project_main_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Brazillian Medical Appointments\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "In this project patients' appointment schedule records would be analyzed  to show how certain recorded patient information like Age, Alcholic habits, Gender among a few others did or did not have significant relationships with  patients actually showing up for their appointments.  \n",
    "This particular dataset consists of information of over 100,000 medical appointments in Brazil. The main focus of the dataset is whether the patients showed up for their appointments or not. The dataset consists of 14 columns:\n",
    "\n",
    "**1- PatientId:** The ID of each patient.\n",
    "\n",
    "**2- AppointmentID:** The ID of each appointment.\n",
    "\n",
    "**3- Gender:** The gender of each patient.\n",
    "\n",
    "**4- ScheduledDay:** The day at which the patient set up his appointment.\n",
    "\n",
    "**5- AppointmentDay:** The day at which the patient should show up for his appointment.\n",
    "\n",
    "**6- Age:** The age of each patient.\n",
    "\n",
    "**7- Neighbourhood:** The place at which the appointment takes place.\n",
    "\n",
    "**8- Scholarship:** It indicates whether the patient is enrolled in Brasilian welfare program or not.\n",
    "\n",
    "**9- Hipertension:** It indicates whether the patient suffers from Hipertension or not.\n",
    "\n",
    "**10- Diabetes:** It indicates whether the patient suffers from Diabetes or not.\n",
    "\n",
    "**11- Alcoholism:** It indicates whether the patient suffers from Alcoholism or not.\n",
    "\n",
    "**12- Handcap:** The number of handicaps that the patient has.\n",
    "\n",
    "**13- SMS_received:** It indicates whether the patient received SMS or not.\n",
    "\n",
    "**14- No-show:** It indicates whether the patient showed up for the appointment or not.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions for Analysis\n",
    "Q1. Can gender or age be associated with showups or misses?\n",
    "\n",
    "Q2. Did wait period (time between schedule and appointment day) affect showing up?\n",
    "\n",
    "Q3.  what were the showup characteristics across the various days of the week?\n",
    "\n",
    "Q4. Did patient physiological factors (hypertension, diabetes, if they were handicapped or alcoholics) affect showing up?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing modules\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": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading the dataset and viewing the first five rows**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df= pd.read_csv(\"noshowappointments-kagglev2-may-2016.csv\")\n",
    "schedule_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assessing the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the dataset contains exactly 110,527 rows and 14 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "schedule_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-no missing value in any column.\n",
    "\n",
    "-PatientId and AppointmentID would be converted  string type since they don't have any numeric usability.\n",
    "\n",
    "-**ScheduledDay** and **AppointmentDay** are objects datatype not DayTime.\n",
    "\n",
    "-**No-show** column's datatype needs to be integers to help us make statistic operations on it.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking column names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension',\n",
       "       'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-some columns have case and spacing discrepancies which would have to be cleaned.\n",
    "\n",
    "-some columns also have typographical errors like the \"handcap\" and \"hipertension\" columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**checking for null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking for duplicated rows**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there are no duplicate rows in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking for unique values across all columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-The number of unique appointments >  unique patient IDs. This suggests that some patients had more than one appointment in the timeframe.\n",
    "\n",
    "-There are 5 uniques values in the Handcap column which is supposed to hold two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking for negative values in the age column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
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       "      <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>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06T08:58:13Z</td>\n",
       "      <td>2016-06-06T00:00:00Z</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "99832  4.659432e+14        5775010      F  2016-06-06T08:58:13Z   \n",
       "\n",
       "             AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  \\\n",
       "99832  2016-06-06T00:00:00Z   -1         ROMÃO            0             0   \n",
       "\n",
       "       Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "99832         0           0        0             0      No  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.loc[ schedule_df['Age'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one row with index 99832"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check in column value ranges are within reasonalble limits**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avg age of the patients is 37 years old.\n",
    "\n",
    "Min Age is -1 to confirm the discovery earlier.\n",
    "\n",
    "Max Age is 115 and this could be logically possible.\n",
    "\n",
    "Handcap has min of 0 and max of 4 value to further confirm the discovery earlier.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fixing PatientId type**\n",
    "\n",
    "The patientID and AppointmentID would have to be changed to string type since they do not have any numeric usability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to drop the .0 of the float type\n",
    "schedule_df['PatientId'] = schedule_df['PatientId'].astype(int)\n",
    "#to change the type to string\n",
    "schedule_df['PatientId'] = schedule_df['PatientId'].astype(str) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix AppointmentID datatype\n",
    "schedule_df['AppointmentID'] = schedule_df['AppointmentID'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 object\n",
      "AppointmentID     110527 non-null object\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: int64(7), object(7)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**fixing schedule date and appointmentDay column datatypes**\n",
    "\n",
    "while the data was being assessed it was also noticed that the AppointmentDay time are all set to 0s, so time would be dropped from both 'AppointmentDay\" and \"scheduleDay\" columns since we don't have the actual AppointmentDay time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removeing time for both `AppointmentDay` and `ScheduledDay`\n",
    "schedule_df['AppointmentDay'] = schedule_df['AppointmentDay'].str[:-10]\n",
    "schedule_df['ScheduledDay'] = schedule_df['ScheduledDay'].str[:-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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>29872499824296</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</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>558997776694438</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0   29872499824296       5642903      F   2016-04-29     2016-04-29   62   \n",
       "1  558997776694438       5642503      M   2016-04-29     2016-04-29   56   \n",
       "\n",
       "     Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  \\\n",
       "0  JARDIM DA PENHA            0             1         0           0        0   \n",
       "1  JARDIM DA PENHA            0             0         0           0        0   \n",
       "\n",
       "   SMS_received No-show  \n",
       "0             0      No  \n",
       "1             0      No  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing schedule date data type to datetime \n",
    "schedule_df['ScheduledDay'] = pd.to_datetime(schedule_df['ScheduledDay'])\n",
    "#changing appointment date datatype to datetime \n",
    "schedule_df['AppointmentDay'] = pd.to_datetime(schedule_df['AppointmentDay'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 object\n",
      "AppointmentID     110527 non-null object\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null datetime64[ns]\n",
      "AppointmentDay    110527 non-null datetime64[ns]\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: datetime64[ns](2), int64(7), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a wait_time column (in days)**\n",
    "\n",
    "This column would hold values for the time difference(in days) between the day the patient booked an appointment and the  day the appointment was scheduled to hold.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subtract the two columns and assign the result to the new column \"wait_time\"\n",
    "wait_time = schedule_df['AppointmentDay'].dt.date - schedule_df['ScheduledDay'].dt.date\n",
    "wait_time =wait_time.dt.days\n",
    "\n",
    "#  insert it in the column next to Appointment for legibility.\n",
    "schedule_df.insert(5, 'Wait_time',wait_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>Wait_time</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",
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       "      <th>0</th>\n",
       "      <td>29872499824296</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
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       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</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",
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       "      <td>2016-04-29</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>867951213174</td>\n",
       "      <td>5642828</td>\n",
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       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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>8841186448183</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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 AppointmentDay  \\\n",
       "0   29872499824296       5642903      F   2016-04-29     2016-04-29   \n",
       "1  558997776694438       5642503      M   2016-04-29     2016-04-29   \n",
       "2    4262962299951       5642549      F   2016-04-29     2016-04-29   \n",
       "3     867951213174       5642828      F   2016-04-29     2016-04-29   \n",
       "4    8841186448183       5642494      F   2016-04-29     2016-04-29   \n",
       "\n",
       "   Wait_time  Age      Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "0          0   62    JARDIM DA PENHA            0             1         0   \n",
       "1          0   56    JARDIM DA PENHA            0             0         0   \n",
       "2          0   62      MATA DA PRAIA            0             0         0   \n",
       "3          0    8  PONTAL DE CAMBURI            0             0         0   \n",
       "4          0   56    JARDIM DA PENHA            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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**checking for unrealistric wait_time values using wait_time**\n",
    "\n",
    "Rows where recorded appointment date is before recorded schedule date.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Wait_time</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>27033</th>\n",
       "      <td>7839272661752</td>\n",
       "      <td>5679978</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>-1</td>\n",
       "      <td>38</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>7896293967868</td>\n",
