Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project: Investigate a Dataset - [no_show_appointments]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "> this notebook has an anlysis for (no_show_appointments) datasset.This dataset for 100k medical appointments in Brazi. from this dataset we will be able to answer some questions about this dataset .we will ne able to know:\n",
    "  1. what are What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?\n",
    "  2. what is the percent of patients who missed their scheduled appointment?\n",
    "  3. what is the gender distribution for show and no-show patients?\n",
    "  4. are there patients had more than one appointment?\n",
    "  5. how many patients whose thier ScheduledDay on the same day?\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "> .This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset columns \n",
    " \t        \n",
    "1. PatienID:\t        ( number )\t      identification of a patient.\n",
    "2. AppointmentID:\t ( number )\t      identification of each appointment.\n",
    "3. Gender:\t         ( F or M )\t      it says 'F' if female or 'M' if man.\n",
    "4. ScheduledDay:\t     ( date )\t          tells us on what day the patient set up their appointment.\n",
    "5. AppointmentDay:\t ( date )\t          the day of the actuall appointment, when they have to visit the doctor.\n",
    "6. Age:              ( number )\t       how old is the patient.\n",
    "7. Neighbourhood:\t  ( string )\t      indicates the location of the hospital\n",
    "8. Scholarship:    \t  ( 0 or 1 )\t      indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa >   Família\n",
    "9. Hipertension:\t  ( 0 or 1 )\t      indicates if the patient has hipertension\n",
    "10. Diabetes:\t     ( 0 or 1 )\t      indicates if the patient has diabetes\n",
    "11. Alcoholism:\t     ( 0 or 1 )\t      indicates if the patient is an alcoholic\n",
    "12. Handcap:\t         ( 0 or 1 )\t      indicates if the patient is handicaped\n",
    "13. SMS_received:\t  ( 0 or 1 )\t      1 or more messages sent to the patient\n",
    "14. No-show:\t          ( Yes or No )\t      it says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#liberaries_used\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "import datetime\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load your data and print out a few lines. Perform operations to inspect data\n",
    "df=pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the shape of dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension',\n",
       "       'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the columns in dataset\n",
    "df.columns"
   ]
  },
  {
   "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",
      "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": [
    "# get a view on dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">there are some observation for this dataset:\n",
    "  1. this dataset has not nun_values.\n",
    "  2. Age: the average age of patients is 37 and most of them are less than 55 years old and there is age lees than 0 but this  is not possible.\n",
    "  3. SMS_received: 75% of people had recieved sms for appointment.\n",
    "  4. Handcap: there are 4 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e2a03f28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e2981d68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e293bdd8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e295f668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e2928d68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e2928da0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e2882a58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e285e048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0e2808828>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0e2a03668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize data\n",
    "df.hist(figsize=(11,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">some other obsevations:\n",
    "1. most of people are not alcoholics.\n",
    "2. most of people are not diabetes.\n",
    "3. most of people have not hipertension.\n",
    "4. most of people are not enrolled in Brasilian welfare program Bolsa Família."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">there are some cleaning should be performed for this dataset:\n",
    "   1. column['PatientId'] has float values and should be converted to int \n",
    "   2. columns['ScheduledDay','AppointmentDay'] have string values and should be converted to datetime\n",
    "   3. we could see that column['Age'] has some values less than 0 and that's not possible so we will remove them  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert ['ScheduledDay','AppointmentDay'] to datetime\n",
    "df['ScheduledDay'] = pd.to_datetime(df['ScheduledDay'])\n",
    "df['AppointmentDay'] = pd.to_datetime(df['AppointmentDay'])"
   ]
  },
  {
   "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></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",
       "      <th>waiting_days</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.598513e+13</td>\n",
       "      <td>5626772</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 08:36:51</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>76</td>\n",
       "      <td>REPÚBLICA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.336882e+14</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 15:05:12</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.449833e+12</td>\n",
       "      <td>5630575</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 15:39:58</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.639473e+13</td>\n",
       "      <td>5638447</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 08:02:16</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>21</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.812456e+13</td>\n",
       "      <td>5629123</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 12:48:25</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>19</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender        ScheduledDay AppointmentDay  Age  \\\n",
