Investigate a Dataset (No-show Appointment).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Welcome to the Investigate a Dataset project! You will find tips in quoted sections like this to help organize your approach to your investigation. Before submitting your project, it will be a good idea to go back through your report and remove these sections to make the presentation of your work as tidy as possible. First things first, you might want to double-click this Markdown cell and change the title so that it reflects your dataset and investigation.\n",
    "\n",
    "# Project:Investigate a Dataset (No-show Appointment  )\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": [
    "\n",
    "## Introduction\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   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",
    "\n",
    "#What factors are important  to know in order to predict if a patient will show up for their \n",
    "#scheduled appointment or no?\n",
    "#This dataset collected from 100k medical appointments in Brazil\n",
    "#and is focused on the question of whether or not patients show up for their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> **Tip**: In this section of the report, you will load in the data, check for cleanliness, and then trim and clean your dataset for analysis. Make sure that you document your steps carefully and justify your cleaning decisions.\n",
    "\n",
    "After Observing the dataset and the questions related to this dataset for the analysis we will be keeping only relevent data and deleting the unused data.\n",
    "### General Properties\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>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 your data and print out a few lines. Perform operations to inspect data\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "df=pd.read_csv('appointment.csv')\n",
    "transDF = pd.DataFrame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: You should _not_ perform too many operations in each cell. Create cells freely to explore your data. One option that you can take with this project is to do a lot of explorations in an initial notebook. These don't have to be organized, but make sure you use enough comments to understand the purpose of each code cell. Then, after you're done with your analysis, create a duplicate notebook where you will trim the excess and organize your steps so that you have a flowing, cohesive report.\n",
    "\n",
    "> **Tip**: Make sure that you keep your reader informed on the steps that you are taking in your investigation. Follow every code cell, or every set of related code cells, with a markdown cell to describe to the reader what was found in the preceding cell(s). Try to make it so that the reader can then understand what they will be seeing in the following cell(s).\n",
    "\n",
    "### Data Cleaning (Replace this with more specific notes!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicate\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum age: 115\n",
      "minimum age: -1\n"
     ]
    }
   ],
   "source": [
    "#The maximum value of the Age_column is 115 quite high but realistic .\n",
    "#The minumum value of the Age_column is -1 , it unacceptale will be inspected. print('maximum age:',df['Age'].max())\n",
    "print('minimum age:',df['Age'].min())   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "General observations:\n",
    "*The data set has 14 columns and 110527 rows .\n",
    "*There are 13 independent variables and one dependent (no_show) in the dataset.\n",
    "*The dataset does not contain any missing values ,since each column has 110527  values.\n",
    "*The dataset does not contain any duplicates values.\n",
    "Now,That we have finished the cleaning work , we can continues with explorative analysis ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After discussing the structure of the data and any problems that need to be\n",
    "#   cleaned, perform those cleaning steps in the second part of this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop null value \n",
    "df = df.drop(df[df[\"Age\"]==-1].index)\n",
    "df['Age'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> **Tip**: Now that you've trimmed and cleaned your data, you're ready to move on to exploration. Compute statistics and create visualizations with the goal of addressing the research questions that you posed in the Introduction section. It is recommended that you be systematic with your approach. Look at one variable at a time, and then follow it up by looking at relationships between variables.\n",
