Investigate_the_No-show_appointments_Databaset_20180108.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Project: Investigate the 'No-show appointments' Dataset\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",
    "> **Key notes**: \"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.\n",
    "‘ScheduledDay’ tells us on what day the patient set up their appointment.\n",
    "‘Neighborhood’ indicates the location of the hospital.\n",
    "‘Scholarship’ indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.\n",
    "Be careful about the encoding of the last column: it says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.\" \n",
    "\n",
    "> **Questions to explore**: \n",
    "><li><a href=\"#q1\"> 1. What is the infomation regarding the oldest patient?</a></li>\n",
    "><li><a href=\"#q2\"> 2. What is the patients number of male versus female?</a></li>\n",
    "><li><a href=\"#q3\"> 3. What is the distribution of male and female patients?</a></li>\n",
    "><li><a href=\"#q4\"> 4. On which years, months and days did most patients as well as least patients made appointments?  How many patients are there in total in the list?</a></li>\n",
    "><li><a href=\"#q5\"> 5. Hou many patients's appintments were scheduled in the April and May of 2016? What is the relationship between scheduled_day and appointment_day?</a></li>\n",
    "><li><a href=\"#q6\"> 6. Among all the 4 kinds of dieseas, which has highest morbility？</a></li>\n",
    "><li><a href=\"#q7\"> 7. What is the most frequent neighbourhood, and it is mostly associated with what age group?</a></li>\n",
    "><li><a href=\"#q8\"> 8. What is the ratio of patients suffering from different kinds of diseases, respectively?</a>\n",
    "><li><a href=\"#q9\"> 9. What is the ratio of both male and female patients suffering from different kinds of diseases, respectively?</a></li>\n",
    "><li><a href=\"#q10\"> 10. Bwtween 16: 00  and 17: 00pm, how many patients's appointments were scheduled on daily bases?</a></li>\n",
    "><li><a href=\"#q11\"> 11. What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?</a></li>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: prettytable in /Users/shilinli/anaconda3/lib/python3.6/site-packages (0.7.2)\r\n"
     ]
    }
   ],
   "source": [
    "# Set up import statements for all of the packages that are planed to use\n",
    "# Include a 'magic word' so that visualizations are plotted\n",
    "# call on dataframe to display the first 5 rows\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import calendar\n",
    "from statistics import mode\n",
    "! pip install prettytable\n",
    "from prettytable import PrettyTable\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "df = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> **Key notes**: In this section of the report, the following work will be done: load the data; check for cleanliness; trim and clean dataset for analysis.\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data and print out a few lines\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a tuple of the dimensions of the dataframe\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 PatientId\n",
      "1 AppointmentID\n",
      "2 Gender\n",
      "3 ScheduledDay\n",
      "4 AppointmentDay\n",
      "5 Age\n",
      "6 Neighbourhood\n",
      "7 Scholarship\n",
      "8 Hipertension\n",
      "9 Diabetes\n",
      "10 Alcoholism\n",
      "11 Handcap\n",
      "12 SMS_received\n",
      "13 No-show\n"
     ]
    }
   ],
   "source": [
    "# print the column labels in the dataframe\n",
    "\n",
    "for i, v in enumerate(df.columns):\n",
    "    print(i, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         float64\n",
       "AppointmentID       int64\n",
       "Gender             object\n",
       "ScheduledDay       object\n",
       "AppointmentDay     object\n",
       "Age                 int64\n",
       "Neighbourhood      object\n",
       "Scholarship         int64\n",
       "Hipertension        int64\n",
       "Diabetes            int64\n",
       "Alcoholism          int64\n",
       "Handcap             int64\n",
       "SMS_received        int64\n",
       "No-show            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return the datatypes of the columns\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates in the data\n",
    "\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  check if any value is NaN in DataFrame and in how many columns\n",
    "\n",
    "df.isnull().any().any(), sum(df.isnull().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      "PatientId         110527 non-null float64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# displays a concise summary of the dataframe\n",
    "# including the number of non-null values in each column\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generates descriptive statistics, excluding NaN values\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change column name into lower case for the convenience of analysis\n",
    "# Confirm changes\n",
    "\n",