       "0  2.987250e+13        5642903      F 2016-04-29 18:38:08     2016-04-29   62   \n",
       "1  5.589978e+14        5642503      M 2016-04-29 16:08:27     2016-04-29   56   \n",
       "2  4.262962e+12        5642549      F 2016-04-29 16:19:04     2016-04-29   62   \n",
       "3  8.679512e+11        5642828      F 2016-04-29 17:29:31     2016-04-29    8   \n",
       "4  8.841186e+12        5642494      F 2016-04-29 16:07:23     2016-04-29   56   \n",
       "5  9.598513e+13        5626772      F 2016-04-27 08:36:51     2016-04-29   76   \n",
       "6  7.336882e+14        5630279      F 2016-04-27 15:05:12     2016-04-29   23   \n",
       "7  3.449833e+12        5630575      F 2016-04-27 15:39:58     2016-04-29   39   \n",
       "8  5.639473e+13        5638447      F 2016-04-29 08:02:16     2016-04-29   21   \n",
       "9  7.812456e+13        5629123      F 2016-04-27 12:48:25     2016-04-29   19   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "5          REPÚBLICA            0             1         0           0   \n",
       "6         GOIABEIRAS            0             0         0           0   \n",
       "7         GOIABEIRAS            0             0         0           0   \n",
       "8         ANDORINHAS            0             0         0           0   \n",
       "9          CONQUISTA            0             0         0           0   \n",
       "\n",
       "   Handcap  SMS_received No-show  waiting_days  \n",
       "0        0             0      No             0  \n",
       "1        0             0      No             0  \n",
       "2        0             0      No             0  \n",
       "3        0             0      No             0  \n",
       "4        0             0      No             0  \n",
       "5        0             0      No             2  \n",
       "6        0             0     Yes             2  \n",
       "7        0             0     Yes             2  \n",
       "8        0             0      No             0  \n",
       "9        0             0      No             2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build new column for waiting days\n",
    "df['waiting_days']=df['AppointmentDay'].dt.day-df['ScheduledDay'].dt.day\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the distibution of waiting day for patients')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0e25dcba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.waiting_days.hist()\n",
    "plt.title(\"the distibution of waiting day for patients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 15 columns):\n",
      "PatientId         110527 non-null int64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null datetime64[ns]\n",
      "AppointmentDay    110527 non-null datetime64[ns]\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "waiting_days      110527 non-null int64\n",
      "dtypes: datetime64[ns](2), int64(10), object(3)\n",
      "memory usage: 12.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# convert ['PatientId'] to int\n",
    "df['PatientId']=df['PatientId'].astype(int)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <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",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>465943158731293</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06 08:58:13</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PatientId  AppointmentID Gender        ScheduledDay  \\\n",
       "99832  465943158731293        5775010      F 2016-06-06 08:58:13   \n",
       "\n",
       "      AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "99832     2016-06-06   -1         ROMÃO            0             0         0   \n",
       "\n",
       "       Alcoholism  Handcap  SMS_received No-show  waiting_days  \n",
       "99832           0        0             0      No             0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get ages less than 0\n",
    "invalid_age=df[df.Age<0]\n",
    "invalid_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove un valid ages (ages less than 0)\n",
    "df=df[df.Age>=0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions\n",
    "  1. what are What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?\n",
    "  2. what is the percent of patients who missed their scheduled appointment?\n",
    "  3. what is the gender distribution for show and no-show patients?\n",
    "  4. are there patients had more than one appointment?\n",
    "  5. how many patients whose thier ScheduledDay on the same day?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1.  what are What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?\n",
    "> we will know if there are some features affect the distribution of show\\ no-show. and if there are ,we will be able to know how this factoe affect in our dataset. this helps us to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
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       "      <th>SMS_received</th>\n",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "      <td>88207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId  AppointmentID  Gender  ScheduledDay  AppointmentDay  \\\n",
       "No-show                                                                   \n",
       "No           88207          88207   88207         88207           88207   \n",
       "Yes          22319          22319   22319         22319           22319   \n",
       "\n",
       "           Age  Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "No-show                                                              \n",
       "No       88207          88207        88207         88207     88207   \n",
       "Yes      22319          22319        22319         22319     22319   \n",
       "\n",
       "         Alcoholism  Handcap  SMS_received  waiting_days  \n",
       "No-show                                                   \n",
       "No            88207    88207         88207         88207  \n",
       "Yes           22319    22319         22319         22319  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group data by no-show and calculate counts for each class\n",
    "grouped_data=df.groupby(['No-show']).count()\n",