       "      <td>5715660</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>-1</td>\n",
       "      <td>19</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>24252258389979</td>\n",
       "      <td>5664962</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>-1</td>\n",
       "      <td>22</td>\n",
       "      <td>CONSOLAÇÃO</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>998231581612122</td>\n",
       "      <td>5686628</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>-6</td>\n",
       "      <td>81</td>\n",
       "      <td>SANTO ANTÔNIO</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>3787481966821</td>\n",
       "      <td>5655637</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>TABUAZEIRO</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>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PatientId AppointmentID Gender ScheduledDay AppointmentDay  \\\n",
       "27033    7839272661752       5679978      M   2016-05-10     2016-05-09   \n",
       "55226    7896293967868       5715660      F   2016-05-18     2016-05-17   \n",
       "64175   24252258389979       5664962      F   2016-05-05     2016-05-04   \n",
       "71533  998231581612122       5686628      F   2016-05-11     2016-05-05   \n",
       "72362    3787481966821       5655637      M   2016-05-04     2016-05-03   \n",
       "\n",
       "       Wait_time  Age  Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "27033         -1   38    RESISTÊNCIA            0             0         0   \n",
       "55226         -1   19  SANTO ANTÔNIO            0             0         0   \n",
       "64175         -1   22     CONSOLAÇÃO            0             0         0   \n",
       "71533         -6   81  SANTO ANTÔNIO            0             0         0   \n",
       "72362         -1    7     TABUAZEIRO            0             0         0   \n",
       "\n",
       "       Alcoholism  Handcap  SMS_received No-show  \n",
       "27033           0        1             0     Yes  \n",
       "55226           0        1             0     Yes  \n",
       "64175           0        0             0     Yes  \n",
       "71533           0        0             0     Yes  \n",
       "72362           0        0             0     Yes  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#query for rows with wait_time less than 0\n",
    "schedule_df.query('Wait_time<0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop records with wrong entry of appointement date compared with scheduled date\n",
    "schedule_df.drop ( index = (schedule_df.query('Wait_time<0').index), axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Wait_time</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Wait_time, Age, Neighbourhood, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, SMS_received, No-show]\n",
       "Index: []"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.query('Wait_time<0')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning the age column**\n",
    "\n",
    "we would first query the table to see where we discovered the negative age during data assessment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>Wait_time</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>99832</th>\n",
       "      <td>465943158731293</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PatientId AppointmentID Gender ScheduledDay AppointmentDay  \\\n",
       "99832  465943158731293       5775010      F   2016-06-06     2016-06-06   \n",
       "\n",
       "       Wait_time  Age Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "99832          0   -1         ROMÃO            0             0         0   \n",
       "\n",
       "       Alcoholism  Handcap  SMS_received No-show  \n",
       "99832           0        0             0      No  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.query(\"Age < 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since it is just a row, it can simply be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the row using the index where age = -1.\n",
    "schedule_df.drop(index=schedule_df[schedule_df[\"Age\"]==-1].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wait_time</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>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.184345</td>\n",
       "      <td>37.089386</td>\n",
       "      <td>0.098271</td>\n",
       "      <td>0.197257</td>\n",
       "      <td>0.071869</td>\n",
       "      <td>0.030401</td>\n",
       "      <td>0.022231</td>\n",
       "      <td>0.321043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.255153</td>\n",
       "      <td>23.109885</td>\n",
       "      <td>0.297682</td>\n",
       "      <td>0.397929</td>\n",
       "      <td>0.258272</td>\n",
       "      <td>0.171690</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>0.466879</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>0.000000</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>4.000000</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>15.000000</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>179.000000</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": [
       "           Wait_time            Age    Scholarship   Hipertension  \\\n",
       "count  110521.000000  110521.000000  110521.000000  110521.000000   \n",
       "mean       10.184345      37.089386       0.098271       0.197257   \n",
       "std        15.255153      23.109885       0.297682       0.397929   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000      18.000000       0.000000       0.000000   \n",
       "50%         4.000000      37.000000       0.000000       0.000000   \n",
       "75%        15.000000      55.000000       0.000000       0.000000   \n",
       "max       179.000000     115.000000       1.000000       1.000000   \n",
       "\n",
       "            Diabetes     Alcoholism        Handcap   SMS_received  \n",
       "count  110521.000000  110521.000000  110521.000000  110521.000000  \n",
       "mean        0.071869       0.030401       0.022231       0.321043  \n",
       "std         0.258272       0.171690       0.161494       0.466879  \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       1.000000  \n",
       "max         1.000000       1.000000       4.000000       1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add a weekday column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create day names from appointment Day column and store it in the weekday column\n",
    "schedule_df['weekday'] = pd.to_datetime(schedule_df['AppointmentDay']).dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>29872499824296</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>867951213174</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId AppointmentID Gender ScheduledDay AppointmentDay  \\\n",
       "0   29872499824296       5642903      F   2016-04-29     2016-04-29   \n",
       "1  558997776694438       5642503      M   2016-04-29     2016-04-29   \n",
       "2    4262962299951       5642549      F   2016-04-29     2016-04-29   \n",
       "3     867951213174       5642828      F   2016-04-29     2016-04-29   \n",
       "4    8841186448183       5642494      F   2016-04-29     2016-04-29   \n",
       "\n",
       "   Wait_time  Age      Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "0          0   62    JARDIM DA PENHA            0             1         0   \n",
       "1          0   56    JARDIM DA PENHA            0             0         0   \n",
       "2          0   62      MATA DA PRAIA            0             0         0   \n",
       "3          0    8  PONTAL DE CAMBURI            0             0         0   \n",
       "4          0   56    JARDIM DA PENHA            0             1         1   \n",
       "\n",
       "   Alcoholism  Handcap  SMS_received No-show weekday  \n",
       "0           0        0             0      No  Friday  \n",
       "1           0        0             0      No  Friday  \n",
       "2           0        0             0      No  Friday  \n",
       "3           0        0             0      No  Friday  \n",
       "4           0        0             0      No  Friday  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.head()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**cleeaning the handicap column**\n",
    "\n",
    "Set values greater than 1 in the column to 1, since they should either be True or False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule_df.loc[schedule_df['Handcap'] > 1, 'Handcap'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108282\n",
       "1      2239\n",
       "Name: Handcap, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df['Handcap'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning no-show column**\n",
    "\n",
    "from the dataset description the \"no-show\" column holds indicators for when patients misses an appointment so a \"yes\" there means the patient was absent and a \"no\" means the patient actually went for the appointment. This would to a large extend look counter-intuitive.\n",
    "\n",
    "So, for sake of simplicity the column would be renamed to \"show_up\" and all the values interchanged so that a \"yes\" means the patient went for the appointment while a value of \"no\" would mean absence.\n",
    "\n",
    "we would start this process by first converting the yes/no strings to 1/0 integers the performing the equivalent of the afore described operatoons on it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first convert the \"yes\" and \"no\" string indicators to integer values of 1 and 0 respectively.\n",
    "schedule_df['No-show'] = schedule_df['No-show'].apply(lambda val: 0 if val == 'No' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: No-show, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df['No-show'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#then rename the column \"No-show\" to show up\n",
    "schedule_df.rename(columns={'No-show':'show_up'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Wait_time', 'Age', 'Neighbourhood', 'Scholarship',\n",
       "       'Hipertension', 'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received',\n",
       "       'show_up', 'weekday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#flip the values(change all 1s to 0s and 0s to 1s) to reflect the logical change in the \n",
    "#column name from no_show to show_up\n",
    "schedule_df['show_up'] = schedule_df['show_up'].apply(lambda val: 1 if val == 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.show_up.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the first five rows in the column that were 0s have now been changed to 1s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**cleaning column names**\n",
    "\n",
    "a few columms names like Hipertension and Handcap both contain typographical mistakes while PatientId, AppointmentID, ScheduledDay and AppointmentDay have issues with spacing inbetween words. The whole column names would then be changed to lowercase for consistency and spaces created with underscores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule_df.rename(columns={'Hipertension':'hypertension','Handcap':'handicap','PatientId':'patient_id ','AppointmentID':'appointment_id','ScheduledDay':'scheduled_day','AppointmentDay':'appointment_day'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_id ', 'appointment_id', 'Gender', 'scheduled_day',\n",
       "       'appointment_day', 'Wait_time', 'Age', 'Neighbourhood', 'Scholarship',\n",
       "       'hypertension', 'Diabetes', 'Alcoholism', 'handicap', 'SMS_received',\n",
       "       'show_up', 'weekday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert all column names to lowercase for consistency\n",
    "schedule_df.columns= schedule_df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_id ', 'appointment_id', 'gender', 'scheduled_day',\n",