    "\n",
    "### Research Question 1 (Is there a specific gender that is more committed to attending appointment?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "#showed_up = df['no_show'] =='No'\n",
    "#not_showed_up = df['no_show'] =='Yes'\n",
    "#In the dataset description, it mention that in no_show column No means patient showed up\n",
    "#and yes means patient did not showed up.                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>57246</td>\n",
       "      <td>14594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>30962</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show     No    Yes\n",
       "Gender               \n",
       "F        57246  14594\n",
       "M        30962   7725"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1=df.groupby('Gender')['No-show'].value_counts().unstack()\n",
    "A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>79.69</td>\n",
       "      <td>20.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>80.03</td>\n",
       "      <td>19.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show     No    Yes\n",
       "Gender               \n",
       "F        79.69  20.31\n",
       "M        80.03  19.97"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1=df.groupby('Gender')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Out of 71831 appointments made by females, 14588 were missed with the ratio of 20%.\n",
    "#Out of 38685 appointments made by males, 7723 were missed with the ratio of 19%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LqbeIYN26dVRXV9O9e/dSl2NNRKkvUj8OdIuI3sAc4Fe7+gYRMTEiqiKiqmPHjg1eoDVtmzZton379mUdDgCSaN++fZM4ErLGI8+AWA0cXLBembZtExHrIuKTdPU+4GvF7mvWUMo9HGo0lXFY45FnQMwHekrqLmkvYCgwo3ADSQcVrJ4NLEuXZwOnSTpA0gHAaWmbmZntIbkFRERsAS4n+Yd9GTA1IpZIuknS2elmV0haIull4Arg4nTf9cBPSEJmPnBTzQVrs7xJ4pprrtm2fvvttzN27Njdft+LL76YRx99dLffx2xPyfMiNRExC5hVq+2GguXrgOvq2HcyMDnP+syy7L333kybNo3rrruODh06lLocs5Ip9UVqs0anVatWjBw5knHjxm3Xt3LlSgYOHEjv3r055ZRTWLVqVeZ7jB49ml69etG7d29++MMfbmufO3cuxx57LD169Nh2NBERXHvttRx++OEcccQRPPzwwwCMGjWKGTOSs7Lnnnsu3/ve9wCYPHky119/fYOO2SyLA8Isw6hRo3jwwQfZsGHDF9q///3vM2LECBYvXszw4cO54oorttt33bp1TJ8+nSVLlrB48WJ+9KMfbetbs2YN8+bNY+bMmYwenUwuMG3aNBYtWsTLL7/M008/zbXXXsuaNWsYMGAAzz77LACrV69m6dLkK0TPPvssJ5xwQl5DN9vGAWGW4ctf/jLf/e53ueuuu77Q/txzz/Gd73wHgIsuuoh58+Ztt+9+++1HRUUFl156KdOmTaNNmzbb+s455xxatGhBr169eOuttwCYN28ew4YNo2XLlnTq1IkTTzyR+fPnbwuIpUuX0qtXLzp16sSaNWt47rnnOPbYY3McvVnCAWFWh6uuuopJkybx4Ycf7nTbQYMG0bdvXy677DJatWrFCy+8wHnnncfMmTMZPHjwtu323nvvbcsRscP37Ny5M++99x5PPvkkJ5xwAgMGDGDq1Knsu+++tG3btv4DMyuSA8KsDu3ateP8889n0qRJ29qOPfZYpkyZAsCDDz7IgAEDAJg9ezaLFi3ivvvuY+PGjWzYsIEzzjiDcePG8fLLL+/wcwYMGMDDDz/M1q1bWbt2LXPnzqVfv34AHH300dx5553bAuL222/f9plmecv1LiazcnfNNddwzz33bFu/++67ueSSS7jtttvo2LEjv/zlL7fb54MPPmDIkCFs2rSJiOCOO+7Y4Wece+65PPfcc/Tp0wdJ/PSnP+XAAw8EkvB46qmn+OpXv0rXrl1Zv369A8L2GO3sMLecVFVVxYIFC+q389j9GraYoj93w863sdwsW7aMww47rNRlNJimNp5y0230EyX53JW3fLPe+0paGBFVWX0+xWRmZpkcEGZmlskBYWZmmRwQZmaWyQFhZmaZHBBmZpbJ34MwK9DQtykWc/uhJK6++mp+9rOfAcn04hs3bmyQKcbNdoePIMxKrGZ68XfeeafUpZh9gQPCrMQaYnpxszw4IMwagd2ZXtwsL7kGhKTBkl6TtFzS6Iz+qyUtlbRY0u8ldS3o2yppUfqaUXtfs6Zkd6YXN8tLbgEhqSVwL3A60AsYJqlXrc1eAqoiojfwKPDTgr6PI6Jv+jobsyZuV6YXN9sT8jyC6Acsj4gVEbEZmAIMKdwgIv4QER+lq88DlTnWY9ao7cr04mZ7Qp63uXYG3ixYrwb672D7S4HfFaxXSFoAbAFuiYjfZu0kaSQwEqBLly67U6/Zbs2K2RDqM724WV4axfcgJF0IVAEnFjR3jYjVknoAz0h6JSL+s/a+ETERmAjJdN97pGCzBrRx48Zty506deKjjz7att61a1eeeeaZUpRlluspptXAwQXrlWnbF0g6FbgeODsiPqlpj4jV6Z8rgD8CR+ZYq5mZ1ZJnQMwHekrqLmkvYCjwhbuRJB0JTCAJh7cL2g+QtHe63AE4DliaY61mZlZLbqeYImKLpMuB2UBLYHJELJF0E7AgImYAtwH7Ao9IAliV3rF0GDBB0mckIXZLRDggzMz2oFyvQUTELGBWrbYbCpZPrWO/PwNH5FmbmZntmL9JbWZmmRwQZmaWqVHc5mrWaIzdr4Hfb8MOuyOCAQMGcP3113P66acD8MgjjzBp0iSefPLJhq3FbBc5IMxKSBLjx4/n29/+NieffDJbtmxhzJgxDgdrFBwQZiV2+OGHc9ZZZ3Hrrbfy4YcfcuGFF3LzzTfz6quv8umnnzJ27FiGDBnCkiVLuOSSS9i8eTOfffYZjz32GD179ix1+daEOSDMGoEbb7yRo446ir322oszzzyTgQMHMnnyZN577z369evHqaeeyvjx47nyyisZPnw4mzdvZuvWraUu25o4B4RZI7DPPvtwwQUXsO+++zJ16lQef/xxbr/9dgA2bdrEqlWrOOaYY7j55puprq7mW9/6lo8eLHcOCLNGokWLFrRo0YKI4LHHHuOQQw75Qv9hhx1G//79eeKJJzjjjDOYMGECAwcOLFG11hz4NlezRmbQoEHcfffdRCRzT7700ksArFixgh49enDFFVcwZMgQFi9eXMoyrRnwEYRZoZ3clron/PjHP+aqq66id+/efPbZZ3Tv3p2ZM2cydepUHnjgAVq3bs2BBx7ImDFjSl2qNXEOCLNGYuzYsduWJ0yYsF3/6NGjGT16uyf3muXGp5jMzCyTA8LMzDI5IKzZq7kYXO6ayjis8XBAWLNWUVHBunXryv4f14hg3bp1VFRUlLoUa0J8kdqatcrKSqqrq1m7dm2pS9ltFRUVVFZWlroMa0IcENastW7dmu7du5e6DLNGKddTTJIGS3pN0nJJ292fJ2lvSQ+n/X+R1K2g77q0/TVJg/Ks08zMtpdbQEhqCdwLnA70AoZJ6lVrs0uBdyPiq8A