    "grouped_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>1.476809e+14</td>\n",
       "      <td>5.681135e+06</td>\n",
       "      <td>37.790504</td>\n",
       "      <td>0.093904</td>\n",
       "      <td>0.204394</td>\n",
       "      <td>0.073838</td>\n",
       "      <td>0.030417</td>\n",
       "      <td>0.022742</td>\n",
       "      <td>0.291337</td>\n",
       "      <td>-1.551124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1.467523e+14</td>\n",
       "      <td>5.652259e+06</td>\n",
       "      <td>34.317667</td>\n",
       "      <td>0.115507</td>\n",
       "      <td>0.169004</td>\n",
       "      <td>0.064071</td>\n",
       "      <td>0.030333</td>\n",
       "      <td>0.020297</td>\n",
       "      <td>0.438371</td>\n",
       "      <td>-2.550069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PatientId  AppointmentID        Age  Scholarship  Hipertension  \\\n",
       "No-show                                                                      \n",
       "No       1.476809e+14   5.681135e+06  37.790504     0.093904      0.204394   \n",
       "Yes      1.467523e+14   5.652259e+06  34.317667     0.115507      0.169004   \n",
       "\n",
       "         Diabetes  Alcoholism   Handcap  SMS_received  waiting_days  \n",
       "No-show                                                              \n",
       "No       0.073838    0.030417  0.022742      0.291337     -1.551124  \n",
       "Yes      0.064071    0.030333  0.020297      0.438371     -2.550069  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group data by no-show and calculate counts for each class\n",
    "mean_data=df.groupby(['No-show']).mean()\n",
    "mean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build a function to plot how some features affect on show\\no-show patients\n",
    "def plot_data(factor):\n",
    "    \"\"\"\n",
    "    this function take features of data as factor and groub data by this feature \n",
    "    and return plots for this feature effct on show\\ no-show \"\"\"\n",
    "    df.groupby([factor])['No-show'].value_counts().unstack('No-show').plot(kind='bar',figsize=(20,10))\n",
    "    plt.xlabel(factor)\n",
    "    plt.ylabel('Propotion')\n",
    "    plt.title(f\"Propotion of show \\ no-show by {factor}\",fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c82a3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how gender affect on propotion show\\no-show patients\n",
    "plot_data('Gender')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that females show thier ScheduledDay more than males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c85c9b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how Age affect on propotion show\\no-show patients\n",
    "plot_data('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that the young patients show thier ScheduledDay more than older patients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c7a27b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how Scholarship affect on propotion show\\no-show patients\n",
    "plot_data('Scholarship')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patients not enrolled in Brasilian welfare program Bolsa > Família show thier ScheduledDay more than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c11d160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how Hipertension affect on propotion show\\no-show patients\n",
    "plot_data('Hipertension')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patient with no Hipertension show thier ScheduledDay more than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c121a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how Diabetes affect on propotion show\\no-show patients\n",
    "plot_data('Diabetes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patient with no Diabetes show thier ScheduledDay more than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c221ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how Alcoholism affect on propotion show\\no-show patients\n",
    "plot_data('Alcoholism')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patient not Alcoholism show thier ScheduledDay more than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c306a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how Handcap affect on propotion show\\no-show patients\n",
    "plot_data('Handcap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patient with no Handcap show thier ScheduledDay more than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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THXfcMfvss0+uvvrq+/W5++67c/jhh2f77bfPDjvscJ/rfPSjH81uu+2WX//1X88FF1yQZLRW1atf/erssMMO2WmnnXLuuecmSV7ykpfk0ksvTZLstNNOedvb3pYkeetb35p/+qd/WuX3nQiiAAAAALo78sgjc/rpp+fmm2++T/vrX//6zJ07N5deemkOOeSQvPGNb7zfuYsWLcoPf/jDfOMb38hll12WV7/61fceu+uuu/K1r30t733ve3P88ccnSU4++eQkyWWXXZYPf/jDOeyww3LnnXdmr732ygUXXJBbbrkls2bNyn/+538mSS688MLsueeeE7lvQRQAAABAZ+uvv37mzp2b+fPn36f9y1/+cg4++OAkyaGHHpoLL7zwfuc++clPzpVXXpk3vOEN+exnP5v111//3mOveMUrkiQ777xzrrrqqiSjYOnQQw9Nkjz96U/P1ltvne985zvZc8898x//8R+58MIL89KXvjS33XZb7rjjjlx11VV52tOeNonbFkQBAAAArA5vetObsmDBgtx+++3L7VNVufvuuzNnzpzMmTMnf/mXf5mNNtoo//Vf/5W99947J598cn7/93//3v7rrrtukmSttdbKXXfdlSRprU157V133TULFy7MBRdckL322is77bRT/vEf/zE777zzKrzL+xJEAQAAAKwGG2+8cQ488MAsWLDg3rbnPe95OeOMM5Ikp59+evbYY4+stdZaWbRoURYtWpS3ve1tue6663LPPffkla98Zd7+9rfn4osvXuH77LXXXjn99NOTJN/5zndy9dVX52lPe1rWWWedbLXVVjnzzDOz++67Z88998wJJ5wwsWl5iSAKAAAAYLU5+uij7/P0vPnz5+eUU07JjjvumNNOOy0nnXTS/c754Q9/mL333jtz5szJ4Ycfnne9610rfI/Xve51ufvuu7PDDjvkVa96VT74wQ/eO3Jqzz33zOabb57HPOYx2XPPPbN48eKJBlG1vOFZM9Uuu+zSFi5cuLrLAAAAAGa4yy+/PM94xjNWdxmr3FT3VVVfb63tsrJzjYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAABghqqqHH300ffun3DCCTnuuONWWz2zVts7AwAAADyCbHPMv6/S61317peutM+6666bj3/843nLW96STTfddJW+/4MhiILlWNX/A8HMNp3/AAAAAPQ2a9aszJs3LyeeeGLe+c533ufYD37wg7zmNa/JkiVLstlmm+WUU07Jk570pInWY2oeAAAAwAx25JFH5vTTT8/NN998n/bXv/71mTt3bi699NIccsgheeMb3zjxWgRRAAAAADPY+uuvn7lz52b+/Pn3af/yl7+cgw8+OEly6KGH5sILL5x4LYIoAAAAgBnuTW96UxYsWJDbb799uX2qauJ1CKIAAAAAZriNN944Bx54YBYsWHBv2/Oe97ycccYZSZLTTz89e+yxx8TrEEQBAAAAPAIcffTRue666+7dnz9/fk455ZTsuOOOOe2003LSSSdNvAZPzQMAAADoYHU8bfu22267d3vzzTfPHXfcce/+Nttsky9+8Ytd6zEiCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAgBmotZY99tgjn/nMZ+5tO/PMM7PvvvuutppmrbZ3BgAAAHgkOW6DVXy9m1d4uKryvve9LwcccECe//zn5+67786f//mf57Of/eyqreMBMCIKAAAAYIbafvvt87KXvSzvec97cvzxx2fu3Ll5ylOeklNPPTW77bZb5syZk9e97nW55557ctddd+XQQw/NDjvskO233z7z589f5fUYEQUAAAAwgx177LF5znOek3XWWScLFy7MN77xjXziE5/Il770pcyaNSvz5s3LGWeckac85Sm57rrrctlllyVJbrrpplVey0RHRFXVhlX1sar676q6vKp+o6o2rqpzquq7w8+Nhr5VVfOr6oqqurSqnjN2ncOG/t+tqsPG2neuqsuGc+ZXVU3yfgAAAAAebh772MfmVa96VQ499NCsu+66+fznP5+LLroou+yyS+bMmZPzzz8/3/ve9/LUpz413/72t3PUUUfl7LPPzgYbrOKphJn81LyTkny2tfb0JM9OcnmSY5J8obW2XZIvDPtJsl+S7YbXvCT/kCRVtXGSY5M8N8luSY5dGl4NfeaNnbf6VtsCAAAAWEM96lGPyqMeNYqBWmt5zWtek0WLFmXRokX59re/nbe+9a3ZZJNNcumll2aPPfbI/Pnz89rXvnbV17HKrzioqvWT7JVkQZK01n7RWrspyf5JTh26nZrk5cP2/kk+1Ea+kmTDqnpikhcnOae1dkNr7cYk5yTZdzi2fmvty621luRDY9cCAAAAYAovfOELc+aZZ+a6665Lklx//fW5+uqrs2TJkrTWcsABB+T444/PxRdfvMrfe5JrRD05yZIkp1TVs5N8PclRSTZvrf0oSVprP6qqJwz9t0xyzdj5i4e2FbUvnqIdAAAAgOXYYYcdcuyxx+aFL3xh7rnnnqy99tp53/vel7XWWitHHHFEWmupqrznPe9Z5e89ySBqVpLnJHlDa+2rVXVSfjUNbypTre/UHkT7/S9cNS+jKXx50pOetKKaAQAAACbjuJtX31sfd9x99g8++OAcfPDB9+t3ySWXTLSOSa4RtTjJ4tbaV4f9j2UUTP1kmFaX4edPx/pvNXb+7CTXrqR99hTt99Nae39rbZfW2i6bbbbZQ7opAAAAAB6ciQVRrbUfJ7mmqp42NO2T5FtJzkqy9Ml3hyX55LB9VpK5w9Pzdk9y8zCF7+wkL6qqjYZFyl+U5Ozh2K1VtfvwtLy5Y9cCAAAAYA0zyal5SfKGJKdX1TpJrkzy6ozCrzOr6ogkVyc5YOj76SQvSXJFkjuGvmmt3VBVb09y0dDvba21G4btP0rywSSPTvKZ4QUAAADAGmiiQVRrbVGSXaY4tM8UfVuSI5dznQ8k+cAU7QuTbP8QywQAAACYiKULf88Uo/jmwZvkGlEAAAAAj1jrrbderr/++occ3qwpWmu5/vrrs9566z3oa0x6ah4AAADAI9Ls2bOzePHiLFmyZHWXssqst956mT179so7LocgCgAAAGAC1l577Wy77baru4w1iql5AAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANDFRIOoqrqqqi6rqkVVtXBo27iqzqmq7w4/Nxraq6rmV9UVVXVpVT1n7DqHDf2/W1WHjbXvPFz/iuHcmuT9AAAAAPDg9RgR9fzW2pzW2i7D/jFJvtBa2y7JF4b9JNkvyXbDa16Sf0hGwVWSY5M8N8luSY5dGl4NfeaNnbfv5G8HAAAAgAdjdUzN2z/JqcP2qUlePtb+oTbylSQbVtUTk7w4yTmttRtaazcmOSfJvsOx9VtrX26ttSQfGrsWAAAAAGuYSQdRLcnnqurrVTVvaNu8tfajJBl+PmFo3zLJNWPnLh7aVtS+eIr2+6mqeVW1sKoWLlmy5CHeEgAAAAAPxqwJX/83W2vXVtUTkpxTVf+9gr5Tre/UHkT7/Rtbe3+S9yfJLrvsMmUfAAAAACZroiOiWmvXDj9/muQTGa3x9JNhWl2Gnz8dui9OstXY6bOTXLuS9tlTtAMAAACwBppYEFVVj62qxy/dTvKiJN9IclaSpU++OyzJJ4fts5LMHZ6et3uSm4epe2cneVFVbTQsUv6iJGcPx26tqt2Hp+XNHbsWAAAAAGuYSU7N2zzJJ0YZUWYl+ZfW2mer6qIkZ1bVEUmuTnLA0P/TSV6S5IokdyR5dZK01m6oqrcnuWjo97bW2g3D9h8l+WCSRyf5zPACAAAAYA00sSCqtXZlkmdP0X59kn2maG9JjlzOtT6Q5ANTtC9Msv1DLhYAAACAiZv0U/MAAAAAIIkgCgAAAIBOBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuJh5EVdVaVXVJVX1q2N+2qr5aVd+tqo9U1TpD+7rD/hXD8W3GrvGWof3bVfXisfZ9h7YrquqYSd8LAAAAAA9ejxFRRyW5fGz/PUlObK1tl+TGJEcM7UckubG19tQkJw79UlXPTHJQkmcl2TfJ3w/h1lpJTk6yX5JnJvm9oS8AAAAAa6CJBlFVNTvJS5P807BfSV6Q5GNDl1OTvHzY3n/Yz3B8n6H//knOaK39vLX2/SRXJNlteF3RWruytfaLJGcMfQEAAABYA016RNR7k/xpknuG/U2S3NRau2vYX5xky2F7yyTXJMlw/Oah/73ty5yzvPb7qap5VbWwqhYuWbLkod4TAAAAAA/CxIKoqvrtJD9trX19vHmKrm0lxx5o+/0bW3t/a22X1toum2222QqqBgAAAGBSZk3w2r+Z5Heq6iVJ1kuyfkYjpDasqlnDqKfZSa4d+i9OslWSxVU1K8kGSW4Ya19q/JzltQMAAACwhpnYiKjW2ltaa7Nba9tktNj4F1trhyQ5N8nvDt0OS/LJYfusYT/D8S+21trQftDwVL1tk2yX5GtJLkqy3fAUvnWG9zhrUvcDAAAAwEMzyRFRy/PmJGdU1TuSXJJkwdC+IMlpVXVFRiOhDkqS1to3q+rMJN9KcleSI1trdydJVb0+ydlJ1krygdbaN7veCQAAAADT1iWIaq2dl+S8YfvKjJ54t2yfO5McsJzz35nknVO0fzrJp1dhqQAAAABMyKSfmgcAAAAASQRRAAAAAHQiiAIAAACgC0EUAABmXo1SAAAYbklEQVQAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuZk2nU1VtluQPkmwzfk5r7TWTKQsAAACAmWZaQVSSTya5IMnnk9w9uXIAAAAAmKmmG0Q9prX25olWAgAAAMCMNt01oj5VVS+ZaCUAAAAAzGjTDaKOyiiMurOqbh1et0yyMAAAAABmlmlNzWutPX7ShQAAAAAws013jahU1e8k2WvYPa+19qnJlAQAAADATDStqXlV9e6Mpud9a3gdNbQBAAAAwLRMd0TUS5LMaa3dkyRVdWqSS5IcM6nCAAAAAJhZprtYeZJsOLa9waouBAAAAICZbbojot6V5JKqOjdJZbRW1FsmVhUAAAAAM850n5r34ao6L8muGQVRb26t/XiShQEAAAAws6xwal5VPX34+ZwkT0yyOMk1SbYY2gAAAABgWlY2IupPksxL8jdTHGtJXrDKKwIAAABgRlphENVamzds7tdau3P8WFWtN7GqAAAAAJhxpvvUvC9Nsw0AAAAAprTCEVFV9WtJtkzy6KraKaOFypNk/SSPmXBtAAAAAMwgK1sj6sVJDk8yO8nfjrXfmuTPJlQTAAAAADPQytaIOjXJqVX1ytbav3aqCQAAAIAZaLprRH2hqv62qhYOr7+pqg0mWhkAAAAAM8p0g6gFGU3HO3B43ZLklEkVBQAAAMDMs7I1opZ6SmvtlWP7x1fVokkUBAAAAMDMNN0RUT+rqj2W7lTVbyb52WRKAgAAAGAmmu6IqD/KaNHyDZJUkhuSHDaxqgAAAACYcaYVRLXWFiV5dlWtP+zfMtGqAAAAAJhxpjU1r6o2qar5Sc5Lcm5VnVRVm0y0MgAAAABmlOmuEXVGkiVJXpnkd4ftj0yqKAAAAABmnumuEbVxa+3tY/vvqKqXT6IgAAAAAGam6Y6IOreqDqqqRw2vA5P8+yQLAwAAAGBmmW4Q9dok/5LkF8PrjCR/UlW3VpWFywEAAABYqek+Ne/xky4EAAAAgJltumtEpap+J8lew+55rbVPTaYkAAAAAGaiaU3Nq6p3JzkqybeG11FDGwAAAABMy3RHRL0kyZzW2j1JUlWnJrkkyTGTKgwAAACAmWW6i5UnyYZj2xus6kIAAAAAmNmmOyLqXUkuqapzk1RGa0W9ZWJVAQAAADDjrDSIqqpKcmGS3ZPsmlEQ9ebW2o8nXBsAAAAAM8hKg6jWWquqf2ut7ZzkrA41AQAAADADTXeNqK9U1a4TrQQAAACAGW26a0Q9P8kfVtVVSW7PaHpea63tOKnCAAAAAJhZpjsiar8kT07ygiQvS/Lbw8/lqqr1quprVfVfVfXNqjp+aN+2qr5aVd+tqo9U1TpD+7rD/hXD8W3GrvWWof3bVfXisfZ9h7YrquqYB3LjAAAAAPS1wiBqCJPelOT/JNk3yQ9baz9Y+lrJtX+e5AWttWcnmZNk36raPcl7kpzYWtsuyY1Jjhj6H5HkxtbaU5OcOPRLVT0zyUFJnjXU8PdVtVZVrZXk5IxCsmcm+b2hLwAAAABroJWNiDo1yS5JLsso8Pmb6V64jdw27K49vFpGo6o+Nnb9lw/b+w/7GY7vMzyxb/8kZ7TWft5a+36SK5LsNryuaK1d2Vr7RZIzhr4AAAAArIFWtkbUM1trOyRJVS1I8rUHcvFh1NLXkzw1o9FL30tyU2vtrqHL4iRbDttbJrkmSVprd1XVzUk2Gdq/MnbZ8XOuWab9uQ+kPgAAAAD6WdmIqF8u3RgLj6attXZ3a21OktkZjWB6xlTdhp+1nGMPtP1+qmpeVS2sqoVLlixZeeEAAAAArHIrC6KeXVW3DK9bk+y4dLuqbpnum7TWbkpyXpLdk2xYVUtHYs1Ocu2wvTjJVkkyHN8gyQ3j7cucs7z2qd7//a21XVpru2y22WbTLRsAAACAVWiFQVRrba3W2vrD6/GttVlj2+uv6Nyq2qyqNhy2H53khUkuT3Jukt8duh2W5JPD9lnDfobjX2yttaH9oOGpetsm2S6jKYIXJdlueArfOhktaH7WA7t9AAAAAHpZ2RpRD8UTk5w6rBP1qCRnttY+VVXfSnJGVb0jySVJFgz9FyQ5raquyGgk1EFJ0lr7ZlWdmeRbSe5KcmRr7e4kqarXJzk7yVpJPtBa++YE7wcAAACAh2BiQVRr7dIkO03RfmVG60Ut235nkgOWc613JnnnFO2fTvLph1wsAAAAABO3sjWiAAAAAGCVEEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhi1uouAAAAeGi2OebfV3cJPIxc9e6Xru4SgEcwI6IAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAFxMLoqpqq6o6t6our6pvVtVRQ/vGVXVOVX13+LnR0F5VNb+qrqiqS6vqOWPXOmzo/92qOmysfeequmw4Z35V1aTuBwAAAICHZpIjou5KcnRr7RlJdk9yZFU9M8kxSb7QWtsuyReG/STZL8l2w2tekn9IRsFVkmOTPDfJbkmOXRpeDX3mjZ237wTvBwAAAICHYGJBVGvtR621i4ftW5NcnmTLJPsnOXXodmqSlw/b+yf5UBv5SpINq+qJSV6c5JzW2g2ttRuTnJNk3+HY+q21L7fWWpIPjV0LAAAAgDVMlzWiqmqbJDsl+WqSzVtrP0pGYVWSJwzdtkxyzdhpi4e2FbUvnqIdAAAAgDXQxIOoqnpckn9N8qbW2i0r6jpFW3sQ7VPVMK+qFlbVwiVLlqysZAAAAAAmYKJBVFWtnVEIdXpr7eND80+GaXUZfv50aF+cZKux02cnuXYl7bOnaL+f1tr7W2u7tNZ22WyzzR7aTQEAAADwoEzyqXmVZEGSy1trfzt26KwkS598d1iST461zx2enrd7kpuHqXtnJ3lRVW00LFL+oiRnD8durardh/eaO3YtAAAAANYwsyZ47d9McmiSy6pq0dD2Z0neneTMqjoiydVJDhiOfTrJS5JckeSOJK9OktbaDVX19iQXDf3e1lq7Ydj+oyQfTPLoJJ8ZXgAAAACsgSYWRLXWLszU6zglyT5T9G9JjlzOtT6Q5ANTtC9Msv1DKBMAAACATro8NQ8AAAAABFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAK/l979x+ze1nXAfz9FsSZJNpIdwAtl0ipmQGC2LIfboCsppUs3FJGztNctvVPi60/RFqzrdoaak1qTN0a4CyKFXSk05BchpAZP2oga6lnMIFhFBWgcPXH82Xde85zlOM557rPw3m9tnvPfX++3+v7/TzPP8+9967r+gIAAABTHL3uBgCeES45bt0dsF1c8vC6OwAAgLUxIwoAAACAKQRRAAAAAEwhiAIAAABgCkEUAAAAAFMIogAAAACYQhAFAAAAwBSCKAAAAACmEEQBAAAAMIUgCgAAAIApBFEAAAAATCGIAgAAAGAKQRQAAAAAUwiiAAAAAJhCEAUAAADAFIIoAAAAAKYQRAEAAAAwxSELotpe0fb+tnes1L6r7Q1tv7j8fOFSb9vL2t7T9ra2p66MuXA5/4ttL1ypn9b29mXMZW17qH4XAAAAAA7coZwR9dEk526qXZxk9xjj5CS7l89J8uYkJy+vnUn+MNkIrpK8L8mZSc5I8r6nwqvlnJ0r4zbfCwAAAIDDyCELosYYNyV5aFP5LUk+trz/WJK3rtQ/Pjb8Q5IXtN2R5JwkN4wxHhpjfC3JDUnOXY49f4zx2THGSPLxlWsBAAAAcBiavUfUi8cY9yXJ8vNFS/3EJF9ZOW/PUvtm9T1b1AEAAAA4TB0um5Vvtb/T+DbqW1+83dn21ra3PvDAA99miwAAAAAciKMn3++rbXeMMe5bltfdv9T3JHnJynknJbl3qf/4pvqNS/2kLc7f0hjj8iSXJ8npp5++z8AKAADgGe+S49bdAdvFJQ+vuwOegWbPiLo2yVNPvrswyV+s1N+5PD3v9UkeXpbu7UpydtsXLpuUn51k13Lsv9q+fnla3jtXrgUAAADAYeiQzYhqe2U2ZjMd33ZPNp5+99tJPtH2XUm+nOT85fTrkpyX5J4k/5PkoiQZYzzU9jeT3LKcd+kY46kN0N+TjSfzPTfJ9csLAAAAgMPUIQuixhhv38ehN21x7kjyy/u4zhVJrtiifmuSVx9IjwAAAADMc7hsVg4AAADAM5wgCgAAAIApBFEAAAAATCGIAgAAAGAKQRQAAAAAUwiiAAAAAJhCEAUAAADAFIIoAAAAAKYQRAEAAAAwhSAKAAAAgCkEUQAAAABMIYgCAAAAYApBFAAAAABTCKIAAAAAmEIQBQAAAMAUgigAAAAAphBEAQAAADCFIAoAAACAKQRRAAAAAEwhiAIAAABgCkEUAAAAAFMIogAAAACYQhAFAAAAwBSCKAAAAACmEEQBAAAAMIUgCgAAAIApBFEAAAAATCGIAgAAAGAKQRQAAAAAUwiiAAAAAJhCEAUAAADAFIIoAAAAAKYQRAEAAAAwhSAKAAAAgCkEUQAAAABMIYgCAAAAYApBFAAAAABTCKIAAAAAmEIQBQAAAMAUgigAAAAAphBEAQAAADCFIAoAAACAKQRRAAAAAEwhiAIAAABgCkEUAAAAAFMIogAAAACYQhAFAAAAwBSCKAAAAACmEEQBAAAAMIUgCgAAAIApBFEAAAAATCGIAgAAAGAKQRQAAAAAUwiiAAAAAJhCEAUAAADAFIIoAAAAAKYQRAEAAAAwhSAKAAAAgCkEUQAAAABMIYgCAAAAYApBFAAAAABTCKIAAAAAmEIQBQAAAMAUgigAAAAAphBEAQAAADCFIAoAAACAKQRRAAAAAEwhiAIAAABgCkEUAAAAAFMIogAAAACYQhAFAAAAwBSCKAAAAACmEEQBAAAAMIUgCgAAAIAptn0Q1fbctne1vaftxevuBwAAAICtbesgqu1RST6c5M1JXpnk7W1fud6uAAAAANjKtg6ikpyR5J4xxr+NMR5PclWSt6y5JwAAAAC2sN2DqBOTfGXl856lBgAAAMBh5uh1N3CAukVt7HVSuzPJzuXjI23vOqRdAUecJscneXDdfbANvH+rf10AMI/vLTxtvrewf77n6Zy03YOoPUlesvL5pCT3bj5pjHF5kstnNQUcedreOsY4fd19AAB8K763AOu03Zfm3ZLk5LYva3tMkguSXLvmngAAAADYwraeETXG+Ebb9ybZleSoJFeMMe5cc1sAAAAAbGFbB1FJMsa4Lsl16+4DOOJZ/gsAbBe+twBr0zH22tsbAAAAAA667b5HFAAAAADbhCAKAAAAgCm2/R5RALO1/f4kb0lyYpKR5N4k144x/nWtjQEAABzmzIgC2A9tfz3JVUma5HNJblneX9n24nX2BgCwP9petO4egCOPzcoB9kPbu5O8aozx9U31Y5LcOcY4eT2dAQDsn7ZfHmO8dN19AEcWS/MA9s+TSU5I8qVN9R3LMQCAw0bb2/Z1KMmLZ/YCkAiiAPbXrybZ3faLSb6y1F6a5OVJ3ru2rgAAtvbiJOck+dqmepP8/fx2gCOdIApgP4wx/rrtK5KckY3NyptkT5JbxhhPrLU5AIC9/WWSY8cYX9h8oO2N89sBjnT2iAIAAABgCk/NAwAAAGAKQRQAAAAAUwiiAAAAAJhCEAUAsIW2v9H2zra3tf1C2zPb3tj2y227ct6ft31kef+stpe1vaPt7W1vafuyNfR+UJ6E1fajbd92MK4FAJB4ah4AwF7anpXkp5KcOsZ4rO3xSY5ZDv9Hkh9J8pm2L0iyY2Xozyc5IclrxhhPtj0pyX8/zXsedbCevjnGeMPBuA4AwMFmRhQAwN52JHlwjPFYkowxHhxj3LscuyrJBcv7n03yZ5vG3TfGeHIZt2eM8bV93aTtI20vbXtzkrPantb2023/se2utjuW817e9m/a/nPbz7f9vqX+a8usq9vavn/1usvPq9uet1L/aNufa3tU299ZGftLy/G2/VDbf2n7V0ledAB/QwCAvQiiAAD29qkkL2l7d9s/aPtjK8d2J3lj26OyEUhdvXLsE0l+elnK93ttf/hb3Od5Se4YY5yZ5OYkH0zytjHGaUmuSPJby3l/kuTDY4wfSvKGJPe1PTvJyUnOSPLaJKe1feOm61+VjVlaaXtMkjcluS7Ju5I8PMZ4XZLXJXn3soTwZ5KckuQHk7x7uRcAwEFjaR4AwCZjjEfanpbkR5P8RJKr2168HH4iyWeyEfA8d4zx709tGTXG2NP2lCQ/ubx2tz1/jLF7H7d6IsmfLu9PSfLqJDcs1zsqG4HTdyY5cYxxzXKPR5NkCaLOTvJPy/hjsxFM3bRy/euTXNb2OUnOTXLTGON/l7GvWdn/6bhl7BuTXLksEby37d/u1x8OAOBbEEQBAGxhCWNuTHJj29uTXLhy+Kok1yS5ZItxj2UjALq+7VeTvDUbs6i28ujKvlBNcucY46zVE9o+fx9jm+QDY4yPfJPf4dG2NyY5JxvB2ZUrY39ljLFr073OSzL2dT0AgANlaR4AwCZtT2l78krptUm+tPL575J8IP8f7Dw17tS2Jyzvn5XkNZvGfTN3JfnuZaP0tH1221eNMf4zyZ62b13qz2n7HUl2JfnFtscu9RPbbrWn01VJLsrG7K6ngqddSd7T9tnL2Fe0fV42ZlNdsOwhtSMbs8EAAA4aM6IAAPZ2bJIPLk/F+0aSe5LsTPLJJBljjCS/u8W4FyX5o2UpXJJ8LsmHns4NxxiPL0vlLmt7XDa+p/1+kjuTvCPJR9pemuTrSc4fY3yq7Q8k+eyylO+RJL+Q5P5Nl/5Uko8nuXaM8fhS++Mk35vk890Y/EA2Zm5dk40lhbcnuTvJp59O7wAAT1c3vkcBAAAAwKFlaR4AAAAAU1iaBwBwiLW9OclzNpXfMca4fR39AACsi6V5AAAAAExhaR4AAAAAUwiiAAAAAJhCEAUAAADAFIIoAAAAAKYQRAEAAAAwxf8Bu9QcMjzO3PYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c899b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how SMS_received affect on propotion show\\no-show patients\n",
    "plot_data('SMS_received')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patient who received SMS show thier ScheduledDay more than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c190dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how waiting_days affect on propotion show\\no-show patients\n",
    "plot_data('waiting_days')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> from this we can see that patient with no waiting_days show thier ScheduledDay more than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">from this analysis we can see that all this feature affects almost equally on our dataset and we can't say that one affect more than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. what is the percent of patients who missed their scheduled appointment?\n",
    ">we will calculate  how many patient missed the appointment, and show thw distribution of show / no-show patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110526"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of all appointments\n",
    "all_appo=len(df.ScheduledDay)\n",
    "all_appo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22319"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##get the number of missed appointments\n",
    "notattend=df[df['No-show']==\"Yes\"]\n",
    "missed_appo =notattend.shape[0]\n",