       "       'appointment_day', 'wait_time', 'age', 'neighbourhood', 'scholarship',\n",
       "       'hypertension', 'diabetes', 'alcoholism', 'handicap', 'sms_received',\n",
       "       'show_up', 'weekday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check\n",
    "schedule_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Final check of the dataframe**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110521 entries, 0 to 110526\n",
      "Data columns (total 16 columns):\n",
      "patient_id         110521 non-null object\n",
      "appointment_id     110521 non-null object\n",
      "gender             110521 non-null object\n",
      "scheduled_day      110521 non-null datetime64[ns]\n",
      "appointment_day    110521 non-null datetime64[ns]\n",
      "wait_time          110521 non-null int64\n",
      "age                110521 non-null int64\n",
      "neighbourhood      110521 non-null object\n",
      "scholarship        110521 non-null int64\n",
      "hypertension       110521 non-null int64\n",
      "diabetes           110521 non-null int64\n",
      "alcoholism         110521 non-null int64\n",
      "handicap           110521 non-null int64\n",
      "sms_received       110521 non-null int64\n",
      "show_up            110521 non-null int64\n",
      "weekday            110521 non-null object\n",
      "dtypes: datetime64[ns](2), int64(9), object(5)\n",
      "memory usage: 14.3+ MB\n"
     ]
    }
   ],
   "source": [
    "schedule_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id          62298\n",
       "appointment_id     110521\n",
       "gender                  2\n",
       "scheduled_day         111\n",
       "appointment_day        27\n",
       "wait_time             129\n",
       "age                   103\n",
       "neighbourhood          81\n",
       "scholarship             2\n",
       "hypertension            2\n",
       "diabetes                2\n",
       "alcoholism              2\n",
       "handicap                2\n",
       "sms_received            2\n",
       "show_up                 2\n",
       "weekday                 6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>wait_time</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>show_up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "      <td>110521.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.184345</td>\n",
       "      <td>37.089386</td>\n",
       "      <td>0.098271</td>\n",
       "      <td>0.197257</td>\n",
       "      <td>0.071869</td>\n",
       "      <td>0.030401</td>\n",
       "      <td>0.020259</td>\n",
       "      <td>0.321043</td>\n",
       "      <td>0.798102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.255153</td>\n",
       "      <td>23.109885</td>\n",
       "      <td>0.297682</td>\n",
       "      <td>0.397929</td>\n",
       "      <td>0.258272</td>\n",
       "      <td>0.171690</td>\n",
       "      <td>0.140884</td>\n",
       "      <td>0.466879</td>\n",
       "      <td>0.401419</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.000000</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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           wait_time            age    scholarship   hypertension  \\\n",
       "count  110521.000000  110521.000000  110521.000000  110521.000000   \n",
       "mean       10.184345      37.089386       0.098271       0.197257   \n",
       "std        15.255153      23.109885       0.297682       0.397929   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000      18.000000       0.000000       0.000000   \n",
       "50%         4.000000      37.000000       0.000000       0.000000   \n",
       "75%        15.000000      55.000000       0.000000       0.000000   \n",
       "max       179.000000     115.000000       1.000000       1.000000   \n",
       "\n",
       "            diabetes     alcoholism       handicap   sms_received  \\\n",
       "count  110521.000000  110521.000000  110521.000000  110521.000000   \n",
       "mean        0.071869       0.030401       0.020259       0.321043   \n",
       "std         0.258272       0.171690       0.140884       0.466879   \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       1.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "             show_up  \n",
       "count  110521.000000  \n",
       "mean        0.798102  \n",
       "std         0.401419  \n",
       "min         0.000000  \n",
       "25%         1.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>wait_time</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>show_up</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29872499824296</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>558997776694438</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_id  appointment_id gender scheduled_day appointment_day  \\\n",
       "0   29872499824296        5642903      F    2016-04-29      2016-04-29   \n",
       "1  558997776694438        5642503      M    2016-04-29      2016-04-29   \n",
       "\n",
       "   wait_time  age    neighbourhood  scholarship  hypertension  diabetes  \\\n",
       "0          0   62  JARDIM DA PENHA            0             1         0   \n",
       "1          0   56  JARDIM DA PENHA            0             0         0   \n",
       "\n",
       "   alcoholism  handicap  sms_received  show_up weekday  \n",
       "0           0         0             0        1  Friday  \n",
       "1           0         0             0        1  Friday  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>wait_time</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>show_up</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>92134931435557</td>\n",
       "      <td>5630323</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</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",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>377511518121127</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>41</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</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",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            patient_id  appointment_id gender scheduled_day appointment_day  \\\n",
       "110525   92134931435557        5630323      F    2016-04-27      2016-06-07   \n",
       "110526  377511518121127        5629448      F    2016-04-27      2016-06-07   \n",
       "\n",
       "        wait_time  age neighbourhood  scholarship  hypertension  diabetes  \\\n",
       "110525         41   38   MARIA ORTIZ            0             0         0   \n",
       "110526         41   54   MARIA ORTIZ            0             0         0   \n",
       "\n",
       "        alcoholism  handicap  sms_received  show_up  weekday  \n",
       "110525           0         0             1        1  Tuesday  \n",
       "110526           0         0             1        1  Tuesday  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General dataset analysis\n",
    "\n",
    "To aid this study certain analysis would be conducted on the data to get a surface overview of all the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88207"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of appointments the patients were present\n",
    "schedule_df.loc[(schedule_df['show_up'] == 1)].count()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22314"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of missed appointments\n",
    "schedule_df.loc[(schedule_df['show_up'] == 0)].count()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181d372780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a visual plot of the above information with output as percentage to enhance legibilty\n",
    "label_Names = [\"present\", \"missed\"]\n",
    "data = [schedule_df.loc[(schedule_df['show_up'] == 1)].count()[0], schedule_df.loc[(schedule_df['show_up'] == 0)].count()[0]]\n",
    "explode = (0, 0.15)\n",
    "plt.axis('equal'); \n",
    "plt.pie(data,radius=1.5,shadow=True ,labels = label_Names,explode=explode, startangle=180,autopct='%0.2f%%',textprops = {\"fontsize\":15})\n",
    "plt.title(\"Percentage of missed and present appointemnts\",y=1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The percentage of missed appointments in the timeframe was about 20% with a total count of 22,314 while the count of present appointment was 88,207 with an approximate percentage of 80."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a histogram of all columns in the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181d370d30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedule_df.hist(figsize=(12,12));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">the age distribution could be said to be more densly popluated in the left with a peak around 0, while positive indicators for Alcoholism, hypertension and diabetes can be said to be fractional compared to negative indicators. The wait time is densly packed on Zero with a very steep decline as the number of days progresses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can age or gender be associated with showups or misses?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>wait_time</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>show_up</th>\n",
       "      <th>weekday</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29872499824296</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "      <td>senior_citizen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>558997776694438</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>Friday</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>Friday</td>\n",
       "      <td>senior_citizen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_id  appointment_id gender scheduled_day appointment_day  \\\n",
       "0   29872499824296        5642903      F    2016-04-29      2016-04-29   \n",
       "1  558997776694438        5642503      M    2016-04-29      2016-04-29   \n",
       "2    4262962299951        5642549      F    2016-04-29      2016-04-29   \n",
       "\n",
       "   wait_time  age    neighbourhood  scholarship  hypertension  diabetes  \\\n",
       "0          0   62  JARDIM DA PENHA            0             1         0   \n",
       "1          0   56  JARDIM DA PENHA            0             0         0   \n",
       "2          0   62    MATA DA PRAIA            0             0         0   \n",
       "\n",
       "   alcoholism  handicap  sms_received  show_up weekday       age_group  \n",
       "0           0         0             0        1  Friday  senior_citizen  \n",
       "1           0         0             0        1  Friday           adult  \n",
       "2           0         0             0        1  Friday  senior_citizen  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create  Age Groups for the sake of this analysis\n",
    "bins = [0, 13, 19, 60, 116]\n",
    "labels = ['child','teen','adult','senior_citizen']\n",
    "schedule_df['age_group'] = pd.cut(schedule_df['age'], bins=bins, labels=labels, include_lowest= True, right= False)\n",
    "#check\n",
    "schedule_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age group classifications are as follows;\n",
    "\n",
    ">child = 0-13 years\n",
    "\n",
    ">teen = 14-19 years\n",
    "\n",
    ">adult= 20-60 years\n",
    "\n",
    ">senior_citizen = 61 years and above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_group       show_up\n",
       "child           0           4306\n",
       "                1          16729\n",
       "teen            0           2040\n",
       "                1           5790\n",
       "adult           0          12727\n",
       "                1          47757\n",
       "senior_citizen  0           3241\n",
       "                1          17931\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get a count of show_up by age group\n",
