Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Project: Investigate a Dataset - [No Show 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",
    "This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row, we will drop a brief about each column's significance, but more details are offered on the website [here](https://docs.google.com/document/d/e/2PACX-1vTlVmknRRnfy_4eTrjw5hYGaiQim5ctr9naaRd4V9du2B5bxpd8FEH3KtDgp8qVekw7Cj1GLk1IXdZi/pub?embedded=True). \n",
    "\n",
    "The columns are as follows:\n",
    "\n",
    "    • PatientID : icluding the ID for each patient\n",
    "    • AppointmentID: the ID of the appointment\n",
    "    • ScheduledDay: the day when the patient made the reservation\n",
    "    • AppointmentDay: the day of the appointment at the clinic\n",
    "    • Age: patient age\n",
    "    • Neighbourhood: the area including the hospital\n",
    "    • Hipertension,Diabetes,Alcoholism: binary columns(1=Yes, 0=No)      indicating if the patient suffers these diseases\n",
    "    • Handcap: on a scale from 0 to 4, it indicates the degree of handcap of the patient\n",
    "    • Scholarship: a binary column telling whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.\n",
    "    • sms_received: binary column indicating whether or not a message was received \n",
    "    • no-show: binary column indicating whether or not the patient DID NOT attend their appointment\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Worth a note: many modifications on columns names and content are to be done based on the project need, each specified before execution ..  \n",
    "\n",
    "\n",
    "\n",
    "### Question(s) for Analysis:\n",
    "\n",
    "Here, we should have ideally specified two or three variables to study their effect on patients not attending their appointments, however, as long as we have the sufficient amount of data, we will analyze as much columns as possible, in order to know exactly, what are the factors affecting the most the \"no-show\" incidents that happened and how can we work on them, which is actually the purpose of our research. \n",
    "Therefore, our research questions will be practically two main questions repeated for each aspect that we study, these aspects are explained clearly below before defining each of our two primary functions in the research.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this cell to set up import statements for all of the packages that you\n",
    "#   plan to use.\n",
    "\n",
    "# Remember to include a 'magic word' so that your visualizations are plotted\n",
    "#   inline with the notebook. See this page for more:\n",
    "#   http://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this investigation, we will have two procedures, repeating both of them for each aspect (df column) of our dataset. The first procedure in the function below is based on data from patients who did not attend their appointments(no_show), we study the most common charactaristic among people who missed their appointments for each aspect(df column). \n",
    "Example: among people who missed their appointments, what is the more dominant gender. (Here the aspect column being : 'gender') \n",
    "\n",
    "We discover the dominant aspect, in addition to counts of all aspects through printing the count of each aspects of respective column, in addition to plotting all the data in bar plot to visualize data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def first_procedure (df, col, x_labels, title, y_label=\"Counts\"):\n",
    "    \n",
    "    '''\n",
    "    df: the original cleaned dataframe\n",
    "    col: the col of interest for analyzing the dominant feature among it for no_show appointments for this function call\n",
    "    x_labels: labels of the bars on the x-axis\n",
    "    title: the plot title\n",
    "    y_label: y-axix label\n",
    "    '''\n",
    "    df_noshow = df.query('Attendance == \"0\"')\n",
    "    print(\"The distribution of {} of people with no show is as follows: \\n\".format(col))\n",
    "    \n",
    "    print(df_noshow[col].value_counts()) \n",
    "    #lets plot\n",
    "    heights = df_noshow[col].value_counts()\n",
    "    print(\"And here is the visualization of these results : \\n\")\n",
    "    plt.bar(x_labels, heights, color ='black')\n",
    "    plt.title(title)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.xlabel(col.title());\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this procedure, we do it the other way round, that is for each aspect(column), we group it into all groups of all its possible charactaristics (Example: Females and Males for 'gender' column), then study for each group, what is the more dominant behvaviour, attending or not attending, by calculating the mean of 'Attendance' column value for each group, knowing that after editing the dataset, we have the column 'Attendance' with 1 for show and 0 for no show, therefore, the group with the lowest mean for 'Attendance' is the most probable group not to show up, that we should deal seriously with to prevent these 'no-shows' from happening. Also, the mean (scaled by 100) for each group is plotted in a bar plot to visualize which is the group with least mean easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#second_procedure_function\n",
    "def second_procedure(df,col,x_labels,title,y_label=\"Mean Attendance\"):\n",
    "    '''\n",
    "    df: the original cleaned dataframe\n",
    "    col: the col of interest for analyzing its constituents' means for this function call\n",
    "    x_labels: labels of the bars on the x-axis\n",
    "    title: the plot title\n",
    "    y_label: y-axix label\n",
    "    '''\n",
    "    df_mean = df.groupby(col)['Attendance'].mean() \n",
    "    print(\"The mean attendace per all categories of {} is as follows: \\n\".format(col))\n",
    "    print(df_mean)\n",
    "    #let's visualize\n",
    "    print(\"\\n \\n Here is the visualization of these results : \\n\")\n",
    "    heights = df_mean * 100\n",
    "\n",
    "    plt.bar(x_labels, heights, color ='black')\n",
    "    plt.title(title)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.xlabel(col.title());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upgrade pandas to use dataframe.explode() function. \n",
    "#!pip install --upgrade pandas==0.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "Here we load in the data, check for cleanliness, and then trim and clean the dataset for analysis. We will document each cleaning or editing step before execution.\n",
    "\n",
    "\n",
    "> In the following cells we will check our data and search for any unusual entries, in addition to removing duplicates and all similar issues, at the end of this section we will have our clean easy-handled dataset ready for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Casting all columns to lowercase for ease of access:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender          scheduledday  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "\n",
       "         appointmentday  age    neighbourhood  scholarship  hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62  JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handcap  sms_received no-show  \n",
       "0         0           0        0             0      No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "for col in df.columns:\n",
    "    df.rename(columns = {col : col.lower()}, inplace=True)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing the dimensions of our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #data has 110527 rows, 14 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 110527 rows and 14 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      "patientid         110527 non-null float64\n",
      "appointmentid     110527 non-null int64\n",
      "gender            110527 non-null object\n",
      "scheduledday      110527 non-null object\n",
      "appointmentday    110527 non-null object\n",
      "age               110527 non-null int64\n",
      "neighbourhood     110527 non-null object\n",
      "scholarship       110527 non-null int64\n",
      "hipertension      110527 non-null int64\n",
      "diabetes          110527 non-null int64\n",
      "alcoholism        110527 non-null int64\n",
      "handcap           110527 non-null int64\n",
      "sms_received      110527 non-null int64\n",
      "no-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made sure that we have no NaN values in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'no-show':'no_show'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We renamed no-show to no_show to facilitate use in functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "#df[df['no-show']=='Yes']['no-show']='hhhh'\n",