    "df.rename(columns = lambda x: x.lower(), inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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": [
       "     patient_id  appointment_id gender         scheduled_day  \\\n",
       "0  2.987250e+13         5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14         5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12         5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11         5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12         5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "        appointment_day  age      neighbourhood  scholarship  hypertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handicap  sms_received no_show  \n",
       "0         0           0         0             0      No  \n",
       "1         0           0         0             0      No  \n",
       "2         0           0         0             0      No  \n",
       "3         0           0         0             0      No  \n",
       "4         1           0         0             0      No  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change some column name to avoid ambiguity\n",
    "# Confirm changes\n",
    "\n",
    "df.rename(columns = {'patientid': 'patient_id', 'appointmentid': 'appointment_id', 'scheduledday': 'scheduled_day',\\\n",
    "                    'appointmentday': 'appointment_day', 'handcap': 'handicap', 'hipertension': 'hypertension', \\\n",
    "                    'no-show': 'no_show'}, \n",
    "          inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id  appointment_id gender       scheduled_day appointment_day  \\\n",
       "0  2.987250e+13         5642903      F 2016-04-29 18:38:08      2016-04-29   \n",
       "1  5.589978e+14         5642503      M 2016-04-29 16:08:27      2016-04-29   \n",
       "2  4.262962e+12         5642549      F 2016-04-29 16:19:04      2016-04-29   \n",
       "3  8.679512e+11         5642828      F 2016-04-29 17:29:31      2016-04-29   \n",
       "4  8.841186e+12         5642494      F 2016-04-29 16:07:23      2016-04-29   \n",
       "\n",
       "   age      neighbourhood  scholarship  hypertension  diabetes  alcoholism  \\\n",
       "0   62    JARDIM DA PENHA            0             1         0           0   \n",
       "1   56    JARDIM DA PENHA            0             0         0           0   \n",
       "2   62      MATA DA PRAIA            0             0         0           0   \n",
       "3    8  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4   56    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   handicap  sms_received no_show  \n",
       "0         0             0      No  \n",
       "1         0             0      No  \n",
       "2         0             0      No  \n",
       "3         0             0      No  \n",
       "4         0             0      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fix datetime format\n",
    "# Confirm changes\n",
    "\n",
    "df.iloc[:, 3:5] = df.iloc[:, 3:5].apply(pd.to_datetime, errors='coerce')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "<a id='q1'></a>\n",
    "### Research Question 1: What is the infomation regarding the oldest patient?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                 3.19632e+13\n",
       "appointment_id                 5700278\n",
       "gender                               F\n",
       "scheduled_day      2016-05-16 09:17:44\n",
       "appointment_day    2016-05-19 00:00:00\n",
       "age                                115\n",
       "neighbourhood               ANDORINHAS\n",
       "scholarship                          0\n",
       "hypertension                         0\n",
       "diabetes                             0\n",
       "alcoholism                           0\n",
       "handicap                             1\n",
       "sms_received                         0\n",
       "no_show                            Yes\n",
       "Name: 63912, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['age'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The oldest patient is a woman who is 115 yeas old and remains relatively heathy. Except for handicap, she had not been diagnosed suffering any major disease in the list. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q2'></a>\n",
    "### Research Question 2: What is the patients number of male versus female?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of male and female patiens are 38687 and 71840, respectively.\n"
     ]
    }
   ],
   "source": [
    "# Use groupby to find out the value count for the gender column\n",
    "\n",
    "# Print out the respective numbers for both male and female patients\n",
    "\n",
    "all_gender = df.groupby(['gender'])['age'].value_counts()\n",
    "\n",
    "print('The number of male and female patiens are {} and {}, respectively.'.format(\\\n",
    "all_gender.loc['M'].sum(), all_gender.loc['F'].sum()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q3'></a>\n",