    "age_group_stat=schedule_df.groupby([\"age_group\",\"show_up\"])['show_up'].count()\n",
    "age_group_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CyI4sttbOaq3Naa3tlYkb1FzTWnt5ks8lOWEYdkqSTwzTVwzzGZZf01prQ/2lw91S904yN8mNSW5KMne4u+pjhte4YlT7AwAAsC0Z5ZHFtXljko9W1VuTfCXJB4b6B5L8ZVWtyMQRxZcmSWvtlqr6WJJbkzyY5DWttYeSpKpem+TKJDOSfLC1dsu07gkAAMBWalrCYmvt2iTXDtPfysSdTNcc80CSF61l/XOTnDtF/VNJPrUJWwUAACDT8zuLAAAAbGGERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQGdkYbGqtq+qG6vqq1V1S1W9ZajvXVVfqqo7qurSqnrMUH/sML9iWL7XpG2dNdRvr6pjJtWPHWorqurMUe0LAADAtmaURxb/NcnC1toBSQ5McmxVLUjyjiR/2lqbm+T7SU4dxp+a5Puttd9O8qfDuFTVvklemuRpSY5N8udVNaOqZiR5T5LnJNk3yYnDWAAAADbSyMJim/DjYXbW8GhJFia5bKhflOQFw/Rxw3yG5UdVVQ31j7bW/rW1dmeSFUkOGR4rWmvfaq39LMlHh7EAAABspJFeszgcAVyW5N4kVyX5ZpIftNYeHIasTLLnML1nkruTZFj+wyS7Tq6vsc7a6gAAAGykkYbF1tpDrbUDk8zJxJHAfaYaNjzXWpY90nqnqk6rqiVVteS+++5bf+MAAADbuGm5G2pr7QdJrk2yIMnjq2rmsGhOknuG6ZVJnpwkw/Kdk9w/ub7GOmurT/X6F7TW5rfW5s+ePXtT7BIAAMBWbZR3Q51dVY8fpndI8u+S3Jbkc0lOGIadkuQTw/QVw3yG5de01tpQf+lwt9S9k8xNcmOSm5LMHe6u+phM3ATnilHtDwAAwLZk5vqHPGq7J7louGvpdkk+1lr7u6q6NclHq+qtSb6S5APD+A8k+cuqWpGJI4ovTZLW2i1V9bEktyZ5MMlrWmsPJUlVvTbJlUlmJPlga+2WEe4PAADANmNkYbG1tjzJ06eofysT1y+uWX8gyYvWsq1zk5w7Rf1TST610c0CAADwMNNyzSIAAABbFmERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAnQ0Ki1V1+IbUAAAA2Dps6JHFd21gDQAAgK3AzHUtrKrDkvxuktlV9V8nLdopyYxRNgYAAMD4rDMsJnlMkh2HcY+bVP/nJCeMqikAAADGa51hsbX2+SSfr6oLW2vfnqaeAAAAGLP1HVlc7bFVdUGSvSav01pbOIqmAAAAGK8NDYsfT/K+JH+R5KHRtQMAAMDmYEPD4oOttfeOtBMAAAA2Gxv60xn/p6r+c1XtXlW7rH6MtDMAAADGZkOPLJ4yPJ8xqdaSPGXTtgMAAMDmYIPCYmtt71E3AgAAwOZjg8JiVZ08Vb21dvGmbQcAAIDNwYaehvqMSdPbJzkqyZeTCIsAAABboQ09DfW/TJ6vqp2T/OVIOgIAAGDsNvRuqGv6aZK5m7IRAAAANh8bes3i/8nE3U+TZEaSfZJ8bFRNAQAAMF4bes3ieZOmH0zy7dbayhH0AwAAwGZgg05Dba19PsnXkzwuyROS/GyUTQEAADBeGxQWq+rFSW5M8qIkL07ypao6YZSNAQAAMD4behrq/0jyjNbavUlSVbOTfDbJZaNqDAAAgPHZ0Luhbrc6KA5WPYJ1AQAA2MJs6JHFT1fVlUn+aph/SZJPjaYlAAAAxm2dYbGqfjvJk1prZ1TV8UmemaSSfDHJJdPQHwAAAGOwvlNJ/yzJj5KktfY3rbX/2lr7fzNxVPHPRt0cAAAA47G+sLhXa235msXW2pIke42kIwAAAMZufWFx+3Us22FTNgIAAMDmY31h8aaq+o9rFqvq1CRLR9MSAAAA47a+u6H+YZLLq+rl+VU4nJ/kMUleOMrGAAAAGJ91hsXW2j8l+d2q+r0k+w3lT7bWrhl5ZwAAAIzNBv3OYmvtc0k+N+JeAAAA2Eys75pFAAAAtkHCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOiMLCxW1ZOr6nNVdVtV3VJVrx/qu1TVVVV1x/D8hKFeVfXOqlpRVcur6qBJ2zplGH9HVZ0yqX5wVd08rPPOqqpR7Q8AAMC2ZJRHFh9M8t9aa/skWZDkNVW1b5Izk1zdWpub5OphPkmek2Tu8DgtyXuTiXCZ5OwkhyY5JMnZqwPmMOa0SesdO8L9AQAA2GaMLCy21r7bWvvyMP2jJLcl2TPJcUkuGoZdlOQFw/RxSS5uExYneXxV7Z7kmCRXtdbub619P8lVSY4dlu3UWvtia60luXjStgAAANgI03LNYlXtleTpSb6U5Emtte8mE4EyyROHYXsmuXvSaiuH2rrqK6eoT/X6p1XVkqpact99923s7gAAAGz1Rh4Wq2rHJH+d5A9ba/+8rqFT1NqjqPfF1i5orc1vrc2fPXv2+loGAADY5o00LFbVrEwExUtaa38zlP9pOIU0w/O9Q31lkidPWn1OknvWU58zRR0AAICNNMq7oVaSDyS5rbX2/01adEWS1Xc0PSXJJybVTx7uirogyQ+H01SvTHJ0VT1huLHN0UmuHJb9qKoWDK918qRtAQAAsBFmjnDbhyc5KcnNVbVsqP1Rkrcn+VhVnZrkO0leNCz7VJLnJlmR5KdJXpUkrbX7q+pPktw0jDuntXb/MH16kguT7JDk74cHAAAAG2lkYbG19oVMfV1hkhw1xfiW5DVr2dYHk3xwivqSJPttRJsAAABMYVruhgoAAMCWRVgEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOjMHHcDAAAwTgsXLx53C1uVaxYsGHcLbCKOLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6MwcdwNbuoWLF4+7ha3KNQsWjLsFAAAgjiwCAAAwBWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAIDOzHE3AACwOVmy/Lxxt7DVmL//G8bdArARHFkEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6IwsLFbVB6vq3qr62qTaLlV1VVXdMTw/YahXVb2zqlZU1fKqOmjSOqcM4++oqlMm1Q+uqpuHdd5ZVTWqfQEAANjWjPLI4oVJjl2jdmaSq1trc5NcPcwnyXOSzB0epyV5bzIRLpOcneTQJIckOXt1wBzGnDZpvTVfCwAAgEdpZGGxtXZdkvvXKB+X5KJh+qIkL5hUv7hNWJzk8VW1e5JjklzVWru/tfb9JFclOXZYtlNr7YuttZbk4knbAgAAYCNN9zWLT2qtfTdJhucnDvU9k9w9adzKobau+sop6lOqqtOqaklVLbnvvvs2eicAAAC2dpvLDW6mut6wPYr6lFprF7TW5rfW5s+ePftRtggAALDtmO6w+E/DKaQZnu8d6iuTPHnSuDlJ7llPfc4UdQAAADaB6Q6LVyRZfUfTU5J8YlL95OGuqAuS/HA4TfXKJEdX1ROGG9scneTKYdmPqmrBcBfUkydtCwAAgI00c1Qbrqq/SnJkkt2qamUm7mr69iQfq6pTk3wnyYuG4Z9K8twkK5L8NMmrkqS1dn9V/UmSm4Zx57TWVt805/RM3HF1hyR/PzwAAADYBEYWFltrJ65l0VFTjG1JXrOW7XwwyQenqC9Jst/G9AgAAMDUNpcb3AAAALAZERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHSERQAAADrCIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBn5rgbAFi4ePG4W9hqXLNgwbhbAAC2Eo4sAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6M8fdAEy2ZPl5425hqzJ//zeMuwUAALZQjiwCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAICOsAgAAEBHWAQAAKAzc9wNAAAAW48ly88bdwtblfn7v2Fsr+3IIgAAAB1hEQAAgI6wCAAAQEdYBAAAoCMsAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAnZnjbgCATWfJ8vPG3cJWZf7+bxh3CwAwNo4sAgAA0BEWAQAA6AiLAAAAdIRFAAAAOsIiAAAAHWERAACAjrAIAABAR1gEAACgIywCAADQERYBAADoCIsAAAB0hEUAAAA6wiIAAAAdYREAAIDOFh8Wq+rYqrq9qlZU1Znj7gcAAGBrsEWHxaqakeQ9SZ6TZN8kJ1bVvuPtCgAAYMu3RYfFJIckWdFa+1Zr7WdJPprkuDH3BAAAsMWr1tq4e3jUquqEJMe21l49zJ+U5NDW2mvXGHdaktOG2X+b5PZpbZRHYrck3xt3E7AF8x6Cjed9BBvHe2jz91uttdnrGzRzOjoZoZqi1qXf1toFSS4YfTtsrKpa0lqbP+4+YEvlPQQbz/sINo730NZjSz8NdWWSJ0+an5PknjH1AgAAsNXY0sPiTUnmVtXeVfWYJC9NcsWYewIAANjibdGnobbWHqyq1ya5MsmMJB9srd0y5rbYOE4Xho3jPQQbz/sINo730FZii77BDQAAAKOxpZ+GCgAAwAgIiwAAAHSERcaiqi4cfidzzfoeVXXZMH1kVf3dWta/q6p2G3WfsDmoqsdX1X8edx+wNauqV1bVu9czZq+q+towfWBVPXd6uoPpU1Xzq+qdI9ziqlBHAAAH0UlEQVT+X1TVvsP0H62x7B9G9bo8OsIim5XW2j2ttS5Ewjbu8UmERdi8HJhEWGSr01pb0lp73YaOr6pHdMPM1tqrW2u3DrN/tMay330k22L0hEWmRVWdXFXLq+qrVfWXQ/mIqvqHqvrW6qOMk/+v7Rrr71pVn6mqr1TV+UlqOvuHMXt7kn9TVcuq6n9X1RlVddPwnnrL6kFV9YqqunEYd35VzRjqP66qc4f33+KqetLY9gTGpKr+tqqWVtUtVXXaUHtVVX2jqj6f5PBJYx929ktV/XiNbT0myTlJXjK8314yTbsB61RVv15Vnxz+vf9aVb2kqg6uqs8P//1fWVW7D2Ovrap3DJ8b36iqZw31X57ZVVW7DO+d5cPnx/5DfVFVXVBVn0ly8Vp6mVFV51XVzcP6/2XS686vqrcn2WF4D10yLPvx8HzOUF9WVf9YVR8a6j7nppmwyMhV1dOS/I8kC1trByR5/bBo9yTPTPL7mfgyvC5nJ/lCa+3pmfgtzd8cUbuwOTozyTdbawcmuSrJ3CSHZOLIxsFVdURV7ZPkJUkOH8Y9lOTlw/q/nmTx8P67Lsl/nO4dgM3Af2itHZxkfpLXVdWeSd6SiZD4/yTZd0M31Fr7WZI3J7m0tXZga+3SUTQMj8KxSe5prR3QWtsvyaeTvCvJCcN//x9Mcu6k8TNba4ck+cNMfNda01uSfKW1tn8mjgJODoYHJzmutfaytfRyWpK9kzx9WP+SyQtba2cm+ZfhPfTyNZa9efgse3aSVUne7XNuPLbo31lki7EwyWWtte8lSWvt/qpKkr9trf0iya0b8H+Ajkhy/LD+J6vq+6NsGDZjRw+PrwzzO2YiPO6fiQ/um4b31w5J7h3G/CzJ6ut/l2biizFsa15XVS8cpp+c5KQk17bW7kuSqro0yVPH1RxsIjcnOa+q3pGJf/e/n2S/JFcNnw0zknx30vi/GZ6XJtlriu09M8m/T5LW2jXDmV47D8uuaK39yzp6+XdJ3tdae3BY//5HsiM10fAlSf60tba0Jn5b3efcNBMWmQ6VZKof9PzXNcasjx8FhYn3yv9srZ3/sOLE6T0XtdbOmmKdn7df/ajuQ/FvP9uYqjoyE19cD2ut/bSqrk3y9ST7rGWVBzOcfTV8YX3MNLQJG6219o2qOjgT19P+z0ycjXJLa+2wtayy+rvY2j4bpvp+tvrz5CfraWdt3/821KIkK1trH5q0PZ9z08xpqEyHq5O8uKp2TSbOf38U27guw6kGVfWcJE/YdO3BZu9HSR43TF+Z5D9U1Y5JUlV7VtUTM/E+O2GYXn2dyW+NpVvY/Oyc5PtDUPydJAsycVTiyOFIyawkL5o0/q5MHMFIkuOSzJpim5Pfl7BZqKo9kvy0tfbhJOclOTTJ7Ko6bFg+a7g8aENN/v51ZJLvtdb+eQPX/UyS/1TDDXDW8v3v58P7b839+P1MHB2cfKMdn3NjIHUzcq21W6rq3CSfr6qH8qvT5x6JtyT5q6r6cpLPJ/nOpuwRNmettVVVdUNN3Pzp75N8JMkXh9NwfpzkFa21W6vqj5N8pqq2S/LzJK9J8u1x9Q2bkU9n4kvr8iS3J1mciVPxFiX54jD95Uycopck70/yiaq6MRNfUKc6gvK5JGdW1bJMHO133SKbg3lJ/ndV/SITnwOnZ+JI+TuH00dnJvmzJLds4PYWJfnQ8N75aZJTHkEvf5GJU7uXV9XPM/G+WvPnaS4Yln95jesW/1uSPZLcOHzWXdFae7PPuelXvzpiCwAAABOchgoAAEDHaagAAMCjUlXHJHnHGuU7W2svnGo8WxanoQIAANBxGioAAAAdYREAAICOsAgAU6iqF1ZVG36Xb1Ntc1FVvWFTbW+K7d9VVbuNavsAbFuERQCY2olJvpDkpeNuBADGQVgEgDVU1Y5JDk9yaiaFxararqr+vKpuqaq/q6pPVdUJw7KDq+rzVbW0qq6sqt0fweu9oqpurKplVXV+Vc2oqtOr6n9NGvPKqnrX2sZvsp0HgIGwCAC9FyT5dGvtG0nur6qDhvrxSfZKMi/Jq5McliRVNSvJu5Kc0Fo7OMkHk5y7IS9UVfskeUmSw1trByZ5KMnLk1w2vN5qL0ly6TrGA8Am5XcWAaB3YpI/G6Y/Osx/Ockzk3y8tfaLJP+3qj43jPm3SfZLclVVJcmMJN/dwNc6KsnBSW4a1t0hyb2ttfuq6ltVtSDJHcNr3JDkNVONf/S7CgBTExYBYJKq2jXJwiT7VVXLRPBrVfXfk9TaVktyS2vtsEfzkkkuaq2dNcWyS5O8OMnXk1zeWms1kRDXNh4ANhmnoQLAw52Q5OLW2m+11vZqrT05yZ2ZOKr4hST/frh28UlJjhzWuT3J7Kr65WmpVfW0DXy9q5OcUFVPHNbdpap+a1j2N5k4JfbETATH9Y0HgE1GWASAhzsxyeVr1P46ycuG55VJvpbk/CRfSvLD1trPMhEy31FVX02yLMnvrmX7f1xVK1c/Wmu3JvnjJJ+pquVJrkqye5K01r6f5NYkv9Vau3GorXU8AGxK1Vobdw8AsMWoqh1baz8eTle9MRM3mvm/4+4LADY11ywCwCPzd1X1+CSPSfIngiIAWytHFgEAAOi4ZhEAAICOsAgAAEBHWAQAAKAjLAIAANARFgEAAOgIiwAAAHT+f/AfNVn234PpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181a885a58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a visual of the data above\n",
    "fig, (ax1) = plt.subplots(ncols=1, sharey=True)\n",
    "hue_order=[1,0]\n",
    "\n",
    "sns.countplot(x='age_group', data=schedule_df, hue='show_up', palette='rainbow', hue_order=hue_order)\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(8)\n",
    "ax1.set(xlabel='Age Level', ylabel='Count')\n",
    "#place legend in upper left of plot\n",
    "plt.legend(loc='upper right',labels=['showed_up', 'missed']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">not so much can be done by way of comparism from the above graph because they are counts of different variables with unbalanced proportions.\n",
    "so we try to get a proportion of show_ups or misses the total in the age groups and get the percentages.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the total number of patients in each age group and store them in temporary age_group(a_g) variables\n",
    "a_g1 = len(schedule_df.query('age_group == \"child\"'))\n",
    "a_g2 = len(schedule_df.query('age_group == \"teen\"'))\n",
    "a_g3 = len(schedule_df.query('age_group == \"adult\"'))\n",
    "a_g4 = len(schedule_df.query('age_group == \"senior_citizen\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21035 7830 60484 21172\n"
     ]
    }
   ],
   "source": [
    "#check\n",
    "print(a_g1, a_g2,a_g3,a_g4)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now proceed to get the proportion of showups and misses in percentage and rounding to 1 decimal place.\n",
    "#showups\n",
    "child_showup=round(((len(schedule_df.query('age_group == \"child\" & show_up==1'))/a_g1)*100),1)\n",
    "teen_showup=round(((len(schedule_df.query('age_group == \"teen\" & show_up==1'))/a_g2)*100),1)\n",
    "adult_showup=round(((len(schedule_df.query('age_group == \"adult\" & show_up==1'))/a_g3)*100),1)\n",
    "senior_citizen_showup=round(((len(schedule_df.query('age_group == \"senior_citizen\" & show_up==1'))/a_g4)*100),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.5 73.9 79.0 84.7\n"
     ]
    }
   ],
   "source": [
    "#check\n",
    "print(child_showup, teen_showup,adult_showup, senior_citizen_showup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#misses\n",
    "child_misses=round(((len(schedule_df.query('age_group == \"child\" & show_up==0'))/a_g1)*100),1)\n",
    "teen_misses=round(((len(schedule_df.query('age_group == \"teen\" & show_up==0'))/a_g2)*100),1)\n",
    "adult_misses=round(((len(schedule_df.query('age_group == \"adult\" & show_up==0'))/a_g3)*100),1)\n",
    "senior_citizen_misses=round(((len(schedule_df.query('age_group == \"senior_citizen\" & show_up==0'))/a_g4)*100),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.5 26.1 21.0 15.3\n"
     ]
    }
   ],
   "source": [
    "#check\n",
    "print(child_misses, teen_misses,adult_misses, senior_citizen_misses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we then create a visual of the output for comparism (four inline pie charts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Senior Citizens')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181a673438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3,ax4) = plt.subplots(1,4,figsize=(25,7))#ax1,ax2,ax3,ax4 refers to our 4 pie charts\n",
    "# 1,4 denotes 1 row, 4 columns i.e stacking horizontally\n",
    "colors = ['#FF8F33','#33FFDC']#the general colors\n",
    "lb = ['Children misses', 'Children showups']# children labels\n",
    "ax1.axis('equal'); \n",
    "data = [20.5, 79.5]\n",
    "ax1.pie(data,radius=1,labels = lb,colors=colors,explode=explode, startangle=180,autopct='%0.2f%%',textprops = {\"fontsize\":17});\n",
    "ax1.set_title(\"children\",{'fontsize':20,\n",
    "        'fontweight' :100,\n",
    "        'verticalalignment': 'baseline'})\n",
    "lb = ['Teen misses', 'Teen show ups']#teen labels\n",
    "ax2.axis('equal'); \n",
    "data = [26.1, 73.9]\n",
    "ax2.pie(data,radius=1,labels = lb,colors=colors,explode=explode, startangle=180,autopct='%0.2f%%',textprops = {\"fontsize\":17});\n",
    "ax2.set_title(\"Teens\",{'fontsize':20,\n",