44NZ0317AUOAfgcHAv6bvZ2Zme0ieRxD9gOURsSIiNgNTgCG1thkC/CpdfhQ4RZLS9ikR8UlEvAEsT9/PzMz2kDyvQXQG3ixYrwb617VNRGyRtAFon7Y/X2vfzlkfImkkMDJd3SjptXrW2wF4p5771t+/KI93Lc1YGl5TGQd4LI1RUxkHunW3xtK1ro6yv0gdEROBibv7PpIWRERVA5RUck1lLE1lHOCxNEZNZRyQ31jyPMW0Gji4YL0ybcvcRlIrYD9gXZH7mplZjvIMiPlAT0ndJe1FctF5Rq1tZgAj0uXzgGci+cbSDGBoepdTd6An8EKOtZqZWS25nWJKrylcDswGWgKTI2KJpJuABRExA5gEPCBpObCeJERIt5sKLAW2AKMiIu/nK+72aapGpKmMpamMAzyWxqipjANyGovKfYoBMzPLh+diMjOzTA4IMzPL1OwCQtLBkv4gaamkJZKuTNvbSZoj6fX0zwNKXevOSKqQ9IKkl9Ox/Eva3j2dumR5OpXJXqWutRiSWkp6SdLMdL1cx7FS0iuSFklakLaV3c8XgKT9JT0q6a+Slkk6phzHIumQ9O+j5vW+pKvKdCw/SH/fX5X0UPrvQC6/K80uIEguel8TEb2Ao4FR6dQeo4HfR0RP4PfpemP3CTAwIvoAfYHBko4mmbJkXDqFybskU5qUgyuBZQXr5ToOgJMjom/Bvenl+PMF8HPgyYg4FOhD8vdTdmOJiNfSv4++wNeAj4DplNlYJHUGrgCqIuJwkhuAhpLX70pENOsX8G/AN4DXgIPStoOA10pd2y6Oow3wIsm31d8BWqXtxwCzS11fEfVXkvyCDgRmAirHcaS1rgQ61Goru58vku8lvUF6M0s5j6VW/acB/16OY+Hz2SfakdyFOhMYlNfvSnM8gtgmnT32SOAvQKeIWJN2/RfQqVR17Yr0tMwi4G1gDvCfwHsRsSXdpM5pShqZO4H/BXyWrrenPMcBEMBTkhamU8FAef58dQfWAr9MT/3dJ2kfynMshYYCD6XLZTWWiFgN3A6sAtYAG4CF5PS70mwDQtK+wGPAVRHxfmFfJDFcFvf/RsTWSA6bK0kmNDy0tBXtOklnAm9HxMJS19JAjo+Io0hmMh4l6YTCzjL6+WoFHAX8IiKOBD6k1imYMhoLAOm5+bOBR2r3lcNY0mskQ0jC+78B+5DMeJ2LZhkQklqThMODETEtbX5L0kFp/0Ek/yMvGxHxHvAHksPL/dOpS6A8pik5Djhb0kqSWX8Hkpz7LrdxANv+l0dEvE1ynrsf5fnzVQ1UR8Rf0vVHSQKjHMdS43TgxYh4K10vt7GcCrwREWsj4lNgGsnvTy6/K80uINLpxCcByyLijoKuwmk/RpBcm2jUJHWUtH+6/CWSaynLSILivHSzRj+WiLguIiojohvJ4f8zETGcMhsHgKR9JLWtWSY53/0qZfjzFRH/Bbwp6ZC06RSS2Q3KbiwFhvH56SUov7GsAo6W1Cb9t6zm7ySX35Vm901qSccDzwKv8Pn57jEk1yGmAl2AvwHnR8T6khRZJEm9SZ6n0ZIk7KdGxE2SepD8T7wd8BJwYUR8UrpKiyfpJOCHEXFmOY4jrXl6utoK+E1E3CypPWX28wUgqS9wH7AXsAK4hPRnjfIbyz4k/8D2iIgNaVvZ/b2kt7NfQHJH5kvAZSTXHBr8d6XZBYSZmRWn2Z1iMjOz4jggzMwskwPCzMwyOSDMzCyTA8LMzDI5IMzMLJMDwszMMv1/OGsjFnuadLkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A1=df.groupby('Gender')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack().plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As shown above, gender does not seem to affect whether a person shows up to an appointment or not, \n",
    "#as both males and females don't show up to approximately 20 percent of their appointments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Research Question 2(Is there a difference the average age of patient who miss appointments and those who do not?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2900.0</td>\n",
       "      <td>639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1366.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1236.0</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017.0</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show      No    Yes\n",
       "Age                   \n",
       "0        2900.0  639.0\n",
       "1        1858.0  415.0\n",
       "2        1366.0  252.0\n",
       "3        1236.0  277.0\n",
       "4        1017.0  282.0\n",
       "..          ...    ...\n",
       "98          5.0    1.0\n",
       "99          1.0    NaN\n",
       "100         4.0    NaN\n",
       "102         2.0    NaN\n",
       "115         2.0    3.0\n",
       "\n",
       "[103 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_show=df.groupby('Age')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "Age_show2=df.groupby('Age')['No-show'].value_counts().unstack()\n",
    "Age_show2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=df.Age)\n",
    "ax.set_title(\"No of appointments by Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=df.Age)\n",
    "ax.set_title(\"Yes of appointments by age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No-show\n",
       "No     37.790064\n",
       "Yes    34.317667\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('No-show')['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119e6bc40>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('No-show')['Age'].mean().hist(alpha=0.6,bins=25,label='No-show');\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"No-show\")\n",
    "plt.title(\"Age of people who showed\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#age: The patients are 37 years on average. 25% of patients are below 18 and most of them are below 55.\n",
    "#There is a data range problem in the dataset. \n",
    "\n",
    "#age: There are many very young people in the dataset but in general the patients age is\n",
    "#distributed evenly and the number of patients goes drastricly down for patients older than 60 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  ( What factors are important to know in order to predict if a patient will show up for their scheduled appointment?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        Scholarship No-show Gender  Hipertension  Diabetes  Alcoholism  \\\n",