    "### Research Question 3: What is the distribution of male and female patients?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vICCg2uOq9uzXJD8/n/nz5/Pjjz9SXl6OxWKhTZs29O3bl7179+JwOGptKxhjQzdt2lTj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e596128>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 258,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use query to seperate male and female group and plot\n",
    "\n",
    "m_age = df.query('gender == \"M\"').age\n",
    "f_age = df.query('gender == \"F\"').age\n",
    "\n",
    "\n",
    "sns.distplot(m_age, bins=range(0, 100, 5),label='Male')\n",
    "sns.distplot(f_age, bins=range(0, 100, 5),label='Female')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q4'></a>\n",
    "### Research Question 4: On which years, months and days did most patients as well as least patients made appointments?  How many patients are there in total in the list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby patients number with year\n",
    "# Find out in which year did most patients make their appointments\n",
    "\n",
    "max_year = df.groupby(df['appointment_day'].apply(lambda x: x.year))['diabetes'].count()\n",
    "max_year.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110527"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the number of patients in the year when most of patients made their appointment\n",
    "\n",
    "max_year.loc[2016]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the result, most patients made their appointments in 2016, and the number is 110527."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby patients number with year\n",
    "# Find out in which year did least patients make their appointments\n",
    "\n",
    "min_year = df.groupby(df['appointment_day'].apply(lambda x: x.year))['diabetes'].count()\n",
    "min_year.idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the result, least patients made their appointments in 2016, which indicates this dataset only provided the data from 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'May'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby patients number with month\n",
    "\n",
    "max_mon = df.groupby(df['appointment_day'].apply(lambda x: x.month))['diabetes'].count()\n",
    "\n",
    "# Convert month number to month name in the index\n",
    "\n",
    "max_mon.index = max_mon.index.map(lambda x: calendar.month_name[x])\n",
    "\n",
    "# Find out in which month did most patients make their appointments\n",
    "\n",
    "max_mon.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80841"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the number of patients in the month when most patients made their appointment\n",
    "\n",
    "max_mon.loc['May']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the result, most patients made their appointments in May, and the number is 80841."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'April'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby patients numbers with month\n",
    "\n",
    "min_mon = df.groupby(df['appointment_day'].apply(lambda x: x.month))['diabetes'].count()\n",
    "\n",
    "# Convert month number to month name in the index\n",
    "\n",
    "min_mon.index = min_mon.index.map(lambda x: calendar.month_name[x])\n",
    "\n",
    "# Find out in which month least patients made their appointments\n",
    "\n",
    "min_mon.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3235"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the number of patients in the month when least patients made their appointment\n",
    "\n",
    "min_mon.loc['April']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the result, least patients made their appointments in April, and the number is 3235.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Wednesday'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby patients numbers with day\n",
    "\n",
    "max_day = df.groupby(df['appointment_day'].apply(lambda x: x.weekday()))['diabetes'].count()\n",
    "\n",
    "# Convert day number to day name in the index\n",
    "\n",
    "max_day.index = max_day.index.map(lambda x: calendar.day_name[x])\n",
    "\n",
    "# Find out on which day did most patients made their appointments\n",
    "\n",
    "max_day.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25867"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the number of patients on the day when most patients made their appointment\n",
    "\n",
    "max_day.loc['Wednesday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the result, most patients made their appointments on Wednesday, and the number is 25867."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Saturday'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby patients numbers with day\n",
    "\n",
    "min_day = df.groupby(df['appointment_day'].map(lambda x: x.weekday()))['diabetes'].count()\n",
    "\n",
    "# Convert day number to day name in the index\n",
    "\n",
    "min_day.index = min_day.index.map(lambda x: calendar.day_name[x])\n",
    "\n",
    "# Find out on which day least patients made their appointments\n",
    "\n",
    "min_day.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the number of patients on the day when most patients made their appointment\n",