    "        'fontweight' :100,\n",
    "        'verticalalignment': 'baseline'})\n",
    "lb = ['Adult misses', 'Adult show ups']#adult labels\n",
    "ax3.axis('equal'); \n",
    "data = [21.0, 79.0]\n",
    "ax3.pie(data,radius=1,labels = lb,colors=colors,explode=explode, startangle=180,autopct='%0.2f%%',textprops = {\"fontsize\":17});\n",
    "ax3.set_title(\"Adults\",{'fontsize':20,\n",
    "        'fontweight' :100,\n",
    "        'verticalalignment': 'baseline'})\n",
    "\n",
    "lb = ['misses', 'senior_citizen show ups']#senior citizen labels\n",
    "ax4.axis('equal'); \n",
    "data = [15.3, 84.7]\n",
    "ax4.pie(data,radius=1,labels = lb,colors=colors,explode=explode, startangle=180,autopct='%0.2f%%',textprops = {\"fontsize\":17});\n",
    "ax4.set_title(\"Senior Citizens\",{'fontsize':20,\n",
    "        'fontweight' :200,\n",
    "        'verticalalignment': 'baseline'})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the above visual it can deduced that the most percentage of misses was in the teens age group(26.1%) then followed by the adult age group(21.0%) which was closely followed by the children age group (20.50%) then the least showup percentage was in the senoir citizens category(15%) which to a large extent looks very significant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gender**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " An overview of the gender distribution of the patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71836\n",
       "M    38685\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    64.997602\n",
       "M    35.002398\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalize the count to get the fraction of these two unique values and then calculate their percentage by multiplying by 100.\n",
    "schedule_df.gender.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">the gender distribution is unbalanced with females having about 65% of the appointments during the study period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get showup and misses counts for both genders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>show_up</th>\n",
       "      <th>show_up</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender             F    M\n",
       "show_up show_up          \n",
       "0       0        1.0  1.0\n",
       "1       1        1.0  1.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.groupby([\"gender\",\"show_up\"])[\"show_up\"].value_counts().unstack(\"gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create show up and miss proportions for both genders for ease of comparism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.68845704103792"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#female showup proportion\n",
    "f_showup=len(schedule_df.query(\"gender=='F' & show_up ==1\").index)/ len(schedule_df.query(\"gender=='F'\").index)\n",
    "#convert to percentage\n",
    "f_showup= f_showup*100\n",
    "f_showup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.311542958962082"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#female miss proportion\n",
    "f_miss=len(schedule_df.query(\"gender=='F' & show_up ==0\").index)/ len(schedule_df.query(\"gender=='F'\").index)\n",
    "#convert to percentage\n",
    "f_miss = f_miss*100\n",
    "f_miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0361897376244"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#male showup proportion\n",
    "m_showup=len(schedule_df.query(\"gender=='M' & show_up ==1\").index)/ len(schedule_df.query(\"gender=='M'\").index)\n",
    "#convert to percentage\n",
    "m_showup=m_showup*100\n",
    "m_showup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.9638102623756"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#female miss proportion\n",
    "m_miss=len(schedule_df.query(\"gender=='M' & show_up ==0\").index)/ len(schedule_df.query(\"gender=='M'\").index)\n",
    "#convert to percentage\n",
    "m_miss= m_miss*100\n",
    "m_miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181a58ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kYGn7fClwSl/blyR1M+pjBPtX1RqA9nG/EW9fkrSBWXuwOMniJCuSrFi7du24y5G2Cdu1ZqNRB8E9SQ4AaB/v3diCVbWkqiaranJiYmJkBUp9sl1rNhp1EFwCLGqfLwKWjXj7kqQN9Hn66GeBrwGHJbkryRnAB4ATkqwCTminJUljNK+vN66q12zkpeP72qYkafPN2oPFkqTRMAgkaeAMAkkaOINAkgbOIJCkgTMIJGngDAJJGjiDQJIGziCQpIEzCCRp4AwCSRo4g0CSBs4gkKSBMwgkaeAMAkkaOINAkgbOIJCkgRtLECQ5Mcm3k9yW5Oxx1CBJaow8CJLsCHwMeBXwTOA1SZ456jokSY1x7BEcA9xWVaur6kfA54CTx1CHJInxBMGBwHenTN/VzpMkjcG8MWwz08yrpyyULAYWt5MPJfl2r1Vtvn2B749ly5nuK5wTxvad5ZxNfmc/ObI6Zn+7Btv2lpjTbTtVT/k/uFdJng+cU1WvbKffAVBV7x9pIVspyYqqmhx3HXOJ39nc4L/T5pvr39k4uoa+ASxMckiSnYHTgEvGUIckiTF0DVXVo0neBFwG7Ah8sqpuGXUdkqTGOI4RUFV/BfzVOLa9DS0ZdwFzkN/Z3OC/0+ab09/ZyI8RSJJmF4eYkKSBMwi2QJKThj40RpI7kuw77jq07diuh9uu7RrSFklyBzBZVeM531zqwVDbtXsEG0iyIMm3knwiyc1JzkvyiiRXJ1mV5Jgkr0/y0Xb5U9vlbkxyZTvvWUn+NskNSW5KsnC8n2rrJNktyf9uP+PNSX6lfenNSa5LsjLJ4e2yeyf5Yvu5v57kOe38lUn2TOMHSX6tnf/p9vt94jtt51+a5KXt84eS/I92W8uTTIz2G5j7bNdPZbtezyCY3k8DHwGeAxwOvBZ4EfB24J0bLPtu4JVVdSRwUjvvjcBHquooYJJmGI257ETg7qo6sqqOAL7Uzv9+VT0X+DjNdwPwXuD6qnoOzXf15+38q4EXAs8CVgMvbucfB3x9hu3vBlzXbuuvgfds/UcaJNv1k9muWwbB9G6vqpVV9ThwC7C8mj60lcCCDZa9GvhUkjfQXBcB8DXgnUnOAn6yqv5pRHX3ZSXwiiQfTPLiqrq/nX9R+3gt67+XFwGfBqiqLwP7JNkD+Crwkvbn48CzkxwI/GNVPTTD9h8Hzm+ff6bdhjaf7frJbNctg2B6/zLl+eNTph9ng2svquqNwG8BBwM3JNmnqv6C5q+ofwIuS/Ly/kvuT1X9HfA8ml+c9yd5d/vSuu/lMdZ/LxsbS+pKmr+WXgx8BVgLvJrmFwngUZ7cHnfZVEmb9wnUsl1PYbtezyDYSkl+qqquqap30ww6dXCSQ4HVVfVHNMNnPGesRW6lJD8BPFJVnwE+BDx3E4tfCZzervdSmt3sB6rquzQDcy2sqtXAVTS73et+Ye4AjkqyQ5KDaYYrX2cHml8uaLozrtoWn0sbZ7t+iu26XY/lyuLtzH9vD5oFWA7cCJwNvC7Jj4F/AN43xvq2hWfTfM7HgR8D/xG4cCPLngP8WZKbgEeARVNeu4b13QxfBd7P+sZ/NXA7zV9nNwPXTVnvYeBZSa4F7gd+BfXNdv1k57Adt2tPH9Wsl+Shqtp93HVI29Jsatd2DUnSwLlHIEkD5x6BJA2cQSBJA2cQSNLAGQTbuSQvTXLpuOuQAJK8JcmtSc7r6f3PSfL2mZfUVF5HIGmU/hPwqqq6fdyFaD33COaAjiNHHpPkb5Jc3z4eNs377Jbkk0m+0S538jg+j4YpyZ8AhwKXJPnN6dpiO1rnF5P8ZZLbk7wpyZntMl9Psne73BvadW9M8oUku06zvZ9K8qUk1yb56rqRRPVUBsHcMdPIkd8CXlJVR9OMHPl707zHbwJfrqqfBV5Gc1XlbiOoXVo3ftHdNG1vNzbeFo+gad/HAL9LMwzE0TSD3v1au8xFVfWz7eiotwJnTLPJJcCbq+p5NL8nf9zPJ5v77BqaO26vqpUASZ4YOTLJupEj9wCWtsMCFLDTNO/xc8BJU/pQdwHm0/wiSaO0sbYIcEVVPQg8mOR+4C/b+StZP77REUl+B9gT2B24bOqbJ9kdeAHw+eSJ8eL+VR8fZHtgEMwdM40c+ds0v0C/lGQBzUiIGwrw76vq2/2VKXUybVtMcizdRkn9FHBKVd2Y5PXASzd4/x2A+9p7J2gGdg1tP/YAvtc+f/1GlrmM5u5LAUhy9AjqkqaztW3x6cCaJDvRjgo6VVU9ANye5NT2/ZPkyK2sebtlEGw/fp9mTPWrWT8S4oZ+m6bL6KYkN7fT0jhsbVt8F82on5fTHB+bzunAGUlupLkRjydHbIRjDUnSwLlHIEkDZxBI0sAZBJI0cAaBJA2cQSBJA2cQSNLAGQSSNHAGgSQN3P8HcXOSHddeh8oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181a58ed68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we create a visual of both proportions for the two genders as subplots for ease of comparism.\n",
    "labels = ['miss', 'showup']#the general labels\n",
    "locations=[1,2]\n",
    "#male plot\n",
    "heights_m = [m_miss,m_showup]\n",
    "plt.figure(figsize=(30,30))\n",
    "fig, (ax1,ax2) = plt.subplots(1,2,sharey=True)\n",
    "ax1.set(xlabel='male', ylabel='percentage')\n",
    "ax1.bar(locations, heights_m, tick_label=labels, color= ('r',\"g\"))\n",
    "#female plot \n",
    "heights_f = [f_miss,f_showup]\n",
    "ax2.set(xlabel='female')\n",
    "ax2.bar(locations, heights_f, tick_label=labels, color= ('r',\"g\"))\n",
    " \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the visuals above it can be inferred that the percentage of misses and showups for both genders are approximately the same, misses; male=19.96%, female=20.31% while show ups; female = 79.68%, male=80.03.\n",
    "therefore the showup variation as per gender in this dataset is almost insignificant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Did the wait period  affect showing up?\n",
    "The wait period column as created earlier for the sake of this analysis was the time between appointment schedule day and appointment day itself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110521.000000\n",
       "mean         10.184345\n",
       "std          15.255153\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           4.000000\n",
       "75%          15.000000\n",
       "max         179.000000\n",
       "Name: wait_time, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the column\n",
    "schedule_df.wait_time.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The mean wait time over the study period is about 10days, the minmum is 0 i.e same day schedule and appointments while the maximum wait time is 179 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181a670390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#overview of the distribution of the column\n",
    "schedule_df.wait_time.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">the wait time distribution can be said to very skewed to the right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create different dataframes for;\n",
    "\n",
    "-same day scheduling and appointments (wait time =0)\n",
    "\n",
    "-when wait time is greater than 0 days but less than 7 days (same week)\n",
    "\n",
    "-when wait time is greater than 7 days but less than 31 days (same month)\n",
    "\n",
    "-when wait time is greater than 31\n",
    "\n",
    "Since a showup is denoted by an integer 1 in the show_up column the mean of show_ups across each of the dataframes would give us a proportion of the showup rows to the entire rows in the particular data\n",
    "frame. So we proceed to find the mean showups across these dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95352938125615894"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#when wait time is 0(i.e same day scheduling and appointments)\n",
    "same_day=schedule_df.query(\"wait_time == 0\")\n",
    "same_day_m=same_day.show_up.mean()#calculating the mean showup for this group\n",
    "same_day_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75852105017865468"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#when wait time is >0 and <=7 \n",
    "same_week=schedule_df.query(\"wait_time>0 & wait_time <= 7\")\n",
    "same_week_m=same_week.show_up.mean()#calculating the mean showup for this group\n",
    "same_week_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68255288013835302"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#when wait time is >7 and =<31 \n",
    "same_month=schedule_df.query(\"wait_time >7 & wait_time<= 31\")\n",
    "same_month_m=same_month.show_up.mean()#calculating the mean showup for this group\n",
    "same_month_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.66979188131052958"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#when wait time is >31 \n",
    "greater=schedule_df.query(\"wait_time >31\")\n",
    "greater_m=greater.show_up.mean()#calculating the mean showup for this group\n",