    "df['no_show'].loc[(df['no_show'] == 'Yes')] = \"no_show\"\n",
    "df['no_show'].loc[(df['no_show'] == 'No')] = \"Attended\"\n",
    "#df[df['no_show']=='Attended'].shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tried naming the column entries as strings, but we will need them as integers for mean calculations, so we will change them to int, and change the convention to be attendance=1, no_show=0, to be more familiar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'no_show':'Attendance'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Attendance'].loc[(df['Attendance'] == 'Attended')] = 1\n",
    "df['Attendance'].loc[(df['Attendance'] == 'no_show')] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Attendance'] = df['Attendance'].astype(int) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renaming no-show column and its entries into Attendance and casting its dtype into int to ease numerical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "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>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>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>3.775115e+14</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T13:30:56Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           patientid  appointmentid gender          scheduledday  \\\n",
       "110526  3.775115e+14        5629448      F  2016-04-27T13:30:56Z   \n",
       "\n",
       "              appointmentday  age neighbourhood  scholarship  hipertension  \\\n",
       "110526  2016-06-07T00:00:00Z   54   MARIA ORTIZ            0             0   \n",
       "\n",
       "        diabetes  alcoholism  handcap  sms_received  Attendance  \n",
       "110526         0           0        0             1           1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking there are no duplicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring data statistics to detect any unusual patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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",
       "      <th>Attendance</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",
       "      <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",
       "      <td>0.798067</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",
       "      <td>0.401444</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",
       "      <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",
       "      <td>1.000000</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",
       "      <td>1.000000</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",
       "      <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",
       "      <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     Attendance  \n",
       "count  110527.000000  110527.000000  \n",
       "mean        0.321026       0.798067  \n",
       "std         0.466873       0.401444  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.000000       1.000000  \n",
       "50%         0.000000       1.000000  \n",
       "75%         1.000000       1.000000  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The describe() shows some ages in negative in 'age' column so we need cleaning this aspect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['age'] >=0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensuring that min age now is 0 (only few months old):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105260e+05</td>\n",
       "      <td>1.105260e+05</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474934e+14</td>\n",
       "      <td>5.675304e+06</td>\n",
       "      <td>37.089219</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197248</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321029</td>\n",
       "      <td>0.798066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560943e+14</td>\n",
       "      <td>7.129544e+04</td>\n",
       "      <td>23.110026</td>\n",
       "      <td>0.297676</td>\n",
       "      <td>0.397923</td>\n",
       "      <td>0.258266</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466874</td>\n",
       "      <td>0.401445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</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>4.172536e+12</td>\n",
       "      <td>5.640285e+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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680572e+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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.438963e+13</td>\n",
       "      <td>5.725523e+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",
       "      <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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patientid  appointmentid            age    scholarship  \\\n",
       "count  1.105260e+05   1.105260e+05  110526.000000  110526.000000   \n",
       "mean   1.474934e+14   5.675304e+06      37.089219       0.098266   \n",
       "std    2.560943e+14   7.129544e+04      23.110026       0.297676   \n",
       "min    3.921784e+04   5.030230e+06       0.000000       0.000000   \n",
       "25%    4.172536e+12   5.640285e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680572e+06      37.000000       0.000000   \n",
       "75%    9.438963e+13   5.725523e+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  110526.000000  110526.000000  110526.000000  110526.000000   \n",
       "mean        0.197248       0.071865       0.030400       0.022248   \n",
       "std         0.397923       0.258266       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     Attendance  \n",
       "count  110526.000000  110526.000000  \n",
       "mean        0.321029       0.798066  \n",
       "std         0.466874       0.401445  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.000000       1.000000  \n",
       "50%         0.000000       1.000000  \n",
       "75%         1.000000       1.000000  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's Done!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensuring all binary columns have only 2 entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patientid          62298\n",
       "appointmentid     110526\n",
       "gender                 2\n",
       "scheduledday      103548\n",
       "appointmentday        27\n",
       "age                  103\n",
       "neighbourhood         81\n",
       "scholarship            2\n",
       "hipertension           2\n",
       "diabetes               2\n",
       "alcoholism             2\n",
       "handcap                5\n",
       "sms_received           2\n",
       "Attendance             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exloring the entries of 'handcap' column that is odd, having 5 entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: handcap, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['handcap'].value_counts() #knowing the scales of handcap column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Casting all dates to datetime to ease analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.scheduledday = pd.to_datetime(df['scheduledday'])\n",
    "df.appointmentday = pd.to_datetime(df['appointmentday'])                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensuring datetime is new datatype of both:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110526 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      "patientid         110526 non-null float64\n",
      "appointmentid     110526 non-null int64\n",
      "gender            110526 non-null object\n",
      "scheduledday      110526 non-null datetime64[ns, UTC]\n",
      "appointmentday    110526 non-null datetime64[ns, UTC]\n",
      "age               110526 non-null int64\n",
      "neighbourhood     110526 non-null object\n",
      "scholarship       110526 non-null int64\n",
      "hipertension      110526 non-null int64\n",
      "diabetes          110526 non-null int64\n",
      "alcoholism        110526 non-null int64\n",
      "handcap           110526 non-null int64\n",
      "sms_received      110526 non-null int64\n",
      "Attendance        110526 non-null int64\n",
      "dtypes: datetime64[ns, UTC](2), float64(1), int64(9), object(2)\n",
      "memory usage: 12.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to study the age effect on attendance, we need to divide each patients' 'age' to belong to one of the 'age_bins':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "age_bin= []\n",
    "for value in df['age']:\n",
    "    if 0 <= value <= 25:\n",
    "        age_bin.append(\"0-25\")\n",
    "    elif 25 < value <= 50:\n",