    "\n",
    "min_day.loc['Saturday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to the result, least patients made their appointments on 'Saturday' , and the number is 39."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As all the data is from 2016, so the total patients number from the dastaset is 110527"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add prettytable to print out the result for better view\n",
    "\n",
    "x = PrettyTable()\n",
    "y = PrettyTable()\n",
    "x.field_names = [\"max_year\", \"min_year\", \"max_month\", \"min_month\", \"max_day\", \"min_day\"]\n",
    "y.field_names = [\"max_year_number\", \"min_year_number\", \"max_month_number\", \"min_month_number\", \\\n",
    "                 \"max_day_number\", \"min_day_number\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+----------+-----------+-----------+-----------+----------+\n",
      "| max_year | min_year | max_month | min_month |  max_day  | min_day  |\n",
      "+----------+----------+-----------+-----------+-----------+----------+\n",
      "|   2016   |   2016   |    May    |   April   | Wednesday | Saturday |\n",
      "+----------+----------+-----------+-----------+-----------+----------+ +-----------------+-----------------+------------------+------------------+----------------+----------------+\n",
      "| max_year_number | min_year_number | max_month_number | min_month_number | max_day_number | min_day_number |\n",
      "+-----------------+-----------------+------------------+------------------+----------------+----------------+\n",
      "|      110527     |      110527     |      80841       |       3235       |     25867      |       39       |\n",
      "+-----------------+-----------------+------------------+------------------+----------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "x.add_row([max_year.idxmax(), min_year.idxmax(), max_mon.idxmax(), min_mon.idxmin(), \\\n",
    "           max_day.idxmax(), min_day.idxmin()])\n",
    "\n",
    "y.add_row([max_year.loc[2016], min_year.loc[2016], max_mon.loc['May'], min_mon.loc['April'], \\\n",
    "          max_day.loc['Wednesday'],  min_day.loc['Saturday']])\n",
    "\n",
    "print(x, y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q5'></a>\n",
    "### Research Question 5: Hou many patients's appintments were scheduled in the April and May of 2016? What is the relationship between scheduled_day and appointment_day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106813"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locate the desired time period\n",
    "# Calculate the numbers\n",
    "a_m_mon = df.query('scheduled_day > \"2016-03-31\"')\n",
    "a_m_mon.groupby(['scheduled_day'])['diabetes'].count().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q6'></a>\n",
    "### Research Question 6:  Among all the 4 kinds of dieseas, which has highest morbility？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------------+------------+----------+\n",
      "| diabetes | hypertension | alcoholism | handicap |\n",
      "+----------+--------------+------------+----------+\n",
      "|   7943   |    21801     |    3360    |   2042   |\n",
      "+----------+--------------+------------+----------+\n",
      "hypertension has the highest mobility among the five kinds of dieseas, and the number is 21801.\n"
     ]
    }
   ],
   "source": [
    "# Use query function to seperate each dieseas, calculate the ratio of patients, seperately\n",
    "# and print using PrettyTable\n",
    "\n",
    "dia = df.query('diabetes == 1').diabetes.value_counts().tolist()[0]\n",
    "hyp = df.query('hypertension == 1').hypertension.value_counts().tolist()[0]\n",
    "alc = df.query('alcoholism == 1').alcoholism.value_counts().tolist()[0]\n",
    "han = df.query('handicap == 1').handicap.value_counts().tolist()[0]\n",
    "\n",
    "\n",
    "dise_dict = {\"diabetes\":dia, \"hypertension\":hyp, \"alcoholism\":alc, \"handicap\":han}\n",
    "z = PrettyTable()\n",
    "z.field_names =[\"diabetes\", \"hypertension\", \"alcoholism\", \"handicap\"]\n",
    "z.add_row([dia, hyp, alc, han])\n",
    "\n",
    "print(z)\n",
    "print('{} has the highest mobility among the five kinds of dieseas, and the number is {}.'\\\n",
    ".format(max(dise_dict, key=dise_dict.get), max(dise_dict.values())))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q7'></a>\n",
    "### Research Question 7:  What is the most frequent neighbourhood, and it is mostly associated with what age group?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'JARDIM CAMBURI'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use groupby to find out the name most frequent neighbourhood\n",
    "\n",
    "fre_neigh = df.groupby(['neighbourhood'])['age'].count()\n",
    "fre_neigh.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7717"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the corresponding frequency\n",
    "\n",
    "fre_neigh.loc['JARDIM CAMBURI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    49\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the max age in this group\n",
