investigate-a-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate a Dataset (Medical Appointment No Shows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": [
    "## Introduction <a id='intro'></a>\n",
    "In this project, I will analyze <a href='https://www.kaggle.com/joniarroba/noshowappointments' target=\"_blank\">Medical Appointment No Shows Dataset</a> and communicate my findings about it using Python libraries NumPy, pandas, Matplotlib, and seaborn. The dataset collects information from 110k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "110,527 medical appointments, 14 associated variables (characteristics). <br>\n",
    "The most important one if the patient show-up or no-show for the appointment. <br><br>\n",
    "*PatientId:* Identification of a patient.\n",
    "\n",
    "*AppointmentID:* Identification of each appointment.\n",
    "\n",
    "*Gender:* Male (M) or Female (F).\n",
    "\n",
    "*ScheduledDay:* The day someone called or registered the appointment, this is before appointment of course.\n",
    "\n",
    "*AppointmentDay:* The day of the actuall appointment, when they have to visit the doctor.\n",
    "\n",
    "*Age:* How old is the patient. \n",
    "\n",
    "*Neighbourhood:* Where the appointment takes place.\n",
    "\n",
    "*Scholarship:* True (1) of False (0). <br> \n",
    "Observation, this is a broad topic, consider reading this article <a href='https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia' target=\"_blank\">here</a>.\n",
    "\n",
    "*Hipertension:* True (1) or False (0).\n",
    "\n",
    "*Diabetes:* True (1) or False (0).\n",
    "\n",
    "*Alcoholism:* True (1) or False (0).\n",
    "\n",
    "*Handcap:* The handcap refers to the number of desabilites a person has. For example, if the person is blind and can't walk the total is 2, \n",
    "<a href='https://www.kaggle.com/joniarroba/noshowappointments/discussion/29699' target=\"_blank\">here</a>.\n",
    "\n",
    "*SMS_received:* 1 or more messages sent to the patient.\n",
    " \n",
    "*No-show:* Yes or No."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I will answer the following questions:\n",
    "- Highest five neighbourhoods in terms of the number of no-show appointments?\n",
    "- Can the gender be considered as a factor to predict if a patient will show up for their scheduled appointment?\n",
    "- Can the age be considered as a factor to predict if a patient will show up for their scheduled appointment?\n",
    "- Can having a scholarship be considered as a factor to predict if a patient will show up for their scheduled appointment?\n",
    "- Can the day of the week that the appointment on be considered as a factor to predict if a patient will show up for their scheduled appointment?\n",
    "- Can the waiting period between scheduled day and appointment day be considered as a factor to predict if a patient will show up for their scheduled appointment?\n",
    "- What are the percentages of missed appointments for each special condition (alcoholism, diabetes ... etc) ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling <a id='wrangling'></a>\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>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 in the dataset into a pandas dataframe\n",
    "df = pd.read_csv(\"noshowappointments-kagglev2-may-2016.csv\")\n",
    "# the first 5 rows \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that:\n",
    "- *ScheduledDay* has a time part, while *AppointmentDay* time part is not given (00:00:00).\n",
    "- column names are a little bit messy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of samples and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "110,527 samples (rows) and 14 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         float64\n",
       "AppointmentID       int64\n",
       "Gender             object\n",
       "ScheduledDay       object\n",
       "AppointmentDay     object\n",
       "Age                 int64\n",
       "Neighbourhood      object\n",
       "Scholarship         int64\n",
       "Hipertension        int64\n",
       "Diabetes            int64\n",
       "Alcoholism          int64\n",
       "Handcap             int64\n",
       "SMS_received        int64\n",
       "No-show            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns data types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*ScheduledDay* and *AppointmentDay* should be datetime not object (string)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There aren't any missing values in any column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicated rows\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There aren't any duplicated rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId          62299\n",
       "AppointmentID     110527\n",
       "Gender                 2\n",
       "ScheduledDay      103549\n",
       "AppointmentDay        27\n",
       "Age                  104\n",
       "Neighbourhood         81\n",
       "Scholarship            2\n",
       "Hipertension           2\n",
       "Diabetes               2\n",
       "Alcoholism             2\n",
       "Handcap                5\n",
       "SMS_received           2\n",
       "No-show                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of non-null unique values for each feature\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 62,299 Patient ids so this means that some patients had more than one appointment, this could lead to a useful analysis which i'm not going to cover in this project.\n",
    "- Only 27 unique appointment days.\n",
    "- 81 unique neighbourhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110527.000000\n",
       "mean         37.088874\n",
       "std          23.110205\n",
       "min          -1.000000\n",
       "25%          18.000000\n",
       "50%          37.000000\n",
       "75%          55.000000\n",
       "max         115.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average age in this dataset is 37, and the heighest age is 115, while -1 is the minimum age which is an issue that will be fixed later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2016-04-29T00:00:00Z', '2016-06-08T00:00:00Z')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AppointmentDay'].min(), df['AppointmentDay'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first appointment in this dataset was on 2016-04-29 and the last appointment was on 2016-06-08, \n",
    "so this means these 110,527 appointments were in a period of 40 days only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop extraneous columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PatientId', 'AppointmentID', 'SMS_received'], axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rename columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'scheduled_day',\n",
       " 'appointment_day',\n",
       " 'age',\n",
       " 'neighbourhood',\n",
       " 'scholarship',\n",
       " 'hipertension',\n",
       " 'diabetes',\n",
       " 'alcoholism',\n",
       " 'handcap',\n",
       " 'no_show']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mod_string(col):\n",
    "    \"\"\"modifies a given string\"\"\"\n",
    "    if 'Day' in col:  # ScheduledDay, AppointmentDay\n",
    "        col = (col[:-3] + '_' + col[-3:])  # split the name and put _ \n",
    "    col = col.strip().lower().replace('-','_') # for example: No-show -> no_show\n",
    "    return col\n",
    "    \n",
    "df.rename(columns = lambda x: mod_string(x), inplace=True)\n",
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fix data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender                     object\n",
      "scheduled_day      datetime64[ns]\n",
      "appointment_day    datetime64[ns]\n",
      "age                         int64\n",
      "neighbourhood              object\n",
      "scholarship                 int64\n",
      "hipertension                int64\n",
      "diabetes                    int64\n",
      "alcoholism                  int64\n",
      "handcap                     int64\n",
      "no_show                    object\n",
      "dtype: object\n"
     ]
    },
    {
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       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
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       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</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>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender scheduled_day appointment_day  age    neighbourhood  scholarship  \\\n",
       "0      F    2016-04-29      2016-04-29   62  JARDIM DA PENHA            0   \n",
       "1      M    2016-04-29      2016-04-29   56  JARDIM DA PENHA            0   \n",
       "2      F    2016-04-29      2016-04-29   62    MATA DA PRAIA            0   \n",
       "\n",
       "   hipertension  diabetes  alcoholism  handcap no_show  \n",
       "0             1         0           0        0      No  \n",
       "1             0         0           0        0      No  \n",
       "2             0         0           0        0      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert string to datetime and keep only the date\n",
    "columns = ['scheduled_day', 'appointment_day']\n",
    "for column in columns:\n",
    "    df[column] = pd.to_datetime(pd.to_datetime(df[column]).dt.date)\n",
    "\n",
    "print(df.dtypes)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time was removed because the time in *appointment_day* column was not given as mentioned before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handle incorrect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</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>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender scheduled_day appointment_day  age neighbourhood  scholarship  \\\n",
       "99832      F    2016-06-06      2016-06-06   -1         ROMÃO            0   \n",