    "        age_bin.append(\"25-50\")\n",
    "    elif 50 < value <= 75:\n",
    "        age_bin.append(\"50-75\")   \n",
    "    elif 75 < value <= 100:\n",
    "        age_bin.append(\"75-100\")\n",
    "    elif 100 < value <= 125:\n",
    "        age_bin.append(\"100-125\")\n",
    "       \n",
    "df['age_bin'] = age_bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the first row of our clean, easy-accessed dataset !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>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>Attendance</th>\n",
       "      <th>age_bin</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>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>50-75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender              scheduledday  \\\n",
       "0  2.987250e+13        5642903      F 2016-04-29 18:38:08+00:00   \n",
       "\n",
       "             appointmentday  age    neighbourhood  scholarship  hipertension  \\\n",
       "0 2016-04-29 00:00:00+00:00   62  JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handcap  sms_received  Attendance age_bin  \n",
       "0         0           0        0             0           1   50-75  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "# Exploratory Data Analysis\n",
    "\n",
    "> Now that we've trimmed and cleaned our data, we're ready to move on to exploration. Here we compute statistics and create visualizations with the goal of addressing the research questions that we posed in the Introduction section. We will compute the relevant statistics throughout the analysis when an inference is made about the data. All this is done through function calls to the two primary functions that we constructed earlier, they are the cornerstone of the project..\n",
    "\n",
    "\n",
    "\n",
    "> We will explore all variables from both angles tackled in functions, also bar plots and histograms are obtained to visualize data, let's first take a quick insight about the data!\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of distribution of attendance among the dataset; Show vs No_Show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    88207\n",
       "0    22319\n",
       "Name: Attendance, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attendance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e97f9aa50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plt.bar(['Show','No_Show'], df['Attendance'].value_counts(), color ='black')\n",
    "    plt.title(\"Attendance Distribution\")\n",
    "    plt.ylabel(\"Counts\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is worth to emphasize here clearly, that in this research we deal with \"Attendance\" as our boolean variable, so: \n",
    "### Attendance = 1 = Show => Attended the appointment\n",
    "### Attendance = 0 = No Show => Missed the appointment\n",
    "\n",
    "Therefore, in all the following calculations for mean or median or any study on the \"Attendance\" column, the higher the value the nearer it is to Attending the appointment, and the lower is the closer to No_Show.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick overview of the whole cleaned dataset through histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e95090c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize = (11, 11));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can we make a query of no_show?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this, and more code cells, to explore your data. Don't forget to add\n",
    "#   Markdown cells to document your observations and findings.\n",
    "df_show = df.query('Attendance==1') #people who attended\n",
    "df_noshow = df.query('Attendance==0') #people of no show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>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>Attendance</th>\n",
       "      <th>age_bin</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>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>50-75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender              scheduledday  \\\n",
       "0  2.987250e+13        5642903      F 2016-04-29 18:38:08+00:00   \n",
       "\n",
       "             appointmentday  age    neighbourhood  scholarship  hipertension  \\\n",
       "0 2016-04-29 00:00:00+00:00   62  JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handcap  sms_received  Attendance age_bin  \n",
       "0         0           0        0             0           1   50-75  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_show.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>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>Attendance</th>\n",
       "      <th>age_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.336882e+14</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 15:05:12+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender              scheduledday  \\\n",
       "6  7.336882e+14        5630279      F 2016-04-27 15:05:12+00:00   \n",
       "\n",
       "             appointmentday  age neighbourhood  scholarship  hipertension  \\\n",
       "6 2016-04-29 00:00:00+00:00   23    GOIABEIRAS            0             0   \n",
       "\n",
       "   diabetes  alcoholism  handcap  sms_received  Attendance age_bin  \n",
       "6         0           0        0             0           0    0-25  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_noshow.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Process:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each column of data, it will be studied from both angles, by calling both functions, so the systematic process will be calling the first then the second function on each column then visualize findings and conclude in comments..\n",
    "\n",
    "Some columns have special nature so a customized way of EDA was followed when dealing with them..\n",
    "Let's move through the columns as follows.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of gender of people with no show is as follows: \n",
      "\n",
      "F    14594\n",
      "M     7725\n",
      "Name: gender, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94489d10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['Female', 'Male'] \n",
    "title = \"Relationship between Gender and Attendance\"\n",
    "first_procedure(df,'gender',x_labels,title) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of gender is as follows: \n",
      "\n",
      "gender\n",
      "F    0.796851\n",
      "M    0.800321\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94128290>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['Female', 'Male'] \n",
    "title = \"Relationship between Gender and Attendance\"\n",
    "second_procedure(df,'gender',x_labels,title) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although women who had no shows were more than men who did so, but on average, both genders skip appointments nearly equally, Equality at its Finest !! \n",
    "\n",
    "A limitation here is the huge difference in number of samples that are females than that of men as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94110790>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['gender'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Age Groups:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of age_bin of people with no show is as follows: \n",
      "\n",
      "0-25       8818\n",
      "25-50      7774\n",
      "50-75      4857\n",
      "75-100      867\n",
      "100-125       3\n",
      "Name: age_bin, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94119a50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['0-25', '25-50', '50-75', '75-100', '100-125']\n",
    "title = \"Relationship between Age and Attendance\"\n",
    "first_procedure(df,'age_bin' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of age_bin is as follows: \n",
      "\n",
      "age_bin\n",
      "0-25       0.771548\n",
      "100-125    0.571429\n",
      "25-50      0.787037\n",
      "50-75      0.838267\n",
      "75-100     0.838997\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e71e9d150>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['0-25', '100-125','25-50', '50-75', '75-100']\n",
    "title = \"Relationship between Age and Attendance\"\n",
    "second_procedure(df,'age_bin' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People in the age bin are the ones with least attendance rate, they've got a right !!\n",
    "However, people in the age bin 0-25 are the next in not showing up, this needs explanation !!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day of reservation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will follow a bit different procedure, to know if scheduling the appointment at the day of the appointment affects the attendance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Leave to end\n",
