Investigate_a_Dataset_No_Show_Appointments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate a Dataset - Medical Appointment No Shows Dataset\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "<hr>\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "<hr>\n",
    "\n",
    "This projects attempts to analyse the [Medical Appointment No Shows dataset](https://www.kaggle.com/datasets/joniarroba/noshowappointments).\n",
    "\n",
    "\n",
    "The dataset provides information about 110,527 medical appointments and has 14 associated variables (characteristics). The No-show variable is the dependent variable. Age is a numerical variable while the others are categorical. The columns provided in the table as explained below:\n",
    "\n",
    "1. __PatientId:__ This is an identifier for each patient.\n",
    "2. __AppointmentID:__ This indicates the appointment identification for the patients.\n",
    "3. __Gender:__ The gender of the patients are also provided as one of either males (M) or females (F).\n",
    "4. __ScheduledDay:__ This refers to the day the patient is scheduled to their hospital.\n",
    "5. __AppointmentDay:__ Refers to the day the patient has an appointment to visit the doctor.\n",
    "6. __Age:__ This is the age of patients\n",
    "7. __Neighbourhood:__ Refers tp where the appointment takes place.\n",
    "8. __Scholarship:__ Refers to whether or not the patient is a beneficiary of _Bolsa Família_, government social welfare scheme. It is represented as either 1 (True) or 0 (False) on the dataset. \n",
    "9. __Hipertension:__ Column indicates whether or not a patient has Hypertension. It is represented as either 1 (True) or 0 (False) on the dataset.\n",
    "10. __Diabetes:__ Column indicates whether or not a patient has Diabetes. It is represented as either 1 (True) or 0 (False) on the dataset.\n",
    "11. __Alcoholism:__ This column indicates if a patient has an issue of alcoholism or not. It is represented as either 1 (True) or 0 (False) on the dataset.\n",
    "12. __Handcap:__ Shows if a patient is handicapped or not. It is represented as either 1 (True) or 0 (False) on the dataset.\n",
    "13. __SMS_received:__ Indicates whether or not the patient received one or more messages.\n",
    "14. __No-show:__ This column indicates whether or not patients turn up for their appointment in the hospital. ___It says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question(s) for Analysis\n",
    "\n",
    "<hr>\n",
    "\n",
    "1. Does having scholarship affect the hospital visits and showing up of patients for appointments.\n",
    "2. Does having any of the chronic diseases/conditions listed in the description above (hypertension, diabetes,alcoholism) affect the hospital visits and showing up of patients for appointments.\n",
    "3. Does being handicapped affected turning up for appointments.\n",
    "4. Does sending out SMS reminder messages to patients influence how they turn up for hospital appointments.\n",
    "6. Does sex of the patient have any effect on the showing up for hospital appointments\n",
    "6. What other factors might influence a patient not showing up for their hospital appointments after scheduling such appointments.\n",
    "7. What is the average duration between scheduled visits and actual hospital appointments and does this affect the show up for appointments.\n",
    "8. Which neighbourhoods are most likely to miss appointments.\n",
    "9. What is the average number of patients who missed at least one appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns                       # Fo visualisation\n",
    "import matplotlib.pyplot as plt             # For visualisation\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upgrade pandas to use dataframe.explode() function. \n",
    "#!pip install --upgrade pandas==0.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "<hr>\n",
    "\n",
    "The following steps demonstrate the data wrangling steps i applied on the dataset.\n",
    "\n",
    "### General Properties\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General Overview of the Dataset\n",
    "<hr>\n",
    "\n",
    "I first loaded the Dataset\n",
    "\n",
    "The below command loads the dataset as a Pandas DataFrame.\n",
    "The r (raw) is important because it helps to avoid unicode error when reading or opening the files.\n",
    "\n",
    "An error may occur if you are using a normal string as a path. \n",
    "You can use one of the three following solutions to fix the error problem:\n",
    "\n",
    "1: Put r before your normal string to convert normal string to raw string:\n",
    "\n",
    "`pd.read_csv(r\"C:\\Users\\Hp\\Documents\\Udacity Project 1\\noshowappointments-kagglev2-may-2016.csv\")`\n",
    "\n",
    "2: Use double quotes and forward slash character\n",
    "\n",
    "`pd.read_csv(\"C:/Users/Hp/Documents/Udacity Project 1/noshowappointments-kagglev2-may-2016.csv\")`\n",
    "\n",
    "3: Doubling the backslashes as shown below.\n",
    "\n",
    "`pd.read_csv(\"C:\\\\Users\\\\Hp\\\\Documents\\\\Udacity Project 1\\\\noshowappointments-kagglev2-may-2016.csv\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Hp\\Documents\\Udacity Project 1\\noshowappointments-kagglev2-may-2016.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a Copy of the DataFrame\n",
    "<hr>\n",
    "Before any data wrangling process i would like to create a backup copy of my dataframe (depending on the size of the DataFrame) and name it df1, in case of any unforseen problems or errors. This also gives me an opportunity to quickly revert to checking the original dataset if I am confused about what I did right or wrong.\n",
    "\n",
    "\n",
    "___N.B. I shall be running my analysis on the original dataframe named df.___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy(deep = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell command creates a copy of my original `df` DataFrame and assigns the name `df1` to it.\n",
    "\n",
    "Setting an argument `deep = True` ensures a \"hard copy\" of the dataframe is created and\n",
    "a soft copy vice versa. The soft copy is akin to assigning the dataframe to a variable. Any changes in one will affect the other.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df', 'df1']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%who_ls DataFrame\n",
    "\n",
    "# This code Lists all the Dataframes loaded into memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code cell output shows that I have successfully created and loaded both `df` and `df1` DataFrames.\n",
    "\n",
    "To delete any DataFrame, I will just run the `del df1` command."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To checkout the Dataset \n",
    "<hr>\n",
    "Run the codes below to checkout the dataset so as to have a general overview of the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check for the number of missing values\n",
    "\n",
    "One of the very first steps in data wranglung is to check for missing values. We shall attempt to do this by running the cell below. Using a `pd.isnull(df)` command gives a boolean result. We shall call a `sum` method on it to give a count of the missing values (if present)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                       0\n",
       "ScheduledDay                 0\n",
       "AppointmentDay               0\n",
       "Age                          0\n",
       "Neighbourhood                0\n",
       "Scholarship                  0\n",
       "Hypertension                 0\n",
       "Diabetes                     0\n",
       "Alcoholism                   0\n",
       "Handicapped                  0\n",
       "SMS_received                 0\n",
       "Showed_up_for_appointment    0\n",
       "Delay_time                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as seen above, there are no missing values in this dataset. We can thus proceedw with furtherchecking out our dataset.\n",
    "\n",
    "Checking for number of missing value entries in the dataset is very important.\n",
    "If there are any missing values, we drop them (if statistically feasible) \n",
    "by calling the `df.dropna()` function or we can decide to do otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us check the size of the DataFrame\n",
    "\n",
    "Using the size attribute, assign the size of the DataFrame to a variable named `df_size`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1547378\n"
     ]
    }
   ],
   "source": [
    "df_size = df.size\n",
    "print(df_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code cell output shows that the DataFrame has 1, 547, 378 cell values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking the Dimension of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The DataFrame dimension is: 2\n"
     ]
    }
   ],
   "source": [
    "df_dimension = df.ndim\n",
    "print(f\"The DataFrame dimension is: {df_dimension}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the above cell shows that the DataFrame df is 2-dimensional, having rows and columns. This is to be expected since it is a Pandas DataFrame. If it had returned 1, then we can say it is a Pandas series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check the Number of Unique Appointments\n",
    "I will achieve this by counting how many unique patients are in the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62299"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['PatientId'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result indicates that there are 62, 299 unique patient visits. This shows that some visits were return visits, meaning some patients had more than one appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110527"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['AppointmentID'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code cell output of 110, 527 corresponds to the total number of patients seen as mentioned above in the introduction above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # Checkout the first 5 entries in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Insights from the above analysis\n",
    "\n",
    "1. There are 14 columns in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
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       "      <th>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>110522</th>\n",
       "      <td>2.572134e+12</td>\n",
       "      <td>5651768</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03T09:15:35Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
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       "      <td>2016-06-07T00:00:00Z</td>\n",
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       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>1.557663e+13</td>\n",
       "      <td>5630692</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T16:03:52Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>9.213493e+13</td>\n",
       "      <td>5630323</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:09:23Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>3.775115e+14</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T13:30:56Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "110522  2.572134e+12        5651768      F  2016-05-03T09:15:35Z   \n",
       "110523  3.596266e+12        5650093      F  2016-05-03T07:27:33Z   \n",
       "110524  1.557663e+13        5630692      F  2016-04-27T16:03:52Z   \n",
       "110525  9.213493e+13        5630323      F  2016-04-27T15:09:23Z   \n",
       "110526  3.775115e+14        5629448      F  2016-04-27T13:30:56Z   \n",
       "\n",
       "              AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  \\\n",
       "110522  2016-06-07T00:00:00Z   56   MARIA ORTIZ            0             0   \n",
       "110523  2016-06-07T00:00:00Z   51   MARIA ORTIZ            0             0   \n",
       "110524  2016-06-07T00:00:00Z   21   MARIA ORTIZ            0             0   \n",
       "110525  2016-06-07T00:00:00Z   38   MARIA ORTIZ            0             0   \n",
       "110526  2016-06-07T00:00:00Z   54   MARIA ORTIZ            0             0   \n",
       "\n",
       "        Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "110522         0           0        0             1      No  \n",
       "110523         0           0        0             1      No  \n",
       "110524         0           0        0             1      No  \n",
       "110525         0           0        0             1      No  \n",
       "110526         0           0        0             1      No  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() # Checkout the last 5 entries in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93424</th>\n",
       "      <td>5.221574e+13</td>\n",
       "      <td>5757773</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-01T09:48:31Z</td>\n",
       "      <td>2016-06-01T00:00:00Z</td>\n",
       "      <td>2</td>\n",
       "      <td>SÃO JOSÉ</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>6289</th>\n",
       "      <td>2.227669e+12</td>\n",
       "      <td>5648025</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-02T14:54:35Z</td>\n",
       "      <td>2016-05-04T00:00:00Z</td>\n",
       "      <td>47</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4718</th>\n",
       "      <td>5.681234e+14</td>\n",
       "      <td>5687283</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11T15:15:35Z</td>\n",
       "      <td>2016-05-16T00:00:00Z</td>\n",
       "      <td>36</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>2.426683e+12</td>\n",
       "      <td>5641336</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T13:21:00Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>49</td>\n",
       "      <td>DO CABRAL</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>91895</th>\n",
       "      <td>1.363977e+13</td>\n",
       "      <td>5680656</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-10T12:21:12Z</td>\n",
       "      <td>2016-06-01T00:00:00Z</td>\n",
       "      <td>33</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>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27797</th>\n",
       "      <td>2.644727e+12</td>\n",
       "      <td>5719555</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-19T11:33:36Z</td>\n",
       "      <td>2016-05-25T00:00:00Z</td>\n",
       "      <td>16</td>\n",
       "      <td>ITARARÉ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52873</th>\n",
       "      <td>2.915678e+13</td>\n",
       "      <td>5736214</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-25T08:03:47Z</td>\n",
       "      <td>2016-05-25T00:00:00Z</td>\n",
       "      <td>39</td>\n",
       "      <td>SÃO JOSÉ</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>42816</th>\n",
       "      <td>1.499929e+12</td>\n",
       "      <td>5729778</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-24T07:50:04Z</td>\n",
       "      <td>2016-05-24T00:00:00Z</td>\n",
       "      <td>5</td>\n",
       "      <td>SÃO CRISTÓVÃO</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>51565</th>\n",
       "      <td>8.249496e+12</td>\n",
       "      <td>5720459</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-19T13:51:47Z</td>\n",
       "      <td>2016-05-19T00:00:00Z</td>\n",
       "      <td>46</td>\n",
       "      <td>PIEDADE</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>51479</th>\n",
       "      <td>3.357126e+11</td>\n",
       "      <td>5681056</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10T13:29:46Z</td>\n",
       "      <td>2016-05-10T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>PIEDADE</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "93424  5.221574e+13        5757773      M  2016-06-01T09:48:31Z   \n",
       "6289   2.227669e+12        5648025      F  2016-05-02T14:54:35Z   \n",
       "4718   5.681234e+14        5687283      F  2016-05-11T15:15:35Z   \n",
       "2204   2.426683e+12        5641336      F  2016-04-29T13:21:00Z   \n",
       "91895  1.363977e+13        5680656      F  2016-05-10T12:21:12Z   \n",
       "27797  2.644727e+12        5719555      F  2016-05-19T11:33:36Z   \n",
       "52873  2.915678e+13        5736214      M  2016-05-25T08:03:47Z   \n",
       "42816  1.499929e+12        5729778      F  2016-05-24T07:50:04Z   \n",
       "51565  8.249496e+12        5720459      F  2016-05-19T13:51:47Z   \n",
       "51479  3.357126e+11        5681056      M  2016-05-10T13:29:46Z   \n",
       "\n",
       "             AppointmentDay  Age    Neighbourhood  Scholarship  Hipertension  \\\n",
       "93424  2016-06-01T00:00:00Z    2         SÃO JOSÉ            0             0   \n",
       "6289   2016-05-04T00:00:00Z   47   JARDIM CAMBURI            0             0   \n",
       "4718   2016-05-16T00:00:00Z   36      RESISTÊNCIA            0             0   \n",
       "2204   2016-04-29T00:00:00Z   49        DO CABRAL            0             1   \n",
       "91895  2016-06-01T00:00:00Z   33  JARDIM DA PENHA            0             0   \n",
       "27797  2016-05-25T00:00:00Z   16          ITARARÉ            0             0   \n",
       "52873  2016-05-25T00:00:00Z   39         SÃO JOSÉ            0             0   \n",
       "42816  2016-05-24T00:00:00Z    5    SÃO CRISTÓVÃO            0             0   \n",
       "51565  2016-05-19T00:00:00Z   46          PIEDADE            0             0   \n",
       "51479  2016-05-10T00:00:00Z    0          PIEDADE            0             0   \n",
       "\n",
       "       Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "93424         0           0        0             0      No  \n",
       "6289          0           0        0             0     Yes  \n",
       "4718          0           0        0             0      No  \n",
       "2204          0           0        0             0      No  \n",
       "91895         0           0        0             1      No  \n",
       "27797         0           0        0             1      No  \n",
       "52873         0           0        0             0      No  \n",
       "42816         0           0        0             0      No  \n",
       "51565         0           0        0             0      No  \n",
       "51479         0           0        0             0      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10) # Checkout the random 10 entries in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dates are given in `ISO-8601` date representation style. In addition AppointmentDays are fixed at time T00:00:00Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes # Check the data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=110527, step=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index  # Show the total number of rows index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  # Shows the total number of rows and columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The DataFrame has 110527 rows and 14 columns.\n"
     ]
    }
   ],
   "source": [
    "df_row_count, df_column_count = df.shape\n",
    "print(f\"The DataFrame has {df_row_count} rows and {df_column_count} columns.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension',\n",
       "       'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns  # Shows the names of the column headers/titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Checks the metadata of the DataFrame\n",
    "\n",
    "\n",
    "Row and column totals, column names, \n",
    "datatypes, missing values and their counts, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 44.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info(memory_usage=\"deep\")\n",
    "\n",
    "# N.B. You can use this single command in place of the \n",
    "# df.dtypes, df.shape, and df.columns commands initially used above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Insights\n",
    "\n",
    "I made the following observations about the dataset:\n",
    "1. There are 14 columns as mentioned above\n",
    "2. There are 110, 527 rows indexed from 0 to 110, 526.\n",
    "3. The dataset contains the following data types:\n",
    "- 1 float64: PatientID column\n",
    "- 8 int64: AppointmentID, Age, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, and SMS_received\n",
    "- 5 objects: ScheduledDay, AppointmentDay, Neighbourhood and No-show\n",
    "4. I am using about 44MB of memory on PC (Remeber I have 2 DataFrames loaded).\n",
    "5. There are some typographical errors in the column names: Handcap and Hipertension.\n",
    "6. There are no missing values in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deciding to drop some columns.\n",
    "\n",
    "The command in the cell below will drop the column names PatientId and AppointmentID from the dataframe.\n",
    "The axis = 1 is used to refer to the column\n",
    "If you need to delete rows, set the axis = 0, which is the default value. I initially thought of deleting these columns but I will leave them for now, just in case a need arises as I continue exploring the dataset. So i will comment out the code in the cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we drop the columns\n",
    "\n",
    "#df2_copy.drop([\"PatientId\", \"AppointmentID\"], axis=1, inplace = True)\n",
    "#df2_copy.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next I will like to run a descriptive statistics on the dataset to peek into the data summary statistics of the numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.00</td>\n",
       "      <td>110527.00</td>\n",
       "      <td>110527.0</td>\n",
       "      <td>110527.0</td>\n",
       "      <td>110527.00</td>\n",
       "      <td>110527.00</td>\n",
       "      <td>110527.00</td>\n",
       "      <td>110527.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5675305.12</td>\n",
       "      <td>37.09</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>71295.75</td>\n",
       "      <td>23.11</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5030230.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5640285.50</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5680573.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5725523.50</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5790484.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID        Age  Scholarship  Hipertension  \\\n",
       "count  1.105270e+05      110527.00  110527.00     110527.0      110527.0   \n",
       "mean   1.474963e+14     5675305.12      37.09          0.1           0.2   \n",