       "\n",
       "       hipertension  diabetes  alcoholism  handcap no_show  \n",
       "99832             0         0           0        0      No  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find negative values in age\n",
    "negative_age = df.query('age < 0')\n",
    "negative_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Negative age is not possible, so apparently this is a human error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</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>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [gender, scheduled_day, appointment_day, age, neighbourhood, scholarship, hipertension, diabetes, alcoholism, handcap, no_show]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the row with a negative age\n",
    "df.drop(negative_age.index, inplace=True)\n",
    "# check again\n",
    "df.query('age < 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Row was dropped successfully. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change columns values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### no_show column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     88207\n",
       "Yes    22319\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check value counts in no_show column\n",
    "df.no_show.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 2 values (No, Yes) I will change them to (0, 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88207\n",
       "1    22319\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if the value Yes then change it to 1, otherwise 0\n",
    "df.no_show = np.where(df.no_show == \"Yes\", 1, 0)\n",
    "# confirm\n",
    "df.no_show.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### handcap column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: handcap, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check value counts in handcap column\n",
    "df.handcap.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients weren't handicapped, 2042 with 1 handicap, 183 with 2 handicaps and so on. In my analysis I don't need to know how many handicaps a patient had, I just need to know if he was handicapped or not, so I will change these values to (0,1) only (0 not handicapped, 1 handicapped)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2241\n",
       "Name: handcap, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if the value is greater than 1 change it to 1, otherwise keep it\n",
    "df['handcap'] = np.where(df['handcap'] > 1, 1, df['handcap'])\n",
    "# confirm\n",
    "df.handcap.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis <a id='eda'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### no_show column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# plot a bar chart\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data = df, x = 'no_show', color = base_color)\n",
    "plt.title('Paitents Show Status')\n",
    "plt.xticks([0, 1], ['Show Up', 'No Show'])\n",
    "plt.xlabel('Stauts')\n",
    "plt.ylabel('Number of Patients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WZxmxe5QVm+vo09m+TH06R1i5pW6Hy879PEt1FvbuFmFIzyh7xCIc9JctnLZvKR+srcMAB/bRQ7kF5g/xZKZTvjsN6mrw6wnpubObqw0TH6ngpmPK2aWseVvcyzfVce6/tnLvSeVExC570zHlX95//EMV/GVCOdfPqGL+iixH7lXChQe387R+5YtdgR8B1+Wz0/yvOe0gXefnvd8mqMnaYJ49rJRvD7Gbsr07RVi+ya4tl2+qo1fHxl+yjVWGxIMVXPetMkbuvu3/vH8vruEbfaJsqTa8vSrLI6d24P4FNVTUFMlcNcXv8nxP8RDEZu3VBLfG3i5jDFOerGRIjyj/O+qrz4QnDCzh3vk1ANw7v4YTB21benXWcPLDFZw3vJRTh267o6cma/jjnGquHN2Oipqv9oDVGajO+vJ0lPe6kOcL//N7KCUV2wt4jxCGc9antYy9u4JhvSJE3PT8anwZI3aLctqjW/l0g2GPmPDPUzvQrb3w+rIsf369mjtPaM8DC6qZ/O9Khvb86n/dPSe154Bd7WfLm2ZX0bVcmHRAO4wxnPX4Vt5eWcdx+5Tw6yPLGyunRfRQiu82Af2ddGJNPjrLdzjvBKbkr8O2RcOZF7910okf56Oj/G3W2tENzstbf0r549J4MtM7Hx3l8zPnT9BhR1Th6wAU0ZrTjtY+KS99KeW/KfFkpqPfneRrzXkxutZUxSMGnO13J/6HMxUrJaTHNZVqhUv87iAfa84T0On6VPEZHk9mRvvZQT7CeXEe+lAqCJf62bi/4bQnHQQ+3INSPpno52EVv9ecFxKS6zWV8kE74AK/GvcvnHZH0GTf2lcqHHzba+vnmvMoIC9nUigVoCHxZGZ/Pxr2M5wTfWxbqTA53Y9G/QmnnYzoRF/aVip8TvOjUV/C+Wz24NHGhO+yMKV8sk88mdnP60Z9CdBFNZefUkpt+TGRuW9MLnm6Yrh8OCAqRk9EUMXsJOBtLxv0a+2WqKGk3X/qDj34P9WHAsaMjLzzzgXRaavGRhbuXiY1e/vUr1JBORmPxxjy/GLreDKzL7BoR48ZIEudKdFpnxwbndNtFyqGioRi5PmCpxdbB66Xk06s8qoxP0Jx7M4esMTsHk/WXjhueNWdw0ZU3brm5tqTZi433V4zhm1HbFaqcIz1sjE/NmvHNOfBK+na8/e1p/X8fe1pdGTr5onRmfPOjj6XHSBLh0aEvI52plQrjQMe96oxP8J5aEsX3EL7Tvdljxp5X/YoomRrj4y8Me/8kmmbDpIl+5RIXV8vi1TKB4d52Zinnznjycw+wBLPGsxxsLy3+IKSqSsOj8zv216qB/jRR6HTz5yBqwO6OenEBi8a83rN2eK15s68YQYNfqNm0GCAuCz/bEp02kcTorO7dGHzfiI6Vb0KhQj2Y13Gq8a85OvFp/Uc06ffT2vPH3dg1R3DD666ff2NNRNnLTU95hjD1nz0r9QOjPOqoYJZc27PWmLd/5SdOOZP2Ym0p6ripOisuedG/1s9WD4bEhHTPd/1qDbPsxWUZ58548lMDFhLOGbLJkJd9luReW9PiU5b/83Ie3uVSrZf0DX5TT9zhsJGJ52IedGQl+EcA8z0pDEfDJcPl0wpmbpsfOTN3h2lanDQ9fhBwxkauznpxLLWNuLlZu1AD9vy3Hyz94Af1vxgAEA/Wfn55OjTH5wQfWWX7mwcJqIn6StP7QuEKpwFc3jjM9Nrt1/WnrfbL2vPI8bm9WdGX1h0ZvSF6B6ycpgIvg8WrIreEOC51jbSJsOZawOduvw5e8LoP2dPoIzqyuOjr752XvTZyv3EGRwR0zPo+lRB2teLRtrMZm1TVNGu/NHsuG8+mh2HUFc3NrJw4ZTotLWjIovi7SS7Z9D1qYIxxItGPAlnPJkRYB8v2goLQyQyo274sBl1wwHYV5wPLyiZuvSoyOs9O1I5RERHFVTb5Uk4PdlbG09m+gGftr6cwtCX1csnlTyz5OTorI492bC/SDjmgdG9taHSyUkntrSmAa82a9vUxdPL6NHnhtqz+9xQezad2bLhtOj0RWdFn5e9ZPlQEXYJuj4VCj2BUISzze442UTH2F3Z4w69K3scpdRWHxuZ88bkkmcq9pcPB0bF6NCgbVdPwGlNA16Fs5tH7RS0GkraPVk3+uAnq0cDxoyKvLPogujU1WMiC/uVSe1eQden8qrVKyyvwtnVo3aKiMirdUOHvlo3FICB8tnHU6LTPj02Ord7Zyr21aFZil6v1jag4cyT902//lfVXtT/qtqL6MW6VeeVPLt4YnRG+a6s21+EsqDrU57TNWchWknXnr+rPb3n72pPpyNbN58SneEOzfL5fiJ4ctK0ClxowqmfOVtoC+073Zs9euS92aMpobbGDs3y9KYDZcmAEqnrE3R9qsVCE05dc3qglpLSaXUjDpxWPQKAb8rixVNKpq4YF5nft73UFOTpkW1Yq483exXOzh61o3K8ZgYPfq1m8GCA/rLs0ynRaR8norO7dmHLUB2aJfRafWKKV+HUPY8++9j03eOa2il7XFM7hW5sWHNO9Ll3TyuZ3m43Vg8ToX3Q9alttDpbeh1jAWo4NMvJ0Zlzzo0+VzNAlvbHg80p5QkNZ1u3lbIOD2aPGPFg9giEurqg61FfavVJ615tjuqbIgQMEf14ER6tzoRXf8xqj9pRqlhkW9uAhlMpf9S2tgENp1L+WN/aBrwK50aP2lGqWKxubQNehXOFR+0oVSzWtLYBDadS/ghNOFd61I5SxSI04dQ1p1Jfp+FUKqQ0nEqF1BetbcCrcLa6EKWKyGonnVjX2kY8CaeTTmwFPveiLaWKwGIvGvHyROlFHralVCF7z4tGvAzn2x62pVQh0zWnUiGla06lQiqUa87WT1mmVGGrAT72oiHPwulOd/aJV+0pVaDmO+lEq6/lBO9HzXvL4/aUKjQzvWrI63DO8Lg9pQrNLK8a8jqcL3ncnlKFJrThnA+0+rQlpQrUEied8OzySU/D6aQTdeimrWq7PFtrgj/TKLzkQ5tKFYLQh/NFH9pUqhBM97IxP8K5AA8uNFWqwCx20okPvWzQ83A66YQBnva6XaVC7t9eN+jX3BqP+dSuUmH1pNcN+hXOp4EKn9pWKmy+AGZ73agv4XRHRpjqR9tKhdBj7mFET/k5Zdw/fGxbqTB5xI9G/QxnBtjgY/tKhcFyPD6+Wc+3cDrpRCXwL7/aVyok7vFjkxb8XXMC3ONz+0oFqQ74i1+N+xpOJ52YDiz0sw+lAvS0k074NsCA32tOgFvy0IdSQbjdz8bzEc4H0MvIVPH5FJ8PF/oeTiedqAD+5nc/SuXZHX7tCKqXjzUnwK3YD89KFYMa4E6/O8lLOJ104mPgqXz0pVQePOCkE77PrJevNSfAjXnsSym/1ALX5aOjvIXTPazyXL76U8on9zrpxEf56KgkH53kuBo4Is99qlaoWbOUVU/++sufa9d/QZcx51C+5zDWPHMrprqSklgvehx/JZGyDtssv/G1J9g8/1kQKO0Zp8dxP0JK2rHqP7+lZtUntN/7m3QdNwmA9S8/RLte/ekwYGTenl8z1ZCntSbkd7MWJ52Yiw8XpSr/lHbfnb6Tb6bv5JvpM+kmpLSMDgNHsWbazXQd9x36TrmVDgNHsXHOtpfw1m5azcY3/sOuk/5A3ym3QV0dW96dQfVKO1tB3/NvoWrpIuqqtlC7eS3Vy98PczDBnqrn5KuzvIbTdQ2657YgVX4yn9IufSiJ9aJm7VLK+u0HQHn8QCref6XxheqymNpqTF0WU1tFtFM3JFJif2fqMNlakAgbZj5Al7Hn5PHZNFsNcH0+O8x7OJ104m30crKCtOXdGXQYchgA7XrsydYP5gBQsXgWtZtWb/P4ks492OWQk/n89sksveVcpKwD7fsfRGmPfpR07snyey6j4+Ax1K5bbtvsvXf+nkzz/c3PU/Uak+/PnPV+BpwWYP+qmUy2hq0fzP3y82H34y5j7XN3sOHlh2i/zwgksu2fMlu5mYolc9jtu3cRKevIqn+n2bzoRToN/Rbdjrjoy8etfPQXdDv6+2x45WGqV35MefwAOh9wTN6eWxNsAH6e706D2KzFHaXstiD6Vi2z9aM3aNd7b6IduwJQ2r0fvU+/lj7f+SMd9x1HSdddt1mm0nmLklhvoh1iSLSEDgNHUfX5u197TMWS2bTbdQCmppLq1Z/Q86QkWxa9SF1NZV6eVxP9NB/HNRsKJJyua4ClAfavmmHLO9Pp6G7SAmS3rAfAmDo2vPIPOh9w7DbLlOzSk+pl71FXU4kxxn5m7d7vy/tNtpaNrz/JLiO+jamtAsS9w0DWk1n0vDCPgFYkgYXTSSc2AT8Mqn/VdHU1lVQ6b9Fh0KFf/m7Lu9P5/I6LWPbX7xLt1J2Ow44EoHbTGlb8024BlvUdRIdBo1l+z49Y/rdLwRg6D/9qc3XTmxk67TeeSGk5pT37A4Zld11K2e5DiJR3yutz3A4DXOKkE9kgOhdjgp2MOp7MPAGcGGgRSjXuLieduCCozoPcrK33fWBz0EUo1cBaIBlkAYGH00knlmI/fyoVJlc56cS2x4fyKPBwum4GXgu6CKVcTznphO+XhO1MKMLpXrR6LjpKvAreKiCwz5m5QhFOACedeA+4LOg6VJt3QRDHNBsTmnACuJsSjwZdh2qzbnHSCc8nJGqpUIXTdQHg6TyHSjXBPOCKoIvIFbpwOunEBuBUIFTnb6mithk4w0knqoIuJFfowgngpBPz0M+fKj/qgDOddOL9oAtpKJThBHDSiTuwo/Yp5acrnXQilIPPhTacrsvQUfuUf+5w0onQDjwX6nC6JxyfAbwRdC2q6DwPXBp0ETsS+InvTRFPZnbFTuu9Z9C1qKLwHjDSSSfWB13IjoR6zVnPSSe+AI4DQv1iqoKwGpgQ9mBCgYQTwEkn3gG+DVQHXYsqWGuAI5x04oOgC2mKggkngJNOvIgNaKiOR6mCsA440kkn5gddSFMVVDgBnHQig704W09SUE21DrvGnBd0Ic1RcOEEcNKJZ4AJ6FUsaufWA0c56cSbQRfSXAUZTgAnnXgeu5NoS9C1qNDaABztpBOvB11ISxRsOOHLyZGOATYFXYsKnRXYTdm5QRfSUgUdTgAnnZgFjAe+CLoWFRrvYI9jFuQas17BhxPASSdeAw4B3gq6FhW4F4DR+ZxwyC9FEU4AJ534DBiDzmLWlt0LHFMIJxg0RUGcvtcc8WRGgBuAq4KuReXVz5x04tqgi/BS0YWzXjyZmQTcAbQLuhblq83YcX8eDroQrxVtOAHiycxI7HSDesJ8cXobOMUdHK7oFM1nzsY46cRs4ABg22mXVaG7GzikWIMJRb7mzBVPZi4GbgQ6BF2LapX1wMVOOvFI0IX4rc2EEyCezAwE7gNGBF2LapEZwLlOOvFp0IXkQ1Fv1jbkDuI0Gjuztl56VjjWA98FDm8rwYQ2tubM5a5FbwaOCroWtUMPAv/bnFHYReRq4Cwgix1d72JjzBwRcYBvGGM8n6BIRA4HrjDGTMj53T3AU8aYFg2UXuJNaYXHXYseHU9mJgJ/APrtZBGVXx9gJ679b3MWEpFR2CuWDjLGVIlIDwr0cFqb2qxtjJNOPAYMxp64oJu6wasGrgOGNTeYrj7AamNMFYAxZrUxZlnO/T8QkTdFZKGIDAYQkW4i8oSILBCR2SKyv/v7hSLSRaw1InKe+/v7ReSI5hQlIo6I/FpE5rq3fXa2TJsPJ4CTTlQ46cRPgGHAtKDraaOywD3AICed+KmTTrT0YvpngX4i8r6I3CYi4xrcv9oYcxBwO19Nv/ALYJ4xZn/gJ9idhgAvY/dRDAU+Asa6vx+JHXCuuTYaYw4BbgFu2tmDNZw5nHTifSedOA4YBTwTdD1thAEeBoY66cTk1p6wbozZDBwMXISdzu9hEflOzkMed7++AcTd78cA97vLvwB0F5EYMBM4zL3dDgwTkd2AtW4/DZ9HoyXlfP9QztdRO3suGs5GOOnEbCedOAb7Aj4ddD1F7EngACedOMPLkwmMMVljzEvGmJ8D3wcm5txdP/5Ulq/2uUhjzWAP3Yx1by9hw34KNrQNrQG6NvhdN+xof7ltNvZ9ozScO+CG9Fp2a5kAAAIhSURBVFjsZoyG1BtZ7JVDI5x04kQnnVjgZeMiMkhEBuT86gDgk50sNgM4213+cOym70ZjzGdAD2CAMeYjYBZ2U7ixcC4B+orIELedPYHhfP0yxtNzvr66s+fSZvfWNoeTTswBjo0nMwdhj7edBXQMtqqCswa4E7jdSSd2FpbW6ATcLCJdgFrsXt+LdrJMCrhbRBZgx6WalHPfHCDqfj8Tu+NwVsMG3D3D57jtlAM1wAXGmA05DysTkTnYleKZO3sibfY4Z2vEk5ldsP9pvwvsH3A5YfcGdgfIP1qxk6fgteQYq4azldwrXy7Gbqq0D7icsNgA/Av4i3vxQZun4QyQuzY9ATvx79FAWbAV5V0FkMHuiZwatoloC5GG0wfxZKYzdtjOE4Bj2XYvXrFYB/wHu5Z8xkkntgZcT1HRcPosnsyUYA9kj3e/jqBwdyatw+4Ume7e5jnpRF2wJRUvDWeeuWE9ABvUMe7XPoEWtX3LsXsrp2OP8y3QMOaPhjME4snMHsAQYCAwKOdrPxo/QO61ZcAi7Hiv9V/fcdKJdXnoW22HhjPE4slMe2AAsAfQHXvGSWO3MmyII+7XWuxxthrsGTFrgJXbuX3upBO5x+JUSGg4lQopPX1PqZDScCoVUhpOpUJKw6lUSGk4lQopDadSIaXhVCqkNJxKhZSGU6mQ0nAqFVIaTqVCSsOpVEhpOJUKKQ2nUiGl4VQqpDScSoXU/wE3MRjfRpzvDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a pie chart\n",