    "df.scheduledday = pd.to_datetime(df['scheduledday']).dt.date\n",
    "df.appointmentday = pd.to_datetime(df['appointmentday']).dt.date\n",
    "\n",
    "df_same_day = df.query('scheduledday == appointmentday')\n",
    "df_prior_day = df.query('scheduledday != appointmentday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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>Attendance</th>\n",
       "      <th>age_bin</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>2016-04-29</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>1</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender scheduledday appointmentday  age  \\\n",
       "0  2.987250e+13        5642903      F   2016-04-29     2016-04-29   62   \n",
       "\n",
       "     neighbourhood  scholarship  hipertension  diabetes  alcoholism  handcap  \\\n",
       "0  JARDIM DA PENHA            0             1         0           0        0   \n",
       "\n",
       "   sms_received  Attendance age_bin  \n",
       "0             0           1   50-75  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_same_day.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>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>Attendance</th>\n",
       "      <th>age_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.598513e+13</td>\n",
       "      <td>5626772</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>76</td>\n",
       "      <td>REPÚBLICA</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>75-100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender scheduledday appointmentday  age  \\\n",
       "5  9.598513e+13        5626772      F   2016-04-27     2016-04-29   76   \n",
       "\n",
       "  neighbourhood  scholarship  hipertension  diabetes  alcoholism  handcap  \\\n",
       "5     REPÚBLICA            0             1         0           0        0   \n",
       "\n",
       "   sms_received  Attendance age_bin  \n",
       "5             0           1  75-100  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prior_day.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_same_day[\"Attendance\"].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_prior_day[\"Attendance\"].mean()\n",
    "#Same day has higher attenddance by far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To visualize the previous findings, here is the mean attendance for people who scheduled appointment on the same day compared to those who scheduled it at prior day: \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e71e18050>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('To visualize the previous findings, here is the mean attendance for people who scheduled appointment on the same day compared to those who scheduled it at prior day: \\n')\n",
    "plt.bar(['Same Day','Prior_Day'],[x,y],color='black')\n",
    "plt.title(\"Relationship between the Day of booking and Attendance\")\n",
    "plt.xlabel('Booking Day')\n",
    "plt.ylabel('Mean Attendance');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that people who make an instaneous booking (same day) are more likely to show up to the appointment, Logicallll!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scholarship:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of scholarship of people with no show is as follows: \n",
      "\n",
      "0    19741\n",
      "1     2578\n",
      "Name: scholarship, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e71e18210>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Scholarship','Scholarship']\n",
    "title = \"Relationship between scholarship and attendance\"\n",
    "first_procedure(df,'scholarship' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of scholarship is as follows: \n",
      "\n",
      "scholarship\n",
      "0    0.801926\n",
      "1    0.762637\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94128a10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Scholarship','Scholarship']\n",
    "title = \"Relationship between scholarship and attendance\"\n",
    "second_procedure(df,'scholarship' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like some people do not value what they have got, people enrolled in scholarship are more likely to skip their appointments, SURPRISING !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hipertension:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of hipertension of people with no show is as follows: \n",
      "\n",
      "0    18547\n",
      "1     3772\n",
      "Name: hipertension, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e9459c6d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Hipertension', 'Hipertension']\n",
    "title = \"Relationship between Hipertension and Attendance\"\n",
    "first_procedure(df,'hipertension' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of hipertension is as follows: \n",
      "\n",
      "hipertension\n",
      "0    0.790961\n",
      "1    0.826980\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e71eeea10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Hipertension', 'Hipertension']\n",
    "title = \"Relationship between Hipertension and Attendance\"\n",
    "second_procedure(df,'hipertension' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On mean, people with hipertension tend to attend their appointment more than people without it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alcoholism:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of alcoholism of people with no show is as follows: \n",
      "\n",
      "0    21642\n",
      "1      677\n",
      "Name: alcoholism, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e9459ced0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Alcoholism', 'Alcoholism']\n",
    "title = \"Relationship between Alcoholism and Attendance\"\n",
    "first_procedure(df,'alcoholism' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of alcoholism is as follows: \n",
      "\n",
      "alcoholism\n",
      "0    0.798052\n",
      "1    0.798512\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94234050>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Alcoholism', 'Alcoholism']\n",
    "title = \"Relationship between Alcoholism and Attendance\"\n",
    "second_procedure(df,'alcoholism' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although people with alcoholism are the minority among all no_showers, but on mean, people with alcoholism and without have nearly same attendance rate!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diabetic ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of diabetes of people with no show is as follows: \n",
      "\n",
      "0    20889\n",
      "1     1430\n",
      "Name: diabetes, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e9415f190>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Diabetes', 'Diabetes']\n",
    "title = \"Relationship between Diabetes and Attendance\"\n",
    "first_procedure(df,'diabetes' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of diabetes is as follows: \n",
      "\n",
      "diabetes\n",
      "0    0.796370\n",
      "1    0.819967\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e9777ec10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['No Diabetes', 'Diabetes']\n",
    "title = \"Relationship between Diabetes and Attendance\"\n",
    "second_procedure(df,'diabetes' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is nearly equal, but diabetic people even surpass people with no diabetes in discipline in their appointments, Speedy Recovery!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handcap?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of handcap of people with no show is as follows: \n",
      "\n",
      "0    21912\n",
      "1      366\n",
      "2       37\n",
      "3        3\n",
      "4        1\n",
      "Name: handcap, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e941f2090>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['0', '1', '2', '3', '4']\n",
    "title = \"Relationship between handcap and Attendance\"\n",
    "first_procedure(df,'handcap' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of handcap is as follows: \n",
      "\n",
      "handcap\n",
      "0    0.797645\n",
      "1    0.820764\n",
      "2    0.797814\n",
      "3    0.769231\n",
      "4    0.666667\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94344890>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['0', '1', '2', '3', '4']\n",
    "title = \"Relationship between handcap and attendnace\"\n",