    "\n",
    "nei_age = df.query('neighbourhood == \"JARDIM CAMBURI\"')\n",
    "nei_age.age.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q8'></a>\n",
    "### Research Question 8: What is the ratio of patients suffering from different kinds of diseases, respectively?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z8PCQxJgB3OhWc5/q1aurevXqunTp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\np05p3bp1OfajWrx4sUaMGKERI0boH//4x73pIAAAAG4b87cHB3tiAQA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13bcb828>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 353,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the ratio of patients suffering from different kinds of diseases and plot\n",
    "\n",
    "diab = df['diabetes'].sum()/df['diabetes'].count()\n",
    "hype = df['hypertension'].sum()/df['hypertension'].count()\n",
    "alco = df['alcoholism'].sum()/df['alcoholism'].count()\n",
    "hand = df['handicap'].sum()/df['handicap'].count()\n",
    "\n",
    "\n",
    "x = ['diabetes', 'hypertension', 'alcoholism', 'handicap']\n",
    "y = [diab, hype, alco, hand]\n",
    "\n",
    "plt.subplots(figsize=(10,6))\n",
    "sns.barplot(x,y, alpha = 0.8);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q9'></a>\n",
    "### Research Question 9: What is the ratio of both male and female patients suffering from different kinds of diseases, respectively?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tq3YUoWdOnTpFjRo1dHcYd+zYwbZt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13b67320>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 656,
       "width": 595
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seperate male and famele group by query function\n",
    "# Calculate the ratio of each disease and plot\n",
    "\n",
    "man = df.query('gender == \"M\"')\n",
    "woman = df.query('gender == \"F\"')\n",
    "\n",
    "dia_man = man['diabetes'].sum()/df['diabetes'].count()\n",
    "dia_woman = woman['diabetes'].sum()/df['diabetes'].count()\n",
    "\n",
    "hype_man = man['hypertension'].sum()/df['hypertension'].count()\n",
    "hype_woman = woman['hypertension'].sum()/df['hypertension'].count()\n",
    "\n",
    "alco_man = man['alcoholism'].sum()/df['alcoholism'].count()\n",
    "alco_woman = woman['alcoholism'].sum()/df['alcoholism'].count()\n",
    "\n",
    "hand_man = man['handicap'].sum()/df['handicap'].count()\n",
    "hand_woman = man['handicap'].sum()/df['handicap'].count()\n",
    "\n",
    "x1 = ['diabetes_man', 'diabetes_woman', 'hypertension_man', 'hypertension_woman',\\\n",
    "      'alcoholism_man', 'alcoholism_woman',\\\n",
    "      'handicap_man', 'handicap_woman']\n",
    "\n",
    "y1 = [dia_man, dia_woman, hype_man, hype_woman,\\\n",
    "      alco_man, alco_woman, hand_man, hand_woman]\n",
    "\n",
    "\n",
    "plt.subplots(figsize=(10,10))\n",
    "plt.xticks(rotation=90);\n",
    "sns.barplot(x1,y1, alpha = 0.8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Aa8vLy1NeXp61a9dm3759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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13c03b70>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 608
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bars\n",
    "\n",
    "x2 = [dia_man, hype_man, alco_man, hand_man]\n",
    "y2 = [dia_woman, hype_woman, alco_woman, hand_woman]\n",
    "\n",
    "ind = np.arange(len(x2))\n",
    "width = 0.35\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "man_chart = plt.bar(ind, x2, width, color='r', alpha=.7, label='Man')\n",
    "woman_chart = plt.bar(ind + width, y2, width, color='g', alpha=.7, label='Woman')\n",
    "\n",
    "# title and labels\n",
    "plt.ylabel('Ratio')\n",
    "plt.xlabel('Diseases')\n",
    "plt.title('Ratio of patients by man and woman')\n",
    "locations = ind + width / 2  # xtick locations\n",
    "labels = ['diabetes', 'hypertension', 'alcoholism_man', 'handicap_man']  # xtick labels\n",
    "plt.xticks(locations, labels)\n",
    "#plt.subplots(figsize=(20, 10))\n",
    "#plt.xticks(rotation=90);\n",
    "\n",
    "# legend\n",
    "plt.legend();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q10'></a>\n",
    "### Research Question 10:  Bwtween 16: 00  and 17: 00pm, how many patients's appointments were scheduled on daily bases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542,\n",
       " 5542]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use query to locate the group which was scheduled between 16: 00  and 17: 00 pm and find out how many patients\n",
    "# in total\n",
    "\n",
    "patients_1617 = df.query('scheduled_day.dt.hour.values >= 16 & scheduled_day.dt.hour.values < 17')\n",
    "tot_pat = patients_1617.count().tolist()\n",
    "tot_pat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shilinli/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out how many days this group cross\n",
    "\n",
    "patients_1617['mnth_yr'] = patients_1617['scheduled_day'].apply(lambda x: x.strftime('%d-%B-%Y'))\n",