    "sorted_counts = df.no_show.value_counts()\n",
    "plt.pie(sorted_counts, labels = ['Show Up', 'No Show'], startangle = 90,\n",
    "        counterclock = False, autopct='%1.1f%%');\n",
    "plt.axis('square')\n",
    "plt.title('Paitents Show Status');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients showed up for their appointments (79.8%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### gender column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "# plot a bar chart\n",
    "sns.countplot(data = df, x = 'gender', color = base_color)\n",
    "plt.title('Paitents Gender')\n",
    "plt.xticks([0, 1], ['Female', 'Male'])\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Number of Patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients were females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "# plot a histogram \n",
    "bin_edges = np.arange(0, df['age'].max()+5, 5)\n",
    "plt.hist(data = df, x = 'age', bins = bin_edges)\n",
    "plt.xlabel('Age')\n",
    "plt.title('Distribution of Patients Age');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients were young."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### scholarship column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "# plot a bar chart\n",
    "sns.countplot(data = df, x = 'scholarship', color = base_color)\n",
    "plt.title('Paitents Scholarship Status')\n",
    "plt.xticks([0, 1], ['No Scholarship', 'Scholarship'])\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Number of Patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients didn't have a scholarship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### alcoholism, diabetes, handcap, and hipertension columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7kCSNNocCSpIkSdKQpi2w8hftJUmSJC0vprPH6ij8RXtJkiRJy4FpC6z8RXtJkiRJy4ulfY+Vv2gvSZIkadZZViav8BftJUmSJI2spR1Y+Yv2kiRJkmadpR1Y+Yv2kiRJkmadafuBYH/RXpIkSdLyYtoCK3/RXpIkSdLyYlmZvEKSJEmSRpaBlSRJkiQNycBKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJUp8k/5rkgiS/TPLFJKskeXCSM5NcnORLSVZqeVdur+e35XN7tvO2lv7rJM/sSd+5pc1PcuDSr6EkaUkzsJIkqUeSDYDXA9tU1aOAFYAXAO8HPlxVmwE3Avu1VfYDbqyqTYEPt3wk2byttwWwM/DxJCskWQH4GLALsDmwV8srSRphBlaSJN3bisCqSVYEVgOuAp4GHN+WHw3s0Z7v3l7Tlu+YJC392Kr6S1X9DpgPbNse86vqkqq6Azi25ZUkjTADK0mSelTVFcB/AX+gC6huBs4GbqqqO1u2y4EN2vMNgMvaune2/Ov0pvetM176vSTZP8m8JPMWLFgwfOUkSdPGwEqSpB5J1qLrQXowsD5wP7phe/1qbJVxli1q+r0Tq46oqm2qaps5c+ZMVnRJ0gwysJIkaWFPB35XVQuq6q/A/wFPANZsQwMBNgSubM8vBzYCaMsfANzQm963znjpkqQRZmAlSdLC/gBsn2S1dq/UjsCFwA+A57U8+wBfb89PaK9py79fVdXSX9BmDXwwsBnwM+AsYLM2y+BKdBNcnLAU6iVJmkYrTp5FkqTlR1WdmeR44OfAncAvgCOAbwHHJnl3S/tMW+UzwOeSzKfrqXpB284FSY6jC8ruBF5TVXcBJHktcBLdjINHVtUFS6t+kqTpMSOBVZJ/BV5ON6b8fGBf4EF0MyOtTdeYvaSq7kiyMnAMsDVwPfD8qrq0bedtdNPc3gW8vqpOauk7Ax+la7A+XVWHLr3aSZJGXVUdBBzUl3wJ3Yx+/Xn/DOw5znbeA7xnQPqJwInDl1SStKxY6kMB/X0QSZIkSbPNTN1j5e+DSJIkSZo1lnpg5e+DSJIkSZptZmIooL8PIkmSJGlWmYmhgP4+iCRJkqRZZSYCK38fRJIkSdKsstSnW/f3QSRJkiTNNjPyO1b+PogkSZKk2WTSoYBJDkiyRjqfSfLzJDstjcJJkiRJ0iiYyj1WL6uqW4CdgDnAvsCh01oqSZIkSRohUwmsxqYv3xX4bFWdy+ApzSVJkiRpuTSVwOrsJCfTBVYnJVkd+Nv0FkuSJEmSRsdUJq/YD9gSuKSqbk+yDt1wQEmSJEkSU+uxOqWqfl5VNwFU1fXAh6e3WJIkSZI0OsbtsUqyCrAasG6Stbjnvqo1gPWXQtkkSZIkaSRMNBTwlcAb6IKos7knsLoF+Ng0l0uSJEmSRsa4gVVVfRT4aJLXVdV/L8UySZIkSdJImXTyiqr67yRPAOb25q+qY6axXJIkSZI0MiYNrJJ8DngocA5wV0suwMBKkiRJkpjadOvbAJtXVU13YSRJkiRpFE1luvVfAn833QWRJEmSpFE1lR6rdYELk/wM+MtYYlU9Z9pKJUmSJEkjZCqB1cHTXQhJkiRJGmVTmRXwtCSbAJtV1XeTrAasMP1FkyRJkqTRMOk9VkleARwPfKolbQB8bToLJUmSJEmjZCqTV7wGeCJwC0BVXQw8cDoLJUmSJEmjZCqB1V+q6o6xF0lWpPsdK0mSJEkSUwusTkvydmDVJM8Avgx8Y3qLJUmSJEmjYyqB1YHAAuB84JXAicA7prNQkiRJkjRKpjIr4N+A/2kPSZIkSVKfcQOrJMdV1T8nOZ8B91RV1aOntWSSJEmSNCIm6rE6oP199tIoiCRJkiSNqnHvsaqqq9rTf6mq3/c+gH9ZOsWTJEmSpGXfVCaveMaAtF2WdEEkSZIkaVRNdI/Vq+l6ph6S5LyeRasDP5nugkmSJEnSqJjoHqv/Bb4NvI9uyvUxt1bVDdNaKkmSJEkaIeMGVlV1M3AzsBdAkgcCqwD3T3L/qvrD0imiJEmSJC3bJr3HKsluSS4GfgecBlxK15MlSdKslGTNJMcn+VWSi5I8PsnaSU5JcnH7u1bLmySHJZmf5LwkW/VsZ5+W/+Ik+/Skb53k/LbOYUkyE/WUJC05U5m84t3A9sBvqurBwI54j5UkaXb7KPCdqnoE8BjgIrph8d+rqs2A73HPMPldgM3aY3/gEwBJ1gYOArYDtgUOGgvGWp79e9bbeSnUSZI0jaYSWP21qq4H7pPkPlX1A2DLaS6XJEkzIskawFOAzwBU1R1VdROwO3B0y3Y0sEd7vjtwTHXOANZM8iDgmcApVXVDVd0InALs3JatUVU/raoCjunZliRpRE00ecWYm5LcH/gh8IUk1wJ3Tm+xJEmaMQ8BFgCfTfIY4GzgAGC9sd94rKqr2r3HABsAl/Wsf3lLmyj98gHp95Jkf7qeLTbeeOPhaiVJmlZT6bHaHbgd+FfgO8Bvgd2ms1CSJM2gFYGtgE9U1WOBP7Lw7Lj9Bt0fVYuRfu/EqiOqapuq2mbOnDkTl1qSNKMmDKyS7AG8GnhGVd1ZVUdX1WFtaKAkSbPR5cDlVXVme308XaB1TRvGR/t7bU/+jXrW3xC4cpL0DQekS5JG2LiBVZKP0/VSrQMckuQ/ltROnW1JkrSsqqqrgcuSPLwl7QhcCJwAjLU1+wBfb89PAPZu7dX2wM1tyOBJwE5J1mpt2k7ASW3ZrUm2b+3T3j3bkiSNqInusXoK8JiquivJasCPgEOW0H7HZlt6XpKVgNWAt9PNtnRokgPphl28lYVnW9qObial7XpmW9qGbgjF2UlOaDcIj822dAZwIt1sS04RL0maqtfR3Ve8EnAJsC/dxcjjkuwH/AHYs+U9EdgVmE83dH5fgKq6IckhwFkt37uq6ob2/NXAUcCqdO2TbZQkjbiJAqs7quougKq6fUn1+vTMtvTStu07gDuS7A7s0LIdDZxKF1jdPdsScEbr7XpQy3vKWCOVZGy2pVNpsy219LHZlmy0JElTUlXn0F2467fjgLwFvGac7RwJHDkgfR7wqCGLKUlahkwUWD0iyXnteYCHtteha0cevZj7dLYlSZIkSbPKRIHVI6dxn1sBr6uqM5N8lBmcbQk4AmCbbbYZmEeSJEmSJjNuYFVVv5+mfQ6abelA2mxLrbdqqrMt7dCXfirOtiRJkiRpKZvK71gtUc62JEmSJGm2mWgo4HRytiVJkiRJs8a4gVWS71XVjkneX1VvXZI7dbYlSZIkSbPJRD1WD0ryVOA5SY6lb1KIqvr5tJZMkiRJkkbERIHVf9JNKrEh8KG+ZQU8bboKJUmSJEmjZKJZAY8Hjk/yH1V1yFIskyRJkiSNlEknr6iqQ5I8B3hKSzq1qr45vcWSJEmSpNEx6XTrSd4HHEA3JfqFwAEtTZIkSZLE1KZbfxawZVX9DSDJ0cAvgLdNZ8EkSZIkaVRM9QeC1+x5/oDpKIgkSZIkjaqp9Fi9D/hFkh/QTbn+FOytkiRJkqS7TWXyii8mORV4HF1g9daqunq6CyZJkiRJo2IqPVZU1VXACdNcFkmSJEkaSVO9x0qSJEmSNA4DK0mSJEka0oSBVZL7JPnl0iqMJEmSJI2iCQOr9ttV5ybZeCmVR5IkSZJGzlQmr3gQcEGSnwF/HEusqudMW6kkSZIkaYRMJbB657SXQpIkSZJG2FR+x+q0JJsAm1XVd5OsBqww/UWTJEmSpNEw6ayASV4BHA98qiVtAHxtOgslSZIkSaNkKtOtvwZ4InALQFVdDDxwOgslSZIkSaNkKoHVX6rqjrEXSVYEavqKJEmSJEmjZSqB1WlJ3g6smuQZwJeBb0xvsSRJkiRpdEwlsDoQWACcD7wSOBF4x3QWSpIkSZJGyVRmBfxbkqOBM+mGAP66qhwKKEmSJEnNpIFVkmcBnwR+CwR4cJJXVtW3p7twkiRJkjQKpvIDwR8E/qGq5gMkeSjwLcDASpIkSZKY2j1W144FVc0lwLXTVB5JkiRJGjnj9lgleW57ekGSE4Hj6O6x2hM4aymUTZIkSZJGwkRDAXfreX4N8NT2fAGw1rSVSJIkSZJGzLiBVVXtuzQLIkmSJEmjaiqzAj4YeB0wtzd/VT1n+oolSZIkSaNjKrMCfg34DPAN4G/TWxxJkiRJGj1TmRXwz1V1WFX9oKpOG3tMe8kkSZpBSVZI8osk32yvH5zkzCQXJ/lSkpVa+srt9fy2fG7PNt7W0n+d5Jk96Tu3tPlJDlzadZMkLXlTCaw+muSgJI9PstXYY9pLJknSzDoAuKjn9fuBD1fVZsCNwH4tfT/gxqraFPhwy0eSzYEXAFsAOwMfb8HaCsDHgF2AzYG9Wl5J0gibSmD198ArgEPpfiz4g8B/TWehJEmaSUk2BJ4FfLq9DvA04PiW5Whgj/Z89/aatnzHln934Niq+ktV/Q6YD2zbHvOr6pKqugM4tuWVJI2wqQRW/wg8pKqeWlX/0B5PG3bHDrGQJC3DPgK8hXvuLV4HuKmq7myvLwc2aM83AC4DaMtvbvnvTu9bZ7z0e0myf5J5SeYtWLBg2DpJkqbRVAKrc4E1p2HfDrGQJC1zkjwbuLaqzu5NHpC1Jlm2qOn3Tqw6oqq2qapt5syZM0GpJUkzbSqB1XrAr5KclOSEsccwO3WIhSRpGfZE4DlJLqVrQ55G14O1ZpKx2XQ3BK5szy8HNgJoyx8A3NCb3rfOeOmSpBE2lenWD5qG/Y4NsVi9vZ7yEIskvUMszujZZu86/UMsthtUiCT7A/sDbLzxxkNUR5I0W1TV24C3ASTZAXhTVb0oyZeB59EFW/sAX2+rnNBe/7Qt/35VVbsI+b9JPgSsD2wG/Iyux2qz9juRV9CNvnjhUqqeJGmaTBpYLemp1XuHWLQGC5bsEItBvXDjDrEAjgDYZpttBuaRJKl5K3BskncDv6D7jUfa388lmU/XU/UCgKq6IMlxwIXAncBrquougCSvBU4CVgCOrKoLlmpNJElL3KSBVZJbuScwWQm4L/DHqlpjMfc5NsRiV2AVYA16hli0XqtBQywun+IQCyZIlyRpyqrqVODU9vwSuuHm/Xn+DOw5zvrvAd4zIP1E4MQlWFRJ0gyb9B6rqlq9qtZoj1WAfwIOX9wdVtXbqmrDqppLd1Xv+1X1IuAHdEMoYPAQC+gZYtHSX9BmDXww9wyxOIs2xKLNLPiClleSJEmSpsVUJq9YSFV9je5G3iXtrcC/taEU67DwEIt1Wvq/AQe2clwAjA2x+A5tiEXr8RobYnERcJxDLCRJkiRNp6kMBXxuz8v7ANswzj1Li8ohFpIkSZJmg6nMCrhbz/M7gUtx+nJJkiRJuttUZgXcd2kURJIkSZJG1biBVZL/nGC9qqpDpqE8kiRJkjRyJuqx+uOAtPsB+9FNLmFgJUmSJElMEFhV1QfHnidZHTgA2JfuF+c/ON56kiRJkrS8mfAeqyRr001x/iLgaGCrqrpxaRRMkiRJkkbFRPdYfQB4LnAE8PdVddtSK5UkSZIkjZCJfiD4jcD6wDuAK5Pc0h63Jrll6RRPkiRJkpZ9E91jNVHQJUmSJElqDJ4kSZIkaUgGVpIkSZI0JAMrSZIkSRqSgZUkSZIkDcnASpIkSZKGZGAlSZIkSUMysJIkSZKkIRlYSZIkSdKQDKwkSZIkaUgGVpIkSZI0JAMrSZIkSRqSgZUkSZIkDcnASpIkSZKGZGAlSZIkSUMysJIkSZKkIRlYSZIkSdKQDKwkSZIkaUgGVpIkSZI0JAMrSZIkSRqSgZUkSZIkDcnASpIkSZKGZGAlSZIkSUMysJIkSZKkIRlYSZIkSdKQDKwkSeqRZKMkP0hyUZILkhzQ0tdOckqSiy8t1m8AACAASURBVNvftVp6khyWZH6S85Js1bOtfVr+i5Ps05O+dZLz2zqHJcnSr6kkaUkysJIkaWF3Am+sqkcC2wOvSbI5cCDwvaraDPheew2wC7BZe+wPfAK6QAw4CNgO2BY4aCwYa3n271lv56VQL0nSNFrqgZVXAiVJy7Kquqqqft6e3wpcBGwA7A4c3bIdDezRnu8OHFOdM4A1kzwIeCZwSlXdUFU3AqcAO7dla1TVT6uqgGN6tiVJGlEz0WPllUBJ0khIMhd4LHAmsF5VXQVd8AU8sGXbALisZ7XLW9pE6ZcPSB+0//2TzEsyb8GCBcNWR5I0jZZ6YOWVQEnSKEhyf+ArwBuq6paJsg5Iq8VIv3di1RFVtU1VbTNnzpzJiixJmkEzeo+VVwIlScuiJPelC6q+UFX/15KvaRfvaH+vbemXAxv1rL4hcOUk6RsOSJckjbAZC6y8EihJWha1+3I/A1xUVR/qWXQCMHY/7z7A13vS9273BG8P3NwuEJ4E7JRkrTZUfSfgpLbs1iTbt33t3bMtSdKIWnEmdjrRlcCqumoRrgTu0Jd+Kl4JlCQN54nAS4Dzk5zT0t4OHAocl2Q/4A/Anm3ZicCuwHzgdmBfgKq6IckhwFkt37uq6ob2/NXAUcCqwLfbQ5I0wpZ6YDWFK4GHcu8rga9NcizdRBU3t+DrJOC9PRNW7AS8rTVkt7arhmfSXQn872mvmCRpVqiqHzN49APAjgPyF/CacbZ1JHDkgPR5wKOGKKYkaRkzEz1WXgmUJEmSNKss9cDKK4GSJEmSZpsZnRVQkiRJkmYDAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoa04kwXQJIkSZqqrd98zEwXQSPo7A/sPe37sMdKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJkiRJQ1pxpgsgadmy9ZuPmekiaASd/YG9Z7oIkiTNKHusJEmSJGlIBlaSJEmSNCQDK0mSJEka0qwNrJLsnOTXSeYnOXCmyyNJUi/bKUmaXWZlYJVkBeBjwC7A5sBeSTaf2VJJktSxnZKk2WdWBlbAtsD8qrqkqu4AjgV2n+EySZI0xnZKkmaZ2Trd+gbAZT2vLwe268+UZH9g//bytiS/XgplW56tC1w304VYFuW/9pnpImhqPIfHsQTP4U2W1IaWcbZTyyb/x8dhOzUyPIfHsTTaqdkaWGVAWt0roeoI4IjpL44Aksyrqm1muhzS4vIc1hJkO7UM8n9co85zeGbN1qGAlwMb9bzeELhyhsoiSVI/2ylJmmVma2B1FrBZkgcnWQl4AXDCDJdJkqQxtlOSNMvMyqGAVXVnktcCJwErAEdW1QUzXCw5nEWjz3NYS4Tt1DLL/3GNOs/hGZSqew3pliRJkiQtgtk6FFCSJEmSlhoDK0mSJEkakoHVLJKkknyw5/Wbkhw8yTp7JNl8Gsv02FauZ/al37aY2zs1yZSnEU2yQ5JvtufPSXLg4uxXoynJP7bz7xHt9dwkv1zMbV2aZN1FyP/SJIe3569Ksvfi7FdaHi3p9izJwUmuSHJOkouT/F9v3iSfnqwtXIzPgB2SPKHntZ8DGijJXe3cvCDJuUn+Lcl92rJtkhw2yfp3tzeLsM+3970+fdFLrn4GVrPLX4DnLsoHP7AHMG2BFbAX8OP2d0ZV1QlVdehMl0NL1dj594KZLERVfbKqjpnJMkgjZjrasw9X1ZZVtRnwJeD7SeYAVNXLq+rCxS/uQDsAdwdWfg5oAn9q5+YWwDOAXYGDAKpqXlW9fhr2uVBgVVVPGC+jps7Aana5k242mH/tX5BkkyTfS3Je+7txu5L2HOAD7UrJQ/vW2S3JmUl+keS7SdZr6QcnObL1Hl2SZOA/fJIAzwNeCuyUZJVx8r0lyfntKs2hLW3LJGe08n41yVo9q+yZ5GdJfpPkyS3/Kkk+27bziyT/MGA/vT0I67XtntsefqDMMknuDzwR2I8BgVWSFZL8Vztnzkvyupa+YzuHzm/n+co9q70uyc/bsrFesLWTfK1t44wkjx6wr4OTvKk937T9P53btvXQ/vySlmx71q+qvgScDLywbfPu0RBJPpFkXus9eGffqm9u7c/Pkmza8s9J8pUkZ7XHE5PMBV4F/Gsrz5P9HNBUVNW1wP7Aa9PpHXmzbZLTWxt1epKH96y6UZLvJPl1koPGEpO8uJ2v5yT5VGv7DgVWbWlfaPlu61nnXt/LNDUGVrPPx4AXJXlAX/rhwDFV9WjgC8BhVXU63e+mvLldKflt3zo/BravqscCxwJv6Vn2COCZwLbAQUnuO6AsTwR+17Z7Kt0VmIUk2YXuKuN2VfUY4P+1RccAb23lPZ925aZZsaq2Bd7Qk/4agKr6e7peiqPHC+Saw4DT2j63ApzmePbZA/hOVf0GuCHJVn3L9wceDDx27P+inTNHAc9v59KKwKt71rmuqrYCPgG8qaW9E/hF28bb6c7diXwB+Fg7954AXLW4FZRmuSXZng3yc7q2rN+/V9U2wKOBp/ZdLLmltT+HAx9paR+l6w17HPBPwKer6lLgk9zTS/ajvn34OaBxVdUldN/RH9i36FfAU9r3sv8E3tuzbFvgRcCWdBegt0nySOD5wBOrakvgLuBFVXUg9/SSvah3BxN8L9MUGFjNMlV1C90Xu/5epMcD/9uefw540hQ2tyFwUpLzgTcDW/Qs+1ZV/aWqrgOuBdYbsP5edAEZ7e+g4YBPBz5bVbe38t/QGtE1q+q0ludo4Ck96/xf+3s2MLc9f1KrF1X1K+D3wMMmqNvT6L4cU1V3VdXNE+TVaJrs/Hs68MmquhO6cw94ON3FgN+0PIt67n0fWGfAF0EAkqwObFBVX235/zx27kta2BJuzwbJOOn/nOTnwC/o2r3e4YVf7Pn7+Pb86cDhSc6hC+7WaP/rg3fq54CmZtD5+QDgy+nuFf4wC38vO6Wqrq+qP9G1VU8CdgS2Bs5q5+eOwEMm2e+9vpcNV43ly6z8gWDxEborcZ+dIM9UfsDsv4EPVdUJSXYADu5Z9pee53fRdy4lWYHuyt1zkvw73QfEOklWr6pbe7NOsSy9xvbdu9/xGkgth5KsQxc8PypJ0f0AawEf783Gvc+9yc6jqZ57453TnqfSollS7dkgjwXm9SYkeTBdb/TjqurGJEcBvaMfasDz+wCPb19oe7c13n79HNCEkjyErp25Fnhkz6JDgB9U1T+24aan9izr/z8ounPt6Kp626LsfsC2NEX2WM1C7erCcXT3low5nXvuM3kR3TA/gFuB8a6sPQC4oj3fZxGL8XTg3KraqKrmVtUmwFfoupd7nQy8LMlq0N2v0nqPbky7fwp4CXAaE/shXb1I8jBgY+DXE+T/Hm2IVxtvvMbUq6YR8Dy6oUKbtPNvI+B3dL2wY04GXpVkRejOPbphFnPH7p1g0c+9HeiGC94yKGNLvzzJHi3/ymPnvqR7W4Lt2UKS/BOwE/f0QI1ZA/gjcHO6+4p36Vv+/J6/P23PTwZe27PtLScqj58Dmki6CVU+CRxeVf0BTu/3spf2LXtGu+d3VbrvWj+h+67zvCQPbNteO8kmLf9fx7mN417fy4at0/LEwGr2+iDQO5vS64F9k5xH92XxgJZ+LN3NuL8YcPPswXRdzj8CrlvE/e8FfLUv7Su0G4XHVNV36IZOzGvd1GP3rexDdxPyeXTjhd81yf4+DqzQhi1+CXhpVf1lgvwHAP/Q8p/Nwt3pGn3jnX+9syB9GvgDcF6Sc4EXVtWfgX3pzvvzgb/RNXATORjYpp2rhzL5RYiXAK9v+U8H/m7y6kjLtSXRnsE9E0lcDLwYeFpVLejNUFXn0g0BvAA4ku7Laa+Vk5zZ9jk2scbraZ8BSS6km7QC4BvAP45NXtG3HT8H1GtsIokLgO/SBTf9E6dAd7/T+5L8hG4kRq8f0w2NPQf4SptN8ELgHcDJ7Vw7BXhQy38EXfv3hd6NTPC9TFOQewfDkiRJkqRFYY+VJEmSJA3JwEqSJEmShmRgJUmSJElDMrCSJEmSpCEZWEmSJEnSkAysJEmSJGlIBlaSJEmSNCQDK0mSJEkakoGVJEmSJA3JwEqSJEmShmRgJUmSJElDMrCSJEmSpCEZWEmSJEnSkAysJEmSJGlIBlaSJEmSNCQDK0mSJEkakoGVJEmSJA3JwEqSJEmShmRgpZGR5MlJfj3T5dA9klyQZIeZLockjSLbtXskOTXJy9vzFyU5eabLJC0qAystdUkuTfKnJLcluSbJZ5Pcf7L1qupHVfXwvu08fQmVaW6SSrLiklo3yVFJ3r0kyrcYZaokm073fqpqi6o6dUlvN8kWSU5OcmOSm5KcnWTXtmyHJJcv4vaWyvGQtHyyXVuyquoLVbXTdO+nV5I1kxyZ5Ooktyb5TZK39ixfpHakN1DU8sPASjNlt6q6P7AV8DjgHTNcnllhcRrQZdQ3gFOA9YAHAq8HbpnREknSxGzXRtuHgfsDjwQeADwH+O2Mlkgjx8BKM6qqrgC+DTwKIMm+SS5qV4suSfLKsby9PRVJPgdsDHyjXSF8S0vfPsnprZfj3N5hau3q0SFJftK2f3KSddviH7a/N7XtPT7JpklOS3JzkuuSfGlx65nkW0le15d2XpI92vNK8vpW5+uSfCDJfXryvqwdlxuTnJRkk55lleQ1SS4GLk4yVpdzW12e3/I9O8k57dicnuTRPdu4NMmbWpluTvKlJKu0Zesm+WZb74YkPxorW+/V1SQrJ/lIkivb4yNJVu5975K8Mcm1Sa5Ksu84x2pd4MHA/1TVHe3xk6r6cZL70Z0v67e63ZZk/STbJvlpK+NVSQ5PslLb3r2OR5KXJvlx337vvhqZZNckF7bz5Iokb1qEt1vScmx5adfa/r+crofn5iQ/TLJFz7KjknystX+3JjkzyUN7lj8jya/auocD6Vm20Gd0ulEMp7Q26Jokb2/p4372t+UTtq19Hgf8b1XdWFV/q6pfVdXxbTuD2pG1Wtu4IF3b/M0kG7b87wGeDBze8h+eAb2AWXj44xJ9bzRDqsqHj6X6AC4Fnt6ebwRcABzSXj8LeCjdB+xTgduBrdqyHYDLB22nvd4AuB7Yle6iwTPa6zlt+al0V58eBqzaXh/als0FClixZ3tfBP69bWsV4Enj1Ode67b0o4B3t+f/DJzZs+wxrWwrtdcF/ABYm65h/Q3w8rZsD2A+3VW0Femugp7es62i691ZG1i1J23TnjxbAdcC2wErAPu047dyz7H8GbB+285FwKvasvcBnwTu2x5PBjLgvXwXcAZdD9Mc4PSe93UH4M6W577tPbodWGvA8QxwMfDNVvf1+pYvdB60tK2B7dvxmdvK/4a+Y9R7PF4K/LhvG3fnAa4Cntyer0U7B3348OFj0IPlsF1rr18GrA6sDHwEOKcv7w3Atu2z+QvAsW3ZunSjEJ7X2oR/bW3EWLt392d02/5VwBtbmVcHtmvLpvLZP7BtHVDnT7f3bV9gswHL+9uRdYB/AlZrZfoy8LWe5af27muc9+PuPFN9b3ws2w97rDRTvpbkJuDHwGnAewGq6ltV9dvqnAacTPdFfipeDJxYVSdWd7XpFGAeXYM05rNV9Zuq+hNwHLDlBNv7K7AJsH5V/bmqfjxBXoDr2lWzm1rdXtiz7OvAZkk2a69fAnypqu7oyfP+qrqhqv5A10Dt1dJfCbyvqi6qqjvpjtWW6em1astvaPUa5BXAp6rqzKq6q6qOBv5C1yCNOayqrqyqG+iG4o0dm78CDwI2qaq/VndPQA3Yx4uAd1XVtVW1AHhnqyc923lX28aJwG3Aw/s30rb9D3RfMD4IXNWuhG7Wn7dnnbOr6oyqurOqLgU+RfcFZnH9Fdg8yRrVXb38+RDbkrR8WN7aNarqyKq6tar+AhwMPCbJA3qy/F9V/ay1XV/oKduuwIVVdXxV/ZWuzbt6nDI8G7i6qj7YynxrVZ3Z9j+Vz/7x2tZ+r2tlfC1wYZL5SXYZ78BU1fVV9ZWqur2qbgXeM2Dfi2JR3xstgwysNFP2qKo1q2qTqvqXsYAgyS5Jzmjd/TfRffiuO/Gm7rYJsGdfI/AkuqBgTO8H9+1046nH8xa6K4w/Szf73csm2f+6rU5rVtWawP+OLWiNznHAi9swhL2Az/Wtf1nP89/T9R6N1eujPXW6oZVrg3HWHWQT4I19x2ajnn3A+MfmA3Q9Zie34RQHjrOP9Vu5B9UB4PrWuA7ax0Kq6vKqem1VPbSV/Y/AMeNVLsnD2jCMq5PcQveFZqrnzSD/RHfu/b4NzXj8ENuStHxYrtq1JCskOTTJb9vn7qVj60yhbOvT0261C2rjtWMbMc69TlP87B+vbV1IVf2pqt5bVVvT9UYdB3w5ydrj7Hu1JJ9K8vu27x8CayZZYZx6TGZR3xstgwystMxIdz/OV4D/ohv+tSZwIj3jrvv095pcBnyutxGoqvtV1aFT2P29emCq6uqqekVVrU/Xa/TxDDez3NF0vTo7ArdX1U/7lm/U83xj4Mr2/DLglX31WrWqTp+o/H0uA97Tt43VquqLkxW6XR18Y1U9BNgN+LckOw7IeiXdl4BBdVhsVXUZ8DHa/QoMrusngF/RDd9YA3g745830AVqq429SPJ3ffs8q6p2pxvW+DW6BlaSFsksb9deCOwOPJ1usoe5LX2iz94xV9HT5iUJC7eBvS6jG0o5yFQ++8drW8dVVWNB2v3o7vkd5I10oy62a/t+Sksf23//8f9j+7taT9rdbc80fOfQDDCw0rJkJbpx2guAO1sX/ETTrV4DPKTn9eeB3ZI8s11JWyXdjcEbTmHfC4C/9W4vyZ49695I9yF519Srs7AWSP2Nbnhbf28VwJvbzbAbAQcAYzeufhJ4W9pNwUkekGTPSXbXf2z+B3hVku3SuV+SZyVZfbJyp5v0YtPW8N1CdwwGHYcvAu9IMifdzdP/SfeeLJJ2DN7Z9nmftq2X0d2/NVa3dfqGm6zeynZbkkcAr+7bbP/xOBfYIsmW6SbpOLhn/yul+w2VB7QhKmN1lqRFNZvbtdXphpRfTxcsvHcR1v0W3Wfwc9NN5vB6eoKMPt8E/i7JG9JNkrR6ku16yjDRZz+M37YuJMl/JHlcawNWaXlvAsZ+Z6z/vVkd+BPd5CBrAwf1bXKh/G2I/BV0I1dWaD1SvZN5LNHvHJoZBlZaZrQxyq+n6x24ke5q2AkTrPI+ui/yNyV5U+vZ2J3uitUCuqtcb2YK53lV3U43PvonbXvb080QdGaS21o5Dqiq3y12BTvHAH/P4IDj68DZwDl0jc5nWtm+CrwfOLYNN/glMO647+Zg4OhWl3+uqnl091kdTnds59PdHDwVmwHfpbsn6qfAx2vwb1e9m27s/3nA+cDPW9qiuoPuyud36RrMX9I13i8FqKpf0QVxl7T6rQ+8ie58uZUuiOxvOA9m4ePxG7qJNL5LN1FG/1j2lwCXtuP9Krr7HCRpkczydu0YuqF1VwAXcs/Fr0lV1XXAnsChdIHZZsBPxsl7K92kHbvRDS28mO4+XJj8sx/GaVsH7Qr4LHAdXa/WM4BnVdVtbfnB9LQjdPdrrdrynwF8p297HwWel27GwMNa2ivo3r/rgS3oJnkaMx3fObSUjc3sJWkpSLI3sH9VPakvveiGMsyfmZJJkjS72LZqabPHSlpKkqwG/AtwxEyXRZIkSUuWgZW0FCR5Jt0wjmvomVVJkiRJs4NDASVJkiRpSPZYSZIkSdKQVpzpAiwr1l133Zo7d+5MF0OSlmtnn332dVU1Z6bLsSyynZKkmTdRO2Vg1cydO5d58+bNdDEkabmW5PczXYZlle2UJM28idophwJKkiRJ0pAMrCRJkiRpSAZWkiRJkjQkAytJkiRJGpKBlSRJkiQNycBKkiRJkoZkYCVJkiRJQzKwkiRJkqQhGVhJkiRJ0pBWnOkCzDZbv/mYmS6CRtDZH9h7posgaTlhO6XFYTslTc4eK0mSJEkakoGVJEmSJA3JwEqSJEmShmRgJUmSJElDMrCSJEmSpCEZWEmSJEnSkAysJEmSJGlIBlaSJEmSNCQDK0mSJEkakoGVJEmSJA3JwEqSJEmShmRgJUmSJElDMrCSJEmSpCEZWEmSJEnSkAysJEnLpSRHJrk2yS970tZOckqSi9vftVp6khyWZH6S85Js1bPOPi3/xUn26UnfOsn5bZ3DkmSifUiSRpuBlSRpeXUUsPP/b+/uw+0o63v/vz8mIqAgD0YPEiyoqRZttRoR9bSlogg+ELRQUY8EyjGtRUV7rGJrhUo91Z9aFB9LBQV/lodyrEZFEZFgtQiEB4GglBispHAkyoOgAoLf88fcmyx39k6y96xkZyXv13Wta898575n3ZPMrFnfNffcMy52DHB+Vc0Dzm/zAAcA89prEfAx6JIk4FjgmcBewLEDidLHWtmxevuv4z0kSSPMxEqStEWqqm8At44LLwBObdOnAgcNxE+rzreBHZLsArwAOK+qbq2q24DzgP3bsu2r6qKqKuC0ceua6D0kSSPMxEqSpNUeVVU3A7S/j2zxXYEbB8qtbLG1xVdOEF/be6whyaIkS5MsXbVq1bQ3SpK04ZlYSZK0bpkgVtOIT0lVnVRV86tq/pw5c6ZaXZK0EZlYSZK02o9aNz7a31tafCWw20C5ucBN64jPnSC+tveQJI2wGUmskrwpybIk1yQ5PcnWSfZIcnEbJenMJFu1sg9p88vb8t0H1vO2Fr8uyQsG4vu32PIk3hQsSVpfi4Gxkf0WAp8fiB/WRgfcG7ijdeM7F9gvyY5t0Ir9gHPbsjuT7N1GAzxs3Lomeg9J0gjb6IlVkl2BNwDzq+rJwCzgUOA9wAltlKTbgCNblSOB26rq8cAJrRxJ9mz1nkQ30tJHk8xKMgv4CN0ITnsCr2hlJUl6QJLTgYuAJyRZmeRI4N3A85NcDzy/zQOcA6wAlgP/BPw5QFXdChwPXNpe72wxgNcCn2h1vg98ucUnew9J0gibPYPvu02SXwLbAjcDzwVe2ZafChxHN1TtgjYNcDbw4fbr3wLgjKq6B7ghyXK6oW4BllfVCoAkZ7Sy127gbZIkjZCqesUki/adoGwBR02ynlOAUyaILwWePEH8JxO9hyRptG30K1ZV9V/A+4Af0iVUdwCXAbdX1X2t2ODoSQ+MuNSW3wHszNRHaFqDoy1JkiRJGoaZ6Aq4I90VpD2ARwMPpeu2N97Y6EkbbCQmR1uSJEmSNAwzMXjF84AbqmpVVf0S+CzwbLqHLY51TRwcPemBEZfa8ofTPdBxqiM0SZIkSdIGMROJ1Q+BvZNs2+6V2pfu/qcLgINbmfEjMY2NnnQw8PXW130xcGgbNXAPYB5wCd3Nw/PaKINb0Q1wsXgjbJckSZKkLdRGH7yiqi5OcjZwOXAfcAVwEvAl4Iwkf9diJ7cqJwOfboNT3EqXKFFVy5KcRZeU3QccVVX3AyR5Hd0QuLOAU6pq2cbaPkmSJElbnhkZFbCqjgWOHRdewepR/QbL3g0cMsl63gW8a4L4OXRD40qSJEnSBjcjDwiWJEmSpM2JiZUkSZIk9WRiJUmSJEk9mVhJkiRJUk8mVpIkSZLUk4mVJEmSJPVkYiVJkiRJPZlYSZIkSVJPJlaSJEmS1JOJlSRJkiT1ZGIlSZIkST2ZWEmSJElSTyZWkiRJktSTiZUkSZIk9WRiJUmSJEk9mVhJkiRJUk8mVpIkSZLUk4mVJEmSJPVkYiVJkiRJPZlYSZIkSVJPJlaSJEmS1JOJlSRJkiT1ZGIlSZIkST2ZWEmSJElSTyZWkiRJktSTiZUkSZIk9WRiJUmSJEk9mVhJkiRJUk8mVpIkSZLUk4mVJEnjJHlTkmVJrklyepKtk+yR5OIk1yc5M8lWrexD2vzytnz3gfW8rcWvS/KCgfj+LbY8yTEbfwslScNmYiVJ0oAkuwJvAOZX1ZOBWcChwHuAE6pqHnAbcGSrciRwW1U9HjihlSPJnq3ek4D9gY8mmZVkFvAR4ABgT+AVrawkaYSZWEmStKbZwDZJZgPbAjcDzwXObstPBQ5q0wvaPG35vknS4mdU1T1VdQOwHNirvZZX1Yqquhc4o5WVJI0wEytJkgZU1X8B7wN+SJdQ3QFcBtxeVfe1YiuBXdv0rsCNre59rfzOg/FxdSaLryHJoiRLkyxdtWpV/42TJG0wJlaSJA1IsiPdFaQ9gEcDD6XrtjdejVWZZNlU42sGq06qqvlVNX/OnDnrarokaQaZWEmS9OueB9xQVauq6pfAZ4FnAzu0roEAc4Gb2vRKYDeAtvzhwK2D8XF1JotLkkaYiZUkSb/uh8DeSbZt90rtC1wLXAAc3MosBD7fphe3edryr1dVtfihbdTAPYB5wCXApcC8NsrgVnQDXCzeCNslSdqApp1YJTk6yfbpnJzk8iT7DbNxkiRtbFV1Md0gFJcDV9OdK08C3gr8RZLldPdQndyqnAzs3OJ/ARzT1rMMOIsuKfsKcFRV3d/uw3odcC7wXeCsVlaSNMJmr7vIpP6kqj7YnssxBzgC+CTw1aG0TJKkGVJVxwLHjguvoBvRb3zZu4FDJlnPu4B3TRA/Bzinf0slSZuKPl0Bx26+fSHwyar6DhPfkCtJkiRJm7U+idVlSb5Kl1idm2Q74FfDaZYkSZIkjY4+XQGPBJ4KrKiqnyfZma47oCRJkiRtUfpcsTqvqi6vqtsBquonwAnDaZYkSZIkjY4pbU5v/QAAIABJREFUX7FKsjWwLfCI9hDFsfuqtqd7kKIkSZIkbVGm0xXwT4E30iVRl7E6sfop8JEhtUuSJEmSRsaUuwJW1Qerag/gzVX12Krao72eUlUfXp91JNkhydlJvpfku0melWSnJOclub793bGVTZITkyxPclWSpw2sZ2Erf32ShQPxpye5utU5sT3gUZIkSZI2iGnfY1VVH0ry7CSvTHLY2Gs9q38Q+EpVPRF4Ct0DEo8Bzq+qecD5bR7gALqn1c8DFgEfA0iyE90zRp5J91yRY8eSsVZm0UC9/ae7nZIkSZK0LtMeFTDJp4HHAVcC97dwAaeto972wO8DhwNU1b3AvUkWAPu0YqcCS+iecr8AOK2qCvh2u9q1Syt7XlXd2tZ7HrB/kiXA9lV1UYufBhwEfHm62ypJkiRJa9NnuPX5wJ4t4ZmKxwKrgE8meQrdfVpHA4+qqpsBqurmJI9s5XcFbhyov7LF1hZfOUF8DUkW0V3Z4jGPecwUN0OSJEmSOn2GW78G+G/TqDcbeBrwsar6XeBnrO72N5GJ7o+qacTXDFadVFXzq2r+nDlz1t5qSZIkSZpEnytWjwCuTXIJcM9YsKoOXEe9lcDKqrq4zZ9Nl1j9KMku7WrVLsAtA+V3G6g/F7ipxfcZF1/S4nMnKC9JkiRJG0SfxOq46VSqqv+b5MYkT6iq64B9gWvbayHw7vb3863KYuB1Sc6gG6jijpZ8nQv874EBK/YD3lZVtya5M8newMXAYcCHpreJkiRJkrRu006squrCJL8BzKuqryXZFpi1ntVfD3wmyVbACuAIum6JZyU5EvghcEgrew7wQmA58PNWlpZAHQ9c2sq9c2wgC+C1wKeAbegGrXDgCkmSJEkbTJ9RAV9DN/DDTnSjA+4KfJzuCtRaVdWVdINfjLdG3TY4xlGTrOcU4JQJ4kuBJ6+rHZIkSZI0DH0GrzgKeA7wU4Cquh545FprSJIkSdJmqE9idU97BhUASWYzyeh7kiRJkrQ565NYXZjkr4Btkjwf+BfgC8NpliRJkiSNjj6J1TF0D/q9GvhTukEm3j6MRkmSJEnSKOkzKuCvgH9qL0mSJEnaYk05sUpyVlX9cZKrmeCeqqr6naG0TJIkSZJGxHSuWB3d/r54mA2RJEmSpFE15XusqurmNvnnVfWfgy/gz4fbPEmSJEna9PUZvOL5E8QO6LE+SZIkSRpJ07nH6rV0V6Yem+SqgUXbAd8aVsMkSZIkaVRM5x6rfwa+DPw93ZDrY+6sqluH0ipJkiRJGiFTTqyq6g7gDuAVAEkeCWwNPCzJw6rqh8NtoiRJkiRt2qZ9j1WSlyS5HrgBuBD4Ad2VLEmSJEnaovQZvOLvgL2B/6iqPYB98R4rSZIkSVugPonVL6vqJ8CDkjyoqi4AnjqkdkmSJEnSyJjO4BVjbk/yMOAbwGeS3ALcN5xmSZIkSdLo6HPFagHwc+BNwFeA7wMvGUajJEmSJGmUTOuKVZKDgMcDV1fVucCpQ22VJEmSJI2QKV+xSvJRuqtUOwPHJ/mbobdKkiRJkkbIdLoC/j7w3Kp6G7APcNBQWyRJ0gxLskOSs5N8L8l3kzwryU5Jzktyffu7YyubJCcmWZ7kqiRPG1jPwlb++iQLB+JPT3J1q3NikszEdkqShmc6idW9VXU/QFX9HPBkIEna3HwQ+EpVPRF4CvBd4Bjg/KqaB5zf5gEOAOa11yLgYwBJdgKOBZ4J7AUcO5aMtTKLBurtvxG2SZK0AU3nHqsnJrmqTQd4XJsPUFX1O0NrnSRJG1mS7el6ZxwOUFX3AvcmWUDXUwO6e4uXAG+lG8zptKoq4Nvtatcurex5VXVrW+95wP5JlgDbV9VFLX4aXe+PL2+EzZMkbSDTSax+a+itkCRp0/FYYBXwySRPAS4DjgYeVVU3A1TVzUke2crvCtw4UH9li60tvnKC+BqSLKK7ssVjHvOYflslSdqgppxYVdV/boiGSJK0iZgNPA14fVVdnOSDrO72N5GJusTXNOJrBqtOAk4CmD9//oRlJEmbhj7PsZIkaXO0ElhZVRe3+bPpEq0ftS5+tL+3DJTfbaD+XOCmdcTnThCXJI0wEytJkgZU1f8FbkzyhBbaF7gWWAyMjey3EPh8m14MHNZGB9wbuKN1GTwX2C/Jjm3Qiv2Ac9uyO5Ps3UYDPGxgXZKkETXlroBJzq+qfZO8p6reuiEaJUnSDHs98JkkWwErgCPofow8K8mRwA+BQ1rZc4AXAsuBn7eyVNWtSY4HLm3l3jk2kAXwWuBTwDZ0g1Y4cIUkjbjpDF6xS5I/AA5Mcgbj+opX1eVDaZkkSTOkqq4E5k+waN8JyhZw1CTrOQU4ZYL4UuDJPZspSdqETCexegfdTbxzgX8Yt6yA5/ZtlCRJkiSNkumMCng2cHaSv6mq4zdAmyRJkiRppEznihUAVXV8kgPpHqIIsKSqvjicZkmSJEnS6Jj2qIBJ/p7ugYnXttfRLSZJkiRJW5RpX7ECXgQ8tap+BZDkVOAK4G3DaJgkSZIkjYq+z7HaYWD64T3XJUmSJEkjqc8Vq78HrkhyAd2Q67+PV6skSZIkbYH6DF5xepIlwDPoEqu3tqfVS5IkSdIWpc8VK6rqZmDxkNoiSZIkSSOp7z1WkiRJkrTFM7GSJEmSpJ6mlVgleVCSa4bdGEmSJEkaRdNKrNqzq76T5DFDbo8kSZIkjZw+g1fsAixLcgnws7FgVR3Yu1WSJEmSNEL6JFZ/O7RWSJIkSdII6/McqwuT/AYwr6q+lmRbYNbwmiZJkiRJo2HaowImeQ1wNvCPLbQr8Lkp1J+V5IokX2zzeyS5OMn1Sc5MslWLP6TNL2/Ldx9Yx9ta/LokLxiI799iy5McM91tlCRJkqT10We49aOA5wA/Baiq64FHTqH+0cB3B+bfA5xQVfOA24AjW/xI4LaqejxwQitHkj2BQ4EnAfsDH23J2izgI8ABwJ7AK1pZSZIkSdog+iRW91TVvWMzSWYDtT4Vk8wFXgR8os0HeC7dFTCAU4GD2vSCNk9bvm8rvwA4o6ruqaobgOXAXu21vKpWtPad0cpKkiRJ0gbRJ7G6MMlfAdskeT7wL8AX1rPuB4C3AL9q8zsDt1fVfW1+JV3XQtrfGwHa8jta+Qfi4+pMFl9DkkVJliZZumrVqvVsuiRJkiT9uj6J1THAKuBq4E+Bc4C3r6tSkhcDt1TVZYPhCYrWOpZNNb5msOqkqppfVfPnzJmzllZLkiRJ0uT6jAr4qySnAhfTJS7XVdX6dAV8DnBgkhcCWwPb013B2iHJ7HZVai5wUyu/EtgNWNm6Gz4cuHUgPmawzmRxSZIkSRq6PqMCvgj4PnAi8GFgeZID1lWvqt5WVXOrane6wSe+XlWvAi4ADm7FFgKfb9OL2zxt+ddbArcYOLSNGrgHMA+4BLgUmNdGGdyqvcfi6W6nJEmSJK1LnwcEvx/4w6paDpDkccCXgC9Pc31vBc5I8nfAFcDJLX4y8Okky+muVB0KUFXLkpwFXAvcBxxVVfe3trwOOJfuuVqnVNWyabZJkiRJktapT2J1y1hS1awAbpnKCqpqCbCkTa+gG9FvfJm7gUMmqf8u4F0TxM+hu+dLkiRJkja4KSdWSV7WJpclOQc4i+4eq0PouuFJkiRJ0hZlOlesXjIw/SPgD9r0KmDH3i2SJEmSpBEz5cSqqo7YEA2RJEmSpFE17Xus2kh8rwd2H1xPVR3Yv1mSJEmSNDr6DF7xOboR+74A/Go4zZEkSZKk0dMnsbq7qk4cWkskSZIkaUT1Saw+mORY4KvAPWPBqrq8d6skSZIkaYT0Sax+G3g18FxWdwWsNi9JkiRJW4w+idVLgcdW1b3DaowkSZIkjaIH9aj7HWCHYTVEkiRJkkZVn8TqUcD3kpybZPHYa1gNkyRpJiWZleSKJF9s83skuTjJ9UnOTLJViz+kzS9vy3cfWMfbWvy6JC8YiO/fYsuTHLOxt02SNHx9ugIeO7RWSJK06Tka+C6wfZt/D3BCVZ2R5OPAkcDH2t/bqurxSQ5t5V6eZE/gUOBJwKOBryX5zbaujwDPB1YClyZZXFXXbqwNkyQN37SvWFXVhRO9htk4SZJmQpK5wIuAT7T50A3OdHYrcipwUJte0OZpy/dt5RcAZ1TVPVV1A7Ac2Ku9llfVinaf8hmtrCRphE07sUpyZ5KfttfdSe5P8tNhNk6SpBnyAeAtrB71dmfg9qq6r82vBHZt07sCNwK05Xe08g/Ex9WZLL6GJIuSLE2ydNWqVX23SZK0AfW5YrVdVW3fXlsDfwR8eHhNkyRp40vyYuCWqrpsMDxB0VrHsqnG1wxWnVRV86tq/pw5c9bSaknSTOtzj9WvqarPeQOuJGkz8BzgwCQvBLamu8fqA8AOSWa3q1JzgZta+ZXAbsDKJLOBhwO3DsTHDNaZLC5JGlF9ugK+bOB1cJJ3M8kvbpIkjYqqeltVza2q3ekGn/h6Vb0KuAA4uBVbCHy+TS9u87TlX6+qavFD26iBewDzgEuAS4F5bZTBrdp7OKquJI24PlesXjIwfR/wA7z5VpK0+XorcEaSvwOuAE5u8ZOBTydZTnel6lCAqlqW5CzgWrrz5FFVdT9AktcB5wKzgFOqatlG3RJJ0tBNO7GqqiOG2RBJkjY1VbUEWNKmV9CN6De+zN3AIZPUfxfwrgni5wDnDLGpkqQZNuXEKsk71rK4qur4Hu2RJEmSpJEznStWP5sg9lC6ByTuDJhYSZIkSdqiTDmxqqr3j00n2Y7uyfRH0D3g8P2T1ZMkSZKkzdW07rFKshPwF8Cr6J42/7Squm2YDZMkSZKkUTGde6zeC7wMOAn47aq6a+itkiRJkqQRMp3nWP0v4NHA24Gbkvy0ve5M8tPhNk+SJEmSNn3Tucdq2g8VliRJkqTNkUmSJEmSJPVkYiVJkiRJPZlYSZIkSVJPJlaSJEmS1JOJlSRJkiT1ZGIlSZIkST2ZWEmSJElSTyZWkiRJktSTiZUkSZIk9WRiJUmSJEk9mVhJkiRJUk8mVpIkSZLUk4mVJEmSJPVkYiVJkiRJPZlYSZIkSVJPJlaSJEmS1NPsmW6ApE3L0//ytJlugkbQZe89bKabIEnSjPKKlSRJkiT1tNETqyS7JbkgyXeTLEtydIvvlOS8JNe3vzu2eJKcmGR5kquSPG1gXQtb+euTLByIPz3J1a3OiUmysbdTkiRJ0pZjJq5Y3Qf8r6r6LWBv4KgkewLHAOdX1Tzg/DYPcAAwr70WAR+DLhEDjgWeCewFHDuWjLUyiwbq7b8RtkuSJEnSFmqjJ1ZVdXNVXd6m7wS+C+wKLABObcVOBQ5q0wuA06rzbWCHJLsALwDOq6pbq+o24Dxg/7Zs+6q6qKoKOG1gXZIkSZI0dDN6j1WS3YHfBS4GHlVVN0OXfAGPbMV2BW4cqLayxdYWXzlBfKL3X5RkaZKlq1at6rs5kiRJkrZQM5ZYJXkY8H+AN1bVT9dWdIJYTSO+ZrDqpKqaX1Xz58yZs64mS5IkSdKEZiSxSvJguqTqM1X12Rb+UevGR/t7S4uvBHYbqD4XuGkd8bkTxCVJkiRpg5iJUQEDnAx8t6r+YWDRYmBsZL+FwOcH4oe10QH3Bu5oXQXPBfZLsmMbtGI/4Ny27M4ke7f3OmxgXZIkSZI0dDPxgODnAK8Grk5yZYv9FfBu4KwkRwI/BA5py84BXggsB34OHAFQVbcmOR64tJV7Z1Xd2qZfC3wK2Ab4cntJkiRJ0gax0ROrqvomE98HBbDvBOULOGqSdZ0CnDJBfCnw5B7NlCRJkqT1NqOjAkqStKnxQfaSpOkwsZIk6df5IHtJ0pSZWEmSNMAH2UuSpsPESpKkSfgge0nS+jKxkiRpAj7IXpI0FSZWkiSN44PsJUlTZWIlSdIAH2QvSZqOmXhAsCRJmzIfZC9JmjITK0mSBvgge0nSdNgVUJIkSZJ6MrGSJEmSpJ5MrCRJkiSpJxMrSZIkSerJxEqSJEmSejKxkiRJkqSeTKwkSZIkqScTK0mSJEnqycRKkiRJknoysZIkSZKknkysJEmSJKknEytJkiRJ6snESpIkSZJ6MrGSJEmSpJ5MrCRJkiSpJxMrSZIkSerJxEqSJEmSejKxkiRJkqSeTKwkSZIkqScTK0mSJEnqycRKkiRJknoysZIkSZKknkysJEmSJKknEytJkiRJ6snESpIkSZJ6MrGSJEmSpJ5MrCRJkiSpJxMrSZIkSerJxEqSJEmSejKxkiRJkqSeTKwkSZIkqScTK0mSJEnqycRKkiRJknqaPdMNkCRJktbX0//ytJlugkbQZe89bIO/h1esJEmSJKmnzTaxSrJ/kuuSLE9yzEy3R5KkQZ6nJGnzslkmVklmAR8BDgD2BF6RZM+ZbZUkSR3PU5K0+dksEytgL2B5Va2oqnuBM4AFM9wmSZLGeJ6SpM3M5jp4xa7AjQPzK4Fnji+UZBGwqM3eleS6jdC2LdkjgB/PdCM2RXnfwplugtaP+/AkhrgP/8awVrSJ8zy1afIYn4TnqZHhPjyJjXGe2lwTq0wQqzUCVScBJ2345gggydKqmj/T7ZCmy31YQ+R5ahPkMa5R5z48szbXroArgd0G5ucCN81QWyRJGs/zlCRtZjbXxOpSYF6SPZJsBRwKLJ7hNkmSNMbzlCRtZjbLroBVdV+S1wHnArOAU6pq2Qw3S3Zn0ehzH9ZQeJ7aZHmMa9S5D8+gVK3RpVuSJEmSNAWba1dASZIkSdpoTKwkSZIkqScTqxGXpJK8f2D+zUmOW0edg5LsOcmy45K8eVzsB0keMZQGT96mw5M8egOs99+HvU7NnCR3jZs/PMmHN/B77p7klRtgvX+W5LBhr1faFI3auWrssybJo5OcPYx1DstE265Nx4Y8Tw3+3yd5Z5LnDWO9wzJ+27dEJlaj7x7gZVM8mRwETHiymglJZgGHA0NPrKrq2cNep7YcSWYDuwNDT6yq6uNVddqw1yttokbyXFVVN1XVwTPZBmkiVfWOqvraTLdDv87EavTdRzcCzJvGL0jyG0nOT3JV+/uYJM8GDgTem+TKJI9b3zdKcnySowfm35XkDUn2SfKNJP+a5NokH0/yoFZmvyQXJbk8yb8keViL/yDJO5J8E3gFMB/4TGvTNkmenuTCJJclOTfJLq3ekiTvSXJJkv9I8nst/qQWu7Jt77wWH/vV8cwkLxxo+6eS/FGSWUnem+TSVu9Pp/jvr01Aku2S3JDkwW1++7aPPbjtMx9I8u9JrkmyVyvz0CSntP/7K5IsaPHD2776BeCrwLuB32v71psm22facbAkydlJvpfkM0nSlr27HRtXJXlfix3XfrX/rSSXDGzL7kmuatMTHgfSCNpo56q2zs+142ZZkkUD8bvaues7Sb6d5FEtvkc7V12a5PiB8rsnuaZNz0ryviRXt7a+vsXf0epdk+SkgeN+ss+e45J8OsnXk1yf5DUD7/eXA58tfzsQ/+sk1yX5GvCEqfxbaNOR5CVJLm7nnK8N7H/HtfPRkiQrkrxhoM6E//fte8zBbfoZbT/7TrrvQtu1ffff0n3/urwdU2Pnqsm+s92V5P2t/PlJ5rT445J8pR1T/5bkiS0+4XGzRasqXyP8Au4Ctgd+ADwceDNwXFv2BWBhm/4T4HNt+lPAwZOs7zjgv4ArB173Ao+g++X+8lbuQcD3gZ2BfYC7gcfSDRt8HnBwq/MN4KGtzluBd7TpHwBvGXjfJcD8Nv1g4N+BOW3+5XRDEY+Ve3+bfiHwtTb9IeBVbXorYJuxf5/296XAqQPLbwS2ARYBb2/xhwBLgT1m+v/V16T7+/3j9s0fAh9uyz4JHNSmFw3sJ0uAf2rTvw9c06b/N/A/2vQOwH8AD6W7eroS2Kkt2wf44kAbJtxnWrk76B70+iDgIuC/AzsB17F6FNYd2t/jgDe36SuBx7bptwJvX9tx4MvXqL3YiOeqtnzs+N0GuAbYuc0X8JI2/f8NHMuLgcPa9FED547dBz4zXgv8H2D2uPfYaaBdnx5Y/2SfPccB32ltewTd+ejRwH50yWfaZ8gXW72nA1cD27Z/w+Vjnx2+Nr0Xaz9P7ThwLvifrD5PHdc+7x/S9omftHPApP/3Y8cH3XeaFcAzWnx7uscpbQts3WLzgKVteh8m+M42cHyMfZd6x0C7zwfmtelnAl9f23GzJb82y+dYbWmq6qdJTgPeAPxiYNGzgJe16U/TnUTWxwlV9b6xmSQ/aO/zgyQ/SfK7wKOAK6rqJ+3HuUuqakUrfzrdF8q76bpxfKuV2Yruy+aYMyd5/ycATwbOa/VmATcPLP9s+3sZ3UmPtt6/TjIX+GxVXT9unV8GTkzyEGB/4BtV9Ysk+wG/M/arD90Jfx5ww+T/PJpBv6iqp47NJDmc7monwCeAtwCfA44AXjNQ73SAqvpGuqtZO9B9iTkwq+9V2Bp4TJs+r6punaQNk+0z99IdBytb266k2z+/TXcsfCLJl+i+LI13FvDHdFfHXt5e6zoOpJGysc5VzRuSvLRN70Z3jP6E7jgdOwYvA57fpp8D/NFAG94zwfs9D/h4Vd3XtmfsM+IPk7yF7ovsTsAyumQRJv7sAfh8Vf0C+EWSC4C96M6b+wFXtDIPa+3eDvjXqvp5204fJL1pW9t5ai5wZrreB1vx6981vlRV9wD3JLmF7nvW77Hu//snADdX1aXQHWet7EOBDyd5Kl2y95sDdSb6znY28CtWfzf7/4HPputp9GzgX9q5CLoEENbvuNmimFhtPj4AXE73q/1khvHQsk/Q/aL/34BT1rLuovvV7byqesUk6/rZJPEAy6rqWZMsv6f9vZ+2D1fVPye5GHgRcG6S/1lVX3+gMVV3J1kCvIDuS+vpA+/1+qo6d5L30oioqm+1rg9/AMyqqmsGF48vTvd//0dVdd3ggiTPZPJ9EybZZ5Lsw+p9E9r+Wd2DYPcC9gUOBV4HPHfcOs+kO2l9ttuUuj7Jb7P240AaRRv8XNWOxecBz6qqn7fP/q3b4l9W+3mdgXPIer5vxpdJsjXwUboeFzemG5Bj64EiE332TBYP8PdV9Y/j3uON69E2jYYPAf9QVYvbfnrcwLI1zh9tesr7ZfMm4EfAU+iugN49sGyy/XK8anVvH0wW17PuFsl7rDYT7Zezs4AjB8L/TvdFDuBVwDfb9J10v4BNx7/SXfF5BjD4xXKv1tf2QXSJyzfpfql/TpLHAyTZNslvjl/hBG26DpiT5Fmt3oOTPGltjUryWGBFVZ1Id2n6dyYodgbdlYzfG2j7ucBrs/renN9sv/JoNJ1GlzSP/9L2coAk/x24o6ruoPu/f33ywP0QvzvJOscfL1PaZ9qvfQ+vqnOANwJrnJyq6vt0J9K/YfWvhVM+DqRN3UY6Vz0cuK0lVU8E9l6POt8a14aJfBX4s3SD2pBkJ1YnUT9ux/r4gS4m+uwBWJBk6yRj3ekvpfts+ZOsvhd51ySPpOtS/9J09x9vB7xkPbZHm6aH03VhBVi4HuXX5//+e8CjkzwDHrjneHZ7r5ur6lfAq+l6PYyZ6DsbdHnB2D78SuCb7QrYDUkOaetPkqe0Mutz3GxRTKw2L++n65s75g3AEeluhH81MDbwxBnAX6a7eXJKNwRX1b3ABcBZVXX/wKKL6LoxXUN3aftfq2oV3dWt01sbvg08cZJVfwr4eOs+NYvuwH5Pku/Q9VFe1+h+LweuafWfSPcFe7yv0vVX/1rbDuiuwF0LXJ7uBuV/xCu5o+wzdH3YTx8Xvy3d0PsfZ/UXuuPp+rBf1f7vJ7vx9irgvnQ3Bb+Jqe8z2wFfbMfAhUxw835zJvA/6L50jh1rUz0OpFGwoc9VXwFmt/UdT3fuWZejgaOSXEr3hXQin6C7X+aqdky+sqpuB/6J7j6Yz9ElSIMm+uwBuAT4Umvb8dWNPvhV4J+Bi5JcTdc1a7uqupzu8+FKunu8/m09tkebpuPoeif8G/DjdRVen//7dq54OfChtl+eR5fwfxRYmOTbdN0AB3tirPGdrcV/BjwpyWV0PSve2eKvAo5s618GLGjx9TlutihZfUVcWrf268blwCFj9zG1y9lvrqoXz2TbpHbf04KqevVAbAnd/rl0xhomaYsz2WdP6y541+D9YdLGsrbvbEnuqqqHbfxWbT78ZV7rLd2DGr9IdzVq/OAQ0oxK8iHgALrRIiVJkjYqr1hJkiRJUk/eYyVJkiRJPZlYSZIkSVJPJlaSJEmS1JOJlbSJSfLXSZYluSrJlUmemeSNSbZdj7rrVU6SpOnyPCVNzMErpE1IexjsPwD7VNU9SR4BbEX3AM35VbXW514k+cH6lJMkaTo8T0mT84qVtGnZBfhxVd0D0E48BwOPBi5IcgFAko8lWdp+MfzbFnvDBOXuGltxkoOTfKpNH5LkmvbQ229sxO2TJI02z1PSJLxiJW1CkjwM+CawLfA14MyqunD8L3xJdqqqW5PMAs4H3lBVV01Q7oGH/bWH5764qg5PcjWwf1X9V5Idqur2jb2tkqTR43lKmpxXrKRNSFXdBTwdWASsAs5McvgERf84yeXAFcCTgD2n+FbfAj6V5DXArOm3WJK0JfE8JU1u9kw3QNKvq6r7gSXAkvaL3cLB5Un2AN4MPKOqbmvdJraebHUD0w+Uqao/S/JM4EXAlUmeWlU/Gd5WSJI2V56npIl5xUrahCR5QpJ5A6GnAv8J3Als12LbAz8D7kjyKOCAgfKD5QB+lOS3kjwIeOnA+zyuqi6uqncAPwZ2G/7WSJI2N56npMl5xUratDwM+FCSHYD7gOV03S1eAXw5yc1V9YdJrgCWASvoukuMOWmwHHAM8EXgRuCatn6A97YTY+j6vn9nw2+aJGkz4HlKmoSDV0iSJElST3YFlCRJkqSeTKwkSZIkqScTK0mSJEnqycRKkiRJknoysZIkSZKknkwjfU7GAAAAFUlEQVSsJEmSJKknEytJkiRJ6un/AaPUqGjGznvUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [14, 10]) # larger figure size for subplots\n",
    "\n",
    "# alcoholism column\n",
    "plt.subplot(2, 2, 1) # 2 rows, 2 cols, subplot 1\n",
    "sns.countplot(data = df, x = 'alcoholism', color = base_color)\n",
    "plt.title('Paitents Alcoholism Status')\n",
    "plt.xticks([0, 1], ['Not an Alcoholic', 'Alcoholic'])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Number of Patients');\n",
    "\n",
    "# diabetes column\n",
    "plt.subplot(2, 2, 2) # 2 row, 2 cols, subplot 2\n",
    "sns.countplot(data = df, x = 'diabetes', color = base_color)\n",
    "plt.title('Paitents Diabetes Status')\n",
    "plt.xticks([0, 1], ['Not Diabetic', 'Diabetic'])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('');\n",