       "std    2.560949e+14       71295.75      23.11          0.3           0.4   \n",
       "min    3.921784e+04     5030230.00      -1.00          0.0           0.0   \n",
       "25%    4.172614e+12     5640285.50      18.00          0.0           0.0   \n",
       "50%    3.173184e+13     5680573.00      37.00          0.0           0.0   \n",
       "75%    9.439172e+13     5725523.50      55.00          0.0           0.0   \n",
       "max    9.999816e+14     5790484.00     115.00          1.0           1.0   \n",
       "\n",
       "        Diabetes  Alcoholism    Handcap  SMS_received  \n",
       "count  110527.00   110527.00  110527.00     110527.00  \n",
       "mean        0.07        0.03       0.02          0.32  \n",
       "std         0.26        0.17       0.16          0.47  \n",
       "min         0.00        0.00       0.00          0.00  \n",
       "25%         0.00        0.00       0.00          0.00  \n",
       "50%         0.00        0.00       0.00          0.00  \n",
       "75%         0.00        0.00       0.00          1.00  \n",
       "max         1.00        1.00       4.00          1.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)\n",
    "\n",
    "# This is used to check the summary statistics of the dataset, especially for numerical data.\n",
    "# You can decide to round the result up for better reading\n",
    "# This also gives a general overview of some abnormalities in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Insights\n",
    "\n",
    "The average age of the patients is 37 years, minimum age is `-1`, maximum age is `115` and a standard deviation of about `23`. Count is the number of values and this shows there are no missing values in the dataset. Note that the count is thesame (110, 527) across the Count row, which tallies with the total number of Patients.\n",
    "\n",
    "I will not bother about the other columns because they are mostly categorical but mainly coded as numbers for ease.\n",
    "\n",
    "Obviously the `-1` age stands out. No one is aged `-1`. Its possible for someone to be `115` years old. We cant be too sure though.\n",
    "\n",
    "I shall attempt addressing these later on in the course of this work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above shown overview of the data, I observed some changes that need to be made. These include:\n",
    "\n",
    "\n",
    "1. The PatientID and AppointmentID are just identifiers (most likely computer generated) and may not be needed in the analysis. They can thus be dropped from the table during the data cleaning process. Although the PatientID is a float datatype instead of an integer, but there will be no need to change its data type since I shall be dropping it. \n",
    "\n",
    "\n",
    "2. The ScheduledDay and AppointmentDay are object data types instead of Datetime datatypes. They need to be converted to Datetime.\n",
    "\n",
    "\n",
    "3. The Handcap and Hipertension columns have a typo error and needs to be renamed for better clarity.\n",
    "\n",
    "\n",
    "4. The No-show column can be confusing and also needs to be changed to correctly reflect what it stands for. Further details will be provided during the process. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Converting Some Columns to Appropriate Data Types\n",
    "\n",
    "\n",
    "Here I will Convert ScheduledDay and AppointmentDay to Date time data types from the default objects data type. \n",
    "\n",
    "\n",
    "Finally I will check the datasets data type again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some personal reasons I would like to document how to convert the PatientID from float to an integer. This will be achieved by running the code in the cell below. Note that the code cell is commented out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['PatientId'] = df['PatientId'].astype('int64')   # Converts PatientId from Float to Int64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To Drop Columns I may not need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"PatientId\", \"AppointmentID\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the above command will drop the PateintID and AppointmentID from the dataset. The `axis=1` argument indicates the command will be applied to the column labels while the `inplace=True` argument applies the changes to thesame DataFrame. After running the above code, we can check to see if our operation got executed successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   Gender          110527 non-null  object\n",
      " 1   ScheduledDay    110527 non-null  object\n",
      " 2   AppointmentDay  110527 non-null  object\n",
      " 3   Age             110527 non-null  int64 \n",
      " 4   Neighbourhood   110527 non-null  object\n",
      " 5   Scholarship     110527 non-null  int64 \n",
      " 6   Hipertension    110527 non-null  int64 \n",
      " 7   Diabetes        110527 non-null  int64 \n",
      " 8   Alcoholism      110527 non-null  int64 \n",
      " 9   Handcap         110527 non-null  int64 \n",
      " 10  SMS_received    110527 non-null  int64 \n",
      " 11  No-show         110527 non-null  object\n",
      "dtypes: int64(7), object(5)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes it did !!! Notice we now have 12 columns since we just dropped 2 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.ScheduledDay = df.ScheduledDay.apply(np.datetime64) \n",
    "\n",
    "# Converts ScheduledDay from Object to datetime64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.AppointmentDay = df.AppointmentDay.apply(np.datetime64)\n",
    "\n",
    "# Converts AppointmentDay from Object to datetime64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above 2 cells can be used to achieve conversion of ScheduledDay and AppointmentDay to datetime64 datatype.. However, If you want to change for multiple columns at once, You can also use pandas to_datetime function to achieve this by using the apply method:\n",
    "```\n",
    "df[[\"AppointmentDay\", \"ScheduledDay\"]] = df[[\"AppointmentDay\", \"ScheduledDay\"]].apply(pd.to_datetime)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[[\"AppointmentDay\", \"ScheduledDay\"]] = \\\n",
    "df[[\"AppointmentDay\", \"ScheduledDay\"]].apply(pd.to_datetime)\n",
    "\n",
    "# The backslash is used here to break the lengthy code so that it spans 2 lines \n",
    "# but doesn't break the code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check the dataset to see if our chages to the datetime datatype was successful for the listed colums above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                         object\n",
       "ScheduledDay      datetime64[ns, UTC]\n",
       "AppointmentDay    datetime64[ns, UTC]\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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, I have successfully converted the `ScheduledDay` and `AppointmentDay` datatypes from object to `datetime64`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Modifying the No-show Column\n",
    "\n",
    "Let us examine the No-show column. It can be confusing if one isn't observant. \n",
    "According to the explanation offered by Udacity:\n",
    "\n",
    ">Be careful about the encoding of the last column: it says `\"No\"` if the patient\n",
    "showed up to their appointment, and `‘Yes’` if they did not show up.\n",
    "\n",
    "This means that those who showed up to their appointment are encoded as `\"No\"` and vice versa. \n",
    "We shall attempt to change this into an encoding that is easy to understand like this:\n",
    "\n",
    "`\"Yes\"` if the patient came for appointment and\n",
    "\n",
    "`\"No\"` if they didn't come for appointment \n",
    "\n",
    "\n",
    "This makes more sense. But first things first. We have to count the total number of each entry first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     88208\n",
       "Yes    22319\n",
       "Name: No-show, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"No-show\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to understand the last column in the dataframe is this. After running the code cell below, those who came for appointments have `True` values and those who didnt have `False` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     88208\n",
       "False    22319\n",
       "Name: No-show, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"No-show\"] == \"No\").value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above table indicates that:\n",
    "\n",
    "88, 208 patients showed up for appointment while \n",
    "\n",
    "22, 319 patients didnt show up.\n",
    "\n",
    "The `No-show` column shall be renamed to `Showed_up_for_appointment`. We want the table to thus show:\n",
    "\n",
    "`Yes` for those who showed up for appointments (88, 208 patients) and \n",
    "\n",
    "`No` for those who didnt (22, 319 patients)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rename some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_rename = {\n",
    "    \"Hipertension\": \"Hypertension\",\n",
    "    \"No-show\": \"Showed_up_for_appointment\",\n",
    "    \"Handcap\": \"Handicapped\"\n",
    "}\n",
    "\n",
    "df.rename(columns = to_rename,  inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code renames three of the column names. First a dictionary is created and assigned the variable name `to_rename`. This dictionary contains `key:value` pairs as `old-name: new-name`. We then passed this `to_rename` variable into the DataFrame rename function as shown above.\n",
    "\n",
    "The `inplace` argument helps to ensure all changes are effected on the original DataFramme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to rename columns is shown below:\n",
    "```\n",
    "df.rename(columns={\"Hipertension\": \"Hypertension\",\"Handcap\": \"Handicapped\",  \"No-show\": \"Showed_up_for_appointment\"}, inplace=True)\n",
    "```\n",
    "\n",
    "You may want to rename some columns whose titles have typos or aren't descriptive enough for you.\n",
    "Thereafter call the df.head() function to see the effects of your changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we change the row entries into the appropriate coding. The next code below does the following action:\n",
    "\n",
    "`\"No\"` will be replaced by `\"Yes\"` and\\\n",
    "`\"Yes\"` will be replaced by `\"No\"`.\n",
    "\n",
    "Remember that we have to ensure the total counts remain thesame as the original Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_values = {\"No\": \"Yes\",\n",
    "                 \"Yes\": \"No\"\n",
    "                 }\n",
    "df = df.replace({\"Showed_up_for_appointment\": replace_values})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let us confirm our total count still remains thesame as explained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    88208\n",
       "No     22319\n",
       "Name: Showed_up_for_appointment, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Showed_up_for_appointment\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes it does!!!\n",
    "\n",
    "The above result shows that the table now indicates that:\n",
    "\n",
    "88, 208 patients showed up for appointment. \n",
    "22, 319 patients didnt show up.\n",
    "\n",
    "Remember that `\"No-show\"` column has beeen renamed to `\"Showed_up_for_appointment\"`.\n",
    "\n",
    "The table thus shows:\n",
    "\n",
    "`\"Yes\"` for those who showed up for appointments (88, 208 patients) and \n",
    "\n",
    "\n",
    "`\"No\"` for those who didnt (22, 319 patients)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at our dataset after these modifications by running the `df.head()` command"
   ]
  },
  {
   "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",
       "    }\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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "0      F 2016-04-29 18:38:08+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00    8   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hypertension  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",
       "   Handicapped  SMS_received Showed_up_for_appointment  \n",
       "0            0             0                       Yes  \n",
       "1            0             0                       Yes  \n",
       "2            0             0                       Yes  \n",
       "3            0             0                       Yes  \n",
       "4            0             0                       Yes  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now see that the`No-show` column has been renamed to `Showed_up_for_appointment`. In addition, those who showed up for appointment are being indicated by `Yes` and those who didn't indicated by `No`. All the other renamed columns can also been confirmed from above.\n",
    "\n",
    "This is easier to understand. We can then proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To get the sex distribution of the patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show there are 65% (71, 840) female patients while 35% (38, 687) are males. \n",
    "\n",
    "We can pass the optional argument `normalize=True` to the `value_counts` function if we want to see the proportions of the total for each `Gender`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Identify unique values in each column\n",
    "This will give us a good idea of the uniqueness of the data entries. We shall write a function to carry out this task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_val():\n",
    "    print(f\"Unique Values in Gender column: {df.Gender.unique()}\")\n",
    "    print(f\"Unique Values in Scholarship column: {df.Scholarship.unique()}\")\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(f\"Unique Values in Hypertension column: {df.Hypertension.unique()}\")\n",
    "    print(f\"Unique Values in Diabetes column: {df.Diabetes.unique()}\")\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(f\"Unique Values in Alcololism column: {df.Alcoholism.unique()}\")\n",
    "    print(f\"Unique Values in Handicapped column: {df.Handicapped.unique()}\")\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(f\"Unique Values in SMS_recieved column: {df.SMS_received.unique()}\")\n",
    "    print(f\"Unique Values in Showed_up_for_appointment column: {df.Showed_up_for_appointment.unique()}\")\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(f\"Unique Values in Age column: {df.Age.unique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then call the `unique_val` function we just defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in Gender column: ['F' 'M']\n",
      "Unique Values in Scholarship column: [0 1]\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Unique Values in Hypertension column: [1 0]\n",
      "Unique Values in Diabetes column: [0 1]\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Unique Values in Alcololism column: [0 1]\n",
      "Unique Values in Handicapped column: [0 1 2 3 4]\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Unique Values in SMS_recieved column: [0 1]\n",
      "Unique Values in Showed_up_for_appointment column: ['Yes' 'No']\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Unique Values in Age column: [ 62  56   8  76  23  39  21  19  30  29  22  28  54  15  50  40  46   4\n",
      "  13  65  45  51  32  12  61  38  79  18  63  64  85  59  55  71  49  78\n",
      "  31  58  27   6   2  11   7   0   3   1  69  68  60  67  36  10  35  20\n",
      "  26  34  33  16  42   5  47  17  41  44  37  24  66  77  81  70  53  75\n",
      "  73  52  74  43  89  57  14   9  48  83  72  25  80  87  88  84  82  90\n",
      "  94  86  91  98  92  96  93  95  97 102 115 100  99  -1]\n"
     ]
    }
   ],
   "source": [
    "unique_val() # Function call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, after calling the function, we can see that `Handicapped` column has 4 unique entries while the others have 2 each, except for `Age` column.\n",
    "\n",
    "The `Age` column showed that there is a `-1` age. This is most unlikely to be true and may probably be an error. Before we decide on what to do, let's count the number of occurences of these values i.e. see how many patients have the entry of `-1` age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Count the number of values in each column\n",
    "\n",
    "First we define a function for this and then call the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_counts():\n",
    "    print(df['Gender'].value_counts())\n",
    "    print(df['Scholarship'].value_counts())\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(df['Hypertension'].value_counts())\n",
    "    print(df['Diabetes'].value_counts())\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(df['Alcoholism'].value_counts())\n",
    "    print(df['Handicapped'].value_counts())\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(df['SMS_received'].value_counts())\n",
    "    print(df['Showed_up_for_appointment'].value_counts())\n",
    "    print(df['ScheduledDay'].value_counts())\n",
    "    print(\"+++++\" * 20) # This line is used to separate the output for easier reading.\n",
    "    print(df['Age'].value_counts())\n",
    "    print(df['AppointmentDay'].value_counts())\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F    71840\n",
      "M    38687\n",
      "Name: Gender, dtype: int64\n",
      "0    99666\n",
      "1    10861\n",
      "Name: Scholarship, dtype: int64\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "0    88726\n",
      "1    21801\n",
      "Name: Hypertension, dtype: int64\n",
      "0    102584\n",
      "1      7943\n",
      "Name: Diabetes, dtype: int64\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "0    107167\n",
      "1      3360\n",
      "Name: Alcoholism, dtype: int64\n",
      "0    108286\n",
      "1      2042\n",
      "2       183\n",
      "3        13\n",
      "4         3\n",
      "Name: Handicapped, dtype: int64\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "0    75045\n",
      "1    35482\n",
      "Name: SMS_received, dtype: int64\n",
      "Yes    88208\n",
      "No     22319\n",
      "Name: Showed_up_for_appointment, dtype: int64\n",
      "2016-05-06 07:09:54+00:00    24\n",
      "2016-05-06 07:09:53+00:00    23\n",
      "2016-04-25 17:18:27+00:00    22\n",
      "2016-04-25 17:17:46+00:00    22\n",
      "2016-04-25 17:17:23+00:00    19\n",
      "                             ..\n",
      "2016-05-02 09:53:25+00:00     1\n",
      "2016-05-30 09:12:28+00:00     1\n",
      "2016-05-16 09:10:04+00:00     1\n",
      "2016-05-09 10:17:48+00:00     1\n",
      "2016-04-27 13:30:56+00:00     1\n",
      "Name: ScheduledDay, Length: 103549, dtype: int64\n",
      "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      " 0      3539\n",
      " 1      2273\n",
      " 52     1746\n",
      " 49     1652\n",
      " 53     1651\n",
      "        ... \n",
      " 115       5\n",
      " 100       4\n",
      " 102       2\n",
      " 99        1\n",
      "-1         1\n",
      "Name: Age, Length: 104, dtype: int64\n",
      "2016-06-06 00:00:00+00:00    4692\n",
      "2016-05-16 00:00:00+00:00    4613\n",
      "2016-05-09 00:00:00+00:00    4520\n",
      "2016-05-30 00:00:00+00:00    4514\n",
      "2016-06-08 00:00:00+00:00    4479\n",
      "2016-05-11 00:00:00+00:00    4474\n",
      "2016-06-01 00:00:00+00:00    4464\n",
      "2016-06-07 00:00:00+00:00    4416\n",
      "2016-05-12 00:00:00+00:00    4394\n",
      "2016-05-02 00:00:00+00:00    4376\n",
      "2016-05-18 00:00:00+00:00    4373\n",
      "2016-05-17 00:00:00+00:00    4372\n",
      "2016-06-02 00:00:00+00:00    4310\n",
      "2016-05-10 00:00:00+00:00    4308\n",
      "2016-05-31 00:00:00+00:00    4279\n",
      "2016-05-05 00:00:00+00:00    4273\n",
      "2016-05-19 00:00:00+00:00    4270\n",
      "2016-05-03 00:00:00+00:00    4256\n",
      "2016-05-04 00:00:00+00:00    4168\n",
      "2016-06-03 00:00:00+00:00    4090\n",
      "2016-05-24 00:00:00+00:00    4009\n",
      "2016-05-13 00:00:00+00:00    3987\n",
      "2016-05-25 00:00:00+00:00    3909\n",
      "2016-05-06 00:00:00+00:00    3879\n",
      "2016-05-20 00:00:00+00:00    3828\n",
      "2016-04-29 00:00:00+00:00    3235\n",
      "2016-05-14 00:00:00+00:00      39\n",
      "Name: AppointmentDay, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "value_counts()   # Function call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Insights\n",
    "\n",
    "The following observations can be made from the result of the value count above.\n",
    "\n",
    "1. There are 71, 840 female patients and 38, 687 males.\n",
    "\n",
    "\n",
    "2. Amongst the patients, 99, 666 didn't enjoy Scholarship while 10, 861 have.\n",
    "\n",
    "\n",
    "3. 88, 726 patients didn't have Hypertension while 21, 801 have.\n",
    "\n",
    "\n",
    "4. 102, 584 patients didn't have Diabetes while 7, 943 have.\n",
    "\n",
    "\n",
    "5. 107, 167 patients didn't have alcoholism issue while 3, 360 have.\n",
    "\n",
    "\n",
    "6. From the introduction above, description of the `Handicapped` data column indicates that its a dichotomous variable with options of `True` or `False`. From the results of the count however, it can be seen that there are 2 extra entries that can't be accounted for:\n",
    "\n",
    "\n",
    "- 108, 286 patients aren't handicapped while 2, 042 patients are.\n",
    "\n",
    "\n",
    "- There are 199 other patients with \"abnormal/questionable/unexplained\" entries. We need to deal with this. This is done later in the analysis below.\n",
    "\n",
    "\n",
    "7. 35, 482 patients recieved SMS while 75, 045 patienst didn't.\n",
    "\n",
    "\n",
    "8. __88, 208 patients showed up for their appointments while 22, 319 patients didn't.__\n",
    "\n",
    "\n",
    "9. Most patients scheduled their appointments on 2016-05-06.\n",
    "\n",
    "\n",
    "10. Only 1 patient had the age of `-1`. We can decide to drop this from the dataset since its negligible. Only five people had their age entries as 115, we can decide to leave it. 3539 patients had ages indicated as `0`. This count is too numerous to be a error in entry, hence we can decide to leave it and assume this represents infant patients less than 1 year old. \n",
    "\n",
    "\n",
    "11. The most hospital appointment was on 2016-06-06 while the least was on 2016-05-14\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us further explore the age column and focus on those patients whose age is less than or equal 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