    "second_procedure(df,'handcap' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, people with handcap of degree 4 were the most probable not to show, understandable with regard to the nature of their medical issue, and definetely needs more care!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sms_received ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of sms_received of people with no show is as follows: \n",
      "\n",
      "0    12535\n",
      "1     9784\n",
      "Name: sms_received, dtype: int64\n",
      "And here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94309810>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['no_sms_received','sms_received']\n",
    "title = \"Relationship between receiving Cancellation SMS and Attendance\"\n",
    "first_procedure(df,'sms_received' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Procedure: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean attendace per all categories of sms_received is as follows: \n",
      "\n",
      "sms_received\n",
      "0    0.832965\n",
      "1    0.724255\n",
      "Name: Attendance, dtype: float64\n",
      "\n",
      " \n",
      " Here is the visualization of these results : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e943090d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['no_sms_received','sms_received']\n",
    "title = \"Relationship between receiving Cancellation SMS and Attendance\"\n",
    "second_procedure(df,'sms_received' , x_labels, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a SMS was received it was more likely that patients do not show up, so this process needs more study.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neighbourhood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>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>Attendance</th>\n",
       "      <th>age_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>9.598723e+13</td>\n",
       "      <td>5595387</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-18</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>37</td>\n",
       "      <td>AEROPORTO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>3.719953e+11</td>\n",
       "      <td>5606885</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>29</td>\n",
       "      <td>ANDORINHAS</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.549277e+12</td>\n",
       "      <td>5597643</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-18</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>28</td>\n",
       "      <td>ANTÔNIO HONÓRIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>8.599713e+10</td>\n",
       "      <td>5629906</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>ARIOVALDO FAVALESSA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>9.279624e+13</td>\n",
       "      <td>5631486</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>34</td>\n",
       "      <td>BARRO VERMELHO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>3.673278e+13</td>\n",
       "      <td>5621388</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>31</td>\n",
       "      <td>BELA VISTA</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>7.382561e+12</td>\n",
       "      <td>5612780</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>BENTO FERREIRA</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>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>6.531269e+13</td>\n",
       "      <td>5635227</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>14</td>\n",
       "      <td>BOA VISTA</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>3.428312e+12</td>\n",
       "      <td>5587516</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-15</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>33</td>\n",
       "      <td>BONFIM</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>5.369588e+12</td>\n",
       "      <td>5623002</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>27</td>\n",
       "      <td>CARATOÍRA</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>4.916356e+12</td>\n",
       "      <td>5521768</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>40</td>\n",
       "      <td>CENTRO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>6.236134e+11</td>\n",
       "      <td>5630432</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>26</td>\n",
       "      <td>COMDUSA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.479497e+13</td>\n",
       "      <td>5633460</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>40</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>7.349270e+14</td>\n",
       "      <td>5531831</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>6.941444e+14</td>\n",
       "      <td>5629172</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>1</td>\n",
       "      <td>CRUZAMENTO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8.545794e+10</td>\n",
       "      <td>5587875</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-15</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>18</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>1.538956e+14</td>\n",
       "      <td>5558848</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>55</td>\n",
       "      <td>DE LOURDES</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>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>3.939988e+12</td>\n",
       "      <td>5564718</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-11</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>43</td>\n",
       "      <td>DO CABRAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>1.762682e+12</td>\n",
       "      <td>5621520</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>80</td>\n",
       "      <td>DO MOSCOSO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75-100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>8.955632e+14</td>\n",
       "      <td>5595905</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-18</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>10</td>\n",
       "      <td>DO QUADRO</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>8.338348e+13</td>\n",
       "      <td>5607987</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>58</td>\n",
       "      <td>ENSEADA DO SUÁ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>4.287628e+12</td>\n",
       "      <td>5631423</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>31</td>\n",
       "      <td>ESTRELINHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>5.931963e+12</td>\n",
       "      <td>5562309</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>19</td>\n",
       "      <td>FONTE GRANDE</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>5.579675e+12</td>\n",
       "      <td>5602845</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-19</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>18</td>\n",
       "      <td>FORTE SÃO JOÃO</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>5.952139e+12</td>\n",
       "      <td>5677114</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>37</td>\n",
       "      <td>FRADINHOS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.336882e+14</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>9.223559e+13</td>\n",
       "      <td>5534656</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>27</td>\n",
       "      <td>GRANDE VITÓRIA</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>6.714635e+09</td>\n",
       "      <td>5576760</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-13</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>19</td>\n",
       "      <td>GURIGICA</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>4.487227e+14</td>\n",
       "      <td>5604164</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>30</td>\n",
       "      <td>HORTO</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4.172398e+13</td>\n",
       "      <td>5620901</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>13</td>\n",
       "      <td>ILHA DAS CAIEIRAS</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75199</th>\n",
       "      <td>8.255992e+12</td>\n",
       "      <td>5663947</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>17</td>\n",
       "      <td>PARQUE INDUSTRIAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>9.588774e+13</td>\n",
       "      <td>5522438</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>28</td>\n",
       "      <td>PARQUE MOSCOSO</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>1.172525e+13</td>\n",
       "      <td>5709076</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>54</td>\n",
       "      <td>PIEDADE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48623</th>\n",
       "      <td>5.564489e+13</td>\n",