    "\n",
    "# hipertension column\n",
    "plt.subplot(2, 2, 3) # 2 rows, 2 cols, subplot 3\n",
    "sns.countplot(data = df, x = 'hipertension', color = base_color)\n",
    "plt.title('Paitents Hypertension Status')\n",
    "plt.xticks([0, 1], ['Not Hypertensive', 'Hypertensive'])\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Number of Patients');\n",
    "\n",
    "# handcap column\n",
    "plt.subplot(2, 2, 4) # 2 row, 2 cols, subplot 4\n",
    "sns.countplot(data = df, x = 'handcap', color = base_color)\n",
    "plt.title('Paitents Handicap Status')\n",
    "plt.xticks([0, 1], ['Not Handicapped', 'Handicapped'])\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients didn't suffer from alcoholism, diabetes, hypertension, or any handicap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Useful Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### days_between (between scheduled_day and appointment_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>days_between</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>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender scheduled_day appointment_day  days_between  age      neighbourhood  \\\n",
       "0      F    2016-04-29      2016-04-29             0   62    JARDIM DA PENHA   \n",
       "1      M    2016-04-29      2016-04-29             0   56    JARDIM DA PENHA   \n",
       "2      F    2016-04-29      2016-04-29             0   62      MATA DA PRAIA   \n",
       "3      F    2016-04-29      2016-04-29             0    8  PONTAL DE CAMBURI   \n",
       "4      F    2016-04-29      2016-04-29             0   56    JARDIM DA PENHA   \n",
       "\n",
       "   scholarship  hipertension  diabetes  alcoholism  handcap  no_show  \n",
       "0            0             1         0           0        0        0  \n",
       "1            0             0         0           0        0        0  \n",
       "2            0             0         0           0        0        0  \n",
       "3            0             0         0           0        0        0  \n",
       "4            0             1         1           0        0        0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate days between scheduled_day and appointment_day\n",
    "days_between = (df['appointment_day'] - df['scheduled_day']).dt.days\n",
    "# insert a new column (days_between) before column 3\n",
    "df.insert(3, 'days_between', days_between)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>days_between</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
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       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27033</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>-1</td>\n",
       "      <td>38</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>-1</td>\n",
       "      <td>19</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>-1</td>\n",
       "      <td>22</td>\n",
       "      <td>CONSOLAÇÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>-6</td>\n",
       "      <td>81</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>TABUAZEIRO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender scheduled_day appointment_day  days_between  age  neighbourhood  \\\n",
       "27033      M    2016-05-10      2016-05-09            -1   38    RESISTÊNCIA   \n",
       "55226      F    2016-05-18      2016-05-17            -1   19  SANTO ANTÔNIO   \n",
       "64175      F    2016-05-05      2016-05-04            -1   22     CONSOLAÇÃO   \n",
       "71533      F    2016-05-11      2016-05-05            -6   81  SANTO ANTÔNIO   \n",
       "72362      M    2016-05-04      2016-05-03            -1    7     TABUAZEIRO   \n",
       "\n",
       "       scholarship  hipertension  diabetes  alcoholism  handcap  no_show  \n",
       "27033            0             0         0           0        1        1  \n",
       "55226            0             0         0           0        1        1  \n",
       "64175            0             0         0           0        0        1  \n",
       "71533            0             0         0           0        0        1  \n",
       "72362            0             0         0           0        0        1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sure there are no negative values\n",
    "negative_days = df.query('days_between < 0')\n",
    "negative_days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it's not right to have a scheduled_day after appointment_day these rows will be droped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>days_between</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>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [gender, scheduled_day, appointment_day, days_between, age, neighbourhood, scholarship, hipertension, diabetes, alcoholism, handcap, no_show]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the rows with negative days\n",
    "df.drop(negative_days.index, inplace=True)\n",
    "# check again\n",
    "df.query('days_between < 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows were dropped successfully. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### waiting period (classify days_between to 4 groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110521.000000\n",
       "mean         10.184345\n",
       "std          15.255153\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           4.000000\n",
       "75%          15.000000\n",
       "max         179.000000\n",
       "Name: days_between, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the min, 25%, 50%, 75%, max days_between values\n",
    "df['days_between'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>days_between</th>\n",
       "      <th>waiting_period</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
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       "      <th>diabetes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>F</td>\n",
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       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender scheduled_day appointment_day  days_between waiting_period  age  \\\n",
       "0      F    2016-04-29      2016-04-29             0         0 days   62   \n",
       "1      M    2016-04-29      2016-04-29             0         0 days   56   \n",
       "2      F    2016-04-29      2016-04-29             0         0 days   62   \n",
       "3      F    2016-04-29      2016-04-29             0         0 days    8   \n",
       "4      F    2016-04-29      2016-04-29             0         0 days   56   \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",
       "\n",
       "   handcap  no_show  \n",
       "0        0        0  \n",
       "1        0        0  \n",
       "2        0        0  \n",
       "3        0        0  \n",
       "4        0        0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bin edges that will be used to \"cut\" the data into groups\n",
    "bin_edges = [-1, 0, 4, 15, 179]\n",
    "# labels for the four waiting period groups\n",
    "bin_names = ['0 days','1-4 days','5-15 days','16 days and above']\n",
    "waiting_period = pd.cut(df['days_between'], bin_edges, labels=bin_names)\n",
    "# insert a new column (waiting_period) before column 4\n",
    "df.insert(4, 'waiting_period', waiting_period)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I won't need *days_between* or *scheduled_day* columns anymore so I will drop them later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### week_day (day of the oppointment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>week_day</th>\n",
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       "      <th>age</th>\n",
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       "      <th>2</th>\n",
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       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender scheduled_day appointment_day  week_day  days_between waiting_period  \\\n",
       "0      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "1      M    2016-04-29      2016-04-29         4             0         0 days   \n",
       "2      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "3      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "4      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "\n",
       "   age      neighbourhood  scholarship  hipertension  diabetes  alcoholism  \\\n",
       "0   62    JARDIM DA PENHA            0             1         0           0   \n",
       "1   56    JARDIM DA PENHA            0             0         0           0   \n",
       "2   62      MATA DA PRAIA            0             0         0           0   \n",
       "3    8  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4   56    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   handcap  no_show  \n",
       "0        0        0  \n",
       "1        0        0  \n",
       "2        0        0  \n",
       "3        0        0  \n",
       "4        0        0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find which day of the week the appointment was on\n",
    "week_day = df.appointment_day.dt.weekday \n",
    "# insert a new column (week_day) before column 3\n",
    "df.insert(3, 'week_day', week_day)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I won't need *appointment_day* column anymore so I will drop it later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### age_group (classify age to 4 groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110521.000000\n",
       "mean         37.089386\n",
       "std          23.109885\n",
       "min           0.000000\n",
       "25%          18.000000\n",
       "50%          37.000000\n",
       "75%          55.000000\n",
       "max         115.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the min, 25%, 50%, 75%, max age values\n",
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>week_day</th>\n",