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       "    }\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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06 08:58:13+00:00</td>\n",
       "      <td>2016-06-06 00:00:00+00:00</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "99832      F 2016-06-06 08:58:13+00:00 2016-06-06 00:00:00+00:00   -1   \n",
       "\n",
       "      Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "99832         ROMÃO            0             0         0           0   \n",
       "\n",
       "       Handicapped  SMS_received Showed_up_for_appointment  \n",
       "99832            0             0                       Yes  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Age\"] < 0] # To get the dataframe of patients whose age is -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above result, only a female patient with `AppointmentID` of `5775010` and `PatientID` of `4.659432e+14` had her age entry as `-1`. She also came for her appointment on `2016-06-06`.\n",
    "\n",
    "If we decide to drop this patient, it won't do our dataset much harm since its just 1 out of 110, 527 entries. We shall wait to see what to do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let us examine the patients who have ages less than or equal to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 08:08:43+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-27 10:46:12+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>SÃO BENEDITO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-25 13:28:21+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>ILHA DAS CAIEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-27 10:48:50+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 08:53:02+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>NOVA PALESTINA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110345</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-16 12:30:58+00:00</td>\n",
       "      <td>2016-06-01 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110346</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-06 14:22:34+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110454</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-03 15:18:44+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110460</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-03 08:56:51+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110507</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 09:04:18+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3540 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "59          F 2016-04-29 08:08:43+00:00 2016-04-29 00:00:00+00:00    0   \n",
       "63          M 2016-04-27 10:46:12+00:00 2016-04-29 00:00:00+00:00    0   \n",
       "64          M 2016-04-25 13:28:21+00:00 2016-04-29 00:00:00+00:00    0   \n",
       "65          M 2016-04-27 10:48:50+00:00 2016-04-29 00:00:00+00:00    0   \n",
       "67          F 2016-04-29 08:53:02+00:00 2016-04-29 00:00:00+00:00    0   \n",
       "...       ...                       ...                       ...  ...   \n",
       "110345      F 2016-05-16 12:30:58+00:00 2016-06-01 00:00:00+00:00    0   \n",
       "110346      M 2016-06-06 14:22:34+00:00 2016-06-08 00:00:00+00:00    0   \n",
       "110454      F 2016-06-03 15:18:44+00:00 2016-06-03 00:00:00+00:00    0   \n",
       "110460      F 2016-06-03 08:56:51+00:00 2016-06-03 00:00:00+00:00    0   \n",
       "110507      F 2016-06-08 09:04:18+00:00 2016-06-08 00:00:00+00:00    0   \n",
       "\n",
       "            Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "59              CONQUISTA            0             0         0           0   \n",
       "63           SÃO BENEDITO            0             0         0           0   \n",
       "64      ILHA DAS CAIEIRAS            0             0         0           0   \n",
       "65              CONQUISTA            0             0         0           0   \n",
       "67         NOVA PALESTINA            0             0         0           0   \n",
       "...                   ...          ...           ...       ...         ...   \n",
       "110345        RESISTÊNCIA            0             0         0           0   \n",
       "110346        RESISTÊNCIA            0             0         0           0   \n",
       "110454        RESISTÊNCIA            0             0         0           0   \n",
       "110460        RESISTÊNCIA            0             0         0           0   \n",
       "110507        MARIA ORTIZ            0             0         0           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment  \n",
       "59                0             0                       Yes  \n",
       "63                0             0                       Yes  \n",
       "64                0             1                       Yes  \n",
       "65                0             0                       Yes  \n",
       "67                0             0                       Yes  \n",
       "...             ...           ...                       ...  \n",
       "110345            0             0                       Yes  \n",
       "110346            0             0                       Yes  \n",
       "110454            0             0                       Yes  \n",
       "110460            0             0                       Yes  \n",
       "110507            0             0                       Yes  \n",
       "\n",
       "[3540 rows x 12 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Age\"] <= 0] # To get the dataframe of patients whose age is less than or equal to 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above result it is obvious that 3539 patients had age entries of zero since only one patient from above had age entry of -1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Dropping the Age of -1 entry in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop('5775010',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above command can be used to drop the abnormal entry of `-1` by using the `AppointmentID`. However remember that I have dropped the AppointmentID column.\n",
    "\n",
    "We shall therefore use the below command to achiev themsame result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.Age >= 0]  \n",
    "\n",
    "# Another command to drop the `-1` Age from the dataset. \n",
    "# This doesn't need the use of AppointmentID in case it has been removed earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's confirm if we were successful with the above command by running the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
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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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handicapped, SMS_received, Showed_up_for_appointment]\n",
       "Index: []"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Age\"] < 0] # To get the dataframe of patients whose age is -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes we were !!! The age of `-1` no longer exists."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Handicapped Error Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"Handicapped\"] >=2).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result shows that 199 patients had Handicapped values of equal to or greater then 2: either 2, 3, 04 r as shown in our analysis of unique values above.\n",
    "\n",
    "Now let us subset the DataFrame of these entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
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       "      <th>Alcoholism</th>\n",
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       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-14 09:26:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>94</td>\n",
       "      <td>BELA VISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-03-30 09:16:41+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>64</td>\n",
       "      <td>SANTA MARTHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-03-30 09:16:41+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>64</td>\n",
       "      <td>SANTA MARTHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 10:08:48+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>64</td>\n",
       "      <td>SANTA MARTHA</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2091</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 08:13:59+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108376</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-01 08:48:28+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>44</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109484</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-31 11:45:57+00:00</td>\n",
       "      <td>2016-06-02 00:00:00+00:00</td>\n",
       "      <td>64</td>\n",
       "      <td>DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109733</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-03 16:11:00+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>34</td>\n",
       "      <td>JUCUTUQUARA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109975</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-02 16:07:36+00:00</td>\n",
       "      <td>2016-06-06 00:00:00+00:00</td>\n",
       "      <td>39</td>\n",
       "      <td>PRAIA DO SUÁ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110107</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-02 06:44:00+00:00</td>\n",
       "      <td>2016-06-06 00:00:00+00:00</td>\n",
       "      <td>44</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "946         M 2016-04-14 09:26:08+00:00 2016-04-29 00:00:00+00:00   94   \n",
       "1665        M 2016-03-30 09:16:41+00:00 2016-04-29 00:00:00+00:00   64   \n",
       "1666        M 2016-03-30 09:16:41+00:00 2016-04-29 00:00:00+00:00   64   \n",
       "2071        M 2016-04-29 10:08:48+00:00 2016-04-29 00:00:00+00:00   64   \n",
       "2091        F 2016-04-29 08:13:59+00:00 2016-04-29 00:00:00+00:00   11   \n",
       "...       ...                       ...                       ...  ...   \n",
       "108376      F 2016-06-01 08:48:28+00:00 2016-06-07 00:00:00+00:00   44   \n",
       "109484      M 2016-05-31 11:45:57+00:00 2016-06-02 00:00:00+00:00   64   \n",
       "109733      F 2016-06-03 16:11:00+00:00 2016-06-07 00:00:00+00:00   34   \n",
       "109975      M 2016-06-02 16:07:36+00:00 2016-06-06 00:00:00+00:00   39   \n",
       "110107      F 2016-06-02 06:44:00+00:00 2016-06-06 00:00:00+00:00   44   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "946       BELA VISTA            0             1         1           0   \n",
       "1665    SANTA MARTHA            0             1         0           1   \n",
       "1666    SANTA MARTHA            0             1         0           1   \n",
       "2071    SANTA MARTHA            0             1         0           1   \n",
       "2091      ANDORINHAS            0             0         0           0   \n",
       "...              ...          ...           ...       ...         ...   \n",
       "108376         ROMÃO            0             1         1           0   \n",
       "109484      DA PENHA            0             1         1           0   \n",
       "109733   JUCUTUQUARA            0             0         0           0   \n",
       "109975  PRAIA DO SUÁ            1             0         0           0   \n",
       "110107   RESISTÊNCIA            0             0         0           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment  \n",
       "946               2             1                       Yes  \n",
       "1665              2             1                       Yes  \n",
       "1666              2             0                       Yes  \n",
       "2071              2             0                       Yes  \n",
       "2091              2             0                       Yes  \n",
       "...             ...           ...                       ...  \n",
       "108376            2             1                       Yes  \n",
       "109484            2             0                       Yes  \n",
       "109733            2             1                       Yes  \n",
       "109975            2             1                       Yes  \n",
       "110107            2             1                       Yes  \n",
       "\n",
       "[199 rows x 12 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Handicapped\"] >= 2] \n",
    "# To get the dataframe of patients whose Handicapped value is greater than or equal 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now let us Delete this entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.Handicapped < 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then check to see if our operation was successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"Handicapped\"] >=2).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returned zero indicating that the command was usccessful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To know the dates covered by the scheduled and appointment registers\n",
    "\n",
    "We shall do this by sorting out the respective dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Timestamp('2015-11-10 07:13:56+0000', tz='UTC'),\n",
       "       Timestamp('2015-12-03 08:17:28+0000', tz='UTC'),\n",
       "       Timestamp('2015-12-07 10:40:59+0000', tz='UTC'), ...,\n",
       "       Timestamp('2016-06-08 19:33:23+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 19:58:52+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 20:07:23+0000', tz='UTC')], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(df['ScheduledDay'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, it can be seen that the patients scheduled their hospital appointments between `2015-11-10` and `2016-06-08`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Timestamp('2016-04-29 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-04-29 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-04-29 00:00:00+0000', tz='UTC'), ...,\n",
       "       Timestamp('2016-06-08 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 00:00:00+0000', tz='UTC')], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(df['AppointmentDay'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, it can be seen that the patients had their hospital appointments between `2016-04-29` and `2016-06-08`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To know time interval/difference between when the patient scheduled an appointment and when he had the appointment\n",
    "\n",
    "I shall name and create a new variable on the dataset, called `Delay_time`. This will indicate the time spent between when the patient scheduled an appointment and when the actual appointment took place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Delay_time\"] = df[\"AppointmentDay\"] - df[\"ScheduledDay\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "      <th>Delay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days +05:21:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "      <td>-1 days +07:51:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "      <td>-1 days +07:40:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "      <td>-1 days +06:30:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</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>Yes</td>\n",
       "      <td>-1 days +07:52:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "0      F 2016-04-29 18:38:08+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00    8   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hypertension  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",
       "   Handicapped  SMS_received Showed_up_for_appointment        Delay_time  \n",
       "0            0             0                       Yes -1 days +05:21:52  \n",
       "1            0             0                       Yes -1 days +07:51:33  \n",
       "2            0             0                       Yes -1 days +07:40:56  \n",
       "3            0             0                       Yes -1 days +06:30:29  \n",
       "4            0             0                       Yes -1 days +07:52:37  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "      <th>Delay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110522</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03 09:15:35+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>34 days 14:44:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110523</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03 07:27:33+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>51</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>34 days 16:32:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 16:03:52+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40 days 07:56:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 15:09:23+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40 days 08:50:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 13:30:56+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40 days 10:29:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "110522      F 2016-05-03 09:15:35+00:00 2016-06-07 00:00:00+00:00   56   \n",
       "110523      F 2016-05-03 07:27:33+00:00 2016-06-07 00:00:00+00:00   51   \n",
       "110524      F 2016-04-27 16:03:52+00:00 2016-06-07 00:00:00+00:00   21   \n",
       "110525      F 2016-04-27 15:09:23+00:00 2016-06-07 00:00:00+00:00   38   \n",
       "110526      F 2016-04-27 13:30:56+00:00 2016-06-07 00:00:00+00:00   54   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "110522   MARIA ORTIZ            0             0         0           0   \n",
       "110523   MARIA ORTIZ            0             0         0           0   \n",
       "110524   MARIA ORTIZ            0             0         0           0   \n",
       "110525   MARIA ORTIZ            0             0         0           0   \n",
       "110526   MARIA ORTIZ            0             0         0           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment       Delay_time  \n",
       "110522            0             1                       Yes 34 days 14:44:25  \n",
       "110523            0             1                       Yes 34 days 16:32:27  \n",
       "110524            0             1                       Yes 40 days 07:56:08  \n",
       "110525            0             1                       Yes 40 days 08:50:37  \n",
       "110526            0             1                       Yes 40 days 10:29:04  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Which months had the highest and lowest amount of scheduled visits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    67295\n",
       "4    25299\n",
       "6    13723\n",
       "3     3608\n",
       "2      280\n",
       "Name: ScheduledDay, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ScheduledDay\"].dt.month.value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above result it is obvious that most visits were scheduled in May while the least visits were scheduled in February. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Which months had the highest and lowest amount of Appointment visits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    80694\n",
       "6    26406\n",
       "4     3227\n",
       "Name: AppointmentDay, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AppointmentDay\"].dt.month.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, it is obvious that most patients had their hospital appointments in May and the least in April.\n",
    "\n",
    "From the foregoing, it can be deduced that the month of May is the busiest in the hospital in terms of ScheduledDay and AppointmentDay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### What is the Minimum and Maximum Delay time before Patients come for Appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('-7 days +10:10:40')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Delay_time\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result indicates that there was a minimum delay time of 10 hours before patients show up for their scheduled appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('178 days 13:19:01')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Delay_time\"].max()  # To get the maximum delay time before appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result indicates that there was a maximum delay time of 178 days before patients show up for their scheduled appointments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To know the duration covered by scheduled and appointment registers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using  a Function to Compute Minimum and Maximum dates/durations for ScheduledDay, AppointmentDay and Delay time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we define the function for the minimum\n",
    "def min(column):        \n",
    "    return column.min()    \n",
    "\n",
    "\n",
    "# Here we define the function for the maximum\n",
    "def max(column):\n",
    "    return column.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Delay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015-11-10 07:13:56+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>-7 days +10:10:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016-06-08 20:07:23+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>178 days 13:19:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ScheduledDay            AppointmentDay        Delay_time\n",
       "min 2015-11-10 07:13:56+00:00 2016-04-29 00:00:00+00:00 -7 days +10:10:40\n",
       "max 2016-06-08 20:07:23+00:00 2016-06-08 00:00:00+00:00 178 days 13:19:01"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next we call the above defined functions on multiple columns of interest\n",
    "# and use the `agg()` DataFrame method.\n",
    "\n",
    "df[[\"ScheduledDay\", \"AppointmentDay\", \"Delay_time\"]].agg([min, max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result shows that the minimum time between scheduling for and showing up for an appointmnet i.e. the delay time is about 10 hours.\n",
    "\n",
    "Similarly the above result shows that the maximum time between scheduling for and showing up for an appointmnet i.e. the delay time is about 178 days hours.\n",
    "\n",