    "missed_appo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the percent of patients who missed their scheduled appointment :  20.193438647919944\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the distribution of show / no-show patients')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cRZ+XAw8Cp0XEq4HrgfcDPZl5H0Bm3hcRf1L6TwUWtUy/uLQN1b64TXslImbTnGHQ09NDX19fF+XXeqbAUTstH9G0ozHSeiWp37Jly8blWNJNOEwGdgHel5lXR8RX+eMlpHba3S/IEbTXjZlzgDkAM2bMyN7e3iHKGNzJZ5/PifO7WfWxddfBveO+TEmrl76+PkZ67BuObu45LAYWZ+bVZfg8mrC4v1wSovz7QEv/bVqm3xpY0qF96zbtkqQJ0jEcMvN3wKKIeGVp2gO4BbgA6H/iaBZwfnl/AXBIeWppd+DRcvnpIuAtEbFpuRH9FuCiMu7xiNi9PKV0SMu8JEkToNtrK+8Dzo6IdYCFwGE0wTIvIg4H7gHeXvpeCOwDLAD+UPqSmUsj4lPAtaXfJzNzaXn/HuB0YArw4/KSJE2QrsIhM28EZrQZtUebvgkcMch85gJz27RfB+zYTS2SpBXP35CWJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSpetwiIhJEfGriPhhGd4uIq6OiDsi4tyIWKe0v6QMLyjjp7XM45jSfltE7NXSPrO0LYiIo8du9SRJIzGcM4f3A7e2DH8e+HJmTgceBg4v7YcDD2fm9sCXSz8iYgfgQOBVwEzglBI4k4CvAXsDOwAHlb6SpAnSVThExNbAvsC/l+EA3gycV7qcAexf3u9Xhinj9yj99wPOycynM/NOYAGwa3ktyMyFmfkMcE7pK0maIJO77PcV4CPAhmV4c+CRzFxehhcDU8v7qcAigMxcHhGPlv5Tgata5tk6zaIB7bu1KyIiZgOzAXp6eujr6+uy/BfrmQJH7bS8c8cxNtJ6JanfsmXLxuVY0jEcIuKtwAOZeX1E9PY3t+maHcYN1t7u7CXbtJGZc4A5ADNmzMje3t523To6+ezzOXF+t7k4du46uHfclylp9dLX18dIj33D0c0R8g3A30bEPsC6wEY0ZxKbRMTkcvawNbCk9F8MbAMsjojJwMbA0pb2fq3TDNYuSZoAHe85ZOYxmbl1Zk6juaF8WWYeDPwMeFvpNgs4v7y/oAxTxl+WmVnaDyxPM20HTAeuAa4Fppenn9Ypy7hgTNZOkjQio7m28lHgnIj4NPAr4NTSfipwVkQsoDljOBAgM2+OiHnALcBy4IjMfA4gIt4LXARMAuZm5s2jqEuSNErDCofM7AP6yvuFNE8aDezzFPD2QaY/ATihTfuFwIXDqUWStOL4G9KSpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpIrhIEmqGA6SpMrkiS5AklZF047+0YQs9/SZ64/LcjxzkCRVDAdJUqVjOETENhHxs4i4NSJujoj3l/bNIuKSiLij/LtpaY+IOCkiFkTETRGxS8u8ZpX+d0TErJb210TE/DLNSRERK2JlJUnd6ebMYTlwVGb+BbA7cERE7AAcDVyamdOBS8swwN7A9PKaDXwdmjABjgV2A3YFju0PlNJndst0M0e/apKkkeoYDpl5X2beUN4/DtwKTAX2A84o3c4A9i/v9wPOzMZVwCYR8VJgL+CSzFyamQ8DlwAzy7iNMvOXmZnAmS3zkiRNgGE9rRQR04C/Aq4GejLzPmgCJCL+pHSbCixqmWxxaRuqfXGb9nbLn01zhkFPTw99fX3DKf8FPVPgqJ2Wj2ja0RhpvZJWPhNxDAFYtmzZuBxLug6HiNgA+B7wgcx8bIjbAu1G5Aja68bMOcAcgBkzZmRvb2+Hqts7+ezzOXH++D/Fe9fBveO+TEkrxqET+CjrSI99w9HV00oRsTZNMJydmd8vzfeXS0KUfx8o7YuBbVom3xpY0qF96zbtkqQJ0s3TSgGcCtyamV9qGXUB0P/E0Szg/Jb2Q8pTS7sDj5bLTxcBb4mITcuN6LcAF5Vxj0fE7mVZh7TMS5I0Abq5tvIG4B+B+RFxY2n7GPA5YF5EHA7cA7y9jLsQ2AdYAPwBOAwgM5dGxKeAa0u/T2bm0vL+PcDpwBTgx+UlSZogHcMhM6+k/X0BgD3a9E/giEHmNReY26b9OmDHTrVIksaHvyEtSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkykoTDhExMyJui4gFEXH0RNcjSWuylSIcImIS8DVgb2AH4KCI2GFiq5KkNddKEQ7ArsCCzFyYmc8A5wD7TXBNkrTGmjzRBRRTgUUtw4uB3QZ2iojZwOwyuCwibhvh8rYAHhrhtCMWnx/vJUpa3bzp86M6fm3bbceVJRyiTVtWDZlzgDmjXljEdZk5Y7TzkaTxNl7Hr5XlstJiYJuW4a2BJRNUiySt8VaWcLgWmB4R20XEOsCBwAUTXJMkrbFWistKmbk8It4LXARMAuZm5s0rcJGjvjQlSRNkXI5fkVld2pckreFWlstKkqSViOEgSaqs1uEQERkRJ7YMfzgijpvAkiRpUNG4MiL2bmk7ICJ+Mt61rNbhADwN/H1EbDHRhUhSJ9ncBH438KWIWDci1gdOAI4Y71pW93BYTnNn/4MDR0TEthFxaUTcVP592fiXJ0kvlpm/AX4AfBQ4FjgzM38bEbMi4pqIuDEiTomItSJickScFRHzI+I3EXHkWNWxUjzKuoJ9DbgpIr4woP1faTb6GRHxTuAkYP9xr06SascDNwDPADMiYkfg74DXl0f/59D8PthvgS0ycyeAiNhkrApY7cMhMx+LiDOBI4EnW0a9Dvj78v4sYGB4SNKEyMwnIuJcYFlmPh0RewKvBa6LCIApNH+P7iLglRHxVeBC4OKxqmG1D4fiKzQpfNoQffyFD0krk+fLC5q/Pzc3Mz8xsFNE/CXNf3dwJPA/+eMfJx2V1f2eAwCZuRSYBxze0vwLmtMygIOBK8e7Lknq0k+BA/ofromIzSPiZRGxJc0vM3+X5v7ELmO1wDXlzAHgROC9LcNHAnMj4p+BB4HDJqQqSeogM+dHxPHATyNiLeBZmqeangNOjeZaU9LcxB4T/vkMSVJljbisJEkaHsNBklQxHCRJFcNBklQxHCRJFcNBklQxHCRJlf8PmKRbA7vr1IkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c40c400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the percentage of missed appointments\n",
    "missed_percent=missed_appo/all_appo*100\n",
    "print(\"the percent of patients who missed their scheduled appointment : \",missed_percent)\n",
    "df['No-show'].hist()\n",
    "plt.title(\"the distribution of show / no-show patients\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### from this analysis we can see that 20% of patients missed thier appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. what is the gender distribution for show and no-show patients?\n",
    "> we will discuss the gender distribution for show and no-show patient to know which gender show thier appointments more. and aslo know which gender missed thier appointments more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the gender distribution of show / no-show patients')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff18c099e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['Gender','No-show'])['Gender'].count().plot(kind='bar')\n",
    "plt.ylabel(\"distribution\")\n",
    "plt.title(\"the gender distribution of show / no-show patients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No-show  Gender\n",
       "No       F         57245\n",
       "         M         30962\n",
       "Yes      F         14594\n",
       "         M          7725\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate number of ech gender for show and no-show\n",
    "df.groupby(['No-show'])['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "show female percent : 79.68512924734476\n",
      "show male percent : 80.03205211052808\n"
     ]
    }
   ],
   "source": [
    "#calculate the percent of females who showed thier appointment\n",
    "female_per=(57245/(57245+14594)*100)\n",
    "#calculate the percent of males who showed thier appointment\n",
    "male_per=(30962/(30962+7725)*100)\n",
    "print(\"show female percent :\",female_per )\n",
    "print(\"show male percent :\",male_per )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### from this analysis we can see that females show thier appointment more than males , but also number of females who missed thier appointment  are more than males. we notice that the percentage of showed  females and male are almost equal. that's because that the number of all females is more than males"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. are there patients had more than one appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "822145925426128    88\n",
       "99637671331        84\n",
       "26886125921145     70\n",
       "33534783483176     65\n",
       "258424392677       62\n",
       "75797461494159     62\n",
       "871374938638855    62\n",
       "6264198675331      62\n",
       "66844879846766     57\n",
       "872278549442       55\n",
       "89239687393655     54\n",
       "8435223536         51\n",
       "853439686798559    50\n",
       "14479974122994     46\n",
       "65433599726992     46\n",
       "81894521843749     42\n",
       "9452745294842      42\n",
       "188232341789524    40\n",
       "2271579924275      38\n",
       "9496196639835      38\n",
       "13364929297498     37\n",
       "1484143378533      35\n",
       "88834999836575     34\n",
       "986162815579582    34\n",
       "712458866975343    33\n",
       "416755661551767    30\n",
       "6128878448536      30\n",
       "81213966782532     29\n",