    "greater_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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giaRWMW5b7/Tr1IL/N2gv3pj5Lf/38cJEh+OcczGLZ7JQNcuqnk5fAxwlaQpwFLAE2BLjtki6RFKRpKLS0tLdjbdOXHxEV47dqzV/eu1LPi9ZnehwnHMuJvFMFiVAx4j5DsDSyAJmttTMzjCzfsDvwmWrY9k2LPuImRWaWWFBQUFtxx8XkrjrrP3Jz8vm8lGTWbPR+y+cc8kvnsliItBDUldJ2cAQ4OXIApLyJVXG8Fvg8XD6DeB4SS3Cju3jw2UpoUXjbB4Y1o8lqzbw2xc/9/4L51zSi1uyMLMtwHCCH/lZwFgzmynpFkmnhsUGArMlzQHaALeF264A/kiQcCYCt4TLUsYBnVvymxN68ernX/PMhEWJDsc553ZIqXJWW1hYaEVFRYkOY6ds22Zc+H8T+bi4jH/96lD6tG+W6JCcc2lG0iQzK4xWzu/gTqCMDHH32X1p2Tib4aMms9b7L5xzScqTRYK1bJzN/UP7sXjlBq7/9wzvv3DOJSVPFklgQNeWXHVcT/47bSmjP1scfQPnnKtjniySxC+P6s4RPfK56b8z+WLpmkSH45xz3+PJIklkZIh7zulL84ZZDB81mfKKLYkOyTnnvuPJIonk5+Vw/9B+LCxbx+//7fdfOOeShyeLJHNwt1Zc+aOe/GfqUsYWef+Fcy45eLJIQpcfvSeH7dmKG1+eyexv1iY6HOec82SRjDIzxL3n9CMvJ4vLR01m/Sbvv3DOJZYniyRV0CSH+4f0ZV5pOX/4z8xEh+OcS3OeLJLYoXvmM/KYHrw4uYTnvf/COZdAniyS3Mhje3BIt1bc8NJM5n7r/RfOucTwZJHkMjPEfUP60jgnk8tHTWbDpq2JDsk5l4Y8WdQDrZvmcs85fZm7rJwbX56R6HCcc2nIk0U9cUSPAoYfvSdji0r495SSRIfjnEsznizqkSuO7cGAri353b9nULysPNHhOOfSiCeLeqRBZgb3D+lHblYmw0dNZuNm779wztUNTxb1TNtmudx99v58+c1abv6v33/hnKsbnizqoYG9WvPLgd0Z/dliXpq6JNHhOOfSgCeLeurq43pS2LkF1//rc+aXev+Fcy6+PFnUUw0yM7h/aD+yG2Rw+agp3n/hnIsrTxb1WLvmDbn77L7M+noNf3zli0SH45xLYZ4s6rmj92rNpUd249lPv+KV6UsTHY5zLkV5skgB15zQi/6dmnPdi5+zcPm6RIfjnEtBnixSQFZmBg8M609mhhg+ejIVW7z/wjlXuzxZpIj2zRty11n7M2PJGv706qxEh+OcSzFxTRaSBkmaLalY0nXVrO8kaZykKZKmSzopXN5F0gZJU8PX3+MZZ6o4rncbLj68K//3ySJe//zrRIfjnEshDeK1Y0mZwEPAcUAJMFHSy2YWednO74GxZvawpN7Aa0CXcN08M+sbr/hS1bWD9mLiopVc+8J09mnXjE6tGiU6JOdcCohnzWIAUGxm881sEzAGGFyljAFNw+lmgF/Os5uyG2Tw4NB+SHj/hXOu1kRNFpKelvQLSXvt5L7bA5HPAi0Jl0W6CThPUglBrWJExLquYfPUe5KOqCG2SyQVSSoqLS3dyfBSV8eWjfjLWfszvWQ1d7z+ZaLDcc6lgFhqFk8AewAPSJon6UVJV8SwnapZZlXmhwJPmlkH4CTgaUkZwNdAJzPrB1wFjJLUtMq2mNkjZlZoZoUFBQUxhJQ+TtinLT8/tAtPfLSQN2Z+k+hwnHP1XNRkYWbvArcBfwAeBQqBX8aw7xKgY8R8B37YzHQRMDZ8n0+AXCDfzCrMrCxcPgmYB/SM4T1dhN+etBf7tm/Gb56fxuIV6xMdjnOuHoulGeod4CPgHGA2cKCZxdIkNRHoIamrpGxgCPBylTJfAceG77M3QbIolVQQdpAjqRvQA5gf20dylXIaZPLQsP6YwYjRU9i0ZVuiQ3LO1VOxNENNBzYBfYD9gD6SGkbbyMy2AMOBN4BZBFc9zZR0i6RTw2JXA7+QNA0YDfzczAw4EpgeLn8BuMzMVuzkZ3NAp1aNuPMn+zF18Sru/J/3Xzjndo2C3+YYCkp5wAXANUBbM8uJZ2A7q7Cw0IqKihIdRtK64aUZPPXJIh79WSE/6t0m0eE455KEpElmVhitXCzNUMMlPQdMBU4DHgdO3P0QXV26/qS92addU65+fhpLVm1IdDjOuXomlmaohsDdwF5mdqyZ3Rx2ert6JDcr6L/Yus0YMWoym7d6/4VzLnaxXA31F2AjcFlYy9g//mG5eOiS35jbz9iXyV+t4q43Zyc6HOdcPRJLM9RI4Fmgdfh6RtKIHW/lktUp+7fj3IM68Y/35jPuy2WJDsc5V0/E0gx1MXCQmd1gZjcABwO/iG9YLp7+cHJv9t6jKVeNncrXq73/wjkXXSzJQkDkAENbqf7ubFdPBP0X/di0ZRsjR09hi/dfOOeiiHW4j08l3STpJmAC8Fhco3Jx160gjz+dsS8TF67k7rfmJDoc51ySizpEuZndLek94DCCGsUFZjYl7pG5uBvctz2fzCvjb+PncVC3VhzV08fXcs5VL9YhyqcS3En9b6BMUqf4heTq0k2n7sNebZvw6+em8s3qjYkOxzmXpGK5GmoE8C3wFvAK8Gr4r0sBuVmZPDisPxs3b2XkGO+/cM5VL5aaxRVALzPbx8z2M7N9zWy/eAfm6s6erfO49bQ+fLZgBfe9MzfR4TjnklAsyWIxsDregbjEOqN/B846oAMPjivmw7nLEx2Ocy7J1NjBLemqcHI+MF7Sq0BF5XozuzvOsbk6dvPgfZi6eBVXPjeF10YeQeumuYkOyTmXJHZUs2gSvr4i6K/IjljWJP6hubrWKLsBD53bn/KKLVwxZipbt8U2IrFzLvXVWLMws5urLgsfeZpnZmviGpVLmJ5tmvDHwX34zQvTeeDduVz5I39AoXMutquhRklqKqkx8AUwW9Jv4h+aS5SzCjtyRv/23PfOXD4u9v4L51xsHdy9w5rEacBrQCfgp3GNyiXcHwf3oVt+Y654biqlayuib+CcS2mxJIssSVkEyeIlM9sMeGN2imucE/RfrNmwmV8/5/0XzqW7WJLFP4CFQGPgfUmdAe+zSAN7tW3Kzafuw4fFy/nbuOJEh+OcS6BYHn50v5m1N7OTLHhg91fA0fEPzSWDcw7syGl923HP23OYML8s0eE45xIk1rGhvmOBLfEIxiUfSdx6+r50adWYK8ZMYXm59184l452Olm49JOX04AHh/Vn5fqg/2Kb9184l3Y8WbiY9G7XlBtP6c0Hc5fz8HvzEh2Oc66ORX2eBYCkQ4EukeXN7Kk4xeSS1LABnfhkXhl3vzWHAV1bcmCXlokOyTlXR2K5Ke9p4C7gcODA8FUY57hcEpLE7WfsS8cWDRkxagor1m1KdEjOuToSS82ikODGPG+odjTJzeLBYf05428fc/XYqTx2/oFkZPgj2Z1LdbH0WcwA2u7KziUNkjRbUrGk66pZ30nSOElTJE2XdFLEut+G282WdMKuvL+Ljz7tm/GHk/dm3OxSHvlgfqLDcc7VgVhqFvnAF5I+4/tDlJ+6o40kZQIPAccBJcBESS+b2RcRxX4PjDWzhyX1JhhOpEs4PQTYB2gHvC2pp5lt3YnP5uLovIM788n8Mv7yxmwO7NKCAzp7/4VzqSyWZHHTLu57AFBsZvMBJI0BBhMMRljJgKbhdDNgaTg9GBhjZhXAAknF4f4+2cVYXC2TxB1n7seMJR8yYtQUXh15BC0aZyc6LOdcnMRyB/d71b1i2Hd7gqfsVSoJl0W6CThPUglBrWLETmyLpEskFUkqKi0tjSEkV5ua5mbx4LB+lJZX8JsXpuHdWs6lrliuhjpY0kRJ5ZI2SdoqKZaxoarr9az6azIUeNLMOgAnAU+Hz8yIZVvM7BEzKzSzwoKCghhCcrVtvw7Nuf6kvXl71jIe+3BBosNxzsVJLB3cDxL8qM8FGgIXh8uiKQE6Rsx3YHszU6WLgLEAZvYJkEvQRxLLti5J/PzQLpywTxvueP1Lpny1MtHhOOfiIKY7uM2sGMg0s61m9gQwMIbNJgI9JHWVlE3QYf1ylTJfAccCSNqbIFmUhuWGSMqR1BXoAXwWS6yu7knizp/sT9tmuQwfNYXV6zcnOiTnXC2LJVmsD3/sp0q6U9KvCYYr36FwsMHhwBvALIKrnmZKukVS5ZVUVwO/kDQNGA38PByocCZBjeML4H/A5X4lVHJr1jC4/2LZ2o1c4/0XzqUcRfujDp9f8S2QDfya4Kqlv4W1jaRRWFhoRUVFiQ4j7T36wXxufXUWN5zcmwsP75rocJxzUUiaZGZRR+WIeumsmS2S1BDYw8xurpXoXMq66PCuTJi/gttfn8UBnVuwf8fmiQ7JOVcLYrka6hRgKkFzEJL6Sqra9+AcEPRf3HXWfrRuksvw0ZNZvcH7L5xLBbH0WdxEcEPcKgAzm0owAq1z1WreKJsHhvXj61Ubue7F6d5/4VwKiCVZbDGz1XGPxKWU/p1acO2gXrw+4xue+mRRosNxzu2mmAYSlDQMyJTUQ9IDwMdxjsulgIsP78Yxe7XmtldnMWOJn284V5/FkixGEAzoV0Fweesa4Mp4BuVSQ0aG+OtZ+9MqL5vLR01m7Ubvv3CuvoplbKj1ZvY7MzswHFrjd2a2sS6Cc/Vfi8bZPDC0HyUrN3Ddvz73/gvn6qlYroYqlPQvSZPDZ05MlzS9LoJzqaGwS0uuOb4Xr07/mmc//SrR4TjndkEsQ5Q/C/wG+BzYFt9wXKq69MhufLqgjFte+YJ+nZqzT7tmiQ7JObcTYumzKDWzl81sgZktqnzFPTKXUir7L1o0ymL4qCmUV2xJdEjOuZ0QS7K4UdKjkoZKOqPyFffIXMpplZfD/UP6sahsHdd7/4Vz9UoszVAXAHsBWWxvhjLgX/EKyqWug7q14qrjenLXm3M4pHsrhg7olOiQnHMxiCVZ7G9m+8Y9Epc2fjVwTz5dsIKbXp5J347N2XuPptE3cs4lVCzNUBMk9Y57JC5tZGSIe87pS7OGWVw+ajLrvP/CuaQXS7I4nOBZFrPDy2Y/90tn3e7Kz8vhviH9WLh8Hb//zwzvv3AuycXSDDUo7lG4tHRI91ZccWxP7nl7Dod0a8XZB3aMvpFzLiFiep5FXQTi0tPwY/bk0wVl3PDyDPp2ak7PNk0SHZJzrhoxPYPbuXjJzBD3DulLXk4Wv3p2Mus3ef+Fc8nIk4VLuNZNcrlvSF/mlZZzw0szEx2Oc64anixcUjhsz3xGHNODFyaV8MKkkkSH45yrwpOFSxpXHNuDg7u15A//mcHcb9cmOhznXARPFi5pZGaI+4b0o1F2JpePmsyGTVsTHZJzLuTJwiWVNk1zueecvsxdVs7Vz0+lrLwi0SE55/Bk4ZLQkT0LuPaEvfjfjG848s5x3P3mbNb4U/acSyhPFi4p/XJgd9789VEM7NWa+98t5og/j+Ph8fO8acq5BFGqDLNQWFhoRUVFiQ7DxcGMJav565uzGTe7lIImOYw4Zk+GHNiJ7AZ+ruPc7pI0ycwKo5WL61+bpEHhmFLFkq6rZv09kqaGrzmSVkWs2xqx7uV4xumSW5/2zXjiggG8cNkhdM1vzA0vzeTou8bzfNFitmz1hzc6VxfiVrOQlAnMAY4DSoCJwFAz+6KG8iOAfmZ2YThfbmZ5sb6f1yzSg5nxwdzl3PXmbKaXrKZbQWOuPq4XJ/ZpS0aGEh2ec/VOMtQsBgDFZjbfzDYBY4DBOyg/FBgdx3hcCpDEkT0LeOnyw/j7eQeQKXH5qMmc/MCHjPtymY9e61ycxDNZtAcWR8yXhMt+QFJnoCvwbsTiXElFkiZIOq2G7S4JyxSVlpbWVtyuHpDEoD5t+d+VR3LPOftTXrGFC56cyE/+/gkT5pclOjznUk48k0V1bQI1nfYNAV4ws8hLXTqFVaNhwL2Suv9gZ2aPmFmhmRUWFBTsfsSu3snMEKf368A7Vx/Fbaf3YcnKDQx5ZAI/fexTpi1eFX0HzrmYxDNZlACRDyjoACytoewQqjRBmdnS8N/5wHigX+2H6FJFVmYG5x7UmfG/Gcjvf7w3M5asZvBDH3Hp00XM8aFDnNtt8UwWE4Eekrq4HpreAAAQJElEQVRKyiZICD+4qklSL6AF8EnEshaScsLpfOAwoNqOceci5WZlcvER3fjg/x3DVcf15OPiMk64931+/dxUFpWtS3R4ztVbsTwpb5eY2RZJw4E3gEzgcTObKekWoMjMKhPHUGCMfb9ncm/gH5K2ESS0O2q6isq56uTlNGDksT346cGd+cf783ny4wX8d9pSzj6wIyOP6UHbZrmJDtG5esVvynNpYdmajTw0rphRn32FJH52cGd+ObA7rfJyEh2acwkV66WznixcWlm8Yj33vTOXf00uoWFWJhcd3pWLj+xG09ysRIfmXEJ4snBuB4qXlXPPW3N49fOvadYwi8uO6s75h3amUXbcWmadS0qeLJyLQdVxp4YfvSdDBnQkp0FmokNzrk54snBuJxQtXMGdb8zmswUraN+8IVf8qAdn9GtPg0wfrNCltmQY7sO5eqOwS0ueu+RgnrpwAK3ysrn2hekcf+/7vDJ9Kdu2pcYJlXO7w5OFc6Gq4041yBDDR03h5Ac+5N0vv/Vxp1xa82ThXBWV4069fsX2cacufLLIx51yac37LJyLYvPWbYwtWswD7xTzzZqNHNEjn2uO78X+HZsnOjTndpt3cDtXyzZu3sozExbxt/HzWLFuE8f3bsPVx/eiV9smiQ7NuV3mycK5OCmv2MLjHy7gn+/Pp3zTFgbv344rf9STLvmNEx2aczvNk4VzcbZq/Sb+/l4w7tSWrcZZhR0Zeeye7NGsYaJDcy5mniycqyNVx5366cGd+ZWPO+XqCU8WztWxxSvWc/87c3kxHHfqwsO7cvER3WjW0MedcsnLk4VzCVK8rJx73p7Dq9N93CmX/DxZOJdgM5as5u635vDul8vIz8thxDE+7pRLPp4snEsSRQtX8Jc3ZvOpjzvlkpCPDeVckijs0pIxlxzM0xf5uFOu/vJk4VwdkMQRPYJxp/7x0+3jTv3Yx51y9YQnC+fqkCRO2CcYd+rec/qyLmLcqU/m+bhTLnl5snAuATIzxGn92vPO1Ufxp9P3ZcnKDQz95wR++tinTFu8KtHhOfcD3sHtXBLwcadcovjVUM7VQ+UVW3jiwwU84uNOuTriycK5eszHnXJ1xZOFcynAx51y8ebJwrkUEjnuVG5WJhf5uFOulniycC4FVR136tKjuvHzQ7v4uFNulyXFHdySBkmaLalY0nXVrL9H0tTwNUfSqoh150uaG77Oj2ecztUXe7bO46Fh/XllxOEc0LkFd/5vNkfeOZ4nP1pAxZatiQ7PpbC41SwkZQJzgOOAEmAiMNTMvqih/Aign5ldKKklUAQUAgZMAg4ws5U1vZ/XLFw6mrRoBXf+L2LcqWN7cEZ/H3fKxS4ZahYDgGIzm29mm4AxwOAdlB8KjA6nTwDeMrMVYYJ4CxgUx1idq5cO6Lx93Kn8vGyufXE6x9/zPv+d5uNOudoVz2TRHlgcMV8SLvsBSZ2BrsC7O7OtpEskFUkqKi0trZWgnatvKsed+k/luFOZYsRoH3fK1a54JgtVs6ym/7VDgBfMrLLRNaZtzewRMys0s8KCgoJdDNO51FB13Kn1m4Jxp858+GMfd8rttngmixKgY8R8B2BpDWWHsL0Jame3dc5FqBx36u2rgnGnlq7ayNB/TuC8Rz9lqo875XZRPDu4GxB0cB8LLCHo4B5mZjOrlOsFvAF0tTCYsIN7EtA/LDaZoIN7RU3v5x3czlWv6rhTx/Vuw9XH92Svtk0THZpLAgnv4DazLcBwgkQwCxhrZjMl3SLp1IiiQ4ExFpG1wqTwR4IEMxG4ZUeJwjlXs9ysTC4+ohvvX3s0Vx/Xkwnzyjjxvg+4YswUFi5fl+jwXD3hN+U5l2ZWrd/EP96fzxMfLWDzVuPswg6MOKYH7Zr7uFPpyO/gds7t0LK1G/nbuHk8++kiJHHeQZ351dHdyfdxp9KKJwvnXExKVgbjTr0wKRh36sLDuvKLI33cqXThycI5t1Mix51qmtuAywZ293Gn0oAnC+fcLpm5dDV/fXMO7365jPy8HIYf3Z2hB3Uip0FmokNzceDJwjm3W3zcqfTgycI5t9vMjA+Ll3PXG7OZVrKaLq0asV+H5uTn5dAqL5v8vOxwOue76dwsr4HUJ7EmC2+MdM7VqHLcqcP3zOfNL77lyY8WMq1kFWXlmyiv2FLtNnk5DcJEkkOrxtm0ysuhIC87TCg5363Lz8umWcMspOpG93HJxpOFcy6qynGnTtin7XfLNmzaStm6CpaXb6KsvILl5ZXTm1heXkHZugoWla1n8lcrWbFuE9UNgtsgQ7TKy6ZV4xzym+SQ3zib/Cbbk0z+d4klh5aNs8lu4E1gieLJwjm3SxpmZ9IhuxEdWjSKWnbrNmPl+jCJhMkkMslULpu3rJzl5RVUbNlW7X6aNcwKaiaNc8hvEiaZKrWVyiSTl9PAay21yJOFcy7uMjP0XQ0hGjNj3aat36utVCaUsoj52d+spWxdGavWb652P9kNMiiISCTV1VYq17VolOUd91F4snDOJRVJ5OU0IC+nAZ1bNY5aftOWbaxcv4nStRWUrft+baU0/PfbNRv5YukaytZVsHnrD9vDJGjRKOiwr2wSa9U4m4IamsQaZqdfJ74nC+dcvZbdIIM2TXNp0zQ3alkzY82GLSxfV8HyMLlU1yT2ediJv7aGTvxG2Znf1UxaNc6h4Lsmse0d+ZXTzRtmkZFR/5vDPFk459KGJJo1yqJZoyy6F+RFLb9x89bv1VYim8Qq/y1ZuZ6pi1exYl1FtZ34mRmiZePtfSo7ahJrlZedtDc/erJwzrka5GZl0r55Q9rHMCLvtrATv2zdJpavrWB5+G/Zuu936i9Yvo7l5RVs3Fx9J36T3AbbayaRHfnh1WKtImotTXPrrhPfk4VzztWCjAzRKrxBsWebJlHLr6vYEtGvUvFdkilbt31ZcWk5ny6oYGVNnfiZGbTKy6awS0seGNqvtj/S93iycM65BGic04DGOQ3o1Cr6pcebt25j5bpN25vB1lWwfO0mloe1ltZN4j+svCcL55xLclmZGbRumkvrGDrx48UvLHbOOReVJwvnnHNRebJwzjkXlScL55xzUXmycM45F5UnC+ecc1F5snDOOReVJwvnnHNRpcwzuCWVAot2Yxf5wPJaCsfVDj8mycmPS/LZnWPS2cwKohVKmWSxuyQVxfLQcld3/JgkJz8uyacujok3QznnnIvKk4VzzrmoPFls90iiA3A/4MckOflxST5xPybeZ+Gccy4qr1k455yLypOFc865qNI+WUgaJGm2pGJJ1yU6nnQj6XFJyyTNqGF9L0lTI15rJF1Z13GmA0m/ljRT0gxJoyXlVlnfWdKk8DjMlHRZxLrbJC2WVF73kac3SY9JmiZpuqQXJOWFy4+UNFnSFkk/2e33Sec+C0mZwBzgOKAEmAgMNbMvEhpYGpF0JFAOPGVmfaKUzQSWAAeZ2e7cgOmqkNQe+BDobWYbJI0FXjOzJyPKZBP8ZlSEP0gzgEPNbKmkgwluip1rZnkJ+AgpS1ILM1u5g/VNzWxNOH03sMzM7pDUBWgKXAO8bGYv7E4c6V6zGAAUm9l8M9sEjAEGJzimtGJm7wMrYix+LDDPE0XcNAAaSmoANAKWRq40s01mVhHO5hDx+2FmE8zs6zqLNL0USRol6RhJqroyIlEIaAhYuHyhmU0HttVGEOmeLNoDiyPmS8JlLjkNAUYnOohUZGZLgLuAr4CvgdVm9mbVcpI6SppO8HfzZzNbWrWMq3U9gVHAcOALSddLahdZQNITwDfAXsAD8Qgi3ZPFD7I0YVZ2ySVsAjkVeD7RsaQiSS0IatVdgXZAY0nnVS1nZovNbD9gT+B8SW3qNtL0Y2ZbzewVMzsDOBLoBnwlaUBEmQsIjtss4Jx4xJHuyaIE6Bgx34EqVW9Xt8Iz18rO7MsiVp0ITDazbxMVW4r7EbDAzErNbDPwL+DciGNxamThsEYxEzgiAbGmHUnNJF0CvExQ07gImB5Zxsy2As8BZ8Yjhgbx2Gk9MhHoIakrQcfpEGBYYkNKb2a2GOhbzaqheBNUPH0FHCypEbCBoH/oNTP7rklDUgegLOwAbwEcBtydkGjTiKRngEMIatU/M7O5EesEdDez4nD6FODLeMSR1jULM9tC0A74BkH1bayZzUxsVOlF0mjgE6CXpBJJF1VTphHBFWv/quv40oWZfQq8AEwGPif4bag6hMTewKeSpgHvAXeZ2ecAku6UVAI0Co/jTXUWfOobC/Qys+siE0VIwP9J+pzguO0B3AIg6cDwmJwF/EPSbv22pfWls84552KT1jUL55xzsfFk4ZxzLipPFs4556LyZOGccy4qTxbOOeei8mTh0o6kRyX1Dqev30G566vMfxyHWAol3b+T2yyU9Hn4+kLSrZJyajs25yL5pbMurUkqr2mU1B2tSyRJC4FCM1sejv76CLDZzM5PbGQulXnNwtVLkq6VNDKcvkfSu+H0seEdr0h6WFJR+OyFmyO2HR+e0d9BMMrqVEnPVtn/D9ZVPqtB0kBJ70kaK2mOpDsknSvps/Bsv3tYrkDSi5Imhq/DqvkcAyW9Ek7fpOD5HuMlza/8fDtiZuXAZcBpklpKypP0Tvgcg88lDQ73/UdJV0S8722SRkraQ9L74eecIcmH73DVMzN/+avevYCDgefD6Q+Az4As4Ebg0nB5y/DfTGA8sF84P57gzBygfAfvUV7dPDAQWEVwt2wOwVAxN4frrgDuDadHAYeH052AWdW8x0DglXD6JuDjcJ/5QBmQVc02C4H8KsumAgcRDOHTNFyWDxQT3OXbhWBsLQhOEucBrYCrgd9FfE9NEn1s/ZWcr3QfG8rVX5OAAyQ1ASoIhqkoJBjYrvKM/Oxw8LUGBD/svaky+NpumGjh8xskzQMqh/P+HDg6nP4R0DviEQRNJTUxs7U72O+rFjwzokLSMqANwYCX0Sji3z8peKjUNoIh99uY2UJJZZL6hfucYmZlkiYCj0vKAv5jZlNjeC+XhjxZuHrJzDaHbfcXEJyNTyf4ke4OzAoHh7wGONDMVkp6EsitYXe7oiJielvE/Da2/11lAIeY2YZd3O9WYvgbDRNmF4KnPp4LFAAHRHxHlZ/7UeDnQFvgcQgePhUmlh8DT0v6i5k9tRPxujThfRauPnufICG8T9AUdRkw1cyM4HGS64DV4TMXTqxhH5vDs+qdXReLNwkGqgRAUnWj6e6WsIP7bwS1gpVAM4LHam6WdDTQOaL4v4FBwIEEg2ciqXNY/p/AY0D/2o7RpQZPFq4++4CgeekTC55zsTFchplNA6YQPHPhceCjGvbxCDC9agd3DOtiMRIolDRd0hcEyay2jJM0g6Cv5ivg0nD5s+F7FhHUMr4brtqCRwePIxhdeWu4eCAwVdIUgucg3FeLMboU4pfOOpcmJGUQ9O2cZT8c6tq5HfKahXNpILwJsRh4xxOF2xVes3DOOReV1yycc85F5cnCOedcVJ4snHPOReXJwjnnXFSeLJxzzkX1/wH6DQA9UmPleAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18279a66d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wait_time= [\"0\",\"1-7\",\"8-31\",\">31\"]\n",
    "mean_showup= [same_day_m,same_week_m,same_month_m,greater_m]\n",
    "plt.plot(wait_time, mean_showup)\n",
    "plt.title('wait_time group vs mean_showup)')\n",
    "plt.xlabel('wait time in Days')\n",
    "plt.ylabel('mean show')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the visual above visual it can be inferred that there is a very significant decrease in mean show ups across our wait time distributions. Having a peak at the 0 days group(0.95) and the lowest mean was in the >31 days group(0.67).\n",
    "This is indicative of the fact that the longer the interval between the schedule day and the appointment the lower the ratio of show ups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  what are the showup characteristics across the various days of the week?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An overview of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wednesday    25866\n",
       "Tuesday      25638\n",
       "Monday       22713\n",
       "Friday       19019\n",
       "Thursday     17246\n",
       "Saturday        39\n",