    "The scheduling dates extend from `2015-11-10` to `2016-06-08` i.e. about 30 weeks, 1 day.\n",
    "The appointment dates extend from `2016-04-29` to `2016-06-08` i.e. about 5 weeks, 5 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### See the Percentage of scheduled and Appointment visits per month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     0.609959\n",
       "4     0.229309\n",
       "6     0.124385\n",
       "3     0.032703\n",
       "2     0.002538\n",
       "12    0.000553\n",
       "1     0.000544\n",
       "11    0.000009\n",
       "Name: ScheduledDay, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ScheduledDay\"].dt.month.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above we used the `normalize = True` argument to see the proportions of the total for each unque month for the visits. This revealed that May had the most visits making up about 61%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    0.731408\n",
       "6    0.239343\n",
       "4    0.029249\n",
       "Name: AppointmentDay, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AppointmentDay\"].dt.month.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results for AppointmentDay indicates that May also had the most appointments making up about 73% of the total visits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the duration it took between scheduling and showing up for first and last appointment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('170 days 16:46:04')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AppointmentDay\"].min() - df[\"ScheduledDay\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, it took 170 days between the first scheduled visit and when the patient showed up for the first Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('-1 days +03:52:37')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AppointmentDay\"].max() - df[\"ScheduledDay\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, it took 3 hours between the first scheduled visit and when the patient showed up for the first Appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Duration of scheduling Visits and Showing Up for Appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('211 days 12:53:27')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ScheduledDay\"].max() - df[\"ScheduledDay\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result indicates that the visits were scheduled over a duration of 211 days (30 weeks, 1 day)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('40 days 00:00:00')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AppointmentDay\"].max() - df[\"AppointmentDay\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result indicates that the appointments were made over a duration of 40 days (5 weeks, 5 days)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Insights\n",
    "\n",
    "This above results indicate that the duration for the appointments were short compared to the duration alloted for scheduling visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -568160000000000,  -139841000000000,  -135838000000000, ...,\n",
       "       15427019000000000, 15427038000000000, 15427141000000000],\n",
       "      dtype='timedelta64[ns]')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "np.sort(df[\"Delay_time\"])  # Using the Numpy sort function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us attempt to sort the DataFrame based on the Delay_time. This further gives us an insight into the variation in the Delay_time. Same code will be applied to AppointmentDay and ScheduledDay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "      <th>Delay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11 13:49:20+00:00</td>\n",
       "      <td>2016-05-05 00:00:00+00:00</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>-7 days +10:10:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18 14:50:41+00:00</td>\n",
       "      <td>2016-05-17 00:00:00+00:00</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>-2 days +09:09:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05 13:43:58+00:00</td>\n",
       "      <td>2016-05-04 00:00:00+00:00</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>-2 days +10:16:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27033</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10 10:51:53+00:00</td>\n",
       "      <td>2016-05-09 00:00:00+00:00</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>-2 days +13:08:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04 06:50:57+00:00</td>\n",
       "      <td>2016-05-03 00:00:00+00:00</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>-2 days +17:09:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102786</th>\n",
       "      <td>M</td>\n",
       "      <td>2015-12-07 10:43:34+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>67</td>\n",
       "      <td>MARUÍPE</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>Yes</td>\n",
       "      <td>178 days 13:16:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102799</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:43:17+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>74</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>178 days 13:16:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102796</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:43:01+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>80</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>178 days 13:16:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102797</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:42:42+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>48</td>\n",
       "      <td>MARUÍPE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>178 days 13:17:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102795</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:40:59+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>178 days 13:19:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110327 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "71533       F 2016-05-11 13:49:20+00:00 2016-05-05 00:00:00+00:00   81   \n",
       "55226       F 2016-05-18 14:50:41+00:00 2016-05-17 00:00:00+00:00   19   \n",
       "64175       F 2016-05-05 13:43:58+00:00 2016-05-04 00:00:00+00:00   22   \n",
       "27033       M 2016-05-10 10:51:53+00:00 2016-05-09 00:00:00+00:00   38   \n",
       "72362       M 2016-05-04 06:50:57+00:00 2016-05-03 00:00:00+00:00    7   \n",
       "...       ...                       ...                       ...  ...   \n",
       "102786      M 2015-12-07 10:43:34+00:00 2016-06-03 00:00:00+00:00   67   \n",
       "102799      F 2015-12-07 10:43:17+00:00 2016-06-03 00:00:00+00:00   74   \n",
       "102796      F 2015-12-07 10:43:01+00:00 2016-06-03 00:00:00+00:00   80   \n",
       "102797      F 2015-12-07 10:42:42+00:00 2016-06-03 00:00:00+00:00   48   \n",
       "102795      F 2015-12-07 10:40:59+00:00 2016-06-03 00:00:00+00:00   27   \n",
       "\n",
       "        Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "71533   SANTO ANTÔNIO            0             0         0           0   \n",
       "55226   SANTO ANTÔNIO            0             0         0           0   \n",
       "64175      CONSOLAÇÃO            0             0         0           0   \n",
       "27033     RESISTÊNCIA            0             0         0           0   \n",
       "72362      TABUAZEIRO            0             0         0           0   \n",
       "...               ...          ...           ...       ...         ...   \n",
       "102786        MARUÍPE            0             1         1           0   \n",
       "102799  SÃO CRISTÓVÃO            0             1         1           0   \n",
       "102796  SÃO CRISTÓVÃO            0             1         1           0   \n",
       "102797        MARUÍPE            0             1         1           0   \n",
       "102795  SÃO CRISTÓVÃO            1             0         0           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment        Delay_time  \n",
       "71533             0             0                        No -7 days +10:10:40  \n",
       "55226             1             0                        No -2 days +09:09:19  \n",
       "64175             0             0                        No -2 days +10:16:02  \n",
       "27033             1             0                        No -2 days +13:08:07  \n",
       "72362             0             0                        No -2 days +17:09:03  \n",
       "...             ...           ...                       ...               ...  \n",
       "102786            0             0                       Yes 178 days 13:16:26  \n",
       "102799            0             1                       Yes 178 days 13:16:43  \n",
       "102796            0             1                       Yes 178 days 13:16:59  \n",
       "102797            0             1                       Yes 178 days 13:17:18  \n",
       "102795            0             1                        No 178 days 13:19:01  \n",
       "\n",
       "[110327 rows x 13 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"Delay_time\") # Using Pandas sort_values method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Numpy sort function as shown in the 2 cells below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Timestamp('2016-04-29 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-04-29 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-04-29 00:00:00+0000', tz='UTC'), ...,\n",
       "       Timestamp('2016-06-08 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 00:00:00+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 00:00:00+0000', tz='UTC')], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(df['AppointmentDay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Timestamp('2015-11-10 07:13:56+0000', tz='UTC'),\n",
       "       Timestamp('2015-12-03 08:17:28+0000', tz='UTC'),\n",
       "       Timestamp('2015-12-07 10:40:59+0000', tz='UTC'), ...,\n",
       "       Timestamp('2016-06-08 19:33:23+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 19:58:52+0000', tz='UTC'),\n",
       "       Timestamp('2016-06-08 20:07:23+0000', tz='UTC')], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(df['ScheduledDay'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Pandas sort_values method as shown in the 2 cells below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
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       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "      <th>Delay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
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       "      <td>-1 days +05:21:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-03-29 11:09:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>33</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30 days 12:50:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2152</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-29 11:08:52+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>50</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30 days 12:51:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-03-29 17:04:40+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30 days 06:55:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:21:24+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>65</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days +06:38:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106528</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 07:14:03+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JESUS DE NAZARETH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days +16:45:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106526</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 07:17:47+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>35</td>\n",
       "      <td>JESUS DE NAZARETH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days +16:42:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106524</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 07:19:00+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>44</td>\n",
       "      <td>JESUS DE NAZARETH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days +16:41:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106520</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-20 08:33:01+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>53</td>\n",
       "      <td>JESUS DE NAZARETH</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18 days 15:26:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98959</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-02 08:36:40+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>49</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>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5 days 15:23:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110327 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "0           F 2016-04-29 18:38:08+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "2151        M 2016-03-29 11:09:08+00:00 2016-04-29 00:00:00+00:00   33   \n",
       "2152        F 2016-03-29 11:08:52+00:00 2016-04-29 00:00:00+00:00   50   \n",
       "2153        F 2016-03-29 17:04:40+00:00 2016-04-29 00:00:00+00:00   69   \n",
       "2154        F 2016-04-29 17:21:24+00:00 2016-04-29 00:00:00+00:00   65   \n",
       "...       ...                       ...                       ...  ...   \n",
       "106528      F 2016-06-08 07:14:03+00:00 2016-06-08 00:00:00+00:00   56   \n",
       "106526      F 2016-06-08 07:17:47+00:00 2016-06-08 00:00:00+00:00   35   \n",
       "106524      F 2016-06-08 07:19:00+00:00 2016-06-08 00:00:00+00:00   44   \n",
       "106520      F 2016-05-20 08:33:01+00:00 2016-06-08 00:00:00+00:00   53   \n",
       "98959       F 2016-06-02 08:36:40+00:00 2016-06-08 00:00:00+00:00   49   \n",
       "\n",
       "            Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "0         JARDIM DA PENHA            0             1         0           0   \n",
       "2151          MARIA ORTIZ            0             0         0           0   \n",
       "2152          MARIA ORTIZ            0             0         0           0   \n",
       "2153          MARIA ORTIZ            0             0         0           0   \n",
       "2154          MARIA ORTIZ            0             0         0           0   \n",
       "...                   ...          ...           ...       ...         ...   \n",
       "106528  JESUS DE NAZARETH            0             0         0           0   \n",
       "106526  JESUS DE NAZARETH            0             0         0           0   \n",
       "106524  JESUS DE NAZARETH            0             0         0           0   \n",
       "106520  JESUS DE NAZARETH            0             1         1           0   \n",
       "98959       MATA DA PRAIA            0             0         0           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment        Delay_time  \n",
       "0                 0             0                       Yes -1 days +05:21:52  \n",
       "2151              0             1                       Yes  30 days 12:50:52  \n",
       "2152              0             0                       Yes  30 days 12:51:08  \n",
       "2153              0             1                       Yes  30 days 06:55:20  \n",
       "2154              0             0                       Yes -1 days +06:38:36  \n",
       "...             ...           ...                       ...               ...  \n",
       "106528            0             0                       Yes -1 days +16:45:57  \n",
       "106526            0             0                       Yes -1 days +16:42:13  \n",
       "106524            0             0                       Yes -1 days +16:41:00  \n",
       "106520            0             1                       Yes  18 days 15:26:59  \n",
       "98959             0             1                       Yes   5 days 15:23:20  \n",
       "\n",
       "[110327 rows x 13 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"AppointmentDay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3764</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-11-10 07:13:56+00:00</td>\n",
       "      <td>2016-05-04 00:00:00+00:00</td>\n",
       "      <td>51</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
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       "      <td>175 days 16:46:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46292</th>\n",
       "      <td>M</td>\n",
       "      <td>2015-12-03 08:17:28+00:00</td>\n",
       "      <td>2016-05-02 00:00:00+00:00</td>\n",
       "      <td>34</td>\n",
       "      <td>VILA RUBIM</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>150 days 15:42:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102795</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:40:59+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>178 days 13:19:01</td>\n",
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       "    <tr>\n",
       "      <th>102797</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:42:42+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>48</td>\n",
       "      <td>MARUÍPE</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>178 days 13:17:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102796</th>\n",
       "      <td>F</td>\n",
       "      <td>2015-12-07 10:43:01+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>80</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>178 days 13:16:59</td>\n",
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       "      <th>92442</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-08 19:32:25+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:27:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88146</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 19:32:56+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:27:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88147</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-08 19:33:23+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:26:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87219</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 19:58:52+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:01:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87223</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 20:07:23+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +03:52:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110327 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "3764        F 2015-11-10 07:13:56+00:00 2016-05-04 00:00:00+00:00   51   \n",
       "46292       M 2015-12-03 08:17:28+00:00 2016-05-02 00:00:00+00:00   34   \n",
       "102795      F 2015-12-07 10:40:59+00:00 2016-06-03 00:00:00+00:00   27   \n",
       "102797      F 2015-12-07 10:42:42+00:00 2016-06-03 00:00:00+00:00   48   \n",
       "102796      F 2015-12-07 10:43:01+00:00 2016-06-03 00:00:00+00:00   80   \n",
       "...       ...                       ...                       ...  ...   \n",
       "92442       M 2016-06-08 19:32:25+00:00 2016-06-08 00:00:00+00:00   54   \n",
       "88146       F 2016-06-08 19:32:56+00:00 2016-06-08 00:00:00+00:00   43   \n",
       "88147       M 2016-06-08 19:33:23+00:00 2016-06-08 00:00:00+00:00   27   \n",
       "87219       F 2016-06-08 19:58:52+00:00 2016-06-08 00:00:00+00:00   30   \n",
       "87223       F 2016-06-08 20:07:23+00:00 2016-06-08 00:00:00+00:00   27   \n",
       "\n",
       "         Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "3764       RESISTÊNCIA            0             0         0           0   \n",
       "46292       VILA RUBIM            0             1         0           0   \n",
       "102795   SÃO CRISTÓVÃO            1             0         0           0   \n",
       "102797         MARUÍPE            0             1         1           0   \n",
       "102796   SÃO CRISTÓVÃO            0             1         1           0   \n",
       "...                ...          ...           ...       ...         ...   \n",
       "92442   JARDIM CAMBURI            0             0         0           0   \n",
       "88146   JARDIM CAMBURI            0             0         0           0   \n",
       "88147   JARDIM CAMBURI            0             0         0           0   \n",
       "87219   JARDIM CAMBURI            0             0         0           0   \n",
       "87223   JARDIM CAMBURI            0             0         0           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment        Delay_time  \n",
       "3764              0             1                       Yes 175 days 16:46:04  \n",
       "46292             0             1                        No 150 days 15:42:32  \n",
       "102795            0             1                        No 178 days 13:19:01  \n",
       "102797            0             1                       Yes 178 days 13:17:18  \n",
       "102796            0             1                       Yes 178 days 13:16:59  \n",
       "...             ...           ...                       ...               ...  \n",
       "92442             0             0                       Yes -1 days +04:27:35  \n",
       "88146             0             0                       Yes -1 days +04:27:04  \n",
       "88147             0             0                       Yes -1 days +04:26:37  \n",
       "87219             0             0                       Yes -1 days +04:01:08  \n",
       "87223             0             0                       Yes -1 days +03:52:37  \n",
       "\n",
       "[110327 rows x 13 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"ScheduledDay\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gender that showed up for most appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  Showed_up_for_appointment\n",
       "F       Yes                          0.518114\n",
       "M       Yes                          0.279959\n",
       "F       No                           0.132044\n",
       "M       No                           0.069883\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Gender\", \"Showed_up_for_appointment\"]].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Insights\n",
    "\n",
    "The above result indicates that more females (51.8%) showed up for their appointments compared to males (28%).\n",
    "\n",