    "patients_1617['mnth_yr'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of patients whose appointments were scheduled between 16:00 and 17:00 was 71.05 on daily basis.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average number on daily basis\n",
    "\n",
    "avg_num = tot_pat[0]/patients_1617['mnth_yr'].nunique()\n",
    "\n",
    "print('The number of patients whose appointments were scheduled between 16:00 and \\\n",
    "17:00 was {:.2f} on daily basis.'.format(avg_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q11'></a>\n",
    "### Research Question 11: What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate the relationship of neighbourhood and show up\n",
    "\n",
    "df1= df.copy()\n",
    "df1['no_show'].replace(['Yes'], 0, inplace = True)\n",
    "df1['no_show'].replace(['No'], 1, inplace = True)\n",
    "nbr_show = df1.groupby(['neighbourhood'])['no_show'].sum()/df1.groupby(['neighbourhood'])['no_show'].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVaqkKlWqmB0LDuLl5aWBAweyfyHS6dWrl7p0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\n8OSTT6pTp07q27ev+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13b8d0b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 462,
       "width": 714
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the top 10 for plot\n",
    "\n",
    "nbr_show = nbr_show.nlargest(n = 10)\n",
    "\n",
    "plt.subplots(figsize=(12,6))\n",
    "plt.xticks(rotation=90)\n",
    "sns.barplot(nbr_show.index, nbr_show, alpha = 0.8);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The neighbourhood actually had impact on showup, histogram indicated that patients from 'PARQUE INDUSTRIAL' area made 100% show up, much higher even compared to second highest show up ratio (around 85%) which is from ILHADO BOI area. The show up ratio from 2nd high to 10th high are not quite different, ranging from 80% to 85%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Seperate man and woman\n",
    "# Calculate the relationship of neighbourhood and show up\n",
    "\n",
    "man_part = df1.query('gender == \"M\"')\n",
    "woman_part = df1.query('gender == \"F\"')\n",
    "man_show = man_part.groupby(['gender'])['no_show'].sum()/man_part.groupby(['gender'])['no_show'].count()\n",
    "woman_show = woman_part.groupby(['gender'])['no_show'].sum()/woman_part.groupby(['gender'])['no_show'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/z+SDvQs44DwzDwAAhRLzAABQKDEPAACFEvMAAFAoMQ8AAIUS8wAAUCgxDwAA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\nBwCAQol5AAAo1P8PGuHWUHv2TrQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13b132e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 244,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "x_gender = ['M', 'F']\n",
    "y_gender =[man_show.tolist()[0], woman_show.tolist()[0]]\n",
    "#plt.subplots(figsize=(12,6))\n",
    "#sns.barplot(x_gender, y_gender, alpha = 0.8);\n",
    "sns.swarmplot(x=x_gender, y=y_gender);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The gender has almost no impact on show up as indicated by histogram, both genders manifest aournd 80% attendance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop seconds, mintutes, hours and days as those ranges are too narrow\n",
    "\n",
    "df1['scheduled_day'] = df1['scheduled_day'].map(lambda x: x.strftime('%Y-%m'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13ba6f28>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 367,
       "width": 737
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the relationship of scheduled_day and show up\n",
    "# Plot the top 10\n",
    "\n",
    "sday_show = df1.groupby(['scheduled_day'])['no_show'].sum()/df1.groupby(['scheduled_day'])['no_show'].count()\n",
    "sday_show = sday_show.nlargest(n = 10)\n",
    "plt.subplots(figsize=(12,6))\n",
    "sns.barplot(sday_show, sday_show.index, alpha = 0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The scheduled_day has important impact on show up as indicated by histogram, with November in 2005 the highest ratio which is 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate the relationship of appointment_day and show up\n",
    "# Drop seconds, mintutes, hours and days as those ranges are too narrow\n",
    "\n",
    "df1['appointment_day'] = df1['appointment_day'].map(lambda x: x.strftime('%Y-%m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c65b630>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 367,
       "width": 737
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the top 10\n",
    "\n",
    "aday_show = df1.groupby(['appointment_day'])['no_show'].sum()/df1.groupby(['appointment_day'])['no_show'].count()\n",
    "aday_show = aday_show.nlargest(n = 10)\n",
    "plt.subplots(figsize=(12,6))\n",
    "sns.barplot(aday_show, aday_show.index, alpha = 0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The appointment_day has little important impact on show up as indicated by histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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skrfeeitTp06tta+6ujqTJ09OeXl52rVrlwsv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13b670f0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 367,