       "Name: weekday, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.weekday.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">most appointments were scheduled for weekdays with just 39 for saturdays and no one for Sundays.\n",
    "the most apointments were scheduled for Wednesdays(25,866) and the least for Saturdays(39)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "summary table of the showups on the various weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday    show_up\n",
       "Friday     0           4037\n",
       "           1          14982\n",
       "Monday     0           4689\n",
       "           1          18024\n",
       "Saturday   0              9\n",
       "           1             30\n",
       "Thursday   0           3337\n",
       "           1          13909\n",
       "Tuesday    0           5150\n",
       "           1          20488\n",
       "Wednesday  0           5092\n",
       "           1          20774\n",
       "Name: weekday, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_df.groupby([\"weekday\",\"show_up\"])[\"weekday\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for sake of easy comparism we create dataframes for each day of the week and find the mean show up across the datframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79355435213313963"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#monday\n",
    "showup_mon=schedule_df.query(\"weekday == 'Monday'\")\n",
    "showup_mon_m=showup_mon.show_up.mean()#calculating the mean showup for this group\n",
    "showup_mon_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79912629690303461"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tuesday\n",
    "showup_tue=schedule_df.query(\"weekday == 'Tuesday'\")\n",
    "showup_mon_tue=showup_tue.show_up.mean()#calculating the mean showup for this group\n",
    "showup_mon_tue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80313925616639603"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#wednesday\n",
    "showup_wed=schedule_df.query(\"weekday == 'Wednesday'\")\n",
    "showup_wed_m=showup_wed.show_up.mean()#calculating the mean showup for this group\n",
    "showup_wed_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80650585643047668"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#thursday\n",
    "showup_thu=schedule_df.query(\"weekday == 'Thursday'\")\n",
    "showup_thu_m=showup_thu.show_up.mean()#calculating the mean showup for this group\n",
    "showup_thu_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78773857721226137"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#friday\n",
    "showup_fri=schedule_df.query(\"weekday == 'Friday'\")\n",
    "showup_fri_m=showup_fri.show_up.mean()#calculating the mean showup for this group\n",
    "showup_fri_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76923076923076927"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#saturday\n",
    "showup_sat=schedule_df.query(\"weekday == 'Saturday'\")\n",
    "showup_sat_m=showup_sat.show_up.mean()#calculating the mean showup for this day of the week\n",
    "showup_sat_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1818bbecc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we create a visual of mean showups for all the days of the week.\n",
    "labels = ['mean showup'] #the general labels\n",
    "locations=[1]\n",
    "plt.rcParams[\"figure.figsize\"] = (10,5)\n",
    "fig, (ax1,ax2,ax3,ax4,ax5,ax6) = plt.subplots(1,6,sharey=True);\n",
    "\n",
    "#monday\n",
    "ax1.set(xlabel='Monday', ylabel='mean show up')\n",
    "ax1.bar(locations, showup_mon_m, tick_label=labels, color= (\"g\"));\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "\n",
    "#tuesday \n",
    "ax2.set(xlabel='Tuesday')\n",
    "ax2.bar(locations, showup_mon_tue, tick_label=labels, color= (\"g\"));\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "\n",
    "\n",
    "#wednesdays\n",
    "ax3.set(xlabel='Wednesday')\n",
    "ax3.bar(locations, showup_wed_m, tick_label=labels, color= (\"g\"));\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax3.spines['left'].set_visible(False)\n",
    "\n",
    " \n",
    "#thursdays\n",
    "ax4.set(xlabel='Thursday')\n",
    "ax4.bar(locations, showup_thu_m, tick_label=labels, color= (\"g\"));\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax4.spines['left'].set_visible(False)\n",
    "\n",
    " \n",
    "#fridays\n",
    "ax5.set(xlabel='friday')\n",
    "ax5.bar(locations, showup_fri_m, tick_label=labels, color= (\"g\"));\n",
    "ax5.spines['right'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax5.spines['left'].set_visible(False)\n",
    "\n",
    " \n",
    "#saturdays\n",
    "ax6.set(xlabel='Saturday')\n",
    "ax6.bar(locations, showup_sat_m, tick_label=labels, color= (\"g\"));\n",
    "ax6.spines['right'].set_visible(False)\n",
    "ax6.spines['top'].set_visible(False)\n",
    "ax6.spines['left'].set_visible(False)\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the visual above it cann be inferred that the mean showups across the week days are fairly constant at 0.8 (+-0.05). therefore it can be concluded that there is no significant change in mean showups across weekdays.\n",
    "\n",
    ">however, considering the count of appointments it was discovered that Wednesday with an appointment count of 25,866 had the most schedules closely followed by Tuesday(25,638) then Monday (22,713) which was then followed by Friday(19,019) and Thursday(17,246) then finally Saturday with just 39 appointments which presumably may be because these were emergency schedules or some sort of exception.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Did patient physiological factors (hypertension, diabetes, if they were handicapped or alcoholics) affect showing up?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**first create a function that would calculate the attendance rates for patients with and without these medical conditions and visualize it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rateplot(schedule_df,condition):\n",
    "    #this function would do the following;\n",
    "    #1.calculate attendance rate for patients with the condition\n",
    "    #2.calculate attendance rates for patients without the condition\n",
    "    #3.visualize the mean showup for both.\n",
    "    #the function also takes in two arguments schedule_df(the dataframe) and the medical condition.\n",
    "\n",
    "    #mean show up when the condition is positive\n",
    "    cond_pos= schedule_df.query(F\"{condition} == 1\")\n",
    "    cond_pos_m=cond_pos.show_up.mean()#calculating the mean showup for this category\n",
    "    cond_pos_m\n",
    "    print(f\"patients with {condition}:had attndance rate of {cond_pos_m*100:.2f}%\")\n",
    "    \n",
    "    #mean show up when the condition is negative\n",
    "    cond_neg= schedule_df.query(F\"{condition} == 0\")\n",
    "    cond_neg_m=cond_neg.show_up.mean()#calculating the mean showup for this category\n",
    "    cond_neg_m\n",
    "    print(f\"patients without {condition} had attndance rate of {cond_neg_m*100:.2f}%\")\n",
    "    \n",
    "    #create a visual of mean showups for both condition positive and negative.\n",
    "    labels = ['mean showup'] #the general labels\n",
    "    locations=[1]\n",
    "    plt.rcParams[\"figure.figsize\"] = (4,7)\n",
    "    fig, (ax1,ax2) = plt.subplots(1,2,sharey=True);\n",
    "    # hypertension positigve\n",
    "    ax1.set(xlabel=F\"{condition} postive\", ylabel='show up percentage')\n",
    "    ax1.bar(locations,cond_pos_m, tick_label=labels, color= (\"g\"));\n",
    "    ax1.spines['right'].set_visible(False)\n",
    "    ax1.spines['top'].set_visible(False)\n",
    "    \n",
    "    #hypertension negative\n",
    "    ax2.set(xlabel=F'{condition} Negative')\n",
    "    ax2.bar(locations,cond_neg_m , tick_label=labels, color= (\"g\"));\n",
    "    ax2.spines['right'].set_visible(False)\n",
    "    ax2.spines['top'].set_visible(False)\n",
    "    ax2.spines['left'].set_visible(False)\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypertension**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An overview of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hypertension\n",
       "0    88720\n",
       "1    21801\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the total number of hypertension positive patients appointments\n",
    "schedule_df.groupby('hypertension')[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">they were about 21,801 recorded appointments by hypertension positive patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hypertension  show_up\n",
       "0             0          18542\n",
       "              1          70178\n",
       "1             0           3772\n",
       "              1          18029\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of showups and misses\n",
    "schedule_df.groupby(['hypertension',\"show_up\"])[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">of the 21,81 recorded hypertension positve appointments only 3,772 were missed.\n",
    "\n",
    ">in the hypertension negative category 88,720 appointments with 18,542 misses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the rateplot function to get the percentage and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patients with hypertension:had attndance rate of 82.70%\n",
      "patients without hypertension had attndance rate of 79.10%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1818cc4dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rateplot(schedule_df,\"hypertension\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the visual above it can be seen that the mean showup for patients with hypertension(0.82) anad show up percentage of 82% is greater than those without hypertension(0.79) without 79%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Diabetes**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An overview of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes\n",
       "0    102578\n",
       "1      7943\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the total number of diabetes positive patients appointments\n",
    "schedule_df.groupby('diabetes')[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">there were 7,943 appointments booked by patients with cases of diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes  show_up\n",
       "0         0          20884\n",
       "          1          81694\n",
       "1         0           1430\n",
       "          1           6513\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of show ups and misses\n",
    "schedule_df.groupby(['diabetes',\"show_up\"])[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">of the 7,943 appointments by diabetic patients there were 6,513 show ups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply the rateplot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patients with diabetes:had attndance rate of 82.00%\n",
      "patients without diabetes had attndance rate of 79.64%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f181a58e828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rateplot(schedule_df,\"diabetes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the above visual the mean show up for diabetic patients(0.819)with showup percentage of 82 is significantly greater than that of non diabetic patients(0.796) with showup perecentage of 79.6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**handicapped**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An overview of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "handicap\n",
       "0    108282\n",
       "1      2239\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the total number of handicapped and non handicapped patients\n",
    "schedule_df.groupby('handicap')[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">there were only 2,239 appointments scheduled by handicapped patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "handicap  show_up\n",
