to miss that appointment. While patients who did not receive a text missed their appointment at a rate of 17%, those who did get a text missed their appointment at a rate of 28%. It's not possible within the scope of this project to find a cause for this discrepency, though it may relate to a potential difference between the populations of those who are receiving text reminders compared to those who do not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Focusing on the dependent variable of whether a patient attended or missed their appointment and the three independent variables of patient age, disability status, and text reminders, several unexpected results were found. As a group, adults appeared to have a higher rate of 26% missed appointments compared to those 17 and under at 21% and those 65 and over at 15%. While those classified as not having a disability had a higher rate of missed appointments at 20% compared to those with a disabilitiy type '1' at 18%, the rate of missed appointments did increase with each disability classification. The highest rate was 33% for those with the disability type '4'. Finally, patients who received text messages were actually more likely to miss their appointment at a rate of 28%, compared to 17% for those who did not receive text messages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Further research into these findings would look into what factors are causing adults specifically to be more likely to miss their appointments compared to other age groups. Also looking into the definitions of the different disability classifications and what potential obstacles they may be facing in making their appointments. Then, looking into the text reminder program and seeing if there are differences between the group of patients who receive messages from the general patient population. In addition, bringing in demographic information about the various neighborhoods that were included in the original dataset would allow that column to be used to see if there are significant differences in the rates of missed appointments depending on location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The limitations of this project are primarily that of scope, time, and the dataset. Without further study into any of the correlations found, it is not possible to draw definitive conclusions about the causes of these findings. Recommendations for further areas of study are provided above under 'Further Research'. In addition, there were some aspects of the data which proved to be unclear, such as the classification system for disabilities, which was noted on the source website as being a boolean value rather than having five possible classes. Also, having a time field rather than just dates in the AppointmentDay field could be useful in determining if certain times of the day have differing rates of missed appointments. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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