       "width": 714
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the relationship for age and show up\n",
    "\n",
    "age_show = df1.groupby(['age'])['no_show'].sum()/df1.groupby(['age'])['no_show'].count()\n",
    "age_show = age_show.nlargest(n = 10)\n",
    "plt.subplots(figsize=(12,6))\n",
    "sns.barplot(age_show.index, age_show, alpha = 0.8);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Age also seems has little impact on on show up as indicated by histogram, however, there is one group which is -1 year, which might be wrong data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patient_id  appointment_id gender scheduled_day appointment_day  age  \\\n",
       "99832  4.659432e+14         5775010      F       2016-06         2016-06   -1   \n",
       "\n",
       "      neighbourhood  scholarship  hypertension  diabetes  alcoholism  \\\n",
       "99832         ROMÃO            0             0         0           0   \n",
       "\n",
       "       handicap  sms_received  no_show  \n",
       "99832         0             0        1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the rows whose age == 1\n",
    "\n",
    "df1.query('age == -1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id            2.89827e+13\n",
       "appointment_id            5774075\n",
       "gender                          M\n",
       "scheduled_day             2016-06\n",
       "appointment_day           2016-06\n",
       "age                             1\n",
       "neighbourhood      FORTE SÃO JOÃO\n",
       "scholarship                     0\n",
       "hypertension                    0\n",
       "diabetes                        0\n",
       "alcoholism                      0\n",
       "handicap                        0\n",
       "sms_received                    0\n",
       "no_show                         1\n",
       "Name: 99833, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As there is only one row of such error, we can simply delete it without affeting the previous and later on analysis\n",
    "\n",
    "df1.drop(df.index[[99832]], inplace = True)\n",
    "\n",
    "# Comfirm changes\n",
    "\n",
    "df1.iloc[99832]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20c3b6a0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 353,
       "width": 712
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find out the relationship of the 4 kinds of disease and show up\n",
    "\n",
    "\n",
    "hyp_show = df1.groupby(['hypertension'])['no_show'].sum()/df1.groupby(['hypertension'])['no_show'].count()\n",
    "dia_show = df1.groupby(['diabetes'])['no_show'].sum()/df1.groupby(['diabetes'])['no_show'].count()\n",
    "alc_show = df1.groupby(['alcoholism'])['no_show'].sum()/df1.groupby(['alcoholism'])['no_show'].count()\n",
    "han_show = df1.groupby(['handicap'])['no_show'].sum()/df1.groupby(['handicap'])['no_show'].count()\n",
    "\n",
    "x_diseases = ['hypertension', 'diabetes', 'alcoholism', 'handicap']\n",
    "y_diseases = [hyp_show.tolist()[0], dia_show.tolist()[0], \\\n",
    "              alc_show.tolist()[0], han_show.tolist()[0]]\n",
    "\n",
    "plt.subplots(figsize=(12,6))\n",
    "sns.barplot(x, y, alpha = 0.8);\n",
    "                        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The kind of diseases has important impact on show up, with hypertension as the highest ratio at around 20%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vfpcmTZpk9OjRq/fOnjdvXnr16pU5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\n6enpMpvNqlmzpvr06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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2571ff60>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 570,
       "width": 714
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the relationship of scholarship and show up\n",
    "\n",
    "schlar_show = df1.groupby(['neighbourhood'])['no_show'].sum()/df1.groupby(['neighbourhood'])['no_show'].count()\n",
    "schlar_show = schlar_show.nlargest(n = 10)\n",
    "plt.subplots(figsize=(12,8))\n",
    "sns.barplot(schlar_show.index, schlar_show, alpha = 0.8);\n",
    "plt.xticks(rotation=90);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The scholarship has important impact on show up, with 'PARQUE INDUSTRIAL' as the highest ratio at 100%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "> In current study, a good amount of profound analysis has been carried out. \n",
    "Prior to each step, deailed instructions was given and interpretions was also provided afterwards. \n",
    "The dataset included 110527 pieces of patients's information from only 2016, \n",
    "which is substantial but limited to only one year. Therefore, even based on such large amount of data, the analysis would not be very representative. \n",
    "\n",
    "> The good aspect of current study was it didn't include NaN values nor duplicates, which could affect the process \n",
    "of analysis. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_the_No-show_appointments_Databaset_20180108.ipynb'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}