       "      <td>5641757</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>77</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75-100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>4.272659e+13</td>\n",
       "      <td>5634178</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>67</td>\n",
       "      <td>PRAIA DO CANTO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>9.447582e+14</td>\n",
       "      <td>5633576</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>67</td>\n",
       "      <td>PRAIA DO SUÁ</td>\n",
       "      <td>0</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>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>9.491857e+12</td>\n",
       "      <td>5600505</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-19</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>33</td>\n",
       "      <td>REDENÇÃO</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>2.395477e+10</td>\n",
       "      <td>5629428</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>14</td>\n",
       "      <td>REPÚBLICA</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>5.214130e+12</td>\n",
       "      <td>5631419</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>17</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>4.788217e+14</td>\n",
       "      <td>5544176</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-05</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>38</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>2.452831e+14</td>\n",
       "      <td>5596079</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-18</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>46</td>\n",
       "      <td>SANTA CECÍLIA</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2940</th>\n",
       "      <td>2.267664e+11</td>\n",
       "      <td>5602856</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-19</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>21</td>\n",
       "      <td>SANTA CLARA</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>2.558875e+14</td>\n",
       "      <td>5584467</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-14</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>14</td>\n",
       "      <td>SANTA HELENA</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>8.519344e+13</td>\n",
       "      <td>5631875</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>19</td>\n",
       "      <td>SANTA LUÍZA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>2.884273e+12</td>\n",
       "      <td>5565770</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-11</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>22</td>\n",
       "      <td>SANTA LÚCIA</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>9.298734e+13</td>\n",
       "      <td>5521644</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>30</td>\n",
       "      <td>SANTA MARTHA</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>6.234618e+14</td>\n",
       "      <td>5533700</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>27</td>\n",
       "      <td>SANTA TEREZA</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>5.654828e+10</td>\n",
       "      <td>5599192</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-19</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>40</td>\n",
       "      <td>SANTO ANDRÉ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>3.326985e+12</td>\n",
       "      <td>5632495</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>4.266984e+14</td>\n",
       "      <td>5642059</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>SANTOS DUMONT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5.384512e+13</td>\n",
       "      <td>5601706</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-19</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>9</td>\n",
       "      <td>SANTOS REIS</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>5.324252e+13</td>\n",
       "      <td>5630297</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>43</td>\n",
       "      <td>SEGURANÇA DO LAR</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>6.965982e+13</td>\n",
       "      <td>5631088</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>48</td>\n",
       "      <td>SOLON BORGES</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>8.366249e+13</td>\n",
       "      <td>5639495</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>25</td>\n",
       "      <td>SÃO BENEDITO</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.741649e+11</td>\n",
       "      <td>5635414</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>78</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75-100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>6.642793e+13</td>\n",
       "      <td>5521733</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>35</td>\n",
       "      <td>SÃO JOSÉ</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>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>5.717356e+13</td>\n",
       "      <td>5537888</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>17</td>\n",
       "      <td>SÃO PEDRO</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>0</td>\n",
       "      <td>0-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2.817719e+13</td>\n",
       "      <td>5490381</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-03-18</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>51</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14020</th>\n",
       "      <td>3.536273e+13</td>\n",
       "      <td>5676915</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>50</td>\n",
       "      <td>UNIVERSITÁRIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>3.656627e+14</td>\n",
       "      <td>5612155</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>67</td>\n",
       "      <td>VILA RUBIM</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50-75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          patientid  appointmentid gender scheduledday appointmentday  age  \\\n",
       "24987  9.598723e+13        5595387      F   2016-04-18     2016-05-05   37   \n",
       "216    3.719953e+11        5606885      F   2016-04-20     2016-04-29   29   \n",
       "76     6.549277e+12        5597643      M   2016-04-18     2016-04-29   28   \n",
       "256    8.599713e+10        5629906      F   2016-04-27     2016-04-29    0   \n",
       "1363   9.279624e+13        5631486      F   2016-04-28     2016-04-29   34   \n",
       "123    3.673278e+13        5621388      F   2016-04-26     2016-04-29   31   \n",
       "576    7.382561e+12        5612780      F   2016-04-25     2016-04-29   62   \n",
       "4409   6.531269e+13        5635227      F   2016-04-28     2016-05-02   14   \n",
       "136    3.428312e+12        5587516      F   2016-04-15     2016-04-29   33   \n",
       "237    5.369588e+12        5623002      F   2016-04-26     2016-04-29   27   \n",
       "553    4.916356e+12        5521768      M   2016-03-29     2016-04-29   40   \n",
       "1260   6.236134e+11        5630432      F   2016-04-27     2016-04-29   26   \n",
       "17     1.479497e+13        5633460      F   2016-04-28     2016-04-29   40   \n",
       "358    7.349270e+14        5531831      F   2016-03-31     2016-04-29    0   \n",
       "686    6.941444e+14        5629172      F   2016-04-27     2016-04-29    1   \n",
       "137    8.545794e+10        5587875      M   2016-04-15     2016-04-29   18   \n",
       "862    1.538956e+14        5558848      F   2016-04-08     2016-04-29   55   \n",
       "2189   3.939988e+12        5564718      F   2016-04-11     2016-04-29   43   \n",
       "926    1.762682e+12        5621520      M   2016-04-26     2016-04-29   80   \n",
       "1436   8.955632e+14        5595905      M   2016-04-18     2016-04-29   10   \n",
       "1443   8.338348e+13        5607987      F   2016-04-20     2016-04-29   58   \n",
       "1371   4.287628e+12        5631423      M   2016-04-28     2016-04-29   31   \n",
       "651    5.931963e+12        5562309      M   2016-04-08     2016-04-29   19   \n",
       "1893   5.579675e+12        5602845      F   2016-04-19     2016-04-29   18   \n",
       "4362   5.952139e+12        5677114      M   2016-05-09     2016-05-16   37   \n",
       "6      7.336882e+14        5630279      F   2016-04-27     2016-04-29   23   \n",
       "49     9.223559e+13        5534656      F   2016-03-31     2016-04-29   27   \n",
       "1135   6.714635e+09        5576760      F   2016-04-13     2016-04-29   19   \n",
       "1622   4.487227e+14        5604164      F   2016-04-20     2016-04-29   30   \n",
       "2009   4.172398e+13        5620901      M   2016-04-26     2016-04-29   13   \n",