       "0                 0      No      F             1         0           0   \n",
       "1                 0      No      M             0         0           0   \n",
       "2                 0      No      F             0         0           0   \n",
       "3                 0      No      F             0         0           0   \n",
       "4                 0      No      F             1         1           0   \n",
       "...             ...     ...    ...           ...       ...         ...   \n",
       "110522            0      No      F             0         0           0   \n",
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       "110524            0      No      F             0         0           0   \n",
       "110525            0      No      F             0         0           0   \n",
       "110526            0      No      F             0         0           0   \n",
       "\n",
       "        Handcap  sum_desieas  \n",
       "0             0            1  \n",
       "1             0            0  \n",
       "2             0            0  \n",
       "3             0            0  \n",
       "4             0            2  \n",
       "...         ...          ...  \n",
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       "110523        0            0  \n",
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       "\n",
       "[110526 rows x 8 columns]"
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     "execution_count": 68,
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   "source": [
    "col_list=[\"Scholarship\",\"Hipertension\", \"Diabetes\", \"Alcoholism\", \"Handcap\"]\n",
    "transDF=pd.DataFrame(df,columns=[\"Scholarship\",\"No-show\",\"Gender\",\"Hipertension\", \"Diabetes\", \"Alcoholism\", \"Handcap\"])\n",
    "transDF[\"sum_desieas\"] = transDF[col_list].sum(axis=1)\n",
    "transDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Scholarship No-show Gender  Hipertension  Diabetes  Alcoholism  Handcap  \\\n",
       "0              0      No      F             0         0           0        0   \n",
       "1              0      No      F             0         0           0        1   \n",
       "2              0      No      F             0         0           0        2   \n",
       "3              0      No      F             0         0           0        3   \n",
       "4              0      No      F             0         0           1        0   \n",
       "..           ...     ...    ...           ...       ...         ...      ...   \n",
       "122            1     Yes      M             0         1           0        0   \n",
       "123            1     Yes      M             1         0           0        0   \n",
       "124            1     Yes      M             1         0           1        0   \n",
       "125            1     Yes      M             1         1           0        0   \n",
       "126            1     Yes      M             1         1           1        0   \n",
       "\n",
       "     sum_desieas  count_patient  \n",
       "0              0          37711  \n",
       "1              1            413  \n",
       "2              2             36  \n",
       "3              3              3  \n",
       "4              1            361  \n",
       "..           ...            ...  \n",
       "122            2              4  \n",
       "123            2             19  \n",
       "124            3              3  \n",
       "125            3              5  \n",
       "126            4              1  \n",
       "\n",
       "[127 rows x 9 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=transDF.groupby([\"Scholarship\",\"No-show\",\"Gender\",\"Hipertension\", \"Diabetes\", \"Alcoholism\", \"Handcap\",\"sum_desieas\"]).size().reset_index(name='count_patient')\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, let's look at categorical variables\n",
    "col_list = ['Scholarship', 'Hipertension', 'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received']\n",
    "fig = plt.figure(figsize=(16, 11))\n",
    "for i, col in enumerate(col_list):\n",
    "    ax = fig.add_subplot(3, 3, i+1)\n",
    "    df.groupby([col, 'No-show'])[col].count().unstack('No-show').plot(ax=ax, kind='bar', stacked=True)"
   ]
  },
  {