       "0         0          21909\n",
       "          1          86373\n",
       "1         0            405\n",
       "          1           1834\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of show ups and misses\n",
    "schedule_df.groupby(['handicap',\"show_up\"])[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " apply the rateplot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patients with handicap:had attndance rate of 81.91%\n",
      "patients without handicap had attndance rate of 79.77%\n"
     ]
    },
    {
     "data": {
      "image/png": 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slHSjpP3Kl2Zm3ajJafot4KSI2Cci9qa6pYbvn2BmHTUJlZ0jYunQTERcBexcrCIz62pNDtSukXQaGy8heSxwV7mSzKybNempvB94IXAp8N00/TclizKz7tXk2591wImjUIuZ9YAmY39eDpxMdeLb0+0j4pByZZlZt2pyTOViqnE/3wCeLFuOmXW7JqGyISK+WrwSM+sJw4aKpN3S5Pck/R3VQdonhp6PiN8Wrs3MutCWeio3Up2WrzR/Su25AF5Sqigz617DhkpETBnNQsysNzQZ+/OuNJgQSZ+UdKmkg8qXZmbdqMnJb6dFxMOS3gD8GXAe1bdBZmabaRIqQ18jvwP4akRcDuxQriQz62ZNQuU3ks4GjgIWS3pWw9eZ2TaoSTgcBSwBZkTEg8BubPpNkJnZ05qM/XmMajDh0Py9wL0lizKz7lV0N0bSDEmrJK2WNK/D88dLulXScknXSZpWsh4zK69YqEiaQHU3w8OBacAxHULjwojYPyIOBD4P/HupesxsdJTsqUwHVkfEmohYDywAZtUbRMTvarM74wtrm3W9kvdDngTcU5sfAF7b3kjS3wMnUX1N7cspmHW5kj0VdVi2WU8kIuZHxEuBjwOf7LgiaY6kfkn9g4ODmcu08aK+nVstX1u9W5XsqQwAe9XmJwNrt9B+AdDxEgsR0SJdwb+vr8+7SD2qvp3xrnDXKtlTWQZMlTRF0g7AbGBRvYGkqbXZdwB3FqzHzEZBsZ5KRGyQNJfqxLkJwDkRsULSmUB/RCwC5ko6FPg9sA54X6l6zGx0lNz9ISIWA4vblp1em/5wyfc3s9HnMTxmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4VM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XMsnKomFlWDhUzy6poqEiaIWmVpNWS5nV4/iRJKyXdIulKSfuUrMfMyisWKpImAPOBw4FpwDGSprU1uwnoi4gDgEuAz5eqx8xGR8meynRgdUSsiYj1wAJgVr1BRCyNiMfS7M+ByQXrMbNRUDJUJgH31OYH0rLhfAD4YacnJM2R1C+pf3BwMGOJNp7Ut3Or1RrrcmyEJhZctzosi44NpWOBPuBNnZ6PiBbQAujr6+u4Dut+9e3MMJ8VG/9KhsoAsFdtfjKwtr2RpEOBTwBviognCtZjZqOg5O7PMmCqpCmSdgBmA4vqDSQdBJwNzIyI+wrWYmajpFioRMQGYC6wBLgdWBgRKySdKWlmavZvwHOAiyUtl7RomNWZWZcouftDRCwGFrctO702fWjJ9zez0eczas0sK4eKmWXlUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4VM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6yKhoqkGZJWSVotaV6H5w+W9EtJGyQdWbIWMxsdxUJF0gRgPnA4MA04RtK0tma/Bo4DLixVh5mNrokF1z0dWB0RawAkLQBmASuHGkTE3em5pwrWYWajqOTuzyTgntr8QFpmZj2sZKiow7IY0YqkOZL6JfUPDg7+gWXZeFXfzq1Wa6zLsREqufszAOxVm58MrB3JiiKiBbQA+vr6RhRMNv7VtzMj/ANkY69kT2UZMFXSFEk7ALOBRQXfz8zGgWKhEhEbgLnAEuB2YGFErJB0pqSZAJJeI2kAeBdwtqQVpeoxs9FRcveHiFgMLG5bdnptehnVbpGZ9QifUWtmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4VM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWVVNFQkzZC0StJqSfM6PP8sSRel538had+S9ZhZecVCRdIEYD5wODANOEbStLZmHwDWRcTLgLOAz5Wqx8xGR8meynRgdUSsiYj1wAJgVlubWcB5afoS4K2SVLAmMyusZKhMAu6pzQ+kZR3bRMQG4CHgBQVrMrPCJhZcd6ceR4ygDZLmAHPS7COSVv2BtY0XuwP3j3URz5TO6NiZ/FFEzPiD1rvpdl4BPP6HrG8c6ZXt3GgblwyVAWCv2vxkYO0wbQYkTQR2BX7bvqKIaAGtQnWOGUn9EdE31nWMF97OvaHk7s8yYKqkKZJ2AGYDi9raLALel6aPBH4SEZv1VMysexTrqUTEBklzgSXABOCciFgh6UygPyIWAd8Ezpe0mqqHMrtUPWY2OuSOwdiRNCd1+a2HbWvb2aFiZln5NH0zy8qhMg5IOkPSyWNdh5W1rWxnh4qZZbXNhYqkfSX9l6RvSLpN0rckHSrpp5LulDQ9tdtZ0jmSlkm6SdKs2uuvlfTL9Hh9Wv5mSVdJuiSt/1udhhxIOlHSSkm3SFpQe2paev0aSSfW2p+U6rxN0kfSsn8YaiPpLEk/SdNvlXRBmn6kto4jJZ2bps+V9LX0M9wh6c/z/obHB2/nMdzOEbFNPYB9gQ3A/lSheiNwDtXZvbOAy1K7fwGOTdPPA+4AdgZ2AnZMy6dSfT0O8GaqYQaT03qvB97Q4f3XAs8aWm/69wzgZ8CzqM6+fADYHng1cGt63+dQnWV6EPA64OL02muBG1L7TwF/m5Y/UnvPI4Fz0/S5wI9SjVOpTkDccay3i7dz72znba6nktwVEbdGxFNUG/DKqLbErVQfRoC3AfMkLQeuAnYE9qbaqF+XdCtwMdUI7CE3RMRAWu/y2rrqbgG+JelYqg/9kB9ExBMRcT9wH/BHwBuA70bEoxHxCHAp8Eaq/yCvlrQL8ATVB7svPXdtg59/YUQ8FRF3AmuA/9XgNd3I23kMtnPJ0/THsydq00/V5p9i4+9EwF9FxCbjjCSdAfwP8MdUfwXq41Pq632Szr/fdwAHAzOB0yS9aguv7TjIJiJ+L+lu4G+o/vLdArwFeClw+1Cz2kt2bF/FVuZ7hbfzlueL2FZ7Kk0sAU4Y2l+WdFBavitwb/or9ddUZws3Imk7YK+IWAr8A1V3+zlbeMk1wDsl7SRpZ+AINv6FugY4Of17LXA8sDz9JQb4H0mvTO95RNt63yVpO0kvBV4C9MoAzZHwds5sW+2pNPEZ4IvALekDdzfw58BXgO9IehewFHj0GaxzAnCBpF2p/jqdFREPdjjOB0BE/DIdeLshLfpGRNyUpq8FPgFcHxGPSnqcTbvE84DvU11a4jY2/VCvAq6m6nofHxG9Mhp4JLydM/MZtduY9OH9fkRcMta1WDljuZ29+2NmWbmnYmZZuadiZlk5VMwsK4eKmWXVc6GSxmzcVmjd50o6Mk1/Q5vfx2hcSeNUXl+bP17Se8eyppHqle0q6W5J36nNPz1eJ/P7fETSTrX5xZKel/t9OvF5KiMUER8c6xoaeDPwCNXZmETE18a0mi4wStu1T9KrImJFwff4CHAB8BhARLy94Httoud6KskESV+XtELSjyU9G0DSh9Jo1JslfWcoydNfqi9J+lkaPTr0V0uSvpxGm/4A2GPoDdJI0740PUPVSNabJV2Zlk1P67sp/fuKtPw4SZdL+pGqW8J+qtMPIOkRSf83rfdKSS9Myw+U9HNVo1+/K+n5afkmo2JV3UL2eOCjkpZLeqPS9TzSGZg31N5rX0m3pOlXS7pa0o2Slkh6cdYt84fp+u2afAH4x/aFGn7E9E6SFqZte5GqWwQP1fhVSf3pd/LptOxEYE9gqaSladndknaX9DlJf1d7zzMkfSxNn5Le+5ahdY3IWI4kLfFg4+jUA9P8QjaOQn1Brd0/ASfExhGdF1OF7DSqOysC/CVwBdUZknsCDwJHpueuohrc9UKqsxmnpOW7pX+fC0xM04cC30nTxwH3Ut007dlUZ0H2dfg5AnhPmj4d+HKavgV4U5o+E/hibHlU7Mm1dT49TzUQ7iVp+uPAJ6kG0f0MeGFafjTVBcu9XfNt17upznC9HXgZm44sHm7E9MnA2Wn5fun30NdW14RU+wG199m97X13pxr9fHVt+UqqAZRvo7o9itLv6/vAwSPZVr26+3NXRCxP0zeycRTpfpL+iY1jMZbUXnNZVOM8Vkr6o7TsYODbEfEksFbpehZtXgdcExF3AUTE0H2LdgXOkzSVKiC2r73mioh4AEDSpVSjVPvb1vsUcFGavgC4VNVp38+LiKvT8vOo/tPAxlGxlwGXDfN7qVsIHAX8K1V4HA28gupDe4WqU8onUP1HGS96YbtCNZDw34BTgR/Wlr8NmKmNV4cbGjH9BuD/pTpuG+pVJkepugnbRODFVOFZf34TEXGTpD0k7UkVnOsi4tepd/M2YGh4wHOoLplwzXDrGk6vhkr7SNBnp+lzgXdGxM2SjqM65tDpNfVBGls7O1DDtPkMsDQijki7IldtYZ1NzkDcWpvhRsUO5yLg4vThj4i4U9L+wIqI+JMG9YyFXtqu51OFSv24ynAjpjsOGpI0haoX85qIWKfqgG/7SOVOLqHqIb2I6h7nQ+/92Yg4u8Hrt6hXj6kMZxfgXknbA+9p0P4aYLakCenYwls6tLkeeFPawEjaLS3fFfhNmj6u7TWHSdotHRN4J/DTDuvdjmrDA7wbuC4iHgLWSXpjWv7XwNUaflTsw+ln3kxE/DfVf8zT2NgjWgW8UNKfpJ9l+wbhNB5003YFqssaAGdRHVAdMtyI6euoepWo+mZq/7T8uVQDHR9KvbDDa+sadttTBclsqs/X0NigJcD7JT0nvc8kSXsM8/ot6tWeynBOA34B/IrqQj3D/dKHfBc4JLW9g2rE5yYiYjB1Py9N/7nvAw4DPk/VTT4JaO9eX0f1l+plwIUR0amL/CjwKkk3Ul1p7Oi0/H3A19LByDVU19oYblTs94BL0gG/Ezq8x0VU3fAp6WdZnw5mfimtayLVCN6S31Lk0E3bte6bVMeyhmxpxPR5abfnJqrdm4dS7/Imqu2zhk1DrAX8UNK9EbFJaEZ1U79dgN9ExL1p2Y8lvRK4PmXaI8Cx6ed+Rjz2Z5Sl7nlfRMzdSrtHImJL1+CwcaTpdh3huicA20fE46qujXIl8PKIWJ/7vXLY1noqZt1oJ6qvh7en6on+n/EaKOCeiplltq0dqDWzwhwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZ/X+yqWlA426YRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18189cea90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rateplot(schedule_df,\"handicap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">the mean showup for handicapped patients(0.819) with showup percentage of 81.90% was slightly higher than those of non handicapped patients(0.79) with showup percentage of 79%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alcoholism**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An overview of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcoholism\n",
       "0    107161\n",
       "1      3360\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the total number of diabetes positive patients appointments\n",
    "schedule_df.groupby('alcoholism')[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">there were 3,360 appointments scheduled by alcoholic patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcoholism  show_up\n",
       "0           0          21637\n",
       "            1          85524\n",
       "1           0            677\n",
       "            1           2683\n",
       "Name: show_up, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of show ups and misses\n",
    "schedule_df.groupby(['alcoholism',\"show_up\"])[\"show_up\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">of the 3,360 appoinments scheduled by alcoholic patients there were just 677 misses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply the rateplot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patients with alcoholism:had attndance rate of 79.85%\n",
      "patients without alcoholism had attndance rate of 79.81%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1818b54c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rateplot(schedule_df,\"alcoholism\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from the visual above it can be seen that the average showup for alcoholics(0.79851)and showup percentage of 79.85% and non alcohoics(0.7980) with show up percentage of 89.81% are almost fairly equal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. From the analysis it can deduced that the most percentage of misses was in the teens age group(26.1%) followed by the adult age group(21.0%) which was closely followed by the children age group (20.50%) then the least showup percentage was in the senoir citizens category(15%).Also the showups and misses by gender; misses ==> male=19.96% female=20.31%,showups ==>male=80.03 female = 79.68%  were almost identical for both genders. So it can be said that showups to an extent can be associated with Age but is almost gender Neutral.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. From the analysis it was discovered that there was a very significant decrease in mean show ups across our wait time distributions. Having a peak at the 0 days group(0.95) and the lowest mean was in the >31 days group(0.67). This is indicative of the fact that appointments that were scheduled to hold on the very days the schedules were made had very high showup ratio and the longer the interval between the schedule day and the appointment the lower the level of showup.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. From the visual above it cann be inferred that the mean showups across the week days are fairly constant at 0.8 (+-0.05). therefore it can be concluded that there was no significant change in mean showups across weekdays.\n",
    "However, cosidering the number of appointments scheduled on the various days it was discovered that Wednesday with an appointment count of 25,866 had the most schedules, closely followed by Tuesday(25,638) then Monday (22,713) which was then followed by Friday(19,019) and Thursday(17,246) then finally Saturday with just 39 appointments which presumably may be because these were emergency schedules or some sort of exception.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. From the analysis it was discovered that there were significant differences in the mean showups of appointments scheduled by patients with the following physiological conditions;\n",
    "\n",
    "i. hypertensive patients(0.82), non hypertensive patients(0.79).\n",
    "\n",
    "ii.diabetic patients(0.819), non diabetic patients(0.796)\n",
    "\n",
    "iii. handicapped patients(0.819), non handicapped patients(0.79).\n",
    "\n",
    "while the mean showup for alcoholics(0.79851) and non alcohoics(0.7980) were almost fairly equal.\n",
    "\n",
    "\n",
    "This is indicative of the fact that hypertensive, diabetic and hsndicapped patients had slightly higer showup levels compared to non hypertensive, non diabetic and non diabetic patients while the showup levels for alcoholics and non alcoholics were almost the same.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Limitations**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.There were different numbers of rows for each category in columns like the number of Females were greater than the number of males and also number of data for specific days were greater than others. These were handled by taking proportions and use of mean(in cases where the columns were 0 and 1 digit indicators)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.There was no time given for the appointment day, which could have been a big plus to boosting our analysis by creating insights to what time of the day had more or less misses or showups.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.There were some rows where the date of the appointment was before the schedule date and one negatiove age column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.https://stackoverflow.com/\n",
    "\n",
    "2.https://www.geeksforgeeks.org/\n",
    "\n",
    "3.https://www.mathworks.com/\n",
    "\n",
    "4.https://matplotlib.org/stable/index.html\n",
    "\n",
    "5.https://pandas.pydata.org/docs/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
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