    "Likewise, 13.2% of females did not turn up for appointments and 7% males didn't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Further Sort the Dataset by columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "      <th>Delay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87223</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 20:07:23+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +03:52:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87219</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 19:58:52+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:01:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88146</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 19:32:56+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:27:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88144</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 19:02:15+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>49</td>\n",
       "      <td>JARDIM 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>Yes</td>\n",
       "      <td>-1 days +04:57:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104871</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-08 18:34:11+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>46</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>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days +05:25:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-02-17 16:57:54+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>75</td>\n",
       "      <td>BONFIM</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>71 days 07:02:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-02-11 16:03:05+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>84</td>\n",
       "      <td>MARUÍPE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>77 days 07:56:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-01-29 10:46:47+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>48</td>\n",
       "      <td>MARUÍPE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>90 days 13:13:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-01-11 09:57:10+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>REDENÇÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>108 days 14:02:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-01-05 11:53:44+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>84</td>\n",
       "      <td>SANTO ANDRÉ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>114 days 12:06:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110327 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender              ScheduledDay            AppointmentDay  Age  \\\n",
       "87223       F 2016-06-08 20:07:23+00:00 2016-06-08 00:00:00+00:00   27   \n",
       "87219       F 2016-06-08 19:58:52+00:00 2016-06-08 00:00:00+00:00   30   \n",
       "88146       F 2016-06-08 19:32:56+00:00 2016-06-08 00:00:00+00:00   43   \n",
       "88144       F 2016-06-08 19:02:15+00:00 2016-06-08 00:00:00+00:00   49   \n",
       "104871      F 2016-06-08 18:34:11+00:00 2016-06-08 00:00:00+00:00   46   \n",
       "...       ...                       ...                       ...  ...   \n",
       "972         M 2016-02-17 16:57:54+00:00 2016-04-29 00:00:00+00:00   75   \n",
       "933         M 2016-02-11 16:03:05+00:00 2016-04-29 00:00:00+00:00   84   \n",
       "871         M 2016-01-29 10:46:47+00:00 2016-04-29 00:00:00+00:00   48   \n",
       "956         M 2016-01-11 09:57:10+00:00 2016-04-29 00:00:00+00:00   71   \n",
       "954         M 2016-01-05 11:53:44+00:00 2016-04-29 00:00:00+00:00   84   \n",
       "\n",
       "         Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "87223   JARDIM CAMBURI            0             0         0           0   \n",
       "87219   JARDIM CAMBURI            0             0         0           0   \n",
       "88146   JARDIM CAMBURI            0             0         0           0   \n",
       "88144   JARDIM CAMBURI            0             0         0           0   \n",
       "104871      TABUAZEIRO            0             0         0           0   \n",
       "...                ...          ...           ...       ...         ...   \n",
       "972             BONFIM            0             1         1           0   \n",
       "933            MARUÍPE            0             1         0           0   \n",
       "871            MARUÍPE            0             1         0           0   \n",
       "956           REDENÇÃO            0             1         1           0   \n",
       "954        SANTO ANDRÉ            0             1         1           0   \n",
       "\n",
       "        Handicapped  SMS_received Showed_up_for_appointment        Delay_time  \n",
       "87223             0             0                       Yes -1 days +03:52:37  \n",
       "87219             0             0                       Yes -1 days +04:01:08  \n",
       "88146             0             0                       Yes -1 days +04:27:04  \n",
       "88144             0             0                       Yes -1 days +04:57:45  \n",
       "104871            0             0                       Yes -1 days +05:25:49  \n",
       "...             ...           ...                       ...               ...  \n",
       "972               0             1                        No  71 days 07:02:06  \n",
       "933               0             1                       Yes  77 days 07:56:55  \n",
       "871               0             1                       Yes  90 days 13:13:13  \n",
       "956               0             1                       Yes 108 days 14:02:50  \n",
       "954               1             1                       Yes 114 days 12:06:16  \n",
       "\n",
       "[110327 rows x 13 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"Gender\", \"AppointmentDay\", \"ScheduledDay\"], ascending=[True, False, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check for Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender                       0\n",
      "ScheduledDay                 0\n",
      "AppointmentDay               0\n",
      "Age                          0\n",
      "Neighbourhood                0\n",
      "Scholarship                  0\n",
      "Hypertension                 0\n",
      "Diabetes                     0\n",
      "Alcoholism                   0\n",
      "Handicapped                  0\n",
      "SMS_received                 0\n",
      "Showed_up_for_appointment    0\n",
      "Delay_time                   0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing data. Wrap a sum function around it to give the total number of missing data\n",
    "print(df.isna().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results above show no missing data.\n",
    "\n",
    "Check for, fill and drop missing data with isna(), fillna() and dropna() [or drop() in some case] respectively.\n",
    "\n",
    "\n",
    "Ensure you first run the fillna() and dropna() or drop() functions on a copy of the dataset \n",
    "before applying same to the original dataset.\n",
    "\n",
    "The drop() function is used to delete columns you KNOW you wont need in your analysis\n",
    "thus reducing the dataset to the bearest useful minimum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us start with some charts to display our column variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diagrammatic Representation of Patients Show up at the Hospital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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": [
    "fig = sns.countplot(x=\"Showed_up_for_appointment\", data=df)\n",
    "fig.set_title(\"Patient Show up for Appointment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows the distribution of those who showed up for appointment and those who didn't. From the chart, it is obvious that those who came for the appointment out-number those who didn't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Appointment show up based on Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Appointment Show up Based on Age Distribution')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.barplot(x = \"Showed_up_for_appointment\", y = \"Age\", data = df, palette = \"rainbow\")\n",
    "plt.title(\"Appointment Show up Based on Age Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows there is no much difference in age amongst those who showed up and those who did not show up for appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Appointment Show up based on Age, separated by Scholarship status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Appointment Show up based on Age, separated by Scholarship status')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sns.boxplot(x=\"Showed_up_for_appointment\", \n",
    "              y=\"Age\", \n",
    "              hue=\"Scholarship\",\n",
    "              data=df,\n",
    "             palette = \"viridis\")\n",
    "\n",
    "plt.title(\"Appointment Show up based on Age, separated by Scholarship status\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows that amongst those who didn't have scholarships, the median age of those who showed up and those who didn't show up for appointment was about same. Thesame applies to those with scholarship.\n",
    "\n",
    "With the exclusion of some few outliers amongst those who didn't show up for appointment and didn't have scholarship, the age distribution was almost normal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Appointment show up based on Age, amongst handicapped patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Appointment Show up based on Age, separated by Handicap status')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sns.boxplot(x=\"Showed_up_for_appointment\", \n",
    "              y=\"Age\", \n",
    "              hue=\"Handicapped\",\n",
    "              data=df,\n",
    "             palette = \"viridis\")\n",
    "\n",
    "plt.title(\"Appointment Show up based on Age, separated by Handicap status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above chart, the oldest of the patients are those handicapped with the median age of those who showed up for appointment being around 58 years. The median age of those non handicapped patients who didn't show up was about 36 years. those patients with Handicap who showed up for appointments averagely are older. The youngest age groups (starting from zero) had litle or no difference amongst the categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Appointment show up based on Age, separated by Alcolism status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Appointment Show up based on Age, separated by Alcoholism status')"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KKV3f5v3wA2BsRBxEceJ/PcXdye6iOFneyIsndidT3ADivrL2qylaeqBoFWjZV20NoDhZXknxPptAcf0JKaWfUwSyK8v9eA8vPaHv6D34jxQ3q3iqHN/ZfiSldB9F0PgDxQnvGOCWmlnmA/cCj0VES2tNh++tlNJ/U9xgYX45z/zOtl/q6FhrcWlZV7vfcxMRgylaNH5Y+zqmlJrKdbfXVe67FOHleoqT9Qsprt1aQxGOz6AI/F8E3lfTrbhDKaUHKI73H1K8t/8W+Nvy+O7MqymOm6eBJRTv923xnT6drff7wIeiuFvhDyi6tP03xc0ZHqF4T9V+ANbywcmaiLizfPxVita1dRTH3RV1eE5SnxKtu39L6gsi4mJgRUrpKx1MTxQXND9U18K01crWp8tTSiO7mFVdiIj3AuenlNp2pWpv3q8Aq1NKP9nGNWT1HoyIk4HTUkpvr7oWSdoS/fHL3CRJGYniOrsjKFoQdqdoMf15d5ZNKX29B0vLQtl17pMULaiS1KfYTU6S1NcFRRegdRTd5JYA/1BpRZkor0NaTdF974ouZpekXsducpIkSZKyZMuQJEmSpCz1WBiKiIsiYlVE3FMz7lsRcX9ELI6In5e3QG2Z9uWIeCgiHtiCW+tKkiRJ0lbpsW5yEXE4xfcoXJpSOqgcdxQwP6W0MSK+AZBS+lJEHEjxnRRvpvh26N8A+7V8h0FHdt111zR69OgeqV+SJElS/3DHHXc8kVIa3nZ8j91NLqV0U9svsUspXV8zeCsvfvnXscCVKaVngaaIeIgiGLX7LdYtRo8ezcKFC7dd0ZIkSZL6nYh4pL3xVV4zdCrFl4cB7EnrLw5bUY6TJEmSpB5RSRiKiBkU3w4+u2VUO7O1238vIk6LiIURsXD16tU9VaIkSZKkfq7uYSgipgLvA6akFy9YWgGMqpltJLCyveVTSheklManlMYPH/6Sbn+SJEmS1C09ds1QeyLiaOBLwISU0l9qJl0LXBER36W4gcK+wO31rE2SJEnqL55//nlWrFjBhg0bqi6lrgYPHszIkSMZNGhQt+bvsTAUEXOAdwK7RsQK4GvAl4FXAjdEBMCtKaXTU0r3RsRc4D6K7nOf6upOcpIkSZLat2LFCnbYYQdGjx5Ned7d76WUWLNmDStWrKCxsbFby/Tk3eRObGf0hZ3MPxOY2VP1SJIkSbnYsGFDVkEIICLYZZdd2JL7ClR5NzlJkiRJPSSnINRiS5+zYUiSJEnKxM9//nMigvvvvx+ApUuXctBBB23VukaPHs0TTzzR7fkvvvhiPv3pTwNw/vnnc+mll27Vdrclw5AkSZKUiTlz5vD2t7+dK6+8stI6Tj/9dE4++eRKawDDkCRJkpSFZ555hltuuYULL7yw3TC0adMmPv/5zzNmzBjGjh3LD3/4QwDmzZvHuHHjGDNmDKeeeirPPvvs5mV++MMfcsghhzBmzJjNrU1r167luOOOY+zYsRx66KEsXrz4Jds6++yz+fa3vw3AD37wAw488EDGjh3L5MmTN0+fOnUqRx11FKNHj+ZnP/sZX/ziFxkzZgxHH300zz///DbZJ4YhSZIkKQO/+MUvOProo9lvv/0YNmwYd955Z6vpF1xwAU1NTdx1110sXryYKVOmsGHDBk455RSuuuoq7r77bjZu3MiPf/zjzcvsuuuu3Hnnnfz93//95nDzta99jXHjxrF48WLOPffcLluAzjvvvM3bPP/88zeP/9Of/sR1113HNddcw0knncQRRxzB3Xffzate9Squu+66bbJPDEOSJElSBubMmbO55WXy5MnMmTOn1fTf/OY3nH766TQ0FDecHjZsGA888ACNjY3st99+AEydOpWbbrpp8zLHH388AG984xtZunQpADfffDMf/ehHAZg4cSJr1qzhqaee6rCusWPHMmXKFC6//PLN2wZ473vfy6BBgxgzZgybNm3i6KOPBmDMmDGbt/Vy1fVLVyVJkiTV35o1a5g/fz733HMPEcGmTZuICD75yU9uniel9JK7saWUOl3vK1/5SgAGDhzIxo0bO1yms7u8XXfdddx0001ce+21nHPOOdx7772t1j1gwAAGDRq0eR0DBgzYvK2Xy5YhSZIkqZ+7+uqrOfnkk3nkkUdYunQpy5cvp7GxkRUrVmye56ijjuL888/fHDTWrl3L6173OpYuXcpDDz0EwGWXXcaECRM63dbhhx/O7NmzAbjxxhvZdddd2XHHHdud94UXXmD58uUcccQRfPOb3+TJJ5/kmWee2RZPuVsMQ+pVHn74YT7ykY9ss6ZPSVLftmjRIo4//vh2L8CW1H1z5szhAx/4QKtxH/zgBzn33HM3D0+bNo299tqLsWPHcvDBB3PFFVcwePBgfvrTn/LhD3+YMWPGMGDAAE4//fROt3X22WezcOFCxo4dy5lnnskll1zS4bybNm3ipJNOYsyYMYwbN47p06ez8847v6znuiWiq6av3mz8+PFp4cKFVZehbegzn/kMy5cvZ9SoUZvvYCJJyteUKVNYv349Q4YM2fxJs6SuLVmyhAMOOKDqMirR3nOPiDtSSuPbzmvLkHqNhx9+mOXLlwOwfPlyW4ckKXOLFi1i/fr1AKxfv97WIUnbnGFIvcb3vve9VsPf+c53KqpEktQbfOtb32o1/I1vfKOiSiT1V4Yh9RotrUIdDUuS8tLSKtTRsCS9XIYh9RqjRo3qdFiSlJchQ4Z0OixJL5dhSL3G9OnTWw2fccYZFVUiSeoNvvCFL7Qa/tKXvlRRJZL6K8OQeo199tlnc2vQqFGjGD16dLUFSZIq9YY3vGFza9CQIUMYO3ZsxRVJ6m8MQ+pVpk+fznbbbWerkCQJKFqHBgwYYKuQ1M/96le/Yv/99+e1r30t5513Xt226/cMSZIkSf3M1n7P0JnnnMPqp57aZnUM32knzvvqVzudZ9OmTey3337ccMMNjBw5kje96U3MmTOHAw88cKu2uSXfM9SwVVuQJEmS1O+sfuopmj70oW23wquv7nKW22+/nde+9rXss88+AEyePJlrrrlmq8PQlrCbnCRJkqTKPProo63uIjxy5EgeffTRumzbMCRJkiSpMu1dthMRddm2YUiSJElSZUaOHMny5cs3D69YsYI99tijLts2DEmSJEmqzJve9CYefPBBmpqaeO6557jyyit5//vfX5dtewMFSZIkSZVpaGjgRz/6Ee95z3vYtGkTp556Kq9//evrs+26bEWSJElSrzd8p526dQe4LVpfNxxzzDEcc8wx22y73WUYkiRJkgTQ5XcC9TdeMyRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSVJlTTz2V3XbbjYMOOqju2/bW2pIkSZIAOOfr/8DTT63bZuvbcaehfPUr/9TpPKeccgqf/vSnOfnkk7fZdrvLMCRJkiQJgKefWscJH917m63vqsse6XKeww8/nKVLl26zbW4Ju8lJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkypx44okcdthhPPDAA4wcOZILL7ywbtv2bnKSJEmSgOJW2N25A9yWrK8rc+bM2Wbb21KGIUmSJEkAXX4nUH9jNzlJkiRJWTIMSZIkScqSYUiSJEnqh1JKVZdQd1v6nA1DkiRJUj8zePBg1qxZk1UgSimxZs0aBg8e3O1lvIGCJEmS1M+MHDmSFStWsHr16qpLqavBgwczcuTIbs9vGJIkSZL6mUGDBtHY2Fh1Gb2e3eQkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCz5pasZmjVrFk1NTVWX0a7m5mYARowYUXElL9XY2Mi0adOqLkOSJEnbiGFIvcqGDRuqLkGSJEmZMAxlqDe3bsyYMQOAmTNnVlyJJEmS+juvGZIkSZKUJcOQJEmSpCz1WBiKiIsiYlVE3FMzblhE3BARD5a/h9ZM+3JEPBQRD0TEe3qqLkmSJEmCnm0Zuhg4us24M4F5KaV9gXnlMBFxIDAZeH25zL9GxMAerE2SJElS5nosDKWUbgLWthl9LHBJ+fgS4Lia8VemlJ5NKTUBDwFv7qnaJEmSJKne1wztnlJqBih/71aO3xNYXjPfinLcS0TEaRGxMCIWrl69ukeLlSRJktR/9ZYbKEQ741J7M6aULkgpjU8pjR8+fHgPlyVJkiSpv6p3GHo8IkYAlL9XleNXAKNq5hsJrKxzbZIkSZIyUu8wdC0wtXw8FbimZvzkiHhlRDQC+wK317k2SZIkSRlp6KkVR8Qc4J3ArhGxAvgacB4wNyI+DiwDPgyQUro3IuYC9wEbgU+llDb1VG2SJEmS1GNhKKV0YgeTjuxg/pnAzJ6qR5IkSZJq9VgYkiRJfcesWbNoamqquoyXaG5uBmDEiBEVV/JSjY2NTJs2reoyJL0MhiFJktRrbdiwoeoSJPVjhiFJktRrWzhmzJgBwMyZ9qSXtO31lu8ZkiRJkqS6MgxJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJClba9eu5ayzzmLdunVVlyJJkipgGJKUrblz57JkyRKuuuqqqkuRJEkVMAxJytLatWuZP38+KSXmz59v65AkSRkyDEnK0ty5c9m0aRMAmzZtsnVIkqQMGYYkZWnBggWtwtCCBQsqrkiSJNWbYUhSlg499NBWw4cddlhFlUiSpKoYhiRlKaXU6bAkSer/DEOSsnTbbbe1Gr711lsrqkSSJFXFMCQpSxMmTGDgwIEADBw4kAkTJlRckSRJqjfDkKQsTZo0qVUYOuGEEyquSJIk1ZthSFKWhg0bxsSJE4kIJk6cyNChQ6suSZIk1VlD1QVIUlUmTZrEsmXLbBWSJClThiFJ2Ro2bBjnnntu1WVIkqSKGIYk9ahZs2bR1NRUdRntam5uBmDEiBEVV/JSjY2NTJs2reoyJEnq1wxDkrK1YcOGqkuQJEkVMgxJ6lG9uXVjxowZAMycObPiSiRJUhW8m5wkSZKkLFXSMhQR04FpQALuBj4GbAdcBYwGlgKTUkrrqqhPkiRJPae3Xk/am68lBa8n7Ql1bxmKiD2BzwLjU0oHAQOBycCZwLyU0r7AvHJYkiRJqosNGzZ4PWlmqrpmqAF4VUQ8T9EitBL4MvDOcvolwI3Al6ooTpIkST2nt7ZueC1pfureMpRSehT4NrAMaAaeSildD+yeUmou52kGdqt3bZIkSZLyUUU3uaHAsUAjsAcwJCJO2oLlT4uIhRGxcPXq1T1VpiRJkqR+roq7yb0LaEoprU4pPQ/8DHgr8HhEjAAof69qb+GU0gUppfEppfHDhw+vW9GSJEmS+pcqwtAy4NCI2C4iAjgSWAJcC0wt55kKXFNBbZIkSZIyUfcbKKSUbouIq4E7gY3AXcAFwPbA3Ij4OEVg+nC9a5MkSZKUj0ruJpdS+hrwtTajn6VoJZIkSZKkHldFNzlJkiRJqpxhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlKVKwlBE7BwRV0fE/RGxJCIOi4hhEXFDRDxY/h5aRW2SJEmS8lBVy9D3gV+llF4HHAwsAc4E5qWU9gXmlcOSJEmS1CPqHoYiYkfgcOBCgJTScymlJ4FjgUvK2S4Bjqt3bZIkSZLyUUXL0D7AauCnEXFXRMyKiCHA7imlZoDy924V1CZJkiQpE1WEoQbgEODHKaVxwHq2oEtcRJwWEQsjYuHq1at7qkZJkiRJ/VyXYSgido+ICyPiv8vhAyPi4y9jmyuAFSml28rhqynC0eMRMaLcxghgVXsLp5QuSCmNTymNHz58+MsoQ5IkSVLOGroxz8XAT4EZ5fD/AldRXvOzpVJKj0XE8ojYP6X0AHAkcF/5MxU4r/x9zdasX8rVrFmzaGpqqrqMPqVlf82YMaOLOdWisbGRadOmVV2GJEnbRHfC0K4ppbkR8WWAlNLGiNj0Mrf7GWB2RLwCeBj4GEUr1dyy1WkZ8OGXuQ0pK01NTTz0p/vZ/dVDqi6lzxg4cCMAf16/vOJK+obHH1tfdQmSJG1T3QlD6yNiFyABRMShwFMvZ6MppUXA+HYmHfly1ivlbvdXD+HkU8dWXYb6qUsvWlx1CZIkbVPdCUP/F7gWeE1E3AIMBz7Uo1VJkiRJUg/rMgyllO6MiAnA/kAAD6SUnu/xyiRJkiSpB3UZhiLi+Daj9ouIp4C7U0rt3vFNkiRJknq77nST+zhwGPDbcvidwK0UoeifUkqX9VBtkiRJktRjuhOGXgAOSCk9DsX3DgE/Bt4C3AQYhiRJkiT1OV1+6SowuiUIlVYB+6WU1gJeOyRJkiSpT+pOy9DvIuKXwL+Xwx8EboqIIcCTPVWYJEmSJPWk7oShTwHHA28vh28HRqSU1gNH9FRhkiRJktSTunNr7RQRf6K4RmgS0AT8R08X1h/MmjWLpqamqsvoU1r214wZMyqupO9obGxk2rRpVZchSeqFPBfZMp6HbJ2+fC7SYRiKiP2AycCJwBrgKiBSSrYGdVNTUxP3PPQQm3bbrepS+owBA4rL2P7n6acrrqRvGLjKu9tLkjrW1NTEQ3+6n91fPaTqUvqEgQM3AvDn9csrrqTvePyx9VWX8LJ01jJ0P/A74G9TSg8BRMT0ulTVj2zabTf+MmVK1WWon9pu9uyqS5Ak9XK7v3oIJ586tuoy1E9detHiqkt4WTq7m9wHgceA30bEv0XEkUDUpyxJkiRJ6lkdhqGU0s9TSicArwNuBKYDu0fEjyPiqDrVJ0mSJEk9osvvGUoprU8pzU4pvQ8YCSwCzuzpwiRJkiSpJ3XnS1c3SymtTSn9JKU0sacKkiRJkqR62KIwJEmSJEn9hWFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlhqqLkCSpJzMmjWLpqamqsvoM1r21YwZMyqupG9pbGxk2rRpVZch9XqGIUmS6qipqYl7HnqITbvtVnUpfcKAAUUnlv95+umKK+k7Bq5aVXUJUp9hGJIkqc427bYbf5kypeoy1E9tN3t21SVIfYZhSOonmpubWb9+PZdetLjqUtRPPd68nmeGNFddhiRJ24w3UJAkSZKUJVuGpH5ixIgR/Hn9Rk4+dWzVpaifuvSixewwZETVZUiStM3YMiRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckvXZUkSeqnmpubWb9+PZdetLjqUtRPPd68nmeGNFddxlazZUiSJElSlmwZkiRJ6qdGjBjBn9dv5ORTx1ZdivqpSy9azA5DRlRdxlazZUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSstRQdQGStp3HH1vPpRctrrqMPmPdmg0ADN1lcMWV9A2PP7aeHV5TdRWSJG07hiGpn2hsbKy6hD7niVVNAOwwZFTFlfQNO7zG40yS1L8YhqR+Ytq0aVWX0OfMmDEDgJkzZ1ZciSRJqoLXDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsVRaGImJgRNwVEb8sh4dFxA0R8WD5e2hVtUmSJEnq/6psGfocsKRm+ExgXkppX2BeOSxJkiRJPaKSMBQRI4G/AWbVjD4WuKR8fAlwXJ3LkiRJkpSRqlqG/gX4IvBCzbjdU0rNAOXv3SqoS5IkSVIm6h6GIuJ9wKqU0h1bufxpEbEwIhauXr16G1cnSZIkKRdVtAy9DXh/RCwFrgQmRsTlwOMRMQKg/L2qvYVTSheklManlMYPHz68XjVLkiRJ6mfqHoZSSl9OKY1MKY0GJgPzU0onAdcCU8vZpgLX1Ls2SZIkSfnoTd8zdB7w7oh4EHh3OSxJkiRJPaKhyo2nlG4EbiwfrwGOrLIeSZIkSfmoNAxJkiSpZz3+2HouvWhx1WX0CevWbABg6C6DK66k73j8sfXs8Jqqq9h6hiFJkqR+qrGxseoS+pQnVjUBsMOQURVX0nfs8Jq+fZwZhiRJkvqpadOmVV1CnzJjxgwAZs6cWXElqpfedAMFSZIkSaobW4Z6UHNzMwPXr2e72bOrLkX91MBVq2hev77qMiRJkvokW4YkSZIkZcmWoR40YsQIVj39NH+ZMqXqUtRPbTd7NiN23LHqMiRJkvokW4YkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSstRQdQGSJOWkubmZgevXs93s2VWXon5q4KpVNK9fX3UZUp9gy5AkSZKkLNkyJElSHY0YMYJVTz/NX6ZMqboU9VPbzZ7NiB13rLoMqU+wZUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSZIkKUuGIUmSJElZMgxJkiRJypJhSJIkSVKWDEOSJEmSsmQYkiRJkpQlw5AkSZKkLNU9DEXEqIj4bUQsiYh7I+Jz5fhhEXFDRDxY/h5a79okSZIk5aOKlqGNwBkppQOAQ4FPRcSBwJnAvJTSvsC8cliSJEmSekTdw1BKqTmldGf5+M/AEmBP4FjgknK2S4Dj6l2bJEmSpHxUes1QRIwGxgG3AbunlJqhCEzAbh0sc1pELIyIhatXr65brZIkSZL6l8rCUERsD/wH8H9SSk93d7mU0gUppfEppfHDhw/vuQIlSZIk9WuVhKGIGEQRhGanlH5Wjn48IkaU00cAq6qoTZIkSVIeGuq9wYgI4EJgSUrpuzWTrgWmAueVv6+pd22Str1Zs2bR1NRUdRntevDBB3nuuef41Kc+xc4771x1Oa00NjYybdq0qsuQJKlfq6Jl6G3AR4GJEbGo/DmGIgS9OyIeBN5dDktSj3nuuecAePTRRyuuRJIkVaHuLUMppZuB6GDykfWsRVLP662tG7/73e+49957Nw8fc8wxvO1tb6uwIkmSVG91D0O5GbhqFdvNnl11GX3GgHXrAHhhqN+52x0DV62CHXesuow+6fvf/36r4e9973uGIUmSMmMY6kGNjY1Vl9DnNK1ZA0CjJ/jds+OOHmdbaePGjZ0OS5Kk/s8w1IN6a/eg3mzGjBkAzJw5s+JK1N81NDS0CkANDf45lCQpN5V+6aokVWXy5MmthqdMmVJRJZIkqSqGIUlZWrBgQavh+fPnV1SJJEmqimFIUpaWL1/e6bAkSer/DEOSsrTHHnu0Gt5zzz0rqkSSJFXFK4YlZWnvvfdm5cqVrYalevFrF7rPr1zYcn7tgtR9hiFJWVq0aFGr4bvuuquaQpQdb4e/ZfzKha3g1y5I3WYYkpSlCRMm8Ktf/arVsFQPfu3ClvErFyT1JK8ZkpSlo446qtXw0UcfXVElkiSpKoYhSVm6/vrrWw3XthJJkqQ8GIYkZant9wy1HZYkSf2fYUhSlt7ylre0Gj700EMrqkSSJFXFMCQpSxHR6bAkSer/DEOSsnTrrbe2Gv7DH/5QUSWSJKkqhiFJWRo3blyr4UMOOaSiSiRJUlUMQ5Ky1NTU1OmwJEnq/wxDkrLU3NzcanjlypUVVSJJkqpiGJKUpVGjRnU6LEmS+j/DkKQsTZ8+vdXwGWecUVElkiSpKg1VFyBJVdhnn33YY489WLlyJXvuuSejR4+uuiRJysasWbN65bWaLTXNmDGj4kra19jYyLRp06ouo1+xZUhStvbee+9WvyVJeRs8eDCDBw+uugzVkS1DkrK0du1a7rjjDgAWLlzIunXrGDp0aMVVSVIebN1Qb2HLkKQszZ07lxdeeAGAF154gauuuqriiiRJUr0ZhiRlacGCBWzcuBGAjRs3smDBgoorkiRJ9WYYkpSlCRMm0NBQ9BRuaGhgwoQJFVckSZLqzTAkKUuTJk1iwIDiT+CAAQM44YQTKq5IkiTVm2FIUpaGDRvGxIkTiQgmTpzozRMkScqQd5OTlK1JkyaxbNkyW4UkScqUYUhStoYNG8a5555bdRmSJKkidpOTJEmSlCXDkKRsrV27lrPOOot169ZVXYokSaqAYUhStubOncuSJUv8wlVJkjJlGJKUpbVr1zJ//nxSSsyfP9/WIUmSMmQYkpSluXPnsmnTJgA2bdpk65AkSRkyDEnK0oIFC1qFoQULFlRckSRJqjfDkKQsjRs3rtXwIYccUlElkiSpKoYhSVlaunRpq+GmpqZqCpEkSZUxDEnK0sqVKzsdliRJ/Z9hSFKWRo0a1emwJEnq/wxDkrI0ffr0VsNnnHFGRZVIkqSqGIYkZWmfffbZ3Bo0atQoRo8eXW1BkqTKrV27lrPOOsvvnsuIYUhStqZPn852221nq5AkCSi+g27JkiV+91xGDEOSsrXPPvtwxRVX2CokSWLt2rXMnz+flBLz58+3dSgThiFJ2bI7hCSpxdy5c3nhhRcAeOGFF2wdyoRhSFK27A4hSWqxYMECNm7cCMDGjRtZsGBBxRWpHgxDkrJkdwhJUq0JEybQ0NAAQENDAxMmTKi4ItWDYUhSluwOIUmqNWnSJAYMKE6NBwwYwAknnFBxRaoHw5CkLNkdQpJUa9iwYUycOJGIYOLEiQwdOrTqklQHhiFJWbI7hCSprUmTJnHAAQfYKpQRw5CkLNkdQpLU1rBhwzj33HNtFcqIYUhSluwOIUmSGqouQJKqMmnSJJYtW2arkCRJmTIMScpWS3cISZKUJ7vJSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUpV4XhiLi6Ih4ICIeiogzq65HkiRJUv/Uq8JQRAwE/h/wXuBA4MSIOLDaqiRJkiT1R70qDAFvBh5KKT2cUnoOuBI4tuKaJEmSJPVDve17hvYEltcMrwDeUlEt/dasWbNoamqquox2tdQ1Y8aMiit5qcbGRqZNm1Z1GZLUI3rr/wb/L0jqSb0tDEU741KrGSJOA04D2GuvvepRk+po8ODBVZcgSepF/L8gqSdFSqnrueokIg4Dzk4pvacc/jJASumf25t//PjxaeHChXWsUJIkSVJfExF3pJTGtx3f264Z+iOwb0Q0RsQrgMnAtRXXJEmSJKkf6lXd5FJKGyPi08CvgYHARSmleysuS5IkSVI/1KvCEEBK6b+A/6q6DkmSJEn9W2/rJidJkiRJdWEYkiRJkpQlw5AkSZKkLBmGJEmSJGXJMCRJkiQpS4YhSZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlCXDkCRJkqQsGYYkSZIkZckwJEmSJClLhiFJkiRJWTIMSZIkScpSpJSqrmGrRcRq4JGq65B6iV2BJ6ouQpLUa/h/QXrR3iml4W1H9ukwJOlFEbEwpTS+6jokSb2D/xekrtlNTpIkSVKWDEOSJEmSsmQYkvqPC6ouQJLUq/h/QeqC1wxJkiRJypItQ5IkSZKyZBiS+ogo3BwR760ZNykiflVlXZKkakVEiojv1Ax/PiLOrrAkqc8wDEl9RCr6tJ4OfDciBkfEEGAm8KlqK5MkVexZ4PiI2LXqQqS+xjAk9SEppXuA/wS+BHwNuByYERF/jIi7IuJYgIh4fUTcHhGLImJxROxbYdmSpJ61keJmCdPbToiIvSNiXvm/YF5E7FX/8qTeyxsoSH1M2SJ0J/Ac8Evg3pTS5RGxM3A7MA44D7g1pTQ7Il4BDEwp/bWqmiVJPScingH2ABYDBwOfALZPKZ0dEf8JXJ1SuiQiTgXen1I6rrpqpd7FMCT1QRHxT8AzwCRgMMWnggDDgPdQBKIZwKXAz1JKD1ZRpySp50XEMyml7cv/Dc8Df+XFMPQEMCKl9HxEDAKaU0p2p5NKDVUXIGmrvFD+BPDBlNIDbaYviYjbgL8Bfh0R01JK8+tdpCSprv6FoufATzuZx0/BpRpeMyT1bb8GPhMRARAR48rf+wAPp5R+AFwLjK2uRElSPaSU1gJzgY/XjP49MLl8PAW4ud51Sb2ZYUjq284BBgGLI+KechjgBOCeiFgEvI6iu5wkqf/7DlDbDe6zwMciYjHwUeBzlVQl9VJeMyRJkiQpS7YMSZIkScqSYUiSJElSlgxDkiRJkrJkGJIkSZKUJcOQJEmSpCwZhiRJkiRlyTAkSX1QRMyIiHsjYnFELIqIt0TE0ojYteult3ktF0fEh+qwnW+Vz/lbPb2tlysiZkXEgV3Mc1xX82yjWuqyHUnqixqqLkCStGUi4jDgfcAhKaVnywD0iorLqoe/A4anlJ7tzswR0ZBS2tjDNbUrpTStG7MdB/wSuK9nq6nbdiSpz7FlSJL6nhHAEy2hIKX0REppZTntMxFxZ0TcHRGvA4iIYRHxi7IV6daIGFuOvzsido7Cmog4uRx/WUS8KyIGlq0xfyyX/btyekTEjyLivoi4Dtits2JrW6wiYnxE3Fg+Prvc1vyIeDAiPtHJOq4FhgC3RcQJEbF3RMwr65oXEXuV810cEd+NiN8C3+hgXW+OiN9HxF3l7/3L8adExDUR8auIeCAivlaOHx0R90fEJeX2ro6I7cppR5bruTsiLoqIV5bjb4yI8eXjZyJiZkT8T7n/d4+ItwLvB75Vtuy9plzmexFxU0QsiYg3RcTPyn3z9Zr6T4qI28vlfhIRA7dkO529VpKUG8OQJPU91wOjIuJ/I+JfI2JCzbQnUkqHAD8GPl+O+0fgrpTSWOAs4NJy/C3A24DXAw8D7yjHHwrcCnwceCql9CbgTcAnIqIR+ACwPzAG+ATw1pfxXMYCfwMcBvxDROzR3kwppfcDf00pvSGldBXwI+DS8jnNBn5QM/t+wLtSSmd0sM37gcNTSuOAfwDOrZn2ZmAK8Abgwy2BhuL5XlBu72ngkxExGLgYOCGlNIait8Xft7O9IcCtKaWDgZuAT6SUfg9cC3yhfE5/Kud9LqV0OHA+cA3wKeAg4JSI2CUiDgBOAN6WUnoDsKmsd0u3I0nCMCRJfU5K6RngjcBpwGrgqog4pZz8s/L3HcDo8vHbgcvKZecDu0TETsDvgMPLnx8DYyJiT2BtuY2jgJMjYhFwG7ALsG85/5yU0qayRWr+y3g616SU/ppSegL4LUUY6Y7DgCvKx5eVz7HFv6eUNnWy7E7Av0fEPcD3KMJgixtSSmtSSn+l2Jct612eUrqlfHx5OX5/oCml9L/l+Eso9k1bz1F0U4PWr0t7ri1/3w3cm1JqLlsAHwZGAUdSvPZ/LF+XI4F9tmI7kiS8ZkiS+qTyZP9G4MaIuBuYWk5quZ5mEy/+jY/2VkHRevApYC9gBkWLz4coQlLLcp9JKf26dsGIOKZcvrs28uKHb4PbqaOz4e6qXW59F/OeA/w2pfSBiBhNsR+7qqe98e3t1/Y8n1JqWb72dWlPy+v3Qs3jluGGcpuXpJS+/DK3I0nCliFJ6nMiYv+I2Ldm1BuARzpZ5CbKrlQR8U6KrnRPp5SWA7sC+6aUHgZupuha1xKGfg38fUQMKpfdLyKGlOubXF5TNAI4oouSl1K0ZgB8sM20YyNicETsArwT+GMX62rxe2By+XhKWXt37QQ8Wj4+pc20d0dxjdWrKG480NIatFcUN64AOLHc3v3A6Ih4bTn+o8CCLajjz8AOWzA/wDzgQxGxG2y+HmzvHtiOJGXBMCRJfc/2wCXlDQwWAwcCZ3cy/9nA+HLe83ixFQmK7m8t3bx+B+zJi8FiFsUdyO4su5T9hKK14efAgxRduX5M1wHgH4HvR8TvKFosat0OXEdxjdI5NTeC6MpngY+Vz+mjwOe6uRzAN4F/johbgIFtpt1M0e1uEfAfKaWF5fglwNRye8OAH6eUNgAfo+hydzdF6835W1DHlcAXyhswdOvGBiml+4CvANeXtdxAcUONbbodScpFvNiiLklS/UTE2cAzKaVvV10LFHeTA8anlD7dZvxo4JcppYOqqEuS1HNsGZIkSZKUJVuGJEnbRET8HGhsM/pLbW/A0MU6xlDe+a7Gsymlt2xFPR/jpd3nbkkpfWpL1yVJ6p8MQ5IkSZKyZDc5SZIkSVkyDEmSJEnKkmFIkiRJUpYMQ5IkSZKyZBiSJEmSlKX/D2qi8VelCPSEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sns.boxplot(x=\"Showed_up_for_appointment\", \n",