       "      <th>days_between</th>\n",
       "      <th>waiting_period</th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</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>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>62</td>\n",
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       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>56</td>\n",
       "      <td>56 and above</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>62</td>\n",
       "      <td>56 and above</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>8</td>\n",
       "      <td>0-18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>56</td>\n",
       "      <td>56 and above</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender scheduled_day appointment_day  week_day  days_between waiting_period  \\\n",
       "0      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "1      M    2016-04-29      2016-04-29         4             0         0 days   \n",
       "2      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "3      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "4      F    2016-04-29      2016-04-29         4             0         0 days   \n",
       "\n",
       "   age     age_group      neighbourhood  scholarship  hipertension  diabetes  \\\n",
       "0   62  56 and above    JARDIM DA PENHA            0             1         0   \n",
       "1   56  56 and above    JARDIM DA PENHA            0             0         0   \n",
       "2   62  56 and above      MATA DA PRAIA            0             0         0   \n",
       "3    8          0-18  PONTAL DE CAMBURI            0             0         0   \n",
       "4   56  56 and above    JARDIM DA PENHA            0             1         1   \n",
       "\n",
       "   alcoholism  handcap  no_show  \n",
       "0           0        0        0  \n",
       "1           0        0        0  \n",
       "2           0        0        0  \n",
       "3           0        0        0  \n",
       "4           0        0        0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bin edges that will be used to \"cut\" the data into groups\n",
    "bin_edges = [-1, 18, 37, 55, 115]\n",
    "# labels for the four age groups\n",
    "bin_names = ['0-18','19-37','38-55','56 and above']\n",
    "age_group = pd.cut(df['age'], bin_edges, labels=bin_names)\n",
    "# insert a new column (age_group) before column 7\n",
    "df.insert(7, 'age_group', age_group)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I won't need *age* column anymore so I will drop it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop columns that are no longer needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>week_day</th>\n",
       "      <th>waiting_period</th>\n",
       "      <th>age_group</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>no_show</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days</td>\n",
       "      <td>56 and above</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days</td>\n",
       "      <td>56 and above</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days</td>\n",
       "      <td>56 and above</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0-18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days</td>\n",
       "      <td>56 and above</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  week_day waiting_period     age_group      neighbourhood  \\\n",
       "0      F         4         0 days  56 and above    JARDIM DA PENHA   \n",
       "1      M         4         0 days  56 and above    JARDIM DA PENHA   \n",
       "2      F         4         0 days  56 and above      MATA DA PRAIA   \n",
       "3      F         4         0 days          0-18  PONTAL DE CAMBURI   \n",
       "4      F         4         0 days  56 and above    JARDIM DA PENHA   \n",
       "\n",
       "   scholarship  hipertension  diabetes  alcoholism  handcap  no_show  \n",
       "0            0             1         0           0        0        0  \n",
       "1            0             0         0           0        0        0  \n",
       "2            0             0         0           0        0        0  \n",
       "3            0             0         0           0        0        0  \n",
       "4            0             1         1           0        0        0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['scheduled_day', 'appointment_day', 'days_between', 'age'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### age_group column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# plot a bar chart\n",
    "sns.countplot(data = df, x = 'age_group', color = base_color)\n",
    "plt.title('Paitents Age Groups')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Number of Patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Patients age groups were about the same, with the age group (0-18) being slightly higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### waiting_period column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# plot a bar chart\n",
    "sns.countplot(data = df, x = 'waiting_period', color = base_color)\n",
    "plt.title('Paitents Waiting Periods')\n",
    "plt.xlabel('Waiting Period')\n",
    "plt.ylabel('Number of Patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients scheduled their appointments on the same day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### week_day column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts = df.week_day.value_counts()\n",
    "weekday_labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "\n",
    "# plot a line plot\n",
    "plt.figure(figsize = [8, 4])\n",
    "sns.lineplot(x = weekday_counts.index,  y = weekday_counts, marker='o')\n",
    "plt.xticks(np.arange(0, 6+1, 1), weekday_labels)\n",
    "plt.title('Paitents Appointments Based on The Day of The Week')\n",
    "plt.xlabel('Day of The Week')\n",
    "plt.ylabel('Number of Appointments');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most patients prefered to have their appointments on working days (Monday-Friday) especially Tuesday and Wednesday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1: Highest five neighbourhoods in terms of the number of no-show appointments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# no show appointments\n",
    "df_no_show = df[df.no_show == 1]\n",
    "\n",
    "# plot a horizontal bar chart\n",
    "plt.figure(figsize = [8, 4])\n",
    "cat_order = df_no_show['neighbourhood'].value_counts().index[:5]\n",
    "sns.countplot(data = df_no_show, y = 'neighbourhood', color = base_color, order = cat_order)\n",
    "plt.title('Highest 5 Neighbourhoods in Terms of The Number of No Show Appointments')\n",
    "plt.xlabel('Number of No Show Appointments')\n",
    "plt.ylabel('Neighbourhood');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the chart, JARDIM CAMBURI neighbourhood had the heighest number of missed appointments followed by MARIA ORTIZ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2: Can the gender be considered as a factor to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "# percentages of no show patients based on gender\n",
    "no_show_perc_gender = df.groupby('gender').no_show.mean() * 100\n",
    "\n",
    "# plot a bar chart\n",
    "plt.bar(x = no_show_perc_gender.index, height = no_show_perc_gender)\n",
    "plt.title('The Percentages of No Show Patients Based on Gender')\n",
    "plt.xticks([0, 1], ['Female', 'Male'])\n",
    "plt.ylabel('No Show Percentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the chart, the percentages of no show patients based on gender were pretty close. \n",
    "\n",
    "So, we can say that the gender can't be considered as a factor to predict if a patient will show up for their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3: Can the age be considered as a factor to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# percentages of no show patients based on age group\n",
    "no_show_perc_age = df.groupby('age_group').no_show.mean() * 100\n",
    "\n",
    "# plot a bar chart\n",
    "plt.figure(figsize = [8, 4])\n",
    "plt.bar(x = no_show_perc_age.index, height = no_show_perc_age)\n",
    "plt.title('The Percentages of No Show Patients Based on Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('No Show Percentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age group (19-37) missed their appointments more than the other groups. On the other hand, the age group (56 and above) were the most committed to showing up for their appointments. So, we can say that the older the person is the more likely he is going to show up. But we can see that the age group (0-18) didn't follow this rule, and I think that's due to the fact that the child especially in the age of (0-15) is accompanied by an adult. \n",
    "\n",
    "So, we can say that the patient's age could help us to predict if a patient will show up for their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q4: Can having a scholarship be considered as a factor to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "# percentages of no show patients based on scholarship\n",
    "no_show_perc_scholarship = df.groupby('scholarship').no_show.mean() * 100\n",
    "\n",
    "# plot a bar chart\n",
    "plt.bar(x = no_show_perc_scholarship.index, height = no_show_perc_scholarship)\n",
    "plt.title('The Percentages of No Show Patients Based on Scholarship')\n",
    "plt.xticks([0, 1], ['No Scholarship', 'Scholarship'])\n",
    "plt.ylabel('No Show Percentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From what's shown, about 24% of patients with scholarship didn't show up for their appointments, while about 20% of patients without scholarship missed their appointments. So, it seems like the patient with a scholarship is more likely to miss the appointment.\n",
    "\n",