       "8634164126317      24\n",
       "1198157171975      23\n",
       "                   ..\n",
       "98683352133221      1\n",
       "5394313945329       1\n",
       "48689197872217      1\n",
       "9675119787546       1\n",
       "763619586595        1\n",
       "867726299814126     1\n",
       "77425139319169      1\n",
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       "271517596623238     1\n",
       "8249496395977       1\n",
       "137479426839        1\n",
       "6529316371746       1\n",
       "3212962263947       1\n",
       "198193457888        1\n",
       "1425822256863       1\n",
       "216133833234618     1\n",
       "37589497678822      1\n",
       "31638467315         1\n",
       "89279955685         1\n",
       "5883897911366       1\n",
       "56326578686847      1\n",
       "869587212288428     1\n",
       "735858598529        1\n",
       "2886912523138       1\n",
       "68129842443312      1\n",
       "99264711372         1\n",
       "2212945531847       1\n",
       "57863365759569      1\n",
       "Name: PatientId, Length: 62298, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PatientId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>822145925426128</td>\n",
       "      <td>5638995</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 08:38:44</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>38</td>\n",
       "      <td>REDENÇÃO</td>\n",
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       "      <td>2016-04-29 18:02:42</td>\n",
       "      <td>2016-04-29</td>\n",
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       "      <td>2016-04-29 11:27:34</td>\n",
       "      <td>2016-04-29</td>\n",
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       "      <td>2016-05-16 18:38:11</td>\n",
       "      <td>2016-05-16</td>\n",
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       "      <th>6985</th>\n",
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       "      <td>5668887</td>\n",
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       "      <td>2016-05-06 09:54:32</td>\n",
       "      <td>2016-05-06</td>\n",
       "      <td>38</td>\n",
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       "      <td>2016-05-24 16:56:06</td>\n",
       "      <td>2016-05-24</td>\n",
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       "      <td>2016-05-17 17:47:53</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>38</td>\n",
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       "      <td>5736811</td>\n",
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       "      <td>2016-05-25 08:48:46</td>\n",
       "      <td>2016-05-25</td>\n",
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       "    <tr>\n",
       "      <th>7198</th>\n",
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       "      <td>5682166</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10 15:56:47</td>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>38</td>\n",
       "      <td>REDENÇÃO</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PatientId  AppointmentID Gender        ScheduledDay  \\\n",
       "3006  822145925426128        5638995      M 2016-04-29 08:38:44   \n",
       "3060  822145925426128        5642878      M 2016-04-29 18:02:42   \n",
       "3093  822145925426128        5640809      M 2016-04-29 11:27:34   \n",
       "6984  822145925426128        5705135      M 2016-05-16 18:38:11   \n",
       "6985  822145925426128        5668887      M 2016-05-06 09:54:32   \n",
       "7009  822145925426128        5735078      M 2016-05-24 16:56:06   \n",
       "7032  822145925426128        5710752      M 2016-05-17 17:47:53   \n",
       "7129  822145925426128        5736811      M 2016-05-25 08:48:46   \n",
       "7133  822145925426128        5711913      M 2016-05-18 08:13:00   \n",
       "7198  822145925426128        5682166      M 2016-05-10 15:56:47   \n",
       "\n",
       "     AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "3006     2016-04-29   38      REDENÇÃO            0             0         0   \n",
       "3060     2016-04-29   38      REDENÇÃO            0             0         0   \n",
       "3093     2016-04-29   38      REDENÇÃO            0             0         0   \n",
       "6984     2016-05-16   38      REDENÇÃO            0             0         0   \n",
       "6985     2016-05-06   38      REDENÇÃO            0             0         0   \n",
       "7009     2016-05-24   38      REDENÇÃO            0             0         0   \n",
       "7032     2016-05-17   38      REDENÇÃO            0             0         0   \n",
       "7129     2016-05-25   38      REDENÇÃO            0             0         0   \n",
       "7133     2016-05-18   38      REDENÇÃO            0             0         0   \n",
       "7198     2016-05-13   38      REDENÇÃO            0             0         0   \n",
       "\n",
       "      Alcoholism  Handcap  SMS_received No-show  waiting_days  \n",
       "3006           0        0             0      No             0  \n",
       "3060           0        0             0      No             0  \n",
       "3093           0        0             0      No             0  \n",
       "6984           0        0             0      No             0  \n",
       "6985           0        0             0      No             0  \n",
       "7009           0        0             0      No             0  \n",
       "7032           0        0             0      No             0  \n",
       "7129           0        0             0      No             0  \n",
       "7133           0        0             0      No             0  \n",
       "7198           0        0             0      No             3  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('PatientId==822145925426128').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from this analysis we can see that there are some patients with multiple appointment. top 10 in range 88:55 appointment. we can see that this multiple appointments are in different dates , these appointments may be for each examination or each specialist visit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. how many patients whose thier ScheduledDay on the same day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0     39405\n",