    "              y=\"Age\", \n",
    "              hue=\"Alcoholism\",\n",
    "              data=df,\n",
    "             palette = \"rainbow\")\n",
    "\n",
    "plt.title(\"Appointment Show up based on Age, separated by Alcoholism status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart shows that highest age of those with alcoholims who showed up for appointment was about 60 years. Also most people who visited the hospital didn't have alcoholim issue. Amongst those with alcoholims, the minimum age was about 18 years, excluding the possible outliers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Appointment Show up based on Age, separated by Hypertensive status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Appointment Show up based on Age, separated by Hypertensive status')"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sns.boxplot(x=\"Showed_up_for_appointment\", \n",
    "              y=\"Age\", \n",
    "              hue=\"Hypertension\",\n",
    "              data=df,\n",
    "             palette = \"rainbow\")\n",
    "\n",
    "plt.title(\"Appointment Show up based on Age, separated by Hypertensive status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age distribution amongst amongst hypertensives who showed compared to those who didnt show up for appointments has wide variations with outliers. Those without hypertension had a lower median age while those with hypertension had a comparatively higher median age, with the latter also having a lot of outliers This is to be expected especially amongst a population with essential hypertension in which risk tends to increase with increasing age. \n",
    "\n",
    "Rather surprisingly however, there seems to be a significant number of hypertension amongst the young too with the minimum age being about 21 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Appointment Show up based on Age, separated by Scholarship status')"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sns.boxplot(x=\"Showed_up_for_appointment\", \n",
    "              y=\"Age\", \n",
    "              hue=\"Scholarship\",\n",
    "              data=df,\n",
    "             palette = \"rainbow\")\n",
    "\n",
    "plt.title(\"Appointment Show up based on Age, separated by Scholarship status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows that the scholarship status didn't seem to affect the showing up for appointment as the median age amongst all the categories was about 35 to 39 years. Also most of the patients who showed up for appointment are those without scholarships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of Patients based on Neighboorhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 8))\n",
    "fig = sns.countplot(x=df.Neighbourhood, data=df)\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "fig.set_title(\"Distribution of Patient by Neighbourhood\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmsAUYC1gS+AbZjZnyu44YG9g9fTZcrzKLYQQQgghhBBi9mVWmStvDvzR3e8BtgVOTuknA9ul39sCp7v7v9z9LuAOYEMzWxZY2N2vcHcHTsmOEUIIIYQQQgghZjCrlNwpwA/S76Xd/UGA9L1USl8OuC875v6Utlz6XaYLIYQQQgghhBB9jLuSa2bzAG8GfjRMtCLNG9KrzrW3mU01s6nTp0/vVlAhhBBCCCGEELM9s2Im943ANe7+cNp+OJkgk74fSen3Aytkxy0PPJDSl69IH8Ddj3f3ye4+eckll5yJf0EIIYQQQgghxOzArFByd2HEVBngPGCP9HsP4NwsfYqZzWtmKxMBpq5MJs1PmtlGKary7tkxQgghhBBCCCHEDOYaz8zNbAHgDcB7suTPAWeY2V7AvcBOAO5+k5mdAdwMPA3s6+7PpGP2AU4C5gcuTB8hhBBCCCGEEKKPcVVy3f3vwOJF2mNEtOUq+SOBIyvSpwJrj0cZhRBCCCGEEEJMHGZVdGUhhBBCCCGEEGLckZIrhBBCCCGEEGLCICVXCCGEEEIIIcSEQUquEEIIIYQQQogJg5RcIYQQQgghhBATBim5QgghhBBCCCEmDFJyhRBCCCGEEEJMGKTkCiGEEEIIIYSYMEjJFUIIIYQQQggxYZCSK4QQQgghhBBiwiAlVwghhBBCCCHEhEFKrhBCCCGEEEKICYOUXCGEEEIIIYQQEwYpuUIIIYQQQgghJgxScoUQQgghhBBCTBik5AohhBBCCCGEmDBIyRVCCCGEEEIIMWGQkiuEEEIIIYQQYsIwrkqumS1qZmea2a1mdouZvdLMJpnZL8zs9vS9WCZ/qJndYWa3mdkWWfrLzeyGtO8YM7PxLLcQQgghhBBCiNmT8Z7JPRr4mbu/BFgXuAX4CHCxu68OXJy2MbM1gSnAWsCWwDfMbM6Uz3HA3sDq6bPlOJdbCCGEEEIIIcRsyLgpuWa2MPBa4LsA7v5vd38c2BY4OYmdDGyXfm8LnO7u/3L3u4A7gA3NbFlgYXe/wt0dOCU7RgghhBBCCCGEmMF4zuSuAkwHTjSzaWb2HTNbEFja3R8ESN9LJfnlgPuy4+9Pacul32W6EEIIIYQQQgjRx3gquXMBLwOOc/f1gb+RTJNrqPKz9Yb0wQzM9jazqWY2dfr06V3LK4QQQgghhBBiNmc8ldz7gfvd/fdp+0xC6X04mSCTvh/J5FfIjl8eeCClL1+RPoC7H+/uk9198pJLLjnT/ogQQgghhBBCiNmDcVNy3f0h4D4ze3FK2hy4GTgP2COl7QGcm36fB0wxs3nNbGUiwNSVyaT5STPbKEVV3j07RgghhBBCCCGEmMFc45z//sD3zWwe4E7gHYRifYaZ7QXcC+wE4O43mdkZhCL8NLCvuz+T8tkHOAmYH7gwfYQQQgghhBBCiD7GVcl192uByRW7Nq+RPxI4siJ9KrD2TC2cEEIIIYQQQogJx3jP5AohhBBigrLNmWcNlfnJW3acBSURQgghRhjPwFNCCCGEEEIIIcQsRUquEEIIIYQQQogJg5RcIYQQQgghhBATBim5QgghhBBCCCEmDFJyhRBCCCGEEEJMGKTkCiGEEEIIIYSYMEjJFUIIIYQQQggxYZCSK4QQQgghhBBiwiAlVwghhBBCCCHEhEFKrhBCCCGEEEKICYOUXCGEEEIIIYQQEwYpuUIIIYQQQgghJgxScoUQQgghhBBCTBik5AohhBBCCCGEmDBIyRVCCCGEEEIIMWGQkiuEEEIIIYQQYsIwrkqumd1tZjeY2bVmNjWlTTKzX5jZ7el7sUz+UDO7w8xuM7MtsvSXp3zuMLNjzMzGs9xCCCGEEEIIIWZPZsVM7qbuvp67T07bHwEudvfVgYvTNma2JjAFWAvYEviGmc2ZjjkO2BtYPX22nAXlFkIIIYQQQggxm/FcmCtvC5ycfp8MbJeln+7u/3L3u4A7gA3NbFlgYXe/wt0dOCU7RgghhBBCCCGEmMF4K7kOXGRmV5vZ3iltaXd/ECB9L5XSlwPuy469P6Utl36X6UIIIYQQQgghRB9zjXP+G7v7A2a2FPALM7u1QbbKz9Yb0gczCEV6b4AXvehFXcsqhBBCCCGEEGI2Z1xnct39gfT9CHAOsCHwcDJBJn0/ksTvB1bIDl8eeCClL1+RXnW+4919srtPXnLJJWfmXxFCCCGEEEIIMRswbkqumS1oZgv1fgP/C9wInAfskcT2AM5Nv88DppjZvGa2MhFg6spk0vykmW2Uoirvnh0jhBBCCCGEEELMYDzNlZcGzkmr/cwFnObuPzOzq4AzzGwv4F5gJwB3v8nMzgBuBp4G9nX3Z1Je+wAnAfMDF6aPEEIIIYQQQgjRx7gpue5+J7BuRfpjwOY1xxwJHFmRPhVYe2aXUQghhBBCCCHExOK5WEJICCGEEEIIIYQYF6TkCiGEEEIIIYSYMEjJFUIIIYQQQggxYZCSK4QQQgghhBBiwiAlVwghhBBCCCHEhEFKrhBCCCGEEEKICYOUXCGEEEIIIYQQEwYpuUIIIYQQQgghJgxScoUQQgghhBBCTBhaKblmdnGbNCGEEEIIIYQQ4rlkrqadZjYfsACwhJktBljatTDwwnEumxBCCCGEEEII0YlGJRd4D3AgodBezYiS+wTw9fErlhBCCCGEEOPHj856dKjMTjsuMQtKIoSY2TQque5+NHC0me3v7sfOojIJIYQQQgghhBCjYthMLgDufqyZvQpYKT/G3U8Zp3IJIYQQQgghhBCdaaXkmtmpwKrAtcAzKdkBKblCCCGEEEIIIZ43tFJygcnAmu7u41kYIYQQQgghhBBiLLRdJ/dGYJnxLIgQQgghhBBCCDFW2s7kLgHcbGZXAv/qJbr7m8elVEIIIYQQQgghxChoq+R+arQnMLM5ganAn9x9azObBPyQCGJ1N7Czu/8lyR4K7EX4/b7f3X+e0l8OnATMD/wUOECm00IIIYQQQgghSlqZK7v7ZVWfluc4ALgl2/4IcLG7rw5cnLYxszWBKcBawJbAN5KCDHAcsDewevps2fLcQgghhBBCCCH+i2il5JrZk2b2RPr808yeMbMnWhy3PLAV8J0seVvg5PT7ZGC7LP10d/+Xu98F3AFsaGbLAgu7+xVp9vaU7BghhBBCCCGEEGIGbdfJXSjfNrPtgA1bHPpV4ENAfvzS7v5gyvdBM1sqpS8H/C6Tuz+l/Sf9LtOFEEIIIYQQQog+2kZX7sPdfwxs1iRjZlsDj7j71S2ztapTNaRXnXNvM5tqZlOnT5/e8rRCCCGEEEIIISYKrWZyzWyHbHMOYt3cYYGfNgbebGZvAuYDFjaz7wEPm9myaRZ3WeCRJH8/sEJ2/PLAAyl9+Yr0Adz9eOB4gMmTJyswlRBCCCGEEEL8l9F2Jneb7LMF8CThQ1uLux/q7su7+0pEQKlfuftuwHnAHklsD+Dc9Ps8YIqZzWtmKxMBpq5Mps1PmtlGZmbA7tkxQgghhBBCCCHEDNr65L5jJp7zc8AZZrYXcC+wUzrHTWZ2BnAz8DSwr7s/k47Zh5ElhC5MHyGEEEIIIYQQoo+25srLA8cSJsgOXE6sVXt/44EJd78UuDT9fgzYvEbuSODIivSpwNptziWEEELMrmx91kmN+8/fcc9ZUg4hhBBidqatufKJhDnxC4nIxj9JaUIIIYQQQgghxPOGtkruku5+ors/nT4nAUuOY7mEEEIIIYQQQojOtFVyHzWz3cxszvTZDXhsPAsmhBBCCCGEEEJ0pa2S+05gZ+Ah4EHgLcDMDEYlhBBCCCGEEEKMmVaBp4BPA3u4+18AzGwScBSh/AohhBBCCCGEEM8L2s7krtNTcAHc/c/A+uNTJCGEEEIIIYQQYnS0VXLnMLPFehtpJrftLLAQQgghhBBCCDFLaKuofgn4rZmdSayTuzMV69kKIYQQQgghhBDPJa2UXHc/xcymApsBBuzg7jePa8mEEEKIWchWZ3+jcf8FO7xvFpVECCGEEGOhtclxUmql2AohhBBCCCGEeN7S1idXCCGEEEIIIYR43iMlVwghhBBCCCHEhEFKrhBCCCGEEEKICYOWARLPO6Z+a5uhMpPf85NZUBIhhBBCCCHE7IZmcoUQQgghhBBCTBik5AohhBBCCCGEmDBIyRVCCCGEEEIIMWGQkiuEEEIIIYQQYsIwbkqumc1nZlea2XVmdpOZHZ7SJ5nZL8zs9vS9WHbMoWZ2h5ndZmZbZOkvN7Mb0r5jzMzGq9xCCCGEEEIIIWZfxjO68r+Azdz9KTObG7jczC4EdgAudvfPmdlHgI8AHzazNYEpwFrAC4Ffmtka7v4McBywN/A74KfAlsCF41h2IYQQQgghRMbNxz08VGbNfZaeBSURzxce+doFjfuX2m+rWVSSfsZtJteDp9Lm3OnjwLbAySn9ZGC79Htb4HR3/5e73wXcAWxoZssCC7v7Fe7uwCnZMUIIIYQQQgghxAzG1SfXzOY0s2uBR4BfuPvvgaXd/UGA9L1UEl8OuC87/P6Utlz6XaYLIYQQQgghhBB9jKuS6+7PuPt6wPLErOzaDeJVfrbekD6YgdneZjbVzKZOnz69c3mFEEIIIYQQQszezJLoyu7+OHAp4Uv7cDJBJn0/ksTuB1bIDlseeCClL1+RXnWe4919srtPXnLJJWfmXxBCCCGEEEIIMRswntGVlzSzRdPv+YHXA7cC5wF7JLE9gHPT7/OAKWY2r5mtDKwOXJlMmp80s41SVOXds2OEEEIIIYQQQogZjGd05WWBk81sTkKZPsPdzzezK4AzzGwv4F5gJwB3v8nMzgBuBp4G9k2RlQH2AU4C5ieiKiuyshBCCCGEEEKIAcZNyXX364H1K9IfAzavOeZI4MiK9KlAkz+vEEII8V/F1md+b6jM+W/ZbRaURAghhHh+MUt8coUQQgghhBBCiFnBeJoriwnObV/fdqjMi/eV+7QQQgghhBBi1iElVwghhBBCCDFb8OAX7h8qs+yHlh8qIyY2MlcWQgghhBBCCDFhkJIrhBBCCCGEEGLCICVXCCGEEEIIIcSEQUquEEIIIYQQQogJgwJPiVnC9ce9eajMOvucNwtKIoQQQgghnk/c96WHhsqscPAys6AkYqIgJVcIIYToyFZnfatx/wU7vmcWlUQIIYQQJTJXFkIIIYQQQggxYdBMrhBCCCGEEEIIAB752kWN+5fa739nUUlGj2ZyhRBCCCGEEEJMGKTkCiGEEEIIIYSYMEjJFUIIIYQQQggxYZCSK4QQQgghhBBiwqDAU4np3zxhqMyS733nLCiJEEL89/Gmcz7buP+n2x86i0oixPiy41lXDZU5a8cNZkFJhBBi4iIlV4hZyHknvHGozJvfeeEsKIkQQggh2nLejx4dKvPmnZaYBSURQrRB5spCCCGEEEIIISYM46bkmtkKZnaJmd1iZjeZ2QEpfZKZ/cLMbk/fi2XHHGpmd5jZbWa2RZb+cjO7Ie07xsxsvMothBBCCCGEEGL2ZTxncp8GDnb3/wE2AvY1szWBjwAXu/vqwMVpm7RvCrAWsCXwDTObM+V1HLA3sHr6bDmO5RZCCCGEEEIIMZsybkquuz/o7tek308CtwDLAdsCJyexk4Ht0u9tgdPd/V/ufhdwB7ChmS0LLOzuV7i7A6dkxwghhBBCCCGEEDOYJYGnzGwlYH3g98DS7v4ghCJsZkslseWA32WH3Z/S/pN+l+lCCDFb845zhhulnLj9z2ZBSYQQQgghJg7jHnjKzF4AnAUc6O5PNIlWpHlDetW59jazqWY2dfr06d0LK4QQQgghhBBitmZcZ3LNbG5Cwf2+u5+dkh82s2XTLO6ywCMp/X5ghezw5YEHUvryFekDuPvxwPEAkydPrlSEhRBCCCGEEOK/hUeO/dVQmaX232wWlGTWMZ7RlQ34LnCLu38523UesEf6vQdwbpY+xczmNbOViQBTVybT5ifNbKOU5+7ZMUIIIYQQQgghxAzGcyZ3Y+DtwA1mdm1K+yjwOeAMM9sLuBfYCcDdbzKzM4CbicjM+7r7M+m4fYCTgPmBC9Pnv5oHv/GRoTLLvu9zs6AkQjx/+MLpWwyV+dCUn8+Cksw+vPHHBwyVuXC7o2dBScREZ5szh49P/+Qt286CkgghhJjojJuS6+6XU+1PC7B5zTFHAkdWpE8F1p55pRNCCCGEEEKI2ZOHj/l/Q2WWfv9rZkFJnp+Me+ApIYQQQgghhBBiViElVwghhBBCCCHEhEFKrhBCCCGEEEKICYOUXCGEEEIIIYQQEwYpuUIIIYQQQgghJgxScoUQQgghhBBCTBik5AohhBBCCCGEmDCM2zq5QgghhBBCjJVvnv3wUJn37rD0LCiJEGJ2QTO5QgghhBBCCCEmDFJyhRBCCCGEEEJMGGSuLIQQQkxwtj7zB0Nlzn/LLrOgJEIIIep4+OjfDZVZ+oCNZkFJZn+k5AoxRi787puGyrxxr5/OgpIIIYQQQgghpOQKUcEvvzNccX39u6S4CiGEEEII8XxDSq4QQgghhBBipnLHscOjYq+2v6Jii/FBSq4QQgghhJilfOmch4bKHLz9MrOgJGIi89BRdw6VWeaQVWZBScSsRtGVhRBCCCGEEEJMGKTkCiGEEEIIIYSYMEjJFUIIIYQQQggxYRg3JdfMTjCzR8zsxixtkpn9wsxuT9+LZfsONbM7zOw2M9siS3+5md2Q9h1jZjZeZRZCCCGEEEIIMXsznjO5JwFbFmkfAS5299WBi9M2ZrYmMAVYKx3zDTObMx1zHLA3sHr6lHkKIYQQQgghhBDAOEZXdvdfm9lKRfK2wOvS75OBS4EPp/TT3f1fwF1mdgewoZndDSzs7lcAmNkpwHbAheNVbiGEGAsHnDV8HO7oHX82rmV447nvGCpz4bYnjmsZni9sdfbRjfsv2OGAWVQSIYQQQswqZvUSQku7+4MA7v6gmS2V0pcDfpfJ3Z/S/pN+l+lCCCGEEOJ5xGHnPDBU5vDtXzgLSiKE+G/n+RJ4qsrP1hvSqzMx29vMpprZ1OnTp8+0wgkhhBBCCCGEmD2Y1Uruw2a2LED6fiSl3w+skMktDzyQ0pevSK/E3Y9398nuPnnJJZecqQUXQgghhBBCCPH8Z1YruecBe6TfewDnZulTzGxeM1uZCDB1ZTJtftLMNkpRlXfPjhFCCCGEEEIIIfoYN59cM/sBEWRqCTO7HzgM+BxwhpntBdwL7ATg7jeZ2RnAzcDTwL7u/kzKah8iUvP8RMApBZ0SQgghhBBCCFHJeEZX3qVm1+Y18kcCR1akTwXWnolF+6/j/q+9Z6jM8vt9axaU5Lnlsm9vNVRmk3dfMAtKMvtw4sn/O1TmHXtcNAtKIoQQ1Wx75vBo5ee+RasP/rdwytnDY7LsvoNc2oR4vvPIN37UuH+p9+3UuH9WR1cWQgghhBAzmZ3PumWozBk7/s8sKIkQQjz3PF+iKwshhBBCCCGEEGNGSq4QQgghhBBCiAmDzJWFEM9rvnraFkNlDtz15+Naho/+aLg/32d2Gu4XKMTswNZnnjFU5vy37DwLSiKEEEKMDim5QgghhHhe8eYzzx8qc95btp4FJRFCCDE7InNlIYQQQgghhBATBim5QgghhBBCCCEmDDJXFmICcPqJw31Gp7zj+eMz+vXvDfez3Xe38fWzFUIIIYTo8dCXbh0qs8zBL5kFJREzAym5QgghhBBCPAdc8v3pQ2U2fduSs6AkQkwspOQKIYQQQvwXMeXsu4bKnL7DyrOgJEJMfB7+6tVDZZY+8OWzoCT/XUjJfR7x0HFHDpVZZp+PzYKSCCGEEEKI0fDz0x8dKrPFlCXGtQxXnvjIUJkN37EUANd+e7jseu9easxlEmJWIiVXiP8yTj1puD/s2/eUP6wQQgghhJg9kZIrhBBCCFGw/VmXDZU5Z8dNZkFJhBBCdEVKrujj7mO2Gyqz0vt/PO7laMtvj996qMyr9j5/FpRk5nNWi4jJOz6PIiYLIYQQQgjxfEBK7iiY/s1vDpVZ8r3vBeCRb35lqOxS7z1ozGUSQgghhBBCCCElVwgxkzj+1OG+vnu//fnj6/vpHw4v7yfe+vwprxBCCCGEaMccz3UBhBBCCCGEEEKImYVmcoUQYjbgjecN99G+8M3y0RZCiInKb06ZPlRm492XnAUlEWJ0PPL1HzfuX2rf7WbauWYbJdfMtgSOBuYEvuPun3uOiySEEEIIwQ5n/WaozNk7bjwLSiKEmBU8/JXrh8osfdA6s6Akoo7ZQsk1szmBrwNvAO4HrjKz89z95ue2ZEIIIYSYXdjuzF8OlfnxW14/C0oihBBiPJktlFxgQ+AOd78TwMxOB7YFpOQKIYQQQowT7z/nvqEyx2y/wiwoiRBCtGd2UXKXA/Ja9n7gFcMOmn7c94ZmvOQ+u42+VEII8V/Em3586FCZn2732RH5cw5vlt3+sFGVY6uzv9S4/4IdDh5VvkIIIYSYGJi7P9dlGIqZ7QRs4e7vSttvBzZ09/0Lub2BvdPmi4HbiqyWAB5teVrJSvb5WA7JSlaykpWsZCWrtl6ykpVs2nb3weic7v68/wCvBH6ebR8KHDqKfKZKVrKjkX2+lEOykpWsZCUrWcmqrZesZCXb/Jld1sm9CljdzFY2s3mAKcB5z3GZhBBCCCGEEEI8z5gtfHLd/Wkz2w/4ObGE0AnuftNzXCwhhBBCCCGEEM8zZgslF8Ddfwr8dIzZHC9ZyY5S9vlSDslKVrKSlaxkJTs+ss+XckhWspIdm+zsEXhKCCGEEEIIIYRow+zikyuEEEIIIYQQQgxFSu5/IWa2gpl9sIP8BuNZnv92ut6Pjnm/cDzyFWK0mNlcxfbCDbIvGv8SPb8xs/nSMnqjPX5JM1uzIn0tM1uyQz5zj7YMM4PxrCdnN8zswOe6DOOFmS39XJdBiLFgZgs+12WYWZjZC2bn/yMlt8DM5jaz9c1sqZr9a5vZKWY21cyuMrOTzWydlnkvaGa7mdkFNfs3NbP9zGxfM9u0Q5kb800yS5jZPmb2a+BSoLEhMbM1zewIM7sdOK5tWboyHg1a107hc6HED7sfZvZuM1s9/TYzO9HMnjCz683sZR1O9bua869sZlub2VZmtsqQso7quUzHbmxmX+9yTHH8ZnmZi307NBzXuczD3v2x0PU6jEWhMLMVzWyRbHtTMzvazD6QotMPO35VM/u4md04hjJcnv0+tdh9ZbF9aSZ7cbHvx0W+H8p+71Ts+0yH8o1JYTKzOc1siWx7HjPb28xuKeR2aPoMyf+NZnYKcA/w1mL/pKZPkd2xQJUyuzxw9JD/aWa2mZl9B7i/2LdFw3GjVsqLfIa2W2a2lJkdbmZnmtmP0u8xtStmdkb2+/PFvovGknfN+brWkx+oyad1vd5Qlrmy38ea2TF1n5lVBjNbxMzeaWa/BK4ZTblbnmemtPUWfYy1LQaK5mt5TKd6tcsz0VbWWvQVZ4Fs5XUws5dkv+ct9m1Uk9fQZ63L+9q17TSz5cxscm9fqos+A9w+xnxb12kdZXfLfm9c7Nuv2H6fmd1LtD/3mdk9Zva+qnyT/JjrniK/lzV9WmfUZb2h2ekDrA2cAkwlliA6GVinQu6bwFrp9yLAzcANwJ+AXQrZbYmH953AOsC66fftwLY15ZgH2A44A3gCOBHYppBZDvg9cBnwZeAr6feVwHJjyHchYHfgZ8CdwJeA+xuu2YrAR4DrgKuJBZZXqpHdoekz5N4skq7bL4E/VexfCjgcOBP4Ufq99JA85wTemO75w8CZQ+TXBI5I96523S1gU2A/YF9g0wa5lYGtga2AVWpkWt8P4EZg7vR713Q/FgdeD/y/Du/BfcX2wumZuRM4Gzgn/f4RsPBYn8t03HrAF4C7gUuA/TuUd2Pg69n2NVW/q7a7lpkO735x3OLA9sDLh/yXTtcBMGAz4DvAwzUy8wDvAI4Cvph+z1vI/B54YVaGR4GDiTrwOzX5LgscmK7TP4HDgJcWMk8Sdc2T2ae3/UQhO63hvk1rkK3dN5rnodi/BLAP8Gvgj8BRxf4zst+fL/ZdlP2eAvwVeCA9W5sSCuA5wMuK405s+JxQUcbXpufyPuAs4CFggQq5u4j39q50v3q/7wLuLGRvargmN9akv4JQgO8FngL2ABYrZJ5Jz/VAXVB1L4CXZL/LZ3aj7HeXenJjohN2OPBmoo0+nHjnNq55fsvPmJ7flHYDcH3F5wbg+pqyr8fo68lR1+tJ/vLs96kN79geTZ8xlmF+YvDm3PS8Pw68DphjyH9v1SZn8q3a+ppn6+tF2lzpnj1KtMnTgOkpbe6KPIbWq6N9JtrK0qKvOAtk27Qvrev2Ls8aFe9rwzVt3Xam/zMduIIYmNkDeIzocyw7hny71GmtZIGFulxj4ONEoN9VsrRVgJ8AHx/Le5+OeSPRDj+aruFlwJsKmWeJ+vNX6XNJ9vlV63vaVnB2+tBBGSXrAKSH9sfp9zLly0EofytVnG8l4Loi7Q3ACUSH+XvANsDdNeU9B9izIn134Nwx5PuP9PC8hpEgY3fWyP4WuAn4BLB6Srur4Ro/S7zYJ6TPsM5bqwaNDi94km/VKUyyK9JeiW+lLNGtsu1yP67Nfp8GHJBtN3bmi3zuLbZPAj6VX3dCufokcErX5xJ4VfpeI+VxC3A5sD9wT8syrkdNY00HBWgU71Krdx84H1g7/V4WeJCo7G8GDizy7HwdaKFQJLk1gTuIhvH9wAHp9x3Ampnc9dnvo4AvpN9zUHS4gXcTDcgfgP8j6sva977Dc9elw9JFttXzAKyavocqTMBWFcc3nfdGYLX0+2XAv4DtZ8I1u5+oh9/OSKdk6L2oeg+K/X9o2HdbsX0k0U5eDLyLGNCpLAPRwX83UffuNKxMbe8z3erJ3wHrV6SvB/x+DPei6+DaiumzEtGOrph/MrlR15PF+UZdr1c8z0OV+JoyrNi1DEQbuTjw/fTcfJfo08w57Fmn2wBm67a+4rmpVRzTOb9Dej9T2sJExNejs7TW9WqXZ6KjbJe+4njJDr0OwA/SNcyfyWmFTLnd+nkn6v1WEzJ0aztvBial3y8C/k02WDeGfFvXaW1lifr8pW2vMXAbMF9FvvNTtCdd7kX2TEwlBvQXTp/NiPd470zuIOIZv4BoE18w7P2tvPajOej5/qGbMprf2AvIOsgVD8HNDee8udh+lqiAV87S6hrq2xryLTshXfI9iGgUbgQ+CqzaIHsu0cn+GiOKS6Vs2rc9cHp6WD9B6vTVyLZu0Dq+4K07hXRX4s+hhbLU5QXveD+uIRSq+YiZ6bWyfbcUsscCx1R8jmVwhuL2hv98e7E99LlM93TH7LlcLZNpen5aNdZ072x2eZemZb9r3336leGPMtJhW4jBBqr1daCDQpHkLwbeUJH+euCSbPuG4jnaItsuy/vvVN7Jbe5b2p/PpLyuRuZOoo7Ykf5Oxo7AHwvZ+wnzy4Oz373tcsaqraJ0XrouQxUmotPxtg55l/tuHXK95gSWyLbnAfZm8D0+mhjgO5+w3lhw2L2oew+K/RdQjJKn9DcCFxZp04n38S2kTk7D83tN+l6DsJY6kTTAWFUm2newutSTrdvkBrlFgY+V9xRYH3g5UUetTwxovLy8b13uB93qh6aZ52cK2db1eovnudx+ZXoelkrb6xADr+W7ObQMwIsJi6driZnuQ4AVmq5DlkfbNrlrW99FcbydVI8U6XPm/58O9WrHZ2I0sm36iuMlO/Q6EH27HTo+k136MY8xOBHT+5xQyHZpO8syVVrFjCLfLnpGK1miLXlZ22tMcz/q1mK7a90zY3CgSF+cirqVsJL8KNEmnAGsV3e+qs9ss05uR+Z297vLRHe/2wZ93R43s62JUamNgb1ghl/K/IXsf8zsRe5+b55oZisCTxeyLyfM2n5pZncSCuGcNeWtTDezOSr2tc7X3b8CfCXZx+9C+Le90Mw+DJzj7n/IZLdNPgM7Aoeb2WrAoma2obuXPnS4+znAORYO6dsCXzKzxYnOwmWF+NrAX4hG5FZ3f8bMvOZaLOzu0yrOd62ZLVQkn0WYy7wVeMbMzgXq8p1O+KAtTfin3d4gCzEztn1FOU4xs49lSRu7+56FjAM9f+Y8vfX9IBrdqcS9Pc/dbwIws00IpSFnasP/KPdZg2zJ0OfS3fdK92VH4rm8xMx+RjyXTee6Ffh/hHnTHSnfgyrkVjGz81Jevd+9/7FyhXyXd6ntu/+f7PfmwLcB3P1JM3u2yLPLddibGDE9Djjf3f/Z8F5AzFb8okx091+a2bFZ0q8sfAofBBYjRtIxs2WJTkfOC4GdgC8nP54zgEp/YDNbjpiJ+ScxO2LAzmY2PzGT+adM/DLCEqP3e5ts36+LrL9NDBiUvyFmTXLWNbMn0rnnT79J2zN849z9zWa2PNE4TiGu8Wlm9sPyf7n7Ickv6hNmtj4xuj5/+m29c2WHLGVmuU/kC/Jtd/9y77eZTQG+Bfwt1QefAk4llMK3FeU4wCKg0KZE/fBFYGEz2xn4qbs/VZa9JQcB56d8rk5pkwnlZetCdhngf9P5v2pmlxDXYi53L9u4Xrn/YGavJGZqppnZ7jXl8Jrffdsd60kzs8Xc/S/0J06iiDliZisQis8LU56nAZ8mlKTTivI8SMwWQlgHfTnb91DN/2tD6/rB3cv2roku9TpE2749cY0WtRH/cCNcN2LD7IuMKKUfNrPzgfcBnyGs4zqVwd1vI+q88y18MHcl+jKPAAuZ2TLuXnd927bJXdv6tm1ROp0P5FXRp2ldr9Ktzegi26UPOl6yQ6+Du18LXGtm37Tw8zZgeRvx+TZiFj+ny/N+j7uXz2odXdrOvIwQ7cKMbXd//yjzbV2ntZV1978B15jZS8zseuL6rZp+k7ZzP9r7zWxzd++LkWERI+XBsgx0w9z9z2Wiuz9mNpiVu9+V+vbzExNaaxD1UTu6aMSzy4eYyX1RRfqKDI6arEGYsl1L/0zOFsCXCtntCLOLPYmp/7UJn7jbgO0ayrMxMUP6IHAh2ZR82v8VonO3YJa2IGECc8xo86055qVEA1XOppRmG0sRI5q/pRixLeTmJPxQTyXM17aokXsJ4RdzG9GgTAeWqZC7hWpTzUlUzJgw4sf4bUJZeRLYmQrTBkZ8gX9B+K79Bdiwprx31KTPke+rk0v7ake4ivvx2fJ+pH1zldciPRetzDaITn9pRngyoUBbkf4JBn2zOj+Xaf/biNmovxPKxf9WyG0P/JCY4f82oTzeVSG3SdOnQr51mRl596+j4d0nTJP3T2X+C7BoSp+fGn/HNteBfj/y+9M79CAwV02ef6DwZczucz6LYEQn5CD6TevXr7oX2f7liZmVq4n38DPF/nNoaQo+mg/ZbOfM/hAN+McIH59/Ah8G1ihkLmn6ZHKHNX2KPEdt2kx0Bt9MKGCPFvs+kH3uL7Y/UJHXvERb9aX0eScV5mgVz9VbiMHEh4HTiv3TKo55HTEI92TFvkcYsTDp/e5tV/qgZ8dW1pPEQNFVRH2wUPq8jhj1f2/F/f0U8X5/hZhJ/AHV7VCl2WFD+V6WffKZ35dR+Gkn+Vb1ZMNxFxTprev1lH5i0yeTu5mRGf3FCMuI1WvK1qkMhcxkYiDhXuC3NTKt2uSU1qWtb9UWJdkfA7tXpO9GDEZXHdNYr47mmej6/NChrziOssPalz2aPqN91ujmk9vUdm4xhvJ2ybepTntPB9n3Vvy/FZs+mdxahBvUSUTfZz9GXKPWKvLsWvf8Hli3In1d4MpsexVGZnDPJLMu6vLpJDy7fBilMlqRzwY1N+KU9KJeQ3RMB25YTX5zEKPkJxbpcxMmc71gBlcTSuBRwDyF7EU1+W5R5ttQjv8FflmkNZlYrViRtimhOFybyjm5zbnTsbUNWpcXvCLfuYkZo6pOYSclnpbKUtcXvMM1+lD2u1RUKxvJtK8xABfh//AjIvDOWanyuDN9LzLkuZxa91zWlGUS8B4aggTQrWFfEliyxTMw6jLX5LkU4fd9bl629A4cMpOuQ6NCkWQ+nq7TSlnaSoRp7idblOPVFIFUGmTXYFBha20KntJeTChUF6TPURSKZZLbOt2jBwiF7VUtyvdSYoZgJ4pGd9i7Qs1A33h86Gja3JDP/MX2YU2fiuOrTKbfzRDT20x+IQZNererkV0M+EhFeutOYcdrszVhHfBYeu9/TUUAHAZdlR6mYtCo6r61KMMlDZ/GIClZ/XBJzf42QSZb1+sd/9fVxfa1DbJVZfhjlzIQysAmNftatckVx7UdsG8zMNnzC76UqNuOYsQvePkW/2+gXm14JvYGLm0pW/v8FLK9vuJA3JQG2RNnpmySfzEt2qxMfsUOz9qihWxt+zAeH6onF1oF3cvSWtVpXWUrjq0Nopn+xzvTc/5lwtKtyk+39b1I8q8m3HI+RfTXt2Yk7s6rM7lnCf3icMJ1qXEgt+7T81GacJjZusSFWYuoOG8kZmeuG3LcmsSIyy7AX9198ijPvwDwH3f/T9p+MfAmwnTi7Jpj5gdWS+W9w93/XiEzzd3Xb1mGzYjOec886zOE8mPA/3mYHPdkr3H3l3X4f88SI+GXE6ZAfQ+S95tq1OUxD7Czu3+vSN8a+BBx75wYTf6iu/+kRZ6LEUGt5nP3f2Tptf/PzFZ093uKtLmJmYM9iRfSidGuk4GPuvu/k9zChF/qy4gX0onRuWnAXu7+1yzPJ6k2mzLCDGrhqvKWZa/6L2b2WsL0ayuiwd2YiIw38Awl+VWJQEZGzEb+sUouybZ5LicVSQ487h0qmJTHTsBb3X2zlNbzb94/nX8OwjXgWHc/okuZzWxpd3+4kHsjEaAkf9Y+7+4/bVHe+YjG5EfFf6jFK8x0KvJdiBiUObli337Eu7FASvobESX42FI2ya9HPBc7EzMaZ7n717L9O1Qdl5X37Ez2DndfreIccxDBKFbL0l5JmDYfTwwGGvFevDv9t99lstcT9cCtZvYKIijHJjX/ZxFisOFFxAy8EUrrvURQwScK+U71Ws0530AMOr0hbR9TiDjRubjE3S8vjr2fflPXD+Tb3m/afAMNZpXuvk4mu19+H4eUf4bJNGG6+SlGTKY/7e7XZLJzEs/KcsDP3P3GVB9/lFC0189kOz/rFuvyrki8k4/XlLd1PdmEmf3G3TfOtq8jBkx7NnGX5Nt5ebu0s0n+le5+RVv5lnm+geiHbJHK+kOi3lup4Zgu9fqLCUWqt3TLLcDxnpmDm9nj9LsXvDbfdvc3Z7Ivcvd725TBwr0iv8e9d+hX7v6bmvK2bZPnI2J0TC+OX5rwBexb5qvmXANtUbF/M0b6ljf5oFln63o1yc9JWG09mrbnSf/zA+7+ksEchmNmnwW+4u6PVOz7vLt/ONturCOLOqKL7A+AI6quuZmd6u5vL9JeSdQ9v3b3RyyW5/wI8Bp3X6EijzbP2l30P2uWbbu7r5rJjqruSfev5+axBbECxluy/a37c13q9jqq+iYp/XxiAPLGZCp9DTERsCrx7n91SL5zAlPc/fsV+7rUPcsQbg8z3iFiAP6hTOZTNLeHhzeVdUY+E1HJNbPPuPtHO8ivSDycuxAd6BWJmcm7C7mf0HzR8wr/14SSc7uFf+uVRACmNYkp+UMz2S4dzTsJk482stMI84grGJnd+4S7H10eZ2Z/J0wRBnZFtiMdrCS/J83X4uRMdmEiSM1yxKzTL9L2IcTo+rZ1+RTnXNDDt6C3/Uli2Y9bLdZV+xkx0/40sKu7/zKTHVVnd5iy1KVhL/Jt7ETl+0vZiu37iU7+cUSE4CfN7C53H/BZTc/64z3l22IN2e2ITsPXeh2FIWXv6/SntF5DkjtVLEQo/ntVDCK06iBb+Ea9iTCDuiulrZL+6888/PeayroI4ce0K/A/7r5ctu/dxAj4hxjxXZ4MfI4I6398RX7DGrOq65D9LV8lk/0AMZD23eIc+wNzlg2Ome3Qe7+TIoy7P1lRxjUYGah7jOgcH+LuK1bInlhRzry878xkvwK8gIgo/beUtiAxw/JPzwa2zOxCYrDg0uJ8mxCN7BuztKGDONm+Ywgfpg+5+7MpbQ7ins3v7vsX8qViU/7BXLFpGhQ8Mrv2e1RkNYlQDn+Y3zczO6zqvNn5D89ke/fHSMGiCtl7MtnW9ZnFepTbufsdqYN6BdFROadC9iRgBaK9egVRL7ySuGc/LmRbP+tJ/l2kGXTCn35vdz+vzX8YDWZ2X94xNrO7idmBNu/m4wz6jufCb863296PohPdK4cTrinzuHu+Ru2zhGvPnlndd2d5XVN6p3q97SBUel9r8Sz+RsdnsvU7VHHssDb5eKJtKBXJtxEzRfsU6WMamExt/y7EO7V2SutSr3YZhOry/PyVGPB/n7v3rV9bUedeMqS8m9XIvpwRP/8q2enErPgX3L1vHd+KMuT+36sRM+o9/+9vufs/M9kuivbixe45iOfsEMJiY8emvJqwlpMLHftzo+2rNvZNksxN7r5W+v1RYoZ599Sf+E2vn1/02c8llvvcF/ggYc0xtM9uMYh2iLu/u+t/man4LJzGn1Ufui2x0joSHx38A+mPpvZpkpkgYXp0QyF7YvZ5lObob10ixZWmcrXmeVQse0CFrf4o78e5hG3/ewiTq18QJj7r1cgvRygb86TtpYiK7oGKMvcGavYmRrvnBP6HzLY/7f87HdcxLI5fhIq1fbs8a12eUbpFGjyallFZ6bZe22aE2f9TxFIBaxLK4NUMWQs5y2MHosNRpt9Fi3U+iRnxAV9NwnR5Wra9Qfa77XJVraP80WGpqg7PwI1UmFAT/pMDz2TbZ42Oka47lLeLW0WXZWtKn9K+7Yp7NuCzTHTyqiIz/qt4tvJPGeVzWnpO5iUUhCfIlu5qcX3mp4P/15C8WtcPXfOiwWQ6PZNzpN/zpXd/wGc17V+x43+6keRuQPhbXdHh2Epf1CHH3NulfMWxt9MtFsCo7jsxEPjh9IyWMUDWBz5PDAr8gjAXrIv622ltbMJ/8nUV6ZtQRNzu8F9GdQ2KPFq/Q9S3yU0RZwdiKNDfFlXWEQzGxui0/m1x7I7F9lj89puen2mEq951hN/sfPm+sd6rNvmkMiwD/JzonyxRdyzd/L8vafhUugcQyu0e6Xp/j2zZvUxms+z3ysW+0uWty+oeo1pSr+G67pr9bt03oX9pyouJgZmqfa377ES09YvSdf0/IuDbWen6HFQhfwMt+uJUrxgy49P2GZ2o0ZXntDBbHTp6T4dIfD4YNXgGZrZxkZTnsRkRKRN3/7cVEVnd/R1ZPtPy7Qq6RIpbtJgltnzb+0c6/+3FbFsTXWa1iZGtl6bjvkM0wC/y6lmoA4kAMXcA85rZ0YR53ynEqGHOvz29DcTI1enu/gxwi0WE3Jy76I/wWve/NnD3q9Lv+YnAL7sSjc9CROc3H+GvfMZmAq2iyELnqKzzu/sD6fduxMDIl9Js2LVFGb5EDB70LAF+R40lQB3ufraZfbwifcYs85BZ7bk9mXAVx0+3/kjpewFXmdlpxHIxFxEN+6+I0f5LK/JuFeWvmCn/oI/MlFeagldRM6rpXjFz7u7/MqsIM9ieTpGurd5U7iB3/5+sXP8BDjGzTzDEfJ0IAFfH34rtMqJyuZ3zb6+I8uvuT5vZvyrkb254tiqymfGc/NjMpnd81v9R3jbrYNrckXWyOqHvlAya1bWOBk1c32dT+j/N7A9eH+32HKJebMu/PZmQuvudFtY3taTn8E1E/bsl0XH6ZiFTZwVlFKsjVMz+OBG74b6K459qau8rWNlGIr8P4IMzv4sSSlIvsvMG7v5Yccw0Qkn4cOpf7ALMk6wkzvF+S5Mu9TrEOtKXVpTzsjQT2itnaUI/4/kl3CT+me1bruJ5z/Me6sJU8w7NzDZ5jopzDlg8VXAEsH+y/tmF6DOeQSz9dq63NJ1MfIV4lnv821NUZ3e/JrUt5zRl0Ob5iez8RksuIMBUM9vV3a+vyG83oj08tUh/N/A3dy+jj884R1M5UxkeArZIdc5UM9vH3S+skP1H73ly97+Y2W3ufnuFHO6+6ZDz5v9hbmIw5CDCvW5br7e0O4qROu0s+uu3jxPWD2T7t6Pd6h69SMzG8MjRber2zYnVArr2Te6zsBS7P/23n8GM9yrvT7XusxPt9XFEP3FLwjLkNOBtRf3Qo4zoX8fVw0WGM1GV3JcwssRFiZOFyvYOS+fYEH8lYuS1x/VmdhQR8Xc1otPdq5yaGFZpdOn8Xka/YpdvO/0vbKUfTANHdZD9T++HR6j9u2peFgil6sXu/mczexGh7L7WMx++jH+Z2dpEAJFN6TfjXqCQbavEd1WWWjfsRYesHIDoG3Rw97qQ/HXn8VTGX6VK/Y2EovMNYIlMNH9+NgMOTcc/W6FXjanTD2BmL6CiY1Gep2Ffk/l0vq/3H9ei/XJVT5jZul746Vv48+fPZ+vGzMKH6ChGTF6PJe7BK4hBg1K+yk946Zry9sL/D2RD5lLg/ct7bUc07kub2XFE5/ii7Fytl7hJ8psAf3H369Mgyt5m9kfgG+6eK5kr1LwXA416xw7ifDaytE+Zb6PS1IIug4L9J49BtbcTnYecqoZ6EvBFMytNm/MO1fzl//TMBI+wBmqrvDcNIpTPcf6MGSPLTFS5rXQdiCmX3Ojb7tWVNuiLeioRGbdq8Ldp4PL8Ynvg/QMmJWV6F4+lTHrc1ZBvFdNr8u/DzJYgZljfSlhkre9Z3IY6PPxUf2Nm7yfWmp9CmBrPyDr7Paxeh/aDUFUd0knEjNixhHlzj38who5pwzvUtU1+pKb/tgFxn8rzbkHMxJ1ZpO8KTPdYtm3JlPx1ojO/q7tPTXLD+mwDpyy2uyxJ1vn5SYrG+y3iT/ykpl4+mJgRLPkh8Q7WKbmtcfcvm9nFwPfM7E2EVWPOqta/ROBK+cCR97sDVpU1E/X/l23fRbiwfZVQCNdNbXxPOK/Xreb3wLZ3m1z4YPa7XNax3G5Tty+Yvrso2hDv0hHEGvJv9ZG4CBsR1qA9uvTZ53X3k9Lv28zsEMK95Zkq4bp+eNKvphBWiXhFTJLRMFGV3C6j96RK4gTghNTBfCuxRuAK3u/s/l1G/JWOMbNafyWi8j+AiH76v9nIypp0UxBLSkf9xYnK6V5372tgajoFdfzcsgBMFv6uOxIP3AGefIIy7vJiveAGerOS0D8zWTXr8E9Ps2sevq5/qFFwIUYyzyQaoK/4iN/Sm4gR8Jy2SnxXZalLw1434ACDgw4AmNlLGQkMcrOn9XKb8JhxO8/M/kCYUuV0Wa+tdae/aKR7LEaMuo8liEL+7OSUM9rfS2Va1/rXX5xO/fqLBxPX6UTiHjqwAdGB260n1LEx6zKq+UXgAjM7OMlBWCt8geo6opU1QlbuvxFxAL5v4Xf2FmJA7qJM7ONEZMU2/ppfJ0yT5jOz2wj/3J8BryLqz1wp/mB5fEZfo940SJT+Rz4DlK9fWlI141g7KGODa7+2GhS0Eb+4vNPz9yT/nqLslQ21mX2TMHX7apacK0nl2qxOKC+daRpESB3/nP+pFKym68xd+UzU1Zs/J3xRX53V6ZX3sUsbVzf7Y2aTCRO4vOP82bzOsFj7t9cefsoHLUDazvzeQyhaJxLPzF65ElooNHWz5NMJBTOnS70OLQehajqk9xDrIZdt7GNtO6Y2GODHqHmH6N4mfxA4w8K/vPeMTSZmPadUyB9Odb36K8Ja4ReEKSV0W/+2jrLMXQahWj8/DCplF6Zn/QSiHs+Zs0qJcfcnrN9iCusPGlYOXJXvfVmG61Kd8yUG65pti+2mfnJV++JETJbl6V+395fZvnUrjjm72K76XbVdNbmwJdFH6JtcmFkKW8bhKd9Oa6t7BCF7L8QEhKU4N+7eM/XukVsSQnOfvRx4foqYjbZ0znxwtvT3PY94v/YjJqmuJfosvQGdfYl3/oT0315DuG8c7Mn6YRgTNfDUtC5KbkM+K3p/sI8bgXXS6Oh8xBT+ahWd56q85ib8I/7kRbQ76zf97YteCAMjWK2jo9UoHnm+eYN6PRHK/O8Ws9NfJl6a9Ylw6FsUZc6jxZ3lY3DeL/J9hDCv7DEl367oOLXNdxvC3r9RiTez7XoDFpmy9FZiXceXEH43eQS4MUdvrSnvIrSMImtDZg89C86UKp63Ej5FZ7j7n1L6+sBS7v7zTDYf2Stx7w+ecVi5n/Af/7W731DsK5/ND1AoLkVjPWpSo74roeDd7+6vKvYPjfJXkWc+U/6/7r5Etu9ad18v276PWPKnclTTRqI7r52SbgQ+5xXmXF3qNWsZrbN8fs3sVq+J5mlmN7v7mqnu+xPxvDyTnqnrPZk3DSlXVUTqPZqO6dBxnjsN8ORpl7v7q9PvvmieXd5dq5hxHytjaafM7KPu/plRHtt5BQELc9ld3X3fLO0eIvJ5Jb37ZmZLlW3ekHOtn8r3FsLX8HRiuZEVK2S/6u4Hpt8HeGZpYmYnufueLc85EPwMeL2HRdFrUxn2J/xd/8cHA7qc7e51ptO53KdodvM5PJO9pE4uRPsC/LSu11P6mN85M7vO3dfNtn/n7hsNO64rXdvkJLc0Ua/36tWbiABcVZGGr/cisGbLfcsz8h4tQFjJfDTtq4uUbsQyaq2sTiwz1U7bn6rJFxh4fpZ393JWvLdvI++PcH8LEWz1b4XcQsBVRZvR+tkxszk8uT9UlGFZd3+wZt+SKa+Bmfca+VcTbm6LEUECh67EUZPP40T/2wiFqtcXN2LQbbGW+Rzq7p8tyreKu5+Sts8kLCIgVjr5VSbbum4v26VC0e7rm2Qy+xCWHgum//UkESTyG23OWZHfpdQ/k331VJI/l1BcryDMrhcjZvUP8MyaxswuInSbhZLcicBPiPvyNnd/XavyTVAld0/CxGmon5l1i5jcJQroN4lw/zclheUK4BniwT7E3X+QyW7S9H+8P4Jhq+hoaX+peJT55hXijAbLzE4ggsN8vu5/WkO0uExmT3c/yTpEL+xYgZZKfM9f6HIvZp67KvEV/2Vykt+JTFnq0rBbzAY0/LURfxjrEEXWzH5P/+zhh4jZw094tU8EZrYyI8vm3OLud7b5D12wtJyTV1QyXZ7NMZz/BZGV/y11Al/r3fzshuX/EuL+5Mr+rcRz0hvV/D7RKasc1ex4vq+5+34V6X3+vtYtWmeXJW46LWuV7RsWkbpuyY+lgCfqnuEkY8CmxDXext2XLvbn9VRZ5kYl05ojc89DzFznS0+d5v0m23X59swyd3D3bbL01iZ4ZnaGu++cfpfLgVzk7v9bnHNFWqwgUByzHv3LT53t2VJVbQcJLPzCDyesCNrGcegd2/NF3ZEY5T/HM1/U0T6TxTmWJmY9Xp6l5e3h1wmz1U+l7b6BrJT2IXf/Qvq9k/cP4nRa7WEsjLVet2IQyqpnkxcjLF2eKtqhFzXl7Znll82cKP+VbXJXLCye1vTC1z8pDDe7++ot8liDMHk/PG0PDMjkeIPrlI1xGUsz+y4xILPlkDLklliHEIrEPr06wcxWIsyzL3X3L2ayrZ9n6zAIleryT9JhuUAz25wIGuvEmui/qJFbm5j9zevro7wYgO/SF2/CzO519xdl2xcD+7v7zWn7BkIfWZBY/mrLTLZcXqssQ+4C9xBhZfADYnnAv2b75vdsGc2U9nHC8mq/Xt1gsVrF0cDv3f3/CvnOloTDMLMbfMTfd05q/H17dXB6Lu4prudAHVzHRDVX/ifwZ9r5mfVMIowwE3lXQ75d/JVe4+7vTb/fQUQb3c5i5uhC4qEEml8cM/shYcLTI5+p2DyVGQ+H8zKgVRdFwZJS8PeUbz6qM1+FfJNZR49JSbFrKkefjzThj9u2Q1AVnGYl4GNm9il3z2eE3UdMxncAvuth3n21mb1v2Ik8/G+mpsYg74zu3NS4e79Jd2kaCPHsbEOYbuRBH15PshrI8no2DWyUM6OtfSIsTEW+Q5jF9maI1zWzq4mlfp4o5IeupZjkPkn/ck4XErMeT1sEu/hlLt/22bRq01CnYsmE7Jj3EbOjL8jyGBiptObR9hnvs3Xzsy3NaXOz0z6T0y6NWeLbFqObw8rR2gSZbqZyPd8xo9+PzBjxWZuBVS+vsLIPBsU4hjB7Ls3130AsHL9PkY5FMJVdiYXsJzGyvEFJ0yhu1eDL0MA2qRN6HuEC0Yv98Dqi3nlzryOTZKvWXfwH1WaZXUzw8vWK30BEWO3Rdy/M7LdEJNrTgbd4LGt3V5WCa9XLT5lXm/pWKiI2uJbim4glgzq76Xi/L+rrCTegOl/UPtPIinJVvW+TiE7fAUX6nDZizr45UQf2qOo3TSHcDCBmSfL1KbckFPzRvPMDWPUSbp3q9SK/gUGorPxl/daz0LmU/vsAsexVVV29JLFCQv78nkG8u39Ngyk/ItbAXZeo05r6YZFxTZvctl7POJuoW/fz/qXRjqGok6x5uccbzGwzd/9VkxJbRdtBqJaDW8cRz1yTa0ufma67H2VmTwGXpX4ghNnp59z9uOLYGc9zC/K+0h70u4+U9+FAor7fwIvlAs3sIO+3SNuKmLn9K/Axr1lbOcluS9Q9nyWeZyPek7PN7BB3P7cnO6QvXgaXbaKsixbO2wXg9tT/xGI945zSR7eJ5Yh6cQrhYnEFoVucVyq4ibcD63o2cOwRBHBnot74v1SmSktCM6uyJGy0YPHBWBZt/X2fSTJuZmXw0UrrgComqpLbupPn/bOkw/xquvgr5R2AN5AaDXd/yLoFTn1lsd02OlrX0f6vEiPlTxAjwL2gCusTHfeSpui/7u4L+8gs0Ckd/m/rCrROUbKYOf4l/WbPnZR4M/sCsXzAN4tdBxIh8XvPSeuGvRj1NmLA5cNE1OIji/N0iSLbxSfiGGIUc4qPzBAbMRr6NcJvqVfGfC3F41P+6wOXWqzZmvtKv5VYKguiMZsjXYM1iGUs+pTcpBTX4e7+6fSjbyDDwmLhfYSCUOU32hupfF05Umlmk4qRyl5QFaNiXdKM1n62NcpAHV0aM4h70KYcraN1Ng022KC/Zq4Al8rxd4pju0R9fLW7710muvv306BOnu+RxMzivURjfgQw1evNKxc1s+2J53HRrEE2QvHL8/4+0SkbFtjmWGLG4xfF8a8nZj5mPAPl89uEZ7O6Kb+eCd6DhM9S66yK7dYrCAC3EkrONr1nyGKt6iq2MLNDGeJbleqZP6ZPK8xsOcL09nqPWb0liIGEsqM5h4XFyBzZ7149WAbvK9+3nsL2AR80Y/0B0eF/lBiU+H+pXKsRHeuBItf8Lrdbv/M2ZN3mQrx1vZ7lP3QQqkt95oW7gsVM4IeJTnhpftk1GnSXNrlt9NYeHyc69/dYmOAbEXvlu8T1yzkzla9XxrLdf8LC1PY7NCvaM3wauwxCAfmscuXgVk/5pyLGRxPpun4z9ZOsRvGAbquXtB6EIp7RN3i2mkJSwHYj6uSvZLI/IfrAjxHRx8sy5FYhR6R8787SrjOzXxGK3Awl17oFl22ivPeLFuXLFcOli311bdngSWIi4+dETJ15GHGjOtrMLnb3geCRZZ8lpf3D+ifJPk08Q5v5oCXhkcRse4/WgymJtjF6VrEIPGbZ794xKzecs4+JquR2DsmeaLTd9hF/zjYmQY+nl+NPROOxVzp2LoqlDTrSNjoatKgQe7j7CWb2c0Ixuy7b9RAxE00h3yn6r7U37+tSgVbi4UdVHv9VuinxWzPiz5NzNBE5+zJiJrFLw967/3sSQY9+TzRqt1Wcp0sU2dazh8DGXvipubsDR1hYPeR8kjDBujRL+3FqHA4jKtQe5XJOP/D65ZxgcBkZCNOdvYh1aj+d77B2SyZAy5HKlD5jtN3M/uX1o++dogdamNnuS/+z/vWyI92lMetYji5LxpRl7zOVIwK29I7rYhnSJepjU+dnjmJ7b+A2Qnk+32OZm6Z6+zJiZrb3O2+Qf13Irk27wDbLlQougLv/0mKmro/0/D+TRqRXIGbe/+ixRMwA1s4Eb4FUP8xBfyRmo2hfvMMKAnRbfuoURnyr3kXMRM9DjPRfW/G/Wi1HY92WkVuE/pUU8gG9vnvXsfN4pIWJ4bLARVndNgf9HbyqczUFrOliqfQl2i/h1qVe7zQIZYMR1V9LDFiUEdV78qsT969nYfJ+L3zl6X+mNmN4NGho3yb/uC4DM/sNxUCJx2DyR8zscEYsJO7w6pmwHYk6bR1COfqBFwFwzGwed/9hXRkq6DII1doyxbq5cw3IpgGeAVk6rF5Ct0GotssFQjaQ2IK5qwYM3P3uinxbB5e1aisdqKiDgVvNbCt3v6DIY2uiPaNI34OwLnlxSrqFWBu2dsLIY3nSm5Psy4kgtyX3m9nm7n5xcb7N6O8Ht7Yk9G4BbrvoDttmv0sroNZWQRNVye0Skj33Fx1QsLzfX7RnEjSZUJiaTILeQ4yuLgMc6COBETYnZo1mYPVRFI1idtaz6GhF+iX0R0eDbhViXob1Khqae8uEtlized+23m/n36UCrTvfZkTna+TAjkp8HDIYMCG96IsSCtlniRH2Vg27me1LVFwXA1s2KFXQIYpsl9F2ho+m5rRaSzHRZTkn3H2GGZzF7OwBxH04ncxEzka/ZEKZVo5UdqH1TLmFSdNpxELqvVmXlwFXmtnbPDOrsg7BKDqWo8kEeQAbhb/mMLxb1McuS34sw4hp5VctgvPMb4ORknvlaN0A+2Bk7keojsw9h5nNW3bwLfwZ5yrS3g18HnjKzD5NKILXAOub2Qme4h4k2dYmePTXD2Uk5oGgad6/gsBS1Kwg4B2Wn6LbWorQfjma1svIuftKNecaINXRHyXahi8T70UvWue7vD/Az2a99y8Nat+VzvcHC2uA0re+7brmXUw93dsv4dalXoeWg1DWIaJ6qv8/RgzufYHoE1UOBNI9GjR0bJNrGHAtsmpf+A16fSB3nzEYVrwf2wJfsljh4mOerACTsrEBsIQXAQQtAmA+4NlKGB0HoVoPbtHNnauLbJfVS5oGoUraLhc4wwIz1bmrEc/uH6vafuA/ZvYiL1YDSe1e2WZMpmVwWe9gpUPUoxeY2VvoX0nhVRT1ooWL34FEfIxrGOlDfNHMKBXdVEe+lWgTFyT6UNu6+y0V5Xg/cK6ZXU7/ihIb069UtrYktJkQ/C9rb3Z1963SuS5rPKglEzXwVOugNmZ2F4Pmppmor9LrQFmEpL8bOMIHTYJWc/emwEJN5S2V07IQm2ayXQJl9YLgzEEssdILgGPA97w/AFdTGdwHI6S19pW0GBH/nFeb932s+H/T2lagVu17Mwl4ANjd3W/NZBsDkPhgmPOriBfu9iJ9dWL0dnIaOLmI/oa9N4NZVd5niYiQ04ty98w0Sh+V1qQK+2/u/qiZbUT4ttxRMfp4MtGp+7RnL7+ZfYKI+phHn73as2AsRT5lkJdXEGbJSwJf9WRubLGc09vdfZeKPCYRFfnb0rFHu/tfCpm/MbJkQtUSB18u5C8mZr+qRio/UTxr+TPRFyAq5X1NkruUhpF1d9/MUhRMM/sdYcraN0tn4Xf2LXd/RVHWVsEo0v5O72gbrN9U7nQfMZVrbQ7U8jxNEak3JHz0TqJiyQ93/31NnvMRHYRdiUb6YnfftZBpPZtRkX9lZG4Lk/iNiOAdd6e0lYhBzameBUkxs5uId3EhYoR9xfSOLkBELV0rk32WMMG7jornzSsCM40VK1YQqJGZRAT3eav3R/QdVaCnmnPMqPcr8r3R3atm8Hr7q2bK7/BiNjl17E4BFiY6nQcyEq3z/4p3c8wBrWrKeh0xwDvUUsnM7qR/sPCofNv7Awe1rtezfcbIINSbiOuyF9kglHWIqG5mzwD3EYP4A22g9wfMMTpEg077WrXJZZtQyPYFBEppVZF4Z/jCe8XMk4VZ65ZEfbY2MdOXr0xwKbCnD/rUrkbEtKitq21kEGoXoG8Qama0Ael9/qW7v8zMPuLun2sjm6W17qN1IT0/VRZeBszn7nNnsnMRlnLvJIKVzUHMhp9I9Cn/k8luR/TNPkO/YvcR4MN5H2lmvusV/29eRqwZISJ+n1Yq5qkPMaXi2VmJaKM3ytJ+S5hWn0m8A0PdIdK7vCv9K0p8Py+HDQbQnLGLQd1htAEp5yHqnV2Jd+ksIrjhT9L+VpY/Q//vRFRyZzYWwV7eRXSiKiPtmdnt+T7rGP2yQ1k2adrv/T7GXZTngeU3Oparz1fS3Q/O9jUtS3JL8cJ0UXJXLJKcWKtvoKIchRL/RmJ24f/o73gfSszM/zTJdWnYy/KWso2dzZRHVdCRTxKzIU4oK68nAoO8ArjO0yhbkl2YMMl5GWGN4ISfyTRiNuPxTLZczmnGLmBnLyLZdsHMvkgEADueMOV9qkbuUzQrmEfkCWa2FmFGVjlS6ZnVwMxUGs3se6TgKe5eZSY0o8OYbV/l7htk2zOWITGz37h7l0AX+XmOadpfPJPnEvf/PKLB/a2Z3enuQy0mRlm2gYjUKb31kh81+S5ERCs+uUivGvCcRJjUlzMUdXnPQzzv38vS9iOimC9AvA9PEQ3vscWxufJWLrnSV9d1qdsbytpXP5jZHuU1GXL8pCLJqY+Q3uuU9jpC8xMxD3qDdguXxzScN49m3HoZOctmygkXhxkz5YSfZz5Tfq2niJxmdoe7r1a1L23n9628T13aqEWBfd39yLT9L0JZrB1Uz44t3Y9K2Xdmsq3r9ZpyVg5CdenA2kxaCqyhjG3b5LpAOAZ8090HguQV56ldjsYiCvQuwIakuB9VioVlEWQr9vXVA0PKcoi7tzLNNLNXeM2AYIXsNHdf38zeCTzrI24wtbLZ9p60XL2kIc++FQG6YGYvJN7zhdL5nkzpCxMDQf9w9wOKY9YlLMJyxe4od7+ukPs7YTlCkls1bY95IqLmv5RB+gb6CYV82YfYhFim0S1bTWImlOtSmifTKiemKp6VvP7Y2d3PSG1UL8jdJURww2O9sMqp6S/3LH8WbPvsTFRz5V6FeChhl97zi/t8ryLM5Goj4wJ4mDjsQbegU9DBH9a6LT+wAfBDd79vWAG8mxnrn1KH9zQiZHyr0Q9r5yvZ2ryP/uh7jfiIj3Qe5vwWYq3RUrbLtcBj4fTtiMq054N1E7Cj94ed34tms/Cc+T3NLpfXw2L29Z5su0vQkSnE87kAYVa+jMdSSXNRBPHwMKnfycxWJd4NI0Yzq4LCVEV77dE1aFLJwcC/iKAfH7MR8/i+DrKnZTuqsMHASHgs2bU2/SOVvwbe46MMEtXQaerlc7a775YUBDOzxXxwRnoSg/6lixb51AajyPJp4+97ddWxNWXvYipHurYfor9e/ZK7X1/IdYlIjcdaf40WOFnemxLvZO6v9LWqTrR3CE5n1YvU70vMnl1HWMP08v0a8LWkXOP1Jro9k8I5gHms37ywDHg3zWui4JbtVIf64SWWmZO1oDcwlCthC5nZtYT56Yw6yrvHZaga0V+MCDyU+0eX9U7T83wg0RGtnCknFOAeualreZ1LM9i2PrYAWMwgf4KR+3EaoXT32sUeXUw9f+KDkUkr6Viv5+VelJG+ymXufp5FEMserSOq1ymxqZ1vCk7Tig5tctO5zq/bYe184S8GricGUecFdrdsacBsAKYp7sqCDftK3k97/8MfUWGOXWKZO5e7n9BWNqP16iVd24EWZT+ZMJPfmrBQmPEuuvsTFmvA3koRLT0ps20sLbv281tRtC3nEm1Pb0WAa0lB+hJVvuCV+zxcx/axCAC4YJyqejWJVI46P2KI/tgfiZnw17X4Wz3a+l2vmd6Vk4hAfq/2kSjaA/1+r570uQeYZmbTKvZVMiFnctPo7nuIzlivMz6ZiAz2He9fZ683JV6a3S5JmM3Mmcl2MfXsMgLaRfYrhPncXUQEyB95hbP+kOtTNRu4eMp3CtHo9cwf6kwFS1/JY73GV9K6mfedSPPM3V6Z7CJEhbEC0fAY8FJC0dvWm8Oc90wfrm3ooFb9lxWIkbcvtpDt8xPseJ+nESZ1vaAjp1ATdKTIt3YkLW1vQaxJemaRx9uAR2oa9vJ8fWspzkpsjGsIFnlV+WL1cE/rklq3GZW9Cd/CQ+j3vfk8MbP0rUz2J8TMQlUwin08+aZk6bm/b8/P6WXEIFyfv28d5TNZsX9p4p2ewqCp3LaMLMUwNZ3/5cRgYt9SDNZh7WaLGfWmd37zTHYrIlrsEfT7K32cqF9+WplL9X8t35VzabdI/XzENfoz0Wn+ICPBeD6d18cdR8Tz9/ji4n+PpX54pbtf0eKS1JLqz729MKFP+zZlZNDlJq/w409yVTEjHiMtR+P9JoZLEr7hd3jDLKR1mynvzdLkMzSk7VXcfcFM9nFC8TbCnPnXmeyr3X2xiv92GSPP++aEAnaQZ/58ZZmaKO/5ENlO9brFzNvxhB/cXel/rUhErH+vp3VqbZTrmduQtbGfT1i/L/z/NdWj1nK22sy+STzbHy/6iocDy3pFNPma892X18FdZK2bO1cX2RuB7bzF6iUd24GhLnAWcVXeQwSDW6PmOvwh32cjUXkr8TG6gViLQLRt25Ykm88m9+1isJ76GGGl1mrd2yH/Y07Ckur7wCnecuLNzO4mBgnrXDBWzmTnINwAphD6xp3EQPMn3b3R0rEoa3triAmq5N5MNER/LtIXJ3yxakdrrD867jGemZ9Zs0nQXt6/EHMXf9i8oS4b5oFG0cyM6FRNIZzFryMU3nNyhc2GjPZ7zSixhTnITin/pQiTnI8VMl19JXPzPggztyrzvh0rivQiYsR+TndfPpM9hghI8CEfDHM+v/cv2VOlqEwigmrs5YNBfvIyLUFcj12Ikbhz3P2QtO9yd391+n2q1wx0pO3W97ni2D+6+6o15ev5bhnhe9KbCTHgC/lxFv4e27j79CKPZdL/Kpes6u2fqZ0WGzSL7MP7fdNWpGVgJGs/UnmxjcIXqw1JSf0Q/bOtX/TM7C3JrUaYuf+WimAUPrgWcSt/3y7P5JD/saL3R6C+jhg8uruQWwk4t1AwSvPP+4CVvHrt5iq/742Ia/iI95t0X0p0DEozs3WIgbZGk99MfjOiA5r7mLZdpP4MYq2/BYnOyo2Eb+ergfXcvesSJr18x6V+mFlUnHM5YnmIf9I/6DI/sL0nP8tRnOddRFv1R2KpiL3dvbKj2rGdbe0uYh1NxysU7IeJZ6e0XtrTC9NQi5mPAZPwju9qp3rdzI4gFP33+oi550LEElj3uPsn2py3ohyvZXBZolW8eumw3oDRsMBBPdkuA+Cb0DIitI3SF94azEMtAul8hzBrvjYlr0sMDr67rFPqsAof4rayFc/7DHcuMzvQ3b86StmyHmhyR2vdDlQcO+ACl/rgPWuis30wCNNuhGtJHptmOuFS9gNiRYs+Zax8l9tiNYFoibqwLxBt27Yl7R9aT1lMdn2IaHvWLd8bC2uM67xmIGDI/3oPYfk202MSVJxrY6L+3pG4hud4moS0Zsufp/L+fRMT1VzZvGKpGXd/zGrC01uL6LjezSSoKfpluWRNJ9Oo1BheRqzjtx+hkH+OUGjzSLZdliDI83/AzL5LjDx9gPBH/lgh9sWsbI1R5izWVG1l3ufuZ2XHrUJEonxt+n/fLcTHHOY8VShnEPc9T1+IWKx+V2Kt13OIxnr5Iovc9GitYl/5sHW5z4ta/+yz5dveP0BxGSMmWr+meZmUBcqOUMrvodQw91HTaelbSzGTrTKl/YaHKWpJlVnkjOKQomhbtzUEGyMeWv9I5drecl1S6xA90Mz2S896rVlcVtY7knKWB6P4NdHxrOrsLVwquCmfa3vvVaL1M2lDRrkZWX4Hui3F0DoitWeRRlPn9BOEKeB7vYhOSpjiX1ekkTqzAybeNmSGokifUd978yL1a7r72hbuAPdnivXP0kBAfv6hpu75Zs3vqu0u9cOYSR360tz+a8BxFUrb7oRJ4rYV+czJcF++A4G1PJYOWYV4X+ue09ZRpr1FzINMtrbjmzpmVem5md5DRCTcBVN+vf7Ii8zsJe5+q0Ugmp8RHeOnzWxXd/9lluVLzKzPDaB3Kgb9AzvV60Q8hA29f03cJ83sfaR+QvpPx1T91+yY3D+6y7JElYGDkiLbFzgoo6pOnTEAnuXdOiJ0opXbSpb/PoT1yoJp+ykK89Ck+O6Snt8ZgYa8epavqo6CuM9LF7J1gUeNWHpvBkOe9w8Qyyp2lqXbEnWt24EZf6TBBS71wX9rsVLF2RY+xXnsjfmJflvOMoTL4C5EX+YCwkrxJsbGMbRfm7pt2wLt3NouIqyJvKqv4GNYTcLdv2Vm+QouZR+t7EN8FviKV8TPsCIeUUqbEZPIw2riN2b2fuIeTSEsTGDQnL3P8qft/5moSu4TZrZu2RmycD4vR+Vbh723Eb+o/5AtQ9NL9yxEuTf4+1lEos1pu/xAmc9LiYfircTNL5cmcG+/BEFvVHUbojLYmGgYDiVeqDLjT9XlU8HHSQtCN7zYeTn+h7gn6xPK9Hu92sSydZjzOtKoWNlBh4iCfGUq++Xu7mZWVp7QYIpYsW/51Gmw7Ddpe7lCNldcy20nW2C7ToEHqOj4z2cVJqvpGsxfpHXptOSmtD1rgZcBv7di6ZxU5pXLPLK88msxnfZrCDaS3uvrrFjL1Ib7YuVmzXvQ7zdeBqJ4J9HAtS3Tv4iOVxvM2vn7dnkmX0nDKHdBl6UYqhSO3rbTv3YzFuaWnyBmBY/0WBKtiqagGlX7ylnVGTMUFbJtF6n/N8yoZx4o8ijbjjOJEeprs3zzsuTKaGv/RzrUDwDWct1FG4xGDTF6/mYGn+s13X2gTnT3UyzM6PowsynAtxjuy/fvnsLmscZ1uS54fq7WCooNWnkYIwNt+f3tKeM7E/Xyz9z9RgsLjY8S9WRpcrwIg0vf9TrwMwbtiLb60+l3z/R1SWIg9WTCV6/HXbT3ZW1dryeerarH3f0p618b+r3EbNEZxMBQU/1wFu3Xxv4iMTi+sg8GDjqKwqcyla3tAPimXh0R+luEW1PJNG/vC/9xQll+nRfmoWY2yZN5qJnt5u7fS8/vst6/dFxvILRHF8uPJv/c1muH0nwfh8l+m/ol6sr73bodsG7LBT7s7q+wsMjpxd640ItVFWBGu/8zYhByXqJ/e6mZHeGFJWFJen/WBv5UocR1WZs67+NDfdsC0Y/qzWJekf2GGDx8WW/w18zeYe3Wve1KlwHX9wG7mtn7vHC9YjAeEQy2ZaRBgp+nTy+t0+BTHRNVyT0YOC+NCuajPHsQU9051zESHXdDYEPLZnvzkcokU+UzsCRh1tvWvLEvQIB3MIu0mHHu+SQ+Q8xw/W/VCCEdRvvN7DRiZvTXxEu2a9UIUVGWVsG9umBmPyLMP44ifM6eIdbX7JU5n6EvRwlnZEPMBLU534sJM9aSjxLX+TjgNDOrW9x90aT8zkH/9Tai45OTB1QpAzf1bTcprsOwkUBCuxKBFHKl8Wzg26mh/VuSX5AYlSxnf7p0Wr5E+OnkM43nmtk5RMe2HNhp4grS++EdAyO1wZNfrLVfl9Rqfo+Jik53fo6y4QP4CnCRmVX5+34lk+vyTHYZ5T6MWD+2cimGXNA7BK6wWBpkSaLje0VKm9GwF6P9q1r17LNRsYZ2OUORBlAWt3BdeSBXCjrUw10Gq3Yk3p91iPgBP3D3Kn8r6O8slh3J7xT/q/XAlnVbd7G0hHCiY7qb9wf3gZr2zsJlpGrfx4GX+3BfvvyaDmwXbXIXLiae97MJF5x7G2S/S8R6uBI4xszuIQaEPuLFsmypTCu1LMO/U0cYwu3j9NQJv8VidrOUbTv73KVeB3Drn3nOyWeAliUtH0UMZP0QOKscaAPwbmtjdwoc1KPlAPg/U37/NLN7epMWaaD6P0V+8xMzQ5W+8ISbV65kvJ3CPDQpsjsTfcmeD+QHGAlUd2yRRzkQOjewdNn+mNlriIGFnNYK+RC6DBT3yXqNH3YqwwaF7Os6nOceRlzg/g7sVfTFc2X5SkLZ+xVpjeUmknK7FfFcrkTNe2HhS32sRwDLRYg66hlgkkWk6x/k4m3/WJc+Ps39jXL7/bRb97Y/k+F+xF0m3u4k3ovvJ53gkOz9qLpGi1iDhVOhl7Sx/GlkQvrkwoyGvmc6aUQQiK97saizRTj02ovgDWHvrcF/d0jZygABXfwT7yRmXU6v6HSU56nyQ82y7QuYswfh49DWV6RLcK8mR3r3zOzKwom9dz88k+uVeZVM9lKa792mmWyVmc8kohHfzWsCs6RR2l0YCch1GOE38Ie0v+kaD3RGrX1AlXJGxWlY3zM11m8mFJWXEZ3V7Yjw8s9mcnMRDfG7iEbFiM7cdwlT9rITYNC8lmKSax32fhjl+1HsW4q4FwOBkbpiLX2xbGRtyzmIBvV1jDyTl3i/L97TRAM9cDqqFdeuZc79fSHqtT5/367PZHZcb5T7i8Ra4AP1mbVfimFGcAoze4M3BDQb8h679/vNdvWVPJQwsz4ibd9LDGrMDZzs7p9tys8qFqm3USyVkvLZllAWFicGVSpNYs1sCe8eTLBvYMvdl8v2tV53sdi/cPo/dZ3qrxCmoAcWitVXgH+Wyqi19OUbzfVtS7pOOxD1x3yE0na6D8bvuJHkCmMxI/gosFrZfxhyrlXTeXbxtM5vuhfvAh4GbiOU/rvSvr7rYWZfc/f9BnOuPFfXev1u6oPF9LWz2THLEfXDBwg3rVOHlGluItjQLgyujd0XHKg4rnKf9Q+An0FhNdG7hxYWSF9O/+0gRmYOjXhWV0hyRxLWK5/y+sCN5fZt7t6zhijLN+P+WTf/+vOJddGvL/KbDBzmmVuNdQtO1zSIOr+7zzUa2Yr/XRsM0jqsHGIdlgssr2ETFgFj1wYuJN71gdU3MtmbPK1dngZsXufu21n4tl9Y3LeT6b429aYMCdJnHQKUprT5GLLubSbb2o+4Lb0ypXJ8gZid39XDhWjgPpnZY8SAb13d884kN8Pyh7Dg+xQjlj+f9gpT90rcfUJ+iJG+txAN/szOe3XCLPMWolGZu+Px9xbbzxJmoXemz13Z587n+lrW/IebgUkV6YsTI0N52k2EYlf5mUXl3aT4vJaoFObpkMdLSQFRRlmGdxFm0FcQsyNvbpA9rOJzNDHKPaWQ/T5hjfBdYlZuTuCuIWWZP/2flxKNWJvyz00o0qcBjxb7biFG3MpjJgG3drxO97aQWYwIHFS3f2VitmArwpe6zTPR98nk7q54LyvfT2KkfazP6qKEEjTu70U637xEx/9HRAPyCWC5MeZ5TdXvWf0hZi4XLO9PekcurzlmHkKxPYNYauZEIqjPsHPNB+xUs2/O9CyeSgQq3KJCZmtiJuMBYvDlVUPONz+hNJ+b3v/HSYMxhdzNDXkM7CNm0f5EuMD8GfhDr84hBpZ6cnMTCsejRCdpair/UVTUq+k/fSD79G3P4udiDqJT/mjVuctntu0zTAyaHkjMNP2TqLdfmu3fiKjDHyOUz176m4hZ/jyvbcjaR+CTxIDceYSZb90z0aleb/m/XkYMfl1LtDNrVsgsWZO+NhFkJ0/7MRG1t5TdDTivpgx3k9W79NfJd2ZyVW3njE8m9yuiHamtqyq2LwY2ryjbZsSA58BxLfK8seG631BsT6v6XbU9nh+i7/aR9Dxend6jlSrkWl+HIefboNgu65K+TyH7LOGm+CRRn/c+TwJPNFzfC4A9G673wkSb+UfC6u3M9EyeCSxSyC5HDKhcRgy6fCX9vpKirSX6iMcQFgC9373thwvZizret5MIZXGOLM2IuuWULG0+oh77GhHXZ66GPMv7+kZioO3gqmey7X0n3CRWS79fRlhcbt/1WZ2Q5spm9kmisrwa+IKZfdbdv10jW+fID4zM5iTZLv67rQMEEA/v64DfELO0l3u6s2NlZkz312Xt7YN7tTa7suTLkn5v7A2+LE0mD6ksud/qZemY1RlZC+1OT0sltMFj5vwGy3zEKmZcy2NyE5sDaRlQxTus70l0Iv5CKJq3evgfVT4/FuZE93nMSNxgYcr4fxbmeJ+quqdZmf6TyluupQjtTWl75TiW+vdj0UxuBarXn3w78a6U+VaOVJrZwEilt4yq6O1NETvR8N96ATdK+bpr1ivn+5Nc62eyGOU+3BtGuZP8HoSJVL4u9YBvZxssgtvtT8z2NJU3N1+6hObR/s0rEnP/26NT2jPlM2yDi9SfSgTnaTINHog6TnR8evt7VhAbEu/t0e5et8b0Z4DXeAQlegXRxmxSc97vEwN1FxEdkV8R1iGXVoi3XncxzaZsmMpR+hyuSCyPtRrMqA8OSTMXqxHv2h1eE0mXbr58rejSXqS0VxH34zXEWqfbe1oqrCAP+mSEmfz1MGh9lPJ9d8p3eWJw5F1ExPG+etzdf8fIu5On/xQo3XyOJJTingXHbukc6xNBJrfIzt+pXm973SyWvNmaeM9PBw71+iXIjiXce0qWI9x/ds3SugQOAtrXw3VtZwVvT+fs4gvf1jz0JdnzsmrxLJWz5LVxVxj0p/aa31Xb44J1CwbZxfS2PE/fDDHRpveYk7AiGWoy7O5zDJPJeDy9a38i7uleqSxzUdwL7xaItkuQvtZubVT4tw6hrR/xyUTsof9HDMCtRY0LAcU98FjPejLhV13GLBmQb+Dfnlx73P2a9Iyd0/LYkZPNJF3qeYWZ3USM/PzdwvfqZ54tQ1HIVnYieuQdYTN7hhH/3QHl1vsjDbbON8kboej2OkQXES/FXU35NDGzpvvNbG4fNHf6PbG0Q2mmuC7wbXffMEsbMLtKFcMuxAzB2ll6l7Vkn6UhqIv3m2MvSoxircOI8rMO0fF8LzGz8rMWlwPLQvVnZbiQGGkqX/jDs+NmSij2GhOQlxCdiLcSo38vIWYQSvP8a4DXu/ufLSInn04oGusRVg9vyWTLqI9OjNheQpiolmHrh5rSZrJ7NP1HH1lv8BJarD+Z5XsSMeJ/hA9GPFzN3XfPZOuiWvbKkJvRzwU84+6elNNXEB36a4vzf9TdP9P03zLZrv8tv2aHEzMTeXl716wvffBv9Zl9PctIwKb8WgyYV6cG+SAqfDsJ5e2UTLbOZLBXiC+nhvBFNAfXKd/j1ssNJfk/EANLZf01LzGDsnqW9izRqO/pIyakd3q16eZrabFUSsrzekKhcgb92/I2o3X9YGFCb0SQtx+6+30NZe2y7uLtRL1RtSTFdMIMrXJgbiyY2QbuftUojuvSXtxNzHafTgwK9ClreXtoHZYbSvL/Jt7jg3uDGFX3w7pFap+xLJGZnQDc5u6fr/uvtKzXy+Obrlt6fu9kZDCk9/xWuRrNMPUsMbMb83Y+S9+MzMzSKwIHZbJtFfNjikNntFvufnlFvk315YDSbC3MQ7s8P2b2A+BXXkzEmNlehJn3W7O0VqbY44lFbI71iQHv09z9tw11T1fT2xVpsVxgl76TDboDOhVLdiXZNYhZ02WJiMEnpfQtiHtxcCE/FzFzmQ/6/swHA8A1mbnX7huGjSwfWYkXUfbN7A53X60mr9t77aH1L3k0F3BlQ1u0vLvfX7PvNV4MIprZWt4isnX2rPf4QL7txTKltflMUCX3and/ed12h3zKirRVx3xInisQit0Xa/YvSoxefZrw06icgW6Tr3VYtLsiv54v5q6EqV4ZzOTVxEzkiVQE96ppTJYllLBdCQXzs4Qf8A2ZzAwFrlTmKra3T/mtxpCgLmZ2KmFWUio/HycUlhfnHd4h12aGz6jFGqVTCCXlamJ28eKaCvQR+mdgp+Tb3iKgilWs71khM5loJHYiljh5VbYv7zh9HZjuKVK2Da5pV9VQTyLu8YLu/u5h5R0NlkUJtZbrT2b7b6+7j+W+th0Ri1mazxPLH3yaGGm9hmjoT+h1PJNsq9nW0fy34r/0vQttGa0ykY5t7dvZteM4yvJswshyQ5/xweWGsAiStQywX08BtfAb/RrwkLsfmsmuT4tF6q0/6viPfSTq+MoV52/dZnRt1K39wFaXDve4dMZq8qv15Ws4pu/57dheXEp/vIdyULSvTjWz7Yi25QZ3/zkNWP9a6ksTs7l7lkpHR6X8eiKS798Jk9wdfUSB7otz0KVe73LdOj47TX62lc+OxQoRMxQEb/aXbKuYV71zk4ho2T/0bM3XJD8w498VC4uOKe7+/bS9Gg3BpDyb7bOIH3MOEbW9t5zaZMJtYvv8fZ4V9WobbCQGwC7EO7IoMVFwZSH3DDGIasRMaG8Q0ID53H3uTDafIT7dR2aIq+rV1u2fmd3F4Pu+EDE58a6yPWuLmb2QGPB/kHBBMaJPsAywqbs/kMlWKpcWQfr+kO+zbutBt/JvzeRb+RG3GZCoo4Xu0Crg5kx71n0W2fDPyg8xWnte+vyk2D6vkJ2TeFEPIdbOhDDP+S0NPg6EqcSCLcuzBLAPEbn4j8QsWL5/wfRAnJvOewiZ79MY8i1t5Yf6RhIK39FEJ+4pQqEZ8LVMsssARxD+CGcTCsAyFXLvJkbO/0AEx1iHGp9RRuHDUVy/y8n8KTOZ2xv+8yPA6kXapJrP4oTiWJXPqwiTrVuo8LdN17L2U8jeQMwA5Z/7iVmjl7R87qy8FoSfw1y95wF4bb6vwzs28G4QFdtZxMj2TYRvyutqjr88+31qwzNwHeE31bv+fdsV+d7RUObKZ4BooDdIn0Uq9t+UzvkiosFeIqUvQMw+jPYed/pvbd6FGtk1iff0dmBq2+Mq8unk29kx7zl71zVtz0P4At1SIbsF8Z7/kuhMDMv3c4z4jV5DzEh+jmY/o40JRfhBwkpj72zf0YTP0flEvbMgHWMnUOG/S0tfwpr8JhMRzu8FfjuG+9Dkc/irsdzjlM+KtPDla/v8MvN8/uYutr9BWFl8lqhzP9Ehr+WJNvxqoi34TLZvWtXvmvK/k5iBv4aYHeqlr08MpOayner1sV639H58vUi7AHhThewbiaA9edoiRFTjPxIK3o/T70uI9cCrztl07aYRg061MTYIJWtaRXqX52RhYkWJrxHxL4xYU/0ewjy9J3c+Ebis6j39SU3emxKz7/sDm7UtU9dP1b0biywxqPN+ou96X7Gvdbwaov92b7q2r0pplfUqNX3Sjtdhh/y9Kp7Xy4i6aXr6XfVcn0TMnpfp7yeCGuZpXyHcM/L4EAsS670eU8juWPE5KD1j9xeyneJd0OxHvGgm9wz9vstPU+PHnB3TWneYlZ/n9OTj9qdaBpTJHtSLicbsV8TM5K3EDGhV3vukm/hY+twDvK9CbiHCt+5n6SH6UvmAZrJ/I0aVPpwe6B3yzxjybR3og/D/uT1di3cRytxdHa/7EiTrgCL936mimJyl1VVefycUuhuy373tv9Uc0yaoSyflh5HgFndVfAbKTvhG7Es03L8ANhrjM7xi8XkRNYMqhO/eeyvSDyKWdMrTPkb4fp+brlXPmmM14Dcdynddsb1VujbvIKL1rUd00u6kuoGYlv0uO1n5vrs73oeTiSAKVqR/gkFleh7i/X88XYtrCd/mE8g6S0V5yv89rdh+CzFC3eYadvpvxbGNjRujUCZalPfq0exrke8UwufqAaKe2JSoq84hlonIZa9K121fwlS679NwjqEBeagI4kEEKNoCOLFIN0Lx+zbhv/UUMaP6goYyzEl0oE4houueOZb7UXMOA7Ycw/FrEYrVSURne7/0Tt1BRVChiuNXJaxjqhSr3xIDRp8gDSpSP9jZ6vllFO1FxT38DoMBXW4E5ky/Fxjt802sS/zJbLs3kLU4FYNcFccvRyi1eaCYZRkM5NSpXh/NdSPq9C+k9+8SYP9i/xrEQHbv2dk/PTt/IGaJctljiABl+f+aI+V/bM35GxXzdC93GXI/pjXl2+J+npv+33uI2fpfEHXWeuXz05DHDW3PV3HsMU2fhuMa791oZSuOXXG01zbJL0L0G35BtIN/IeIilHJ5EKk8qNTfgac7nK98jt5N+L1uRiiEC6ffV5INdCbZ2kkjwrUg3y6D9F1NQ5C+7LhViPrpD4TuMU+xf+B5bvm/VyXchN4MrNor4yjz6qQ7EPXp3Nn2i4m+auegUm0+E9JcuYeF78RqxNT4H706pHbrpQJsZCHw/bwIygH83tNC4Cn9H8SL8XFSICmr91s4iWbzhHdmsl3yPawmz17Gub/odGJJg68C53usMVeZb5LfiJgN+TMxg3sqoeTOQURN/Fkm28qUK8munmTuK3atSJj53JHJbkp/UJfTvSaoS4OZxseJBnj3quOGYWbvIDq38xGjYWf44KLho8b6Q87f7O6XVMjcTFghPFukzwFc74UvVLp3yxKd+t7SH2sQHfTcL63KPGUxIgDKU+6+fyZ7KXCAD/por0N0WjYp0jv56rTFIvDUdwml51riur2M6AS9y7Nlm8zsCKKyf6+npbPMbCHg68A97v6JlHYr8ZzNQax9uCvRQTbge54FcLNYF3hjYhDqB8Q1rgxON4r/lpv5LEC/6Zf7iJlPa7Ovjudv7dvZMd/WbhXWYbmhJP/apnO7+68z2Wk+OhPwfKmUN7j7ksX+Vv67Hc/5IOHOcmLFvlG/P+n41ktSJPmhbihJrpUvX0ezxRWb/otXBDy0COq1KxHgaBIxYHKeZ2u/dq2TLPwqj3D3Wyr2neojZoB303Lpnor614mo9mXb2JNvVa+n9FbXLR3fMyl/jFhy6RAvTPizfOclrm2vzbmJuNelj/fNRL+r9F2ci1ACB4JiZvWPEfV2ry4aWv+kfN9OTBpsU+xrveyb9fsqzkn0F1/kxdKLdeapw/YNw1rGZUiyre9dR9nzmsro/cFaR1WnpmOXIuqVXRiyXGBqt99HDD6c44XvbM0xLyD60OtlaTcDr/bBQG2LJ9m8ra/9b3X7LGIbDA3SZ4PrQX+vfFeS3Nqemfincr6WWKHi6lK+4vhG0+I2jEJ3+DURBPT2ZNZ/JeH6uCZwlbt/pGsZGss3EZXcVKF9hhgRuofonC5PzNJ+zLMgJF0aMzO7jWIh8JQ+PzEKu0aWdhBRaSxIREr9IfCLuhvf4b+NV755lNDNiBG81xOVS9XLNZWImLgIYXLxRnf/nYWf2A8aXv7lGalMFyAqpHyttC5rxnUJ6lKl/KxPjHq/0yvWB7OIRP02MiWTaLD/lck8S4x+39s7bVGGNzMKLNYkPJtYhuJqmBHkZ35ixOtPmWxTwI/KfS2V5zLNiQbwUuD44j2qXPOybp9FwISDiXfzi4wETzDgC+6+apKbk5h5eyptb0TMwEKMYvZ1LrL884iHN7n7H60IoJaUqw3LhiY1fr/zkbUtB65N30XJ1mNO8gsTHegpxKz2ucQ78etCrlMnti1tlYkheWxMBBnaN0vrrFC0PFer9VNHmfdA0DPiOq8LLO/uc2ayrYJ4WLVPUU9p+Q8x6v4xd7/YOvjvdsHCz+xeYjZgL3f/a7avVcfShsSHaHF8GVH4DMJks/a/WQtfvi7Pr5ld5O7/27K8RxJ+mfcSA1DnEObPVcpzPqCTK1U9xaeMrjydUJS+4O5fL/a1GnQws+WKer2q3plE1H+7eBH0Lh0ztF7vgo0EY9vL0wBz17qkJt9rvfATHravg2Leez9776QTgbMuI8xLH8iP66KIte0vWodgUqNlWLm73LuOstOJSYgfEMvi9A3YeH+w1jLOAIVsu+BBZitWtS8WMWwOZGRFgq+4+2OFzAcqslyMmMX8Wn6PzOyWqgGWqn0N7UVfHyaT3wT4i8f6sTsTyugfgW8UfcpW60En2fOBj7j7jRaDjdcQM9GrEn20rya5VTxNzKXtoQN9bRmF7pAPFH2acM3aN/W3r+7tG3LOpd394Vblm6BK7lcIs96DfGSGZmHiofmHux+QybZuzKzlQuBF+iqkKMLE+rqHEYrdHwq5Tkv9tMnXzM5w953T78+7+4ezfbWdg6S0b0W8BBsT/j+7FjIzGqKKl79tJ2sNYkmCd2RplVEY074b8hfARhEIrEr5qTnXmkQn6zf0K5kbA9t6ig5nHaNo12GDAVXOITqMJxVyuxMBSLbN0q4iFJLbC9nVCeUqX5y9tfLcBWsI7la1zyK4Qi29Z8LMjiKi5vYWlL+LMCWcjzA1+nB9LjSOVJrZ9WVnNdvX96w15D8QebzYvzhhwvw+ojJfIdvXuRPbljbKRMUx6xHXaWfCVOxsdz92DGVYm4h6vCYjg0Rf8sEBrNYBl6zDsmE1ZXo1MUK+GHCkZ5G/rWMQj5r85yRmsb7v7mub2dHEmrs3EJ2wc4mZqk5KQtmoW0TTfXn6L+8kBuou7e2rU6qs36pmOaLNOCTbXyrwzkhE9Q/nHUhrGVG46T8RMzVTKGZq2j6/HRWU1jMOXQd0zGwaYYp+MjHQsWfWlrdtD2dE7R8iNxn4sru/NkvrVK/XKILAgFVIb7DuVYR1yunAd6oGBrpgI9Yx5bs2YB1TceyiRJ8HImjPX+tkW5alyzPUC6TUK2svmFJ53VoHk2o418BAY7F/mHVB63vXUXZOwh95F8Jy4wKinzEQMdfC4uQ4quvUPmvCLqR67GCi/jiBsBarfA5s0KKxN1j/ax+0NumyakirPkyS/TpxreYj6qAXENf5VYRbxNsy2bvpD5AH2Xvq/dYeMyYxzOyjRLyW3S1mtn/T69+Y2Tfc/X3WYaBvNFhYAW1NPBuvplp3uD4r12+IFTh+nLb7gnEWx/XahF2JiPHLtSqUj4MN9HP9IezDq3xD56Twv2TQ97HvU8g2BeW4pEibXCH3UmKG+Y9FemuftJr/W5fvtOx3rd/jkLwXogiYU+ZXkfeAHwbwSqKzv1TaXofo9JWBCpp8Z5v2NQYCI3xaaz8V8hcTpodl+uvLe11zvhWADw6RaQqoclvDcaW/xxuJgZk9GfE7fAcxo/SmQvYcsgXOs/TdyQJnZOnrEya616TP8Yws0D1XJvc4WXA3+gO//aXNs1bzX6cV55mWvo3+4FWrFMcNDYLAoE9c/hnwj8uOM2p8+Qq5xRgJunYf8NWW/3ky0QCP6ppV5LcU4R9XFRhkDcKH+RbCImJ/wlR7rOfcNj3X70zv+rrp9+3EIFEue1jTp5A9seFzQkN5NicsEC6h4r1OMp38x4b8//dUPC89/90niY5Grf9uOm6RdM1+CfyprqyEu8atpGBa5f+gQxyHhuf4IOBHRXoe+PA2wm3lvrb5FnmtOOT5rQtscydFDAvq41nkPtH3Ey42D1IRgIwIPFUZAGnYs0MM0txNWDdB+7a29bWruMed6vVR3J8FCaum8wml7jhiRnK0+V3S9Kk5pm0MhaVIAxlEv6jxPhKWY2O6Pg15b0qHYFJ0851tVV91uXdd7zMR2X5PwqJkoKxtyziK6/q3dI0Ooz/uzAco4s10zPfVhPXnpwif1a0Js/C7CTPm0eZ7c/qej1Cwe/7+xth8tK/Nfl9MWOZU7ftR+n6UaOdnxA6hIf4HsHK6BltR9LFalG1hqnWH7xETjgcRsSkWSOmLMhjzZH5iIONcog/1OLHU6hytyzEeD+Bz/SFG9zrta3MzaQ7KsVYhO43o0B3BkIAdxMxUT3F4GbHeamcnbGKWJN/uqoi+keiwPEZDVLkk24u+lkde623/p5D9ItGJ/gEROOaw9HAfQBGkJ8m8u+J8exHh/8v0toHAetGKb8g+1xOdnGcq5JuCCgxEfE3pjdGuk8yKtAuoUqnQE+a9A/uI2aOTGQlqcDKxnEgp10V53jE926Wici0xaHFxJrtJ06fmfEMj6jJY6f1v9vva7Pc30nfrIAg0B34aOIZ2ivNChO/XT4GHiEGBzagYdBvyLo+6c0A0pEtWpC9NjIDmac8S7/lqWVqnSME1Zbiu5rleqbyn+fsz1vNW5LkVoRxdCGw8RHbazD5/xTnmJjpPpxGm6eX+Vo16WVZikO+E9O7fU+zrmWq+pvccjuYeNz2TNEQUzmSqBsFmfGryXTJ/lhkceH4s/e8TKz5Ngx7zER29s4i26LRi/4dSPbLraK4NUVfeQETbv6llHve2lFuaIhAWHer17P8fSESy3ZuGSOMVx05Kx1RG2055r030l1oF4Wt53iMI372FsrSFiEGLT2dpPyPagS3S9T9pSL7vZiQQmqVn5wmifzB0kmEm/K/WA430B1wq+11PFLID/zvdu/eU966LbNo3LzGQ9COiT/cJYLkKuWnjdM0+Rf3A6Ccr5PcgBun/lj5Tidgxde/XEQxZNaRjeVv3xYklOHu/Ny727Vds/yQ9L9sTAz6LpvT5yeodUp+algN9hIJ6BtE/OpsYRLsz3e+FC9lykOEgog+0cs21mJ/oAx9NuH/20l8FvD3b/j7RBn6XsByYk47BcN19wiq5P656gImAOeUSQq1vZpKfj+jof4kwqduLmoqciBp2GGGmdy0RPXnFphcgbQ9d6qfmfPeW+RAzcS8nKtD1CSX65RSKGh2iyo2iXDczMmq0GNHpWr1GdmmiU3ppusZfIjpoV1BUNETwrZ+SDUoQ0eh+Qqwl21SmlYhRytupHoH8AzBvzf2/PdvuEu26S3TRr9Ay5HzHe9FaeSYa+ZVqrt0/qejIdihHK+uF9NwuVHH8Ivl7wihHKluWtYvi/ChROW/D6KMVDnRiOx5/PMUsVkp/G3BckbY94dd/X3reNq/7bw3nG7BaoMNyQ8Tg4vT0LNxPWj6i4fguyw09SwxK/IQhihXFQOXM+BBK2sAgJ6EElBFyWzfq1Chw6bm/tUg7iPCdu5GIo7Bq13eCUM6vbym7BhVLHqV7fA2xzvRrqRkIIxSNT6V36c9E52061Z3XMc8UUW+ttFx6Ny5O17VyhjjJTqtIm48IYvdslnYs1ZFxj2VQSamS/R7RzmxTyHYdFP1hyus9RH/p6JrjNyDNSBfpbwZeXqTNRcxC9iLITkv37QuMsi4s8r+RNOtTpL+ALJox2eBnm2ck5Tt3+r1rKvvihNXW/xtruVv8r/EaaOwSNbqLbG8w/f9Iy242yDYuhVch35toaVy+Z0geGxTbu6dncVOi37Ao0be9mkJPYMiAyBjuRW9lk4PpX+XkYAatU7ooxEsB3yQGRfMJgE2JwGFNZeoN9J1NMdBHTOR9iv7o50YMxpxS5HNYxedoQv+Y0lSGijJtnP2+juiDzlhSdTTvxUT1ye35p/yDeJCdqKyrgvacRMzoHOEpOm3y4/sEUemMKupuRZnWJTr2OwMPufvG2b7WPmlDznGf9/s1XUp9JFI8C5hjHaLKdaX0ybSGoBOZzKZkERrd/VcVMq0DgWX7Vif82F5BKKQne4VPpUXU5Y2IkbO7U9pKREdjqrsfkdK6RLvuElBlbiJK6Z7E7LQTs8AnE37MtX6gTSR/9RcQATh6ETgXJJTqf3p/sK6b3X3Nmnxq/dNblqNVRF2LoBGvJyIg35vSViQGKC529y+ltHe7+7etYxCEinKtSgqM5iOBp7r48i3gLaPmmtmxDL6fk4gRzQM88xftwpD7VheIbEHCd7R33U4m/DUvqslnmG/ndURH/N7iuBWJdSLzWAfXAzu7+60WATG+4EU07kx2CvAtYjT+dqIhPpWYTfi0D0aRrcynh/cHSakKKAWFv10XzOx0YmDhsiJ9C0Kx2jVLuy6d6xTCauW+Lj6uKY/5iOv+o4p9qzA8jsMOFdkuRswuX96r9xpkZ+CFf3RbXz6L4IpvIgZX78rKfhyxruVXMtlp3t6f8gPAX939u0X6/oT54FcrjtmdGOT6FaGMpL/W759tZnN4Ed0+27esuz+Yfu/RVEbvj45byjoxc32VFxH8u9TraV8e/GUu4EqvDqB0KWEGfXeRvhoR2GazogytYqGMBmsZQyG9R69jxI/xkny7oo8zoz9iZqcRK2UcnbbHFKm8Dc+B3zMA3r+SQhfZZxnxS87ry1HXkynfdxODLh8iJlwgXHc+R1yP4xuOXZORgKZ/9f44JL8j+hZ3F8esRERv3yhLmyn32waDXB7WJO/9K53MqNPK+q1LfdexvAsRg3cnp+3b3X31GtnafYXcJOCX5fVM7cDORL/hZx5Bs7YmBmDnL/7vS4iBp7cCjwAvISwUh/q0z8hjIiq5PcxsM7JlENz94gqZ1jdztJ0gi6VcNidewDcRkVu3y/Z3eQEm1YgZodgt35RXHdYhqtwo8n6cGJ3r8dp820cfgbh1IDCLIDgfI56HLxCdq8alXcxsP6LCXYC4vk8RJsjHZjIH0SHatXUMCGQVIefN7KhcoehChfIM4Zd8MuGb9O9MtrWiMopytI6oa2bvJSrABYn372/A59z9uCHn6AVBqA2gluQal0AZq+LcUL6yE/ssI53Y6WPIt0uEyJPcfc9CZhKhwL616MQuRMz87krM1p2TZAbqHDPbjnjPPkP/QONHiABGP85ku0S4b73cUBM2xsjCHc7TFPl8IMjeaBr14vncgph9esuQcr00nWdnz6KA2mAwlZ5idam7X1DkUcr2HVcqgsWx86byfpEYYM7r1GmE3/SjxTFLEsvj5J2gtUolueGcNxJWIv8u0ucl3rl84GUtQql+gFDaHmxzjuz4Xj3fKkhK0+BE2r8kQF290KVeT/JtowTXBuCzIkiMmd1OLMfnhdychHXB0I5xExXKa84lvbJYh2Wakvw1hFvDX4hrt5mPBJYcU9+n4j/UBpOyjgONLc71JDH4V3cdNhuN7HhhHSdaUj9kl/R5mpgEmFyhzDYN+vbt66LsV+RlMLbleFI+rZdXtFg9oGkiq69vbWYbEDPHD6Xt3Yl66h7gU71rb81LYLVScpPsgFJuMbG4AjE59Ip07lcSUaJ/3JDXZOLe7ERYS76qTRlm+rT88/lDKBQfK9Kaghnd3rBvWovzvYYIYPEAcBFh5rzIGP/DXbT0IyQzJ6Um2Eq2//dk9vFZ+rrEKO9YyrxJ02cM+dYFAtucwUBgzxAz9l+nwyLq6diFqDCZLWRWIZToGwhT3g8TDX7TMbUBVYYc18p3a0ge8xMBqtahwgQsyWxHmG3vmWTXJgJa3UYoGm3OMx+wU0V6brLzgXK7Jq8XDLsPQ+7hHkVaLyjUHwjTq3XKd6jm/9T68nUs03bAvtn2ldn7/ZYx5HsZsTRSmb4BRUArupmpdfLtTHXHKYSSew0x47ruWJ6Fsrx0cO2ghc/8zP7QHB+i1o8yu19fJsytf1ux/7WEqdp96Xl8qO5d7lDe/cZyfIv8h/rykZmeVhx/Y7Hde196n3y7DMRYG+Cl3Ee4SXQKrETHICn0+8c9DJxZIXMYYbr5GA1m20UZGuv1JNeLqdHz53yaCt9OOgSCHPKs1+7LZFYlLKIq7z/NMRRGbd5LDIT+Kb0/387SNwEumAnP/Ho0BJOioz9sh/NOGw/ZmfEhBp2/XqRVxjqp2kc3169a159yX3r+f0V1QLQ6H/ShsTo6Xpu/MxI/pve7t/23QrZT35pohyel368ldJMdCb/jMzO5kwnTZCuO/wRwasv/sVnVNSPcA+ZIv+dL16y1zzPhgvGRtvJzMQFJo/SfAF5I+JucRtzE3dPvnN+Y2ScJUzfP8vgE8LuG03jDPszsPuKhPx043PuXfyhNGVov9ePdTFi2JGbAAD4P/KJB9mDgvDQ6n8+87EH4Mo8ar1lGpzebQnScR8P7gXPN7HL6y7wxEd01p3ZWoaZs2xA+aPd4rG35STPrjXgd4MmMrofHGmRHAkemWZJdiGA3q5Z5p/yXjMP8GOAYG7JkRXl4RX5vBA6lf7mWz7v7Twu5KhPD1WIQst/E0N1/bLFkz8FEcAMjKqidvQixX5xjYGaJ6NTmfJtQPOu2B/C0Vm4Tw0YqC/GvEzOBu/rIEiiN77WHafyZwJk9E59hZcrKNpf3z/x+kHj+e8xDmGctSAQ/ObNt3gUfBM5II6b58hW7F+cDWMDM1qfdyPVH0/HHAaeZ2Q+bCpGekTbuHl2ehaWsf93DF+TbXrh21Mw+r+KjtHgZBbeb2Zsq3sM3Eh32WjyWFLvKzA4mOiT58fn6ux/0kfV3B0zl0zucP9eWbbv3r+f4TiIYUSusw9J3ZnYyMVB2IdEm3liT7b9r0qv2TS625yBM4Q4h/PDK8g6sr2ix3EvJe4j3sDz+zUSk66uL9O8T9+gi4vr9ilACL63I47XE87gVMbC1MRGkpVyv+yAi2usGXphtm9lB3m+23bpeT9tzVshX8UuLZUc+XvSPDk//MedmM9vd3U8p/sduhH/eADVWNLtUybr7Sm0KbGa7ufv30u+N3f032b793L3v+Xb381P7u5D3rxM6NZWtMxbLI/bMZx8jLLzMizXVEwMWUR4zat9Knzbna1xu6PmAVSxRV4g8YWbrln0LC1e/JwvZ6USwu6WJuAe3U98n/x8Ll5iBIhGTEzl3eMtZaxtcjucIwpXt5MYDh9PacqCub53Kt3FF8pw+MlP+VsLl4CzgLDO7NpPbn4gNcUdKdyKezzVEXJL8PDdQ7Xb1ANXt/789uXZ4uH79wTuYH7v7s2b2PsKMfSgT0lzZYv3JXrCiLYmZvZuIhvehQnZh4ma+jAgOld/Mvbx+7a1ha5St6Nlaek2mDNZvg1+aI8zYl7ZbV+BNZg81ZV6GWM9zhok3MdrW+gEchg3x5euY12rAMkTnNS/z7URH5I81x72A6Nz9rWp/krke2MjDPHhrYkZlF8Kndid33yLJbeHuP6/JY2d3PyPbNmJkfj+iM2bECPqxnvm6JdnWZunWwZfFwp/m2vTp5dfDvcVaoHXUdN5Wqel4D3Q2ZgYWpmevd/c/p/KcTlTY6xFmg2/JZPNncWkiAN2envm1J7lcqRrA+9dxvdzdX51+n+rub8/LVrzbV7n7Btn219x9v/T7d575CnXFzJYiFnif4ddOLHxf+vJ1NlOz9mt/70EMRPXM0G8hrCb6OsEd/9dhTfu9WHfROvjMjweps3s+MfOQDzi8Eti65podQFwzp+aaWYf1dy3M/XJyRfAad98xk23tk2bd/aNb+fJZ/1qkFHLzufvcFWWZg4jo+UGibvuMu99cyOxOPI8HE+07RBDGLxDtXO4PeyktfVFTeit/6mJw4sfZ4MTA4LV1M9sel3rdwoT2O8QyVb281yXamXflA4/WLRbKu4k6ZHmi3j2DWOqodhC/bd+nqd9T9XzXDBDMoBwgaEO6H/+P6EPekdLq4jiMykS2Smn0frP/N7h708RGnldr2S7UKPuHuPvAoL7FGubfJwZ4ByZa3P3yQn4R2q2l3TiBUPTT+/rbQ/5b61gdYyX1VR7LB5pSemv/1iR/I7Ceuz+dnru93f3XvX0+6D6zKjFx0nP5/KMNTtKV19dTWSv712b2d2LVDlK+q6btXjsw1A3OivhDjbITVMktfUUeJiJZ/qvhmIGbWSGTV4ZHEZ2EGVRVhhaBVHYlZhMmER3P8zwbMexSKXeU7QW0MiLCZt8shw8JaFX3YnXFOvjydcz3fMLf6PoifTIR3XObIn0fYrazN0L/FDHb+Y2KvGc8Q2Z2AmFa+Pm0nd+DZwjzx93yRryUS9tdAqr0Zl+G+hVZB18WiyAXbyUahXMJ3+Q7GCNdOm9JflwCehT37evAdHf/VNq+1msCnpnZ8ow0xgsQSttH074mxcq9PxjPjEay4v73NaDW7PfyR++fYRsXujTqNcfX+XbuTtQ5HyAUCiMGD79IRHIdlaLbdXDEOvrMjwcWPp+70j/gcJoPBszrdM3SoNmmjMR6WJiI9v9Tr7B6aKkIPk2YyA0cThF3wmaSf/RYsPBFfSdx3S4HPlvVdmfybyT8wtcm6tebCP/+Cwu51r6oWfpQf+qOgxMDnc66feNVr2f5r0IMJEP0j2qtEKxdLJR/E8/LwT5iRdOoILTt+1jHwD02Bt/yhrJuT8tgUtbNd7aL0ngJ9bOb7u6bj0a2DquYTe6i7Kd9SxN95E4TLRaDum8lrssKbRWginy6DAy0jtWR6vZa8rrdzDYiJij+TFignkq42sxBRIP+WSZ7Eh38W83sY0Rb8Sjhs/8yd3eLwbuTPQuIWxzX2t/YzBYgdKl7vCKGQJdBh4Zz3OvuLxom18twwn2I0NOLEUrlpHK7Qn4uYsmPD6bP1lQvEH9iw+eEQrbLsiNdlvqZVvW7Zvuwpk8huxGxbM/Z6fw3Ej4qjwBbjvF+zJR1GivybfLdKn2sOi03RPhAvICoWO4hAhr09t2c/Z5G+HbeR+F7WnV/qFgLlDC3mVb3X1pch9a+LFn6gkSFdS7ROdxkjPfi6HSdzk/5Lth0j2m/kP2Hst/l9a1ai/PG3rub3qvXtnleijzWAE4c5XXoEv7/+1SvCf0eopM6pvejZXmnjVO+v6N++anfjSHfUS0Zwyh85mf1ZyzXjFji581UrL+b9r0nvQ/fAVadGc9DxfM8qqXvxnjN7id8HQ8kW+KHmqV+OuTb2he1RmYyNf7UROd9M8Is/0+EKebOwAuarm+bfTO7Xh/ltXspYSGzEzVLzNDvH38b0ZlvjEtBy75Plzp4FlyLBYml284nBo+Oo/D17vjOPUvL5YaI/mP52Zdop68arWxx3Ho0+xtvT8sl6phJy/dQsUxnh2MvIczwqz4XNxw3bN3tYys+X0vX9+lCdiqhPO9E+OFvlNJfUvHcd/ZvJfr629O/POUahM6xSiE71N+YaHfuJgZm30RYFfyO0B/2qDj/S7Lf85Zly35XLvuX0v/W9B/78pwZD9Xz7UOHAAWE3+5thIL3FcL04LKU9sIxlGE6LdfrTOe+pO5TyI5LBd7lxRpF3gcxxnUaa/LtEhTjNirWMyZMqQaCYhCzA3ekF/dnWfr6ZJVd75qnSuIqYsBjgZr70zqgSsfr0DloGBH0ZCtilHAaYeYz1vvRqvOWZPOF7PNPGfik0/NOKDK/ITp50xgZVFkN+E2F/CvTO7pU2l6HUBTKtetard9H1DvbE2ZUdzLS4d6RwUA4SxFmrJcwsib0pcQMx9JjvR8t71ljQLpC9sk29yzJtl4nt2N5Z8a6qC8lfP/+ONa8ZvK9mCnXjDBTy7dbK4J063B3Dh43DtfsJFoOPHfM95vEQHUZeOVwwly5bT5Gg6JJDEBsQ/XgRB4cqnzf/lOT30yv1zv810VS/fVHwlrrx+n3JcDCDcctT1jFXU0M8leuv07LtoAOgXuS/DZkihERcOc6okO98mify4q0SVQEk+r4zrVWGovjNgF+ScyqDqx73EWW6Ot8Mt2rywl3oHuG5NlG2Z+lAxA15RyVsl/kUbnudrbfiDg3N6R7uU6x/9rsd+0kV9U1G+s1BL6RvrtM0l2XnokNCEV4lZS+FBXB/jq8x5s0fdr+pwlprtyFNN1/rRdr5JnZ+4nFzvcYZb5dTBn6bNyH5NuzZ89t2Unbq7j7QLCMlvle6yNrxZXLjEzzmbA2lw368n2SMG39Q+OB9fn9gGgwvl2k70VUoG/N0lovN5SlL0e8qNf5yBrKyxILx9+btnMzqbmIKL3bEw73x3n7pVFGbb5rHXxZLNYf3oXwsfolsU7c1IFMR+RbBbSqOG5uQjGcQtyLJYr9rZ6prqZnKX0jYFnCb623ZuQahLKdr/f3RcJq41pCCT6f8En/DPAtT+ak1s3n+cSm/+Pu76gob8+8D2rWhB4rZrYY8LgXFf5ozdSG3T8r1sYets9iia8P0f+cfckHXRFam9PObnS5Zh1NEU8aIvvOTPaj7v6ZluU9rGG3exFnYHbCOviiJvlPNuXnI+uqb0T4Ma9KdHLf6e63pH3zu/s/RlneTvX6eGBmxxCBwT6UtZdzEPXk/O6+f4s81iDWKD+8Yl+rvk9Xc0hrGX+jC13a8y4mstkxrZYbsliP+xOE9cqR7n5JQ56tZLuYIFu3JepGvXxPF1LfZG0ibssjDXKbENdjXmLgpXRp+AAd1t1O/cM9iZgAvydcK26rOG8Xl8Qx+7cW5/6Ru+9kZo8Slj9fZYi/cdFH63PzqHEP6NynGwsTUsm1bsGZmtbnrFWMOpant17nLkS0xL71Os3sEWLm6TRiPcLamzIz7Nlr8u0UrKFj3qsRM1P5fViHeIE28faRHst8lyZGjP9Nf1CXeYhAF7kv1MVERXVxkcfmhLnypkV6o71/puRWvcSvA04AlnT3hbL0zgFV2mItg4alBup6YgTWKTq/7v7+TLbz4uxmtigxgAExQ/7Xqs5bByV3PJ/LmwmflH8mJfABYlT19gq51uv3PdekDvcZ7n6rhT/oz4gO+tOEz9QvM9kqpWoj4p4/4llgrOIcwwLv5Y1v3y6KwTgz25aIcfBZ4jkzYgT9UMLf7NxMtlMjaKNc2/y5oOM1G9V9qzhnX7RhMzuW/uvlhPXCJV4EfhmS7wYe0aFnGlYTQTYNkHyQqPt6AyRHeVrneoznbOWLahEBu2RBwkd6cXd/QZKbSjzXvybM/N41GiWq4vyt6/WO+W7WG3Qzs5U9W1XAzHbwLA5JqifX8cFB/LmIGZ184HyHpvN6dXyTMfV9LCYeprj794v0VvE3utBFYes6YNVBabyKcIX6ImEd1FSGLrJd/I27KPudgyC2zPebRHDPmyyCVV1BWElMItqXHxTybZX9xnW3iZnr+dz9KjPblwgoeDERA6D2Wc36iUZYGvYGdQf6iTNbHzCzd7v7t63bJN11pKXSCLPu1zFyDy/xIn5B2z6dVUdtzv9bKwV+oiq5XUZCajtNYxlVMLPP+EjgmhkjdRbRnLf3/kiOixMmk70ZzjMJn7zftzzXmANEdXmxRpF3XYCoDQjf4K1Hm3fKZ1OyoC5eMRNmZmsx4qc0sNyQp8XfM/neC5ZXuE40BEv1FHMz286rHfwXA97j7q3CnFcc37pzURy3JIBXOPyn/Y2WCcVz2SWg1TzA8cTo8l3EdVuRGIR4b0VD0GrGaJyfy3J2bIY1QyHXZ9kwbJ+ZvRjYm/6owsf7KC0WumJmNxG+cG5mexMN1esJk6KT3X3DmuMaR64L2aHR5ZvK6P0RLa8j3sG7izxWIiKurpuljaVOHvWxo8Wql64ABkfaR9th6XLfkvwihPn8rkTE8eWyfVX1wyTC7eCHXsxOFPmuyUhQnL+6e7m8T2dseATZLgMkX3X3A9PvA9z96GzfgPIwhjIvRHRo9yKiBn/J04xRl4G6bICmbIPmAuZx97ky2db1esf/0qUvVVl/Vu2zmRjsqVReUx9rXyLi7HnE0on7ESbR17r7tsXx1xMK29+JZ2xHHwmGdbO7r9m2LFmeXYJJtR6w6qg0Xkq2VFhRlrIMrWWzY4bOJndU9jvVz9Z+2cSb3H2t9PtA4HXuvp3FxMCF3j+T2EXZbwpOdy8xibOvu6+aBqEeIVyd8n76qGZcs/MMTCCl9NcAD3hDEL4O5+hN0u1K9JfLSbq7CV/xunvcN/hhMal3epJ/a/pN2t7ZU1CrmaXAT8h1cum/2OWFL7cXqRlVNCJaZX/iyNIc+ajxN7xYe4+aNWrd/QmiMpiBuz9GWhPNzF5IjMp9NZ3rdHf/WHb+2shrFmvU/SzP21qaAfooZ1NbslJ5vnTOq4Y9yP+/vTMPl6Qq8vYbdEODLAKjoh+rbCKyiKCsIwLjAoKCIE2DA464gwKCwrAquODAjCioI8q+IzsyOCjQMMAA0tLdrLJr48aiCDKIKPH9EaduncrKzMpTVVnLvfE+z31uVeapk1FVWZknzon4RRXUZtoKw3ACL2KhInG5oRux8lF/yTbOXrzCgPtgzFmInbNfiSmLTrwUy61aQMU6XgUcjwkBgIkZxMc4nKjGnMhEaaJ9CKWJgnPYVpoocbAjWQc39PG0SNv17HAsv2xFVX0u2LUkVov2iPAX86SIrKGqDwb7T8UG3o9hpTsaN5NFtWIofxesJiJXRM9XiZ+r6vvCw8r1+0RkU+y7OTn8CRb2NjtMTpTV3u4Xf40mvN6NXUP+DtwntqrSglSfuY6vk0tnr5vaWmM5ZQZ54ayDG/p4TCy0LCZbbzmFYczovhyOey4mmFEYjpr4mVX+3kLbxbCVw92xa8mS2AD1xowNudcHsdWQW7CBW7x9ZWwQOwuLFFgZE+l7LOW9ZPpMqTF6NJZXHh9vnohch01qXh5tf3v0eC9MUKVBV4PMjN3LYvnIe2D3+Ldoa91VaP/dtDzP/IZaakWH6+mnseiaS+N93TqxFUgZSy0q+TW3BZuAmUBz0jYmGufXLe7ovGIpO2Bjoj9iTspHsVX+RbCJtLk5XZ8QXv8slgPZcHA3AH5bZGcHKtdb1ajmcmbC6pM5E1aV65qr6juKjpm9rqa0bUwIqaUCnQOcI83V5EOwWtENlse0JnKdfcw5TkZKosxEZAVtjTKLJ9jfSbiHqOrvcsYxz2N5pbtg45HsBFOLvVJcd/tPqvofYTwP0LbK3SdOoOlnxLwQ9u2Qsy8JtbSti4CLwjUoe99fJbHLz0ePsykVd2TanauqtyT238JkdXKz4VZF+8AEZIpOhJYBgFi41LmY2MWZMFHi4TYR2SM7m9INqvobETkFu0h/DrtIHxY1OQk7qV+JhQZsq6q3ipUvOA8LH2nYG89yH09zlvtiEWmZ5a6ZRUv2LTYgG07AVpNPjTeKlRs6gYJzQETWwD7/jbGL9WczTte/57xsWbGVzd2yjlECKYOL/bEZtrdppjSRiBygUWmiRFKKs38gHH8iX1KtjNCnMaW9rJO7H/Y7AhvMrofdCDbABp//GPbdRquDX4qIrKWq94fHMzQqGyYim2SczPdnXp73XYLl0FwRVh/acp4zbY/EcspmR9suC4Puo7A85bp5MUxu/R7YitZSZ6+IG+bNXMeTNtqaBxX/RrLXTSWaeEnkJRFZSUMKQGTbypjj1DxIxXzRUUFV3xyuzbOwe8e94f81mgn7SiHlexORczAH7xrs/nEdNgifnfA+XsgOCEXkFuw+dD6wS5iwerQXBzdwP5bzt4M2c/4OKGibMkFSdk3tCbH8/g9gE1vrak4Jp0D2dxM/z/0NiaWA7I/pPJwLvDVMjA+ClLHUb8mUKYzoVAKmJboAc46yVHVeV21MUovIDwjlUhqTr1lU9VQR+W+C/kbG5kJnvJ8kTFh17TSGyeStCGVgsNrw3bRtmxAKk+HfC38xlZ19zGGtygG0R5ldF1Z3b8J+hw2eEcu1/jU2TtobIEz4tow/U5x97Np7lViqQrbu9vGhv4PC/1+GPl5Pc4HsPi0pxVWRogWkO8LCTFeIRVku0JDuJlYCaWdMgOuLFV6/GmGiUtvLoD1PyPPt0M2DwL+L6eBcgEW3zk15H8CkVVdOUtdL6PdWYIOc7W8GbstsayhNHki7CmWb8iTmCH4Qu8n9HpsJ3hZLYI/bzY0ed1Jem0dxSYp5A/w+ziO/VMreWAjcIGyoXG4obFsn2D0fc2SmJR5vI+DGHuxNKUVzJ/WUJtqC5kVtByxk5UvYausWmbbzEz/f+Dw+F9iv4L0n2Z/4ua2U0O9y2KrRxeHvaHIUkMlR6o72/aLb7yLxM9gYcxSeBo6Itm9HpjQRrcru19Gq7H5dwjF37sHeHYEHsEiLdcNv718wRfQde/wsPhD9PUKfSsz0YM9MbND9+R77qfy9YfeB+dhkx4phW2V1e2wy/F+AKzPbL8dKS5wEbJbab8nxdqJ62ZF5eb9jbEV5fk7bZTCl0MbjZcNf7v0QG+jvi60gblVi88vY6klWgbxNeTzhc3gVNkH9CBYp88ohnK/P0Czb0XjceP7HHvteLPweLg/f9TOE3L6C9ndFj6dhDu+SOe2SFGcxccbG480z+/bt8r2lqNb/DLun7oNN6Lb8Zdre2YUtHcvApLSlWfKyzdZe7CWhfA8JZROx6L0fh9/8h6Pt78aiGstsEmzi4AfA73P2b4tNVD0d/m4gX5F6KSx14RFsjH9pePxDSpTHK3xmPZU6K3ntzwnlVrEJ0t9gTu4xwEUFr3kdNhl3OzZZcxQ24ZdtdykWun0mOT5OTvuVsSjKO7HUryNJKP83WXNyV87bjEnVH6qq20Vty4o0q6qeFbUtzM/I7pNy5Uk0Ug8UkXOxMNgbsVnxwlkOScuRqWxvnUiCQFSNNjykqqtX3RfCfRcAV2EiBS1oBSGP7PeRaO8z2Pkg2KpmI6pAMAdzmajt3do+W9ZxX0U7qgpazaNVcCDmes0RH8BKXfwRc6S31pAXLVGeq4g8TvHqAKrask8SlPsyv6WLVXXngs/g/cAKqvrt8Px2bAJBMSXRi6K2ZQq5PQll9YO88KqStimq75WKs4vIiliEw3GZ7etjE4LxeXa8dh8J0ej3tJLdqgm5fz3YsDw2q70Tdr5fiOWuFa321WHDWtiqzEwsL+wN2AAk+zvOE+pq1DnfX1V/k2nfWIGbhSmUL42Vrbm9DzZXyfnbEVs5+SqtURaHAAdrpJcg5bljqpFyaPjOLsEGa3NoRm0tht2zft3r++uEiDyPfVen0R4503btq8mGLcv2q+oNXfYbRxecTzO6oDCss9N4J9oeCzwKTS2HRv7jUpn2fRc3lDQxqdlRWyUTIqutubMt97AONnwFy2X/FTZhfylwR95nnNg2Jd+4snK0pOUm3wZ8PHt/CPeR72uB7kRVRGRj7Hq5EzYJtg9whbanH1Tt73RsIuNobSqPC7Z6v7qqtvkhVVZ9JaHCSKK9sRjbt4EnVfWL4flcbc2v/xh2jV4Bu7ddiGlplP2Wl8I+290wUczLsQn4G4teE163AZbatp5WTLGclOHK2ipq8mZahSsuzjTPU6EUbOVqeSxEJupOlsme6GL5CAtlbPgS1flvTKQoN5wmw/oi8mywcbHwuGFzNiy4chhgnYRB9WbSKhB1ldZQKqWEn0lQjos3hovBnJz2PQ1+g2PfywzS+6PHx2f2ZZ//lWLa9klCWaAwCD5SOghaYWGLjcFgFY7E8i+mYTePhoO7JTbD2WAasERCv1rwOO953OeqFPMF7GLcYBEsLGkJbAB6UbRvRbFSGlmE/BC82qkYCthoWzmkLfvSkj5fhUWpzArHvjTbJgxWyiYcG32doAniQVqS+zcIROQGLP/1QmyluhFet4iILKtRuJ0kiFSlohbCfyT2W94I+35vF5HHVXWzqN2SRX0U9PsnbNBxarjmzcT0JFZU1RW7sVUScv5U9TIReRSbIPkMzQmSXbMDYE3LHTsJKwF3esa2PYHv0J7qUAfH0bxmlX4vInKhqu4aHn9dVQ+O9l2jqu/qxoDYia1wD0hhHWzC5z7gflX9u4h0ul82xj7QOv5pcV6rDn4jUlKDqnJQzrYJhy3eqGkhsikhvR/HomG+S7MMTNFnnNI2JQT5UBH514J9Lc6+puUmp6QQNcY8h9CqpZMnUpV19o/GnP0zcvosKxumqnpM9HzznHuTAkeLSLaaw1LYyvFGWK64YOf+HKxs07NR8/2BS0VkD3IWkErs68Q0EZmullKzDXZ+NMj6jd/GUgh212Y+e+lvWZvaRGdIU3j3xHBPbLlvhN/Ae7Ax2DbYhGtl/2qyruTmCVccpKp5K7zx6wQTjTgY+xF8RaN4dzGl0o9hF7A4Bv/rWOH5bD7CUEmZ5Z7sSA+rySKyBHZNaiv/I+0lN8Bm/jbDQnCv7IPtnRSTK5cmkrSar0JG0Apb1W4TtOoGsZyYJeNJo7B6I41VrtSZdKmo3Jftu+w4IvKzzCzySaq6b3h8q6puEu3bq8y+vJtlHUiJ0FBjJjnTvqeZa8ms5IoJVOwU+lwT++3NVNUVCl6/F/BZWhWpv6WqZ2baJa26SEKkTh2E1cN4lWZiF+2rh3MpEanSLsvDZeyZuJaF3/fbM47Mu7Hf5EWZ1+2BraZUXZVZuVt7u109q9j3dCxErnGe3Qv8t7aXxSirq96X0oI5/VaOnMh57Z3ajGCpXEGiQr+CTY40JhAWwibIe74HSGt0wRPYd9IWXVA3qdeULvqPHbYq6uctE42Ze1bKCnFKGZiUtpXPJ0ksdSZpYnrL0RSBLYsySxnzPIk5+yfQuT5spbJhoW1ZJOGDqrpG9Px00ld94wWk3AojKYjIYVh601PASlgYuoqpOZ+hqptHbV9DM5pnOcKEbpVJTrEqJLuE164BXKzNSex3hu3vxUKgzwcuyxuHlx5jkjq5L1OxWHXYV6lIc2i7PfZjmaidBxzXD2emDqQ9DPBuLA+hpzDAcSXlYiAin8JWPBv1Kf+Mzf59J2qTdWoUm1j5mZYUGq9gZy2DC0krC3QAdqH7uGYErYAfawdBKykRHxCRL6jqv4XHH1TVH0b74vJbSQO0FCdTyssTqYaVgQ43qIdVdbWq9g0CSQgFzJm5LgtTK6pbJ1iOzIyo7QvYjelw7LzSksHCnpiQyOewycNGaOhxwDdjR1fKw9HznNwTaWciUkejMiyjgDRFqnagTyJVod9PY5ObjYHXc2SuZaHdrdjA+snM9tdiocKbRtuuKDumNtXJU21NKTtS2QYxpdPrMYGkO0P/GwCvBbbSKBS76DcvIgthefe514NUOjg0eREhE2hrTfNaHLU+3QNmYSkKhWkz0owu2AVoiS6oG2nWpxZgNZq1qoVMferEfis7bKF9x4nGVKcxel1pGZiUtpIQgpzpt9TZl7TyPW1ROyXHTRnzVHb2M30Vlg0L+88AHgaO0cjpEpEjsHvnP0fbWpzezHEK9/UbsUour8PuP8+HbWsCS2S+i/jaswLNBcZXYPeMQzP9NibcZ2H3+Suwccr1mc/meuz+d3H2u0t6H5PUyW3EelcpVl25SPO4ETsLThoicjh2/uyrIRci3Ny/iYmMfblCH5trl4rbvQ4uSvqtXPNVRO7EBDSeyrR7NXbha3M+xZTwZmI3yPUw4ZRLVPWuTLuqBcHb0gM6vL++n/PBaZyt7aHun8Bq7s2Ktp1G+Uz73v20LQ+x/GjBhB0uUNUFJQ5mysx1aSSMtqaJHIBdgxfHblQXAD8p6PdWbBD8WGb7Klj5o3ilvJH7vRAVCs9n+iuN1KkDaS0v1oa2qldnXzsTCwX7urbnMH9IVc8Oj1uuMyKyr6qeFD2vfC0TkflaEBad3RfOnQXY5MhtZJxS7T5fMyXnr7INYiskczVT61dEPgtsqKp7Rdu+gU0I7B8N8BYHvgH8RStoMpRR0aFJmbBrTAwsBJwd+pbwd3bRNb+CnbXdAwqOtwgWcXN2N/Z2Q8p1LaHPFIet8kRj5hhJK8TR65bERPc6RhXltZWE1eTQvmqJutlQOTc5pV5wUp37aF/HiQFpLxv2zbzxilgI8imYUzc3vL8NsMm2vdXSPhptK6/6jgLZyeZo+5rYAseXMtufwlI0z8fGsnWViLTjTUYnt4FUE654meIizS/HgybJD02dIHvjk4o1akPbqvV3K9PLDO5UR0R+AayvGQEwsTDQeaq6Zng+DbtBLY/9YO8WW+0/FFgs78df8fjJg4uK/VYWbJAEQStJFB+QigJR0iqE07jpKZYXsohmVuLqOOfDb/MyrNZynKYwA1P//X3UNk+8aiUsd2aaFoTr9hupGArY7cx1gh2rhr53w8KRjsKuwQ9EbVIE/R6jonhQ9JrKkTr9JgwIi2gZuIX2lUSqqk4SheeVrmVh2wPA2tnvXiwv6l5tDaubhtWdbJQAuwoTD7mn5D13pGjQVNC2sg0icr+qrpXdHva1hCCH9/s17LxpODkrYWOIQ1W1TAehzN6uHJoK/c6mfGyyVZf91nIPkPyat/tgqWDzVHUQOc+lhHNrN1U9p2Pj9tfOprrDVnmiMbSv6jSWloHRVj2AlLYpAlGVnf0yJBPKL2nRHj2LVBU4+3HZsG9nr9EF/ayG+QOCRRI+LCL7xxNvkrDqOwpIujjoKzQqNVk3k9rJjZGmcMXMzAUmbxZPyFdiTplZfT/NGrV3hD43xMJfW2rUSmv93VjJcS+g6/q7Uq54i/YQAjDZyQ56MvsmBkthdWBFLDRzY+ymsClwiPaQ85wyuEjsdwtMzCVXsEFVb4raluWpZgfSf8VuYgdqU3yg7EbdVXhduNl8GsuxuVRVD8zsr+2cF5GtidIUtEPeS3DwDsVCh78BnNLt4LgXwgBmFh1CATvNXEu+8i6Qr1qa0/+6oe9dNQrxlnJF6sJ9VZAxitSRVpGqi2iKVAGt527VSaLwvNK1LDw/Fsur2ldbVzC/BTylkaBRpp8Z2Dl2HJZLlhcmXokyJ1dE3qqqP+vGhg795u4LEwGrY+f4Q70OzlIdmmFT1z1ARC6nWfN2G6yc0yKYjsXcvr+REgoc7n0xh3tuvx3uHIctJR82ZYX458A/qeofROTt2MrZZ7CSl29U1V26aZuxp1MI8mwqOvs5fZeF8qdEe6SMeVKc/Zexie+/0b5A1vF+GPWT1bPIW/V9CzbB/lFVfSanj9fT3/q7lRGR32IRhkXfxdGZ9mVpT6o9CCzm2jdVnNwqSI4Ss0ZhX4l9zcOKlD+W2b4KNrsZrxDfCnxKVe/Msed7qrpxlza8iBXArrzq4Rgici120b42s30b4PDGzLiI3I3Jmb8cnISnMHGAnsQzUgYXXfRdtSxQiqBVrKDbUXxAyvNhW/oO7ZfGVkP3xCaEvqGqT+f0O/RzXkTeCByGhSMdh4ULDkzNvIgwaGgRGippWzmkrQ92NXLi2naRkxMnFcWDQtuySJ2+31DzEMv92p1WUa1zsxMukiZSlbKSW3Qt2xqro7xVtG068GXgo9jATrBJvFNC25cyfczAhEFmYfXXr8BEGLsusSMi79LWaKu1aeZ5/UlVN+rGBhF5hHzVWwH+LTPx8oEyG1X1kpT3FPVbS+REjr2K3YvmarWqDUX91nIPEJG7VHXd8HhasHWlXmztlkE43GUOW6Zdp4nG2VRfIU4pA1O5bdiWlG9c8F5zhdakWih/4YRVwbGqilR15ez3gogsKPiN5K36ZidIcpWYMWc+q8RcC6njUakhPaD0eFPdyZUEJWZJyLeTtBC8WurZpl4InCYi8iasdtdNtM7+bY5NXjRK3vRdjTH0U3lw0cMx+lkSIu63o/hAQl+vwsJMZ2KlSk7UKH8lp/1Qz3kR+SF2wzkeG+S11FjOOjY12rEXtoK5FmF2l3y14soz14nHr7zqm3LTkwTxoNS+6yBMeFyH5SDF9r4Tqw19f5f9VhbL6XAt21FV787pv7GCCbaC+UJOmzMwEb+rsdzptn66JXxvs8Lf34CVgY1yJo0r2yDlNZNbyk11aKvah/rKkUMzC9iCEjGgCn3l2bssFsK9tw62XF/He0Bd981uqNPhruKwlbw2aaIxxwG6G3izqv5NLLz34xrqkEp7qHlK265DkMucfUkTQax8n5c0kaokZz/n9Y0Uyd1V9b0Vj1laY77DZ3Y6iUrM/WbYY65OuJOboMQsCfl2Yiu5O2h+jdortVXA4z5gs+yFTyzE+hYtyCOq8N5G+uQbZcSk0l+LlT+JZ/8eBH6tqg+HdvEqVHbAiQ5gpSiFcAE8iprKAuUc7w1YikBXfYvI89gq3GmYImwL2p7vMWwn9zHaV+Mmconzris12JCiVlz7zHU/vxNJEA8aBUTkIuBCVb0ws31nbCC0c7StskhVqvMeHKrdiVYGgHNU9cVMu8orguHe+XzUbmIXCeF6WUTkZmBp7Fw8X1UfFJFHCwa7tdhQFyJyFRY6eblGpTDCisxO2pry1HPt23CeXKhdRoP1A8kRoJHWSB5oRvMM/Hurw+FOdNi6nmjs4ACllIFJaTubxBDkiquzKSKIlRWeU77PFGc/es0i2Ge3O1bP9WJMaO3KqE1j0rflswrPF1PV6SKyqkZhxhU/s6ErMUum3nuF9o+Sc60Oj1X7XK3CndwEJebM60rz7SShRq3UVH9XRD6smWL2TjVE5EdYTvb8zPaNgKNUdYfwfA0sNGtBpouVgd80Jk5GBalPtblSSaAu+v0i5YIqX4qfi8iHgbOAZTSIdoWb0IeBA7RLldFxQhLVinuZua5oT99WaSRBPChs6ymPuFfybCraJ4kiVeE1S2OCXmClbdqiHAoGWIRtL2IiJ4ep6rUyAiuCYuGjG2Bhx+eq6i1Fg92a7ZhGn68jYlodu2EhytdhDtB/aU6uvvSp9m0/f38djlNLiHfdSGvUVJw+0/U1ItFhS55orOIAhXaVysCkti1539nV5BRnPyU3+Xqq1wtOEalKcfYbgnfvDrZegC0WrFJgVyki8h1V/XTiZzZWSswAYuk7MQth7/cg4OfxxG9fUFX/M0d/cUwG/EfYBe67wLty2r0Rk+i/B7vhTS/pc32sjMcczHk9C1O5zGu7PXAjFjL9VHi8Q4/v6TQsxDPv75Rhf+aj/AfcXbLvrujxj7Cc3GybjbAV+6G/l4xddwKvytn+auDOHvr9ed7jvOc1v7/dgD8BvwFuwGa5H8duFG8Z0me+GpajW3hO9fl491bdh9XNnh4e34/l7E7s65M9ffv+y87RXs7fGr+Lwvfey+eC5QyeDjwTftNzsbzCUzHV8ar9TAv3qdLvGpu0u22An9srgY9gIkCPhvf2tgEev9brCOZIzQz9/S58b+8sOj+6vaYCbwD+d0Cf2Wklf6dm2i6KRcCdBHycknHUOP6F39W22PjvcWzs99u894mpSTcefxtbvW08n5tp+xUsmuxaLG/+H4BHh/xeBXNKfwD8PrPvSSxNYhcszQrgkQp9Lhpecwnwe2yyK96/Yc7fPtjq988ybZ/DJpSuz/m7LufYm2CTB4tH29bM/u4xlf8bgNdH20rfW7iO7BtsfUdm3w/D/6eqfmaY2vuRhAXLaPsRwFnD/h10+CwWwsS/7sZ8qrXrOE5L+Y2pjNrM1TnAOdJUYj4EiAUw4ny7A7Awz6UsYqQ9305NsrxSTLyq/ghzmFoQkcU1CmtKpK0/ovDqLvucKixasm+x6PEqmlMSSlXvCCtno8bCmilLBJaXK1Y2o1uk4HHe87SORbbFVMnjUlxfV9X/yml+OBa2+lAI//xfbFXz0l5sSEXya0XOKn1R/2jLnyzZdx5wg1jtuhew1I1GuH5h7nMnMqs6S2dXebTCqo6IrIh9d3GN2FcWrBgJMFKhqYHXiMjncrYLNrHUurGiSBV2ni+MrXI8F167JDZIPiL8dURV/w7MEyuPV9bulz1eH5JQW5E+FThVTDRmJnCCiKyoBWJ2vZBdgaLm64hajvMFwAUish42WN2L1vvyK0RkA2wwuFh4LDRXG2P7r6R9ZWtZbGXuQ/2wOYuI3KzRypZGOc05bbNCS2cAL2HXm+2wlKD96rBzGITf1dXA1dLMvV4c+LWIZHOvp4nIdLXVym0wp79Bdoz+cWyF+Ls0V4iHEo5ZsJr8+Uyz19JcnT0hrMAuFr3fXNTKnV0EXBSua9n7x5zIji1pKjx/UtvrBT+kJSrOOce+NWfbAzlNN8Qmw34qJmp3PgXjarHScJdgQl2NCiq7imkf7KQmktfwNZaj+mf2GUwU8CERmUtGibnqex4k4T7yEcyHugnTuHm4tuMFj9qpgLTn22VzEVbNtN8L+CytA5Y28ZfQdnnshjRfVf8qVptzf0yd8P/1wfaRKGcyLojIedgs3/cz2/fGVvhnhudl4SKF+4ZFWehaL2Ft0mVJoAr9fgwrF/QFrBQX2ETTsVhawcll76EsvLUOJLFecE02pKoV9xymlmNDXthrA9UC4R5pVWhdHhOsOSjaX9Zv6UB7GIjIUWX7tTVPsbJIlVju2Ns0U9JGRJYAbtUuS4wVIZZbf7qqbtrPfruwY2Xtk1iYSGk+Y63XkeD07YoNkl8H/BCr7zs3ajObirVvw0C/ZTcWFfZgXfd5KVCFjfa/Esst3R0Lu10+2heLPU0Hbu/2HjFOSH691ZQQ2Vrrmld8D13VepbOytFJuclSvV7wnVqzTodYGdBZwd652H3r5Gj/pdg44PTM6/YEdtaCMlWdPrOoXUcl5lFBrK7u37BQ/l9l91eZAE86nju59SBp4i/7Y+GMD2GzUd/EiiufiZU2+G0PdoxkOZNRJwxCLgX+is28gTlXi2Azb40LcSVneFSQmlSbJbEkUEK/9wJb5Nzg/gG4STO5cdJemPxz8XPNCFX1G0msF1yTDSuX7e+Xk9AtIrKzql4cPV8SWw3YHQsLuxQTK1uhoItJQTZKR9JEquZrgahd7EB0YVPZiuA/q+ot3fSbaMMVZftV9X099l9F0KWW60g0CbYWtrJznqre3E1fBf2vgaVUgYU0P96vvnOO1aYKG1am3od9vm/B6j7vCNyoQf01tBsZdeU66MJhS55orOoA9RvpQ63nAmc/pa5vSr3gyiJVvSIiC2GTDh9T1Q9G2ytrM5T03VFtu2ziblSIJqrj+0wsztmzcn3L8dzJTUNMfGIPmoWX78XCybIqlSniLxMDeRFZCXN2354XNpFo60iUMxlnRGQrrEQF2AzZdZn9lZxhpztE5L6sI1u2L2XlrA4ksV7wVCQ7OBaRF4DbsRDRm8IqxsCFhuqiapROykBITL3/HZCbCnC9RnXYE23dktbBR7wiOJBVgTCIXoCtEt1G5j1qhTrPBf2mCLqUXUdUu1eLPy0c+6ex01fQNkXpemksR3s9mvUy1wN+CnwSeLeq/rgLe4vEpAT4T1V9ddT2HCxS7BrMQbkOCxXN+3z7LvY0SqQ4bH063iDrmqcIRFV29iWtru9sqtcLvp7iiAjVSKQqlYRre25UX3CKH4j3VfnMpAsl5lFBRA6k9TtTQu62BjHUvh7PndzqiBWlvwK4mWZc/VvI1E4NbVPq5GZnNXOlyruw9zGGXM5kqtDJGZ7siMgrgJcaA2Gx8MbtgMe0hzw2EbkNU4Kel9m+PvB9VX1bD2bXivSxXvBkQjJhjmKK37thOWvnYrmKP5kM16eUKJ2y1ayce8RjmPBJnpPb9bVdiktdQEaJuZv+K9owDQvRnoU5aldhK573lL6wc789r0CFft6qqj/rwY7XYIPQeKL8O6r6+0y7vND8XKVrETkL+26y9TIPBzYG3qBdKK0W2DCBttYWnoedN2cCF6jqgsk0WZVCisOW2G8tdc27pdNqcuLqbHL5ngKbsgrPG+Y02wRLgXpCVd+a9Kab/e5P9Wv7N4AlgP2jlfrFsdTBv6jqZ6O2HT8z6UKJeVQomEBcFlOp/qKqnt/XA+oIqGyN+h+wIqaUdy0ZBcSw/5+w2fN425yS/uZknj8BfCv6a3k+7Pfvf/7X6Q9TA18jPF4d+ANwYvjNHNtDv1sQbuDADtgN9UtYAfQtCl6zbbDnKWyG8AZguyF/PmsCpw37e8qxa63