    "So, we can say that having a scholarship could help us to predict if a patient will show up for their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q5: Can the day of the week that the appointment on be considered as a factor to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# percentages of no show appointments based on week day\n",
    "no_show_perc_weekday = df.groupby('week_day').no_show.mean() * 100\n",
    "\n",
    "# plot a line plot\n",
    "plt.figure(figsize = [8, 4])\n",
    "sns.lineplot(x = no_show_perc_weekday.index,  y = no_show_perc_weekday, marker='o')\n",
    "plt.title('The Percentages of No Show Appointments Based on The Day of The Week')\n",
    "plt.xticks(np.arange(0, 6+1, 1), weekday_labels)\n",
    "plt.xlabel('Appointment Day')\n",
    "plt.ylabel('No Show Percentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 23% of appointments on Saturday were missed, making it the most likely day for patients to not show up. followed by Friday with about 21.2% of the appointments in that day patients didn't show up. On the other hand, Thursday with about 19.4% and Wednesday with about 19.7% missed appointments, are the least likely among the other days to have missed appointments. So, we could say that patients are more likely to miss their appointments on weekends and the days that are close to it (Monday, Friday).\n",
    "\n",
    "So, we can say that the day of the week that the appointment on could help us to predict if a patient will show up for their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q6: Can the waiting period between scheduled day and appointment day be considered as a factor to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# percentages of no show patients based on waiting period\n",
    "no_show_perc_waiting_period = df.groupby('waiting_period').no_show.mean() * 100\n",
    "\n",
    "# plot a bar chart\n",
    "plt.figure(figsize = [8, 4])\n",
    "plt.bar(x = no_show_perc_waiting_period.index, height = no_show_perc_waiting_period)\n",
    "plt.title('The Percentages of No Show Patients Based on Waiting Period')\n",
    "plt.xlabel('Waiting Period')\n",
    "plt.ylabel('No Show Percentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's pretty clear that the longer the waiting period is, the more likely the patient will not show up for the appointment.\n",
    "\n",
    "So, we can say that the waiting period could help us to predict if a patient will show up for their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q7: What are the percentages of missed appointments for each special condition <br>(alcoholism, diabetes ... etc) ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hypertension': 17.016760594305786,\n",
       " 'Diabetes': 20.3579418344519,\n",
       " 'Alcoholism': 21.644120707596255,\n",
       " 'Handicapped': 19.497907949790793,\n",
       " 'Multiple Conditions': 17.69815418023887}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of special conditions for each patient\n",
    "df['number_of_cond'] = df.hipertension + df.diabetes + df.alcoholism + df.handcap\n",
    "\n",
    "# empty dictionary \n",
    "cond_dict = {}\n",
    "\n",
    "# percentage of no show patients who suffered from hypertension only\n",
    "cond_dict['Hypertension'] = (df[df.number_of_cond <= 1].groupby('hipertension').no_show.mean() * 100)[1]\n",
    "# percentage of no show patients who suffered from diabetes only\n",
    "cond_dict['Diabetes'] = (df[df.number_of_cond <= 1].groupby('diabetes').no_show.mean() * 100)[1]\n",
    "# percentage of no show patients who suffered from alcoholism only\n",
    "cond_dict['Alcoholism'] = (df[df.number_of_cond <= 1].groupby('alcoholism').no_show.mean() * 100)[1]\n",
    "# percentage of no show patients who were handicapped only\n",
    "cond_dict['Handicapped'] = (df[df.number_of_cond <= 1].groupby('handcap').no_show.mean() * 100)[1]\n",
    "\n",
    "# percentage of no show patients who suffered from multiple conditions\n",
    "cond_dict['Multiple Conditions'] = df[df.number_of_cond > 1].no_show.mean() * 100\n",
    "\n",
    "cond_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alcoholism             21.644121\n",
       "Diabetes               20.357942\n",
       "Handicapped            19.497908\n",
       "Multiple Conditions    17.698154\n",
       "Hypertension           17.016761\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the dictionary to a pandas series\n",
    "cond_ser = pd.Series(cond_dict)\n",
    "# sort the values in descending order\n",
    "cond_ser.sort_values(ascending=False, inplace=True)\n",
    "cond_ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a bar chart\n",
    "plt.figure(figsize = [10, 4])\n",
    "plt.bar(x = cond_ser.index, height = cond_ser)\n",
    "plt.title('Percentages of Missed Appointments For Each Special Condition')\n",
    "plt.ylabel('No Show Percentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown 21.64% of patients who suffered from alcoholism only didn't show up for their appointments,\n",
    "20.36% of diabetic patients didn't show up too, and the patients who suffered from multiple conditions 17.70% of them missed their appointments. So, we can say that alcoholics are the most likely to miss their appointments among other patients with special conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions <a id='conclusions'></a>\n",
    "In this project I analyzed <a href='https://www.kaggle.com/joniarroba/noshowappointments' target=\"_blank\">Medical Appointment No Shows Dataset </a> and communicated my findings about it using Python libraries NumPy, pandas, Matplotlib, and seaborn. The dataset contained **110,527** appointments in a period of **40** days, about **20%** of the patients didn't show up for their appointments.\n",
    "\n",
    "Most patients: \n",
    "- Were females.\n",
    "- Were young.\n",
    "- Didn't have a scholarship.\n",
    "- Didn't suffer from any special conditions (alcoholism, diabetes, hypertension, handicap).\n",
    "- Scheduled their appointments on the same day.\n",
    "- Prefered to have their appointments on working days (Monday-Friday) especially Tuesday and Wednesday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following questions were answerd:\n",
    "**Q1: Highest five neighbourhoods in terms of the number of no-show appointments?**\n",
    "\n",
    "|Neighbourhood |Number of no-show appointments|\n",
    "|:-:           |:-:                           |\n",
    "|JARDIM CAMBURI|1,465                         |\n",
    "|MARIA ORTIZ   |1,219                         |\n",
    "|ITARARÉ       |923                           |\n",
    "|RESISTÊNCIA   |905                           |\n",
    "|CENTRO        |703                           |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2: Can the gender be considered as a factor to predict if a patient will show up for their scheduled appointment?**\n",
    "\n",
    "The percentages of no show patients based on gender were pretty close.\n",
    "So, we can say that the gender can't be considered as a factor to predict if a patient will show up for their scheduled appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3: Can the age be considered as a factor to predict if a patient will show up for their scheduled appointment?**\n",
    "\n",
    "Yes, the younger the patient is the more likely he/she is going to miss the appointment. Except the group (0-18) since they probably are accompanied by an adult. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q4: Can having a scholarship be considered as a factor to predict if a patient will show up for their scheduled appointment?**\n",
    "\n",
    "Yes, it seems like a patient with a scholarship is more likely to not show up for the appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q5: Can the day of the week that the appointment on be considered as a factor to predict if a patient will show up for their scheduled appointment?**\n",
    "\n",
    "Somewhat, it looks like patients are more likely to miss their appointments on weekends and the days that are close to it (Monday, Friday)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q6: can the waiting period between scheduled day and appointment day be considered as a factor to predict if a patient will show up for their scheduled appointment?**\n",
    "\n",
    "Yes, the longer the waiting period is, the more likely the patient will not show up for the appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q7: What are the percentages of missed appointments for each special condition (alcoholism, diabetes ... etc) ?**\n",
    "\n",
    "we can say that alcoholics are the most likely to miss their appointments among other patients with special conditions.\n",
    "\n",
    "|Condition          |Percentage of no-show appointments|\n",
    "|:-:                |:-:                               |\n",
    "|Alcoholism only    |21.64%                            |\n",
    "|Diabetes only      |20.36%                            |\n",
    "|Handicapped only   |19.50%                            |\n",
    "|Multiple Conditions|17.70%                            |\n",
    "|Hypertension only  |17.02%                            |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitation:\n",
    "- The dataset covers a short period 40 days only, longer period could lead to a better results.\n",
    "- Appointment time wasn't given, which could be very useful.\n",
    "- Dataset description on kaggle wasn't up to date, so some columns values weren't clear until found in disscussion forms. \n",
    "- Recent data would be much better, since 2016 is about 4 years ago and a lot has changed since then.\n",
    "- Covering more than one country would've been better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:\n",
    "- Several questions on <a href='https://stackoverflow.com' target=\"_blank\">Stack Overflow</a>.\n",
    "- <a href='https://pandas.pydata.org/pandas-docs/stable/index.html' target=\"_blank\">pandas documentation</a>."
   ]
  }
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