       "...             ...            ...    ...          ...            ...  ...   \n",
       "75199  8.255992e+12        5663947      F   2016-05-05     2016-05-05   17   \n",
       "555    9.588774e+13        5522438      F   2016-03-29     2016-04-29   28   \n",
       "3891   1.172525e+13        5709076      F   2016-05-17     2016-05-19   54   \n",
       "48623  5.564489e+13        5641757      M   2016-04-29     2016-05-19   77   \n",
       "619    4.272659e+13        5634178      F   2016-04-28     2016-04-29   67   \n",
       "126    9.447582e+14        5633576      F   2016-04-28     2016-04-29   67   \n",
       "669    9.491857e+12        5600505      F   2016-04-19     2016-04-29   33   \n",
       "1583   2.395477e+10        5629428      F   2016-04-27     2016-04-29   14   \n",
       "1372   5.214130e+12        5631419      F   2016-04-28     2016-04-29   17   \n",
       "1131   4.788217e+14        5544176      F   2016-04-05     2016-04-29   38   \n",
       "1979   2.452831e+14        5596079      F   2016-04-18     2016-04-29   46   \n",
       "2940   2.267664e+11        5602856      F   2016-04-19     2016-04-29   21   \n",
       "1996   2.558875e+14        5584467      F   2016-04-14     2016-04-29   14   \n",
       "1351   8.519344e+13        5631875      F   2016-04-28     2016-04-29   19   \n",
       "568    2.884273e+12        5565770      M   2016-04-11     2016-04-29   22   \n",
       "1025   9.298734e+13        5521644      F   2016-03-29     2016-04-29   30   \n",
       "673    6.234618e+14        5533700      F   2016-03-31     2016-04-29   27   \n",
       "668    5.654828e+10        5599192      F   2016-04-19     2016-04-29   40   \n",
       "104    3.326985e+12        5632495      F   2016-04-28     2016-04-29    0   \n",
       "212    4.266984e+14        5642059      M   2016-04-29     2016-04-29   62   \n",
       "2014   5.384512e+13        5601706      M   2016-04-19     2016-04-29    9   \n",
       "3138   5.324252e+13        5630297      F   2016-04-27     2016-04-29   43   \n",
       "1407   6.965982e+13        5631088      F   2016-04-27     2016-04-29   48   \n",
       "440    8.366249e+13        5639495      F   2016-04-29     2016-04-29   25   \n",
       "44     2.741649e+11        5635414      F   2016-04-28     2016-04-29   78   \n",
       "185    6.642793e+13        5521733      F   2016-03-29     2016-04-29   35   \n",
       "172    5.717356e+13        5537888      F   2016-04-01     2016-04-29   17   \n",
       "336    2.817719e+13        5490381      M   2016-03-18     2016-04-29   51   \n",
       "14020  3.536273e+13        5676915      M   2016-05-09     2016-05-11   50   \n",
       "1430   3.656627e+14        5612155      F   2016-04-25     2016-04-29   67   \n",
       "\n",
       "             neighbourhood  scholarship  hipertension  diabetes  alcoholism  \\\n",
       "24987            AEROPORTO            0             0         0           0   \n",
       "216             ANDORINHAS            0             0         0           0   \n",
       "76         ANTÔNIO HONÓRIO            0             0         0           0   \n",
       "256    ARIOVALDO FAVALESSA            0             0         0           0   \n",
       "1363        BARRO VERMELHO            0             0         0           0   \n",
       "123             BELA VISTA            1             0         0           0   \n",
       "576         BENTO FERREIRA            0             0         0           0   \n",
       "4409             BOA VISTA            0             0         0           0   \n",
       "136                 BONFIM            1             0         0           0   \n",
       "237              CARATOÍRA            1             0         0           0   \n",
       "553                 CENTRO            0             0         0           0   \n",
       "1260               COMDUSA            0             0         0           0   \n",
       "17               CONQUISTA            1             0         0           0   \n",
       "358             CONSOLAÇÃO            0             0         0           0   \n",
       "686             CRUZAMENTO            0             0         0           0   \n",
       "137               DA PENHA            0             0         0           0   \n",
       "862             DE LOURDES            0             0         0           0   \n",
       "2189             DO CABRAL            0             0         0           0   \n",
       "926             DO MOSCOSO            0             0         0           0   \n",
       "1436             DO QUADRO            0             0         0           0   \n",
       "1443        ENSEADA DO SUÁ            0             1         0           0   \n",
       "1371            ESTRELINHA            0             1         0           0   \n",
       "651           FONTE GRANDE            1             0         0           0   \n",
       "1893        FORTE SÃO JOÃO            0             0         0           0   \n",
       "4362             FRADINHOS            0             0         0           0   \n",
       "6               GOIABEIRAS            0             0         0           0   \n",
       "49          GRANDE VITÓRIA            0             0         0           0   \n",
       "1135              GURIGICA            0             0         0           0   \n",
       "1622                 HORTO            0             0         0           0   \n",
       "2009     ILHA DAS CAIEIRAS            0             0         0           0   \n",
       "...                    ...          ...           ...       ...         ...   \n",
       "75199    PARQUE INDUSTRIAL            0             0         0           0   \n",
       "555         PARQUE MOSCOSO            0             0         0           0   \n",
       "3891               PIEDADE            0             1         0           1   \n",
       "48623    PONTAL DE CAMBURI            0             0         0           0   \n",
       "619         PRAIA DO CANTO            0             1         1           0   \n",
       "126           PRAIA DO SUÁ            0             0         1           0   \n",
       "669               REDENÇÃO            0             0         0           0   \n",
       "1583             REPÚBLICA            0             0         0           0   \n",
       "1372           RESISTÊNCIA            0             0         0           0   \n",
       "1131                 ROMÃO            1             1         0           0   \n",
       "1979         SANTA CECÍLIA            0             0         0           0   \n",
       "2940           SANTA CLARA            0             0         0           0   \n",
       "1996          SANTA HELENA            1             0         0           0   \n",
       "1351           SANTA LUÍZA            0             0         0           0   \n",
       "568            SANTA LÚCIA            0             0         0           0   \n",
       "1025          SANTA MARTHA            0             0         0           0   \n",
       "673           SANTA TEREZA            0             0         0           0   \n",
       "668            SANTO ANDRÉ            0             0         0           0   \n",
       "104          SANTO ANTÔNIO            0             0         0           0   \n",
       "212          SANTOS DUMONT            0             1         1           0   \n",
       "2014           SANTOS REIS            1             0         0           0   \n",
       "3138      SEGURANÇA DO LAR            0             0         0           0   \n",
       "1407          SOLON BORGES            1             0         0           0   \n",
       "440           SÃO BENEDITO            1             0         0           0   \n",
       "44           SÃO CRISTÓVÃO            0             1         1           0   \n",
       "185               SÃO JOSÉ            1             0         0           0   \n",
       "172              SÃO PEDRO            0             0         0           0   \n",
       "336             TABUAZEIRO            0             0         0           0   \n",
       "14020        UNIVERSITÁRIO            0             0         0           0   \n",
       "1430            VILA RUBIM            0             1         1           0   \n",
       "\n",
       "       handcap  sms_received  Attendance age_bin  \n",
       "24987        0             0           0   25-50  \n",
       "216          0             1           0   25-50  \n",
       "76           0             0           0   25-50  \n",
       "256          0             0           0    0-25  \n",
       "1363         0             0           0   25-50  \n",