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79924</td>\n",
       "      <td>19741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8283</td>\n",
       "      <td>2578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show         No    Yes\n",
       "Scholarship              \n",
       "0            79924  19741\n",
       "1             8283   2578"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A3=df.groupby('Scholarship')['No-show'].value_counts().unstack()\n",
    "A3"
   ]
  },
  {
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.19</td>\n",
       "      <td>19.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.26</td>\n",
       "      <td>23.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show         No    Yes\n",
       "Scholarship              \n",
       "0            80.19  19.81\n",
       "1            76.26  23.74"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A3=df.groupby('Scholarship')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Percentage of Patients joined the  Scholarship = 9.826558216544374% \n",
    "\n",
    "#Patients Enrolled in Scholarship\n",
    "\n",
    "Rercentage of Patients missed appointment = 23.73630420771568%\n",
    "Percentage of Patients attended appointment = 76.26369579228432%\n",
    "\n",
    "#Patients didnt Enrolled in Scholarship\n",
    "\n",
    "Rercentage of Patients missed appointment = 19.807155900708366%\n",
    "Percentage of Patients attended appointment = 80.19284409929163%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A3=df.groupby('Scholarship')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we can observer that about 10% of all the patients are enrolled in the well fare Scholarship only hence it does not have a great role to play\n",
    "\n",
    "#a slightly better attendance rate vs missing rate exist for the enrolled patients population as Scholarship to the general patients populations\n",
    "\n",
    "#As cleare by the bar graph this attribute does not have a major impact on the general patients trends.\n",
    "\n",
    "#The reason for the slight increase in the attendance / missing ratio for the people wha are enrolled can be due to wavers offered to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handcap</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86373</td>\n",
       "      <td>21912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1676</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>146</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show     No    Yes\n",
       "Handcap              \n",
       "0        86373  21912\n",
       "1         1676    366\n",
       "2          146     37\n",
       "3           10      3\n",
       "4            2      1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A2=df.groupby('Handcap')['No-show'].value_counts().unstack()\n",
    "A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "The value distribution implies differentiations of the handicap-degree. \n",
    "0 appears to be no handicap. The amounts of each degree are quite low, especially from degree 2 on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A2=df.groupby('Handcap')['No-show'].value_counts().unstack().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hipertension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.1</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.7</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show         No   Yes\n",
       "Hipertension            \n",
       "0             79.1  20.9\n",
       "1             82.7  17.3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A4=df.groupby('Hipertension')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "A4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='No'>, <AxesSubplot:ylabel='Yes'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A4.plot.pie(subplots=True, figsize=(9, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we notice that the patients suffer from hypertension they tend to attend appointment 82% times.\n",
    "#Hence it is as expceted that the patients with a diseas tend to attend appointments more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.64</td>\n",
       "      <td>20.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.00</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show      No    Yes\n",
       "Diabetes              \n",
       "0         79.64  20.36\n",
       "1         82.00  18.00"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A5=df.groupby('Diabetes')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "A5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='No'>, <AxesSubplot:ylabel='Yes'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A5.plot.pie(subplots=True, figsize=(9, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we notice that the patients suffer from Diabetes they tend to attend appointment 82% times\n",
    "#Hence it is as expceted that the patients with a diseas tend to attend appointments more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcoholism</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.81</td>\n",
       "      <td>20.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.85</td>\n",
       "      <td>20.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show        No    Yes\n",
       "Alcoholism              \n",
       "0           79.81  20.19\n",
       "1           79.85  20.15"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A6=df.groupby('Alcoholism')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "A6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='No'>, <AxesSubplot:ylabel='Yes'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A6.plot.pie(subplots=True, figsize=(9, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Here we notice that the patients suffer from Alcoholism they tend to attend appointment 79.85% times.\n",
    "Hence it is as expceted that the patients with a diseas tend to attend appointments more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>No-show</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMS_received</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83.30</td>\n",
       "      <td>16.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72.43</td>\n",
       "      <td>27.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "No-show          No    Yes\n",
       "SMS_received              \n",
       "0             83.30  16.70\n",
       "1             72.43  27.57"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A7=df.groupby('SMS_received')['No-show'].value_counts(normalize=True).mul(100).round(2).unstack()\n",
    "A7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='No'>, <AxesSubplot:ylabel='Yes'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A7.plot.pie(subplots=True, figsize=(9, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we notice that around 32% of patients recieve SMS and they tend to attend appointment 72% times\n",
    "#while patents who dont get SMS tend to attend 83%of times\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "I have looked into the dataset and managed a few problems like  removing wrong data.\n",
    "I have also investigated most of independent variables in the dataset and made a few observations comparing them to each other as well as to the dependent one (no_show).\n",
    " As this was only an exploratory analysis, many potential correlations may remain uncovered. \n",
    "The data should be investigated further with more advanced statistical analysis to potentially reveal new insights and correlations.\n",
    "The most important findings are:\n",
    "\n",
    "*General trend is that around 80% patients attend appointment.\n",
    "\n",
    "*A trend related to age could be observerd where people with lower age less than 35 tend to miss apointment more then the people with age greater than 35\n",
    "\n",
    "*Medical Scholarship has very little imaact on the trend\n",
    "\n",
    "*Diseased Patients tend to attend appointment approximately 84% times\n",
    "\n",
    "*Sending SMS has a worse imapct on people as the general trends shows that people who get sms are less likely to attend appointment as compared to those who dosent\n",
    "\n",
    "*The different categories (Columns) Provided in this data set do not provide sufficent information to derive the exact reason behind the behaviour of the patients towards attending or missing the appointment.\n",
    "\n",
    "*We observe a anamolous behviour when we study the patients who recieve SMS miss their appointment often. Now we cannot get to the exact cause of this scenario due to limitation of the data set as it does not discss to whom the sms are sent like is it for older people, for premium clients , or for those who require special medical attention etc.\n",
    "\n",
    "*The features such as different gender or diseased is not a factor to decide if the person would come to his appointment or not!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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