       " 2      6309\n",
       " 1      5803\n",
       " 4      4737\n",
       " 6      3647\n",
       " 5      3626\n",
       " 7      3332\n",
       " 3      2609\n",
       "-23     1863\n",
       "-24     1450\n",
       "-16     1446\n",
       "-2      1425\n",
       "-9      1409\n",
       "-17     1379\n",
       "-26     1332\n",
       " 14     1317\n",
       "-10     1313\n",
       " 8      1287\n",
       "-3      1184\n",
       "-29     1174\n",
       "-22     1118\n",
       " 10     1078\n",
       "-18     1052\n",
       "-8      1025\n",
       "-15     1003\n",
       "-11      986\n",
       " 11      972\n",
       " 9       948\n",
       "-12      946\n",
       "-14      939\n",
       "       ...  \n",
       "-1       922\n",
       "-25      880\n",
       "-4       857\n",
       "-5       792\n",
       " 13      779\n",
       " 12      730\n",
       "-19      725\n",
       "-27      696\n",
       " 15      675\n",
       "-7       647\n",
       "-21      642\n",
       "-6       605\n",
       "-20      561\n",
       "-28      529\n",
       " 16      407\n",
       " 21      348\n",
       " 17      332\n",
       " 18      307\n",
       "-30      270\n",
       " 28      250\n",
       " 19      243\n",
       " 20      231\n",
       " 22      184\n",
       " 29      161\n",
       " 24      157\n",
       " 23      137\n",
       " 25      137\n",
       " 27      133\n",
       " 26      131\n",
       " 30       20\n",
       "Name: waiting_days, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.waiting_days.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No-show  waiting_days\n",
       "No        0              37381\n",
       "          2               4767\n",
       "          1               4479\n",
       "          4               3538\n",
       "          6               2721\n",
       "          5               2597\n",
       "          7               2436\n",
       "          3               1906\n",
       "         -23              1334\n",
       "         -24              1081\n",
       "         -26              1019\n",
       "         -17               996\n",
       "         -9                973\n",
       "         -2                953\n",
       "         -16               945\n",
       "          8                907\n",
       "          14               903\n",
       "         -10               868\n",
       "         -29               863\n",
       "         -3                841\n",
       "         -22               779\n",
       "         -18               730\n",
       "          10               720\n",
       "         -15               695\n",
       "          9                684\n",
       "         -8                667\n",
       "         -14               655\n",
       "         -13               643\n",
       "          11               642\n",
       "         -11               636\n",
       "                         ...  \n",
       "Yes      -14               284\n",
       "         -13               281\n",
       "          9                264\n",
       "          13               256\n",
       "         -6                255\n",
       "         -25               254\n",
       "          12               247\n",
       "         -19               241\n",
       "          15               237\n",
       "         -7                230\n",
       "         -21               187\n",
       "         -27               185\n",
       "         -20               173\n",
       "          16               140\n",
       "         -28               123\n",
       "          17               106\n",
       "          21               100\n",
       "          18                84\n",
       "          19                73\n",
       "          28                71\n",
       "          20                70\n",
       "          22                55\n",
       "          29                45\n",
       "         -30                42\n",
       "          25                41\n",
       "          27                41\n",
       "          24                37\n",
       "          23                36\n",
       "          26                36\n",
       "          30                 2\n",
       "Name: waiting_days, Length: 122, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['No-show'])['waiting_days'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### yes, there are patients who had thier ScheduledDay in the same day. almost 40000 of patients had thier ScheduledDay in the same day and almost 38000 of them showed thier appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "after this analysis there are some Conclusions :\n",
    " 1.  On average, The patients are 37 years . and most of them are less than 55 years old.\n",
    " 2.  Most of patients are not alcoholics.\n",
    " 3.  Most of the patients has not diabetes.\n",
    " 4.  Most of the patients are not handicaped.\n",
    " 5.  Most of the patients has not Hipertension.\n",
    " 5.  Most of the patients recieved SMS for thier appointment.\n",
    " 7.  20% of the appointments were missed.\n",
    " 8.  Almost 40000 of patients has thier ScheduledDay on the same day.\n",
    " 9.  There are some patients who had multiple appointment\n",
    " 10.  There are some important factors to know in order to predict if a patient will show up for their scheduled appointment such as : ( Age ,Scholarship ,Hipertension ,Diabetes ,Alcoholism ,Handcap ,Gender ,SMS_received ,waiting_days)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations\n",
    "1. the range of patient's ages reaches to 115 and there are poeople with 0 age  which might have adversely affected this analysis. \n",
    "2. Although we could see  that number of females who showed thier appointment more than males but we couldn't say that this affect as the number of females in all data is more males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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