       "123          0             1           0   25-50  \n",
       "576          0             1           0   50-75  \n",
       "4409         0             1           0    0-25  \n",
       "136          0             1           0   25-50  \n",
       "237          0             1           0   25-50  \n",
       "553          0             0           0   25-50  \n",
       "1260         0             0           0   25-50  \n",
       "17           0             0           0   25-50  \n",
       "358          0             0           0    0-25  \n",
       "686          0             0           0    0-25  \n",
       "137          0             1           0    0-25  \n",
       "862          0             1           0   50-75  \n",
       "2189         0             0           0   25-50  \n",
       "926          0             0           0  75-100  \n",
       "1436         0             1           0    0-25  \n",
       "1443         0             0           0   50-75  \n",
       "1371         0             0           0   25-50  \n",
       "651          0             1           0    0-25  \n",
       "1893         0             1           0    0-25  \n",
       "4362         0             0           0   25-50  \n",
       "6            0             0           0    0-25  \n",
       "49           0             1           0   25-50  \n",
       "1135         0             1           0    0-25  \n",
       "1622         0             1           0   25-50  \n",
       "2009         0             1           0    0-25  \n",
       "...        ...           ...         ...     ...  \n",
       "75199        0             0           1    0-25  \n",
       "555          0             1           0   25-50  \n",
       "3891         0             0           0   50-75  \n",
       "48623        0             0           0  75-100  \n",
       "619          0             0           0   50-75  \n",
       "126          0             0           0   50-75  \n",
       "669          1             0           0   25-50  \n",
       "1583         1             0           0    0-25  \n",
       "1372         0             0           0    0-25  \n",
       "1131         0             0           0   25-50  \n",
       "1979         0             1           0   25-50  \n",
       "2940         0             1           0    0-25  \n",
       "1996         0             1           0    0-25  \n",
       "1351         0             0           0    0-25  \n",
       "568          0             1           0    0-25  \n",
       "1025         0             1           0   25-50  \n",
       "673          0             1           0   25-50  \n",
       "668          0             0           0   25-50  \n",
       "104          0             0           0    0-25  \n",
       "212          0             0           0   50-75  \n",
       "2014         0             1           0    0-25  \n",
       "3138         0             0           0   25-50  \n",
       "1407         0             0           0   25-50  \n",
       "440          0             0           0    0-25  \n",
       "44           0             0           0  75-100  \n",
       "185          0             1           0   25-50  \n",
       "172          0             1           0    0-25  \n",
       "336          0             1           0   50-75  \n",
       "14020        0             0           0   25-50  \n",
       "1430         0             1           0   50-75  \n",
       "\n",
       "[81 rows x 15 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.groupby('neighbourhood').Attendance.idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "AEROPORTO              0\n",
       "ANDORINHAS             0\n",
       "ANTÔNIO HONÓRIO        0\n",
       "ARIOVALDO FAVALESSA    0\n",
       "BARRO VERMELHO         0\n",
       "BELA VISTA             0\n",
       "BENTO FERREIRA         0\n",
       "BOA VISTA              0\n",
       "BONFIM                 0\n",
       "CARATOÍRA              0\n",
       "CENTRO                 0\n",
       "COMDUSA                0\n",
       "CONQUISTA              0\n",
       "CONSOLAÇÃO             0\n",
       "CRUZAMENTO             0\n",
       "DA PENHA               0\n",
       "DE LOURDES             0\n",
       "DO CABRAL              0\n",
       "DO MOSCOSO             0\n",
       "DO QUADRO              0\n",
       "ENSEADA DO SUÁ         0\n",
       "ESTRELINHA             0\n",
       "FONTE GRANDE           0\n",
       "FORTE SÃO JOÃO         0\n",
       "FRADINHOS              0\n",
       "GOIABEIRAS             0\n",
       "GRANDE VITÓRIA         0\n",
       "GURIGICA               0\n",
       "HORTO                  0\n",
       "ILHA DAS CAIEIRAS      0\n",
       "                      ..\n",
       "PARQUE INDUSTRIAL      1\n",
       "PARQUE MOSCOSO         0\n",
       "PIEDADE                0\n",
       "PONTAL DE CAMBURI      0\n",
       "PRAIA DO CANTO         0\n",
       "PRAIA DO SUÁ           0\n",
       "REDENÇÃO               0\n",
       "REPÚBLICA              0\n",
       "RESISTÊNCIA            0\n",
       "ROMÃO                  0\n",
       "SANTA CECÍLIA          0\n",
       "SANTA CLARA            0\n",
       "SANTA HELENA           0\n",
       "SANTA LUÍZA            0\n",
       "SANTA LÚCIA            0\n",
       "SANTA MARTHA           0\n",
       "SANTA TEREZA           0\n",
       "SANTO ANDRÉ            0\n",
       "SANTO ANTÔNIO          0\n",
       "SANTOS DUMONT          0\n",
       "SANTOS REIS            0\n",
       "SEGURANÇA DO LAR       0\n",
       "SOLON BORGES           0\n",
       "SÃO BENEDITO           0\n",
       "SÃO CRISTÓVÃO          0\n",
       "SÃO JOSÉ               0\n",
       "SÃO PEDRO              0\n",
       "TABUAZEIRO             0\n",
       "UNIVERSITÁRIO          0\n",
       "VILA RUBIM             0\n",
       "Name: Attendance, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('neighbourhood')['Attendance'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among all neighbourhoods, the above scored a mean score of 0 in attendance, which means that the dominant behaviour of patients going to clinics in these areas is not showing up at all.. We should have heads up on these areas!! We used two ways to display them, one explicitly, and one including the 81 rows in the dataframe with other columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "The following findings were obtained based on the previous investigation:\n",
    "    \n",
    "    • Reserving on the day of the appointment lead to higher attendance rate\n",
    "    • Receiving an sms decreases the rate of attendance and increases the no show rate\n",
    "    • 81 areas (neighborhoods) had shared least attendance rate, which says that a more detailed study on their logistics is to be done to find out the reason behind this\n",
    "    • Age group (100 - 125) followed by (0-25) and (25-50) were the most age groups with no show incidents, a closer insight on the (0-50) groups needs to take place, as the first one is a bit logical\n",
    "    • People with serious handcap conditions (degree 4) are likely to skip their appointments, this can also be understood\n",
    "    • People who scheduled the appointment on its same day are more likely to show up then those who schedule it in earlier days\n",
    "\n",
    "Notes:\n",
    "\n",
    "    • Those were the clear findings, as some differences between categories were very slight that we can't tell they really affect attendance (Ex. Alcoholism and Non Alcoholism)\n",
    "    • The second procedure was more likely to infer conclusions from, due its specific nature studying each group on its own, however the first gave a very clear numerical insights on all groups of data\n",
    "\n",
    "### Limitations\n",
    "The following limitations were faced during working on the project:\n",
    "\n",
    "   \n",
    "    • We cannot plot columns data against the ‘Attendance’ data in a scatterplot for better visualization due to its boolean nature\n",
    "    • Equal samples are not taken for each aspect, ex. Not the same number of males and females\n",
    "    • Explanation was only offered for four columns where the rest were left ambiguous, some were really clear but some needed more explanation\n",
    "    • The scarcity of some of the categorical samples decreased the reliability of findings (Example: the age group (100-125) included only 3 samples, which made the findings weak ones, as these 3 may be outliers to the original age group on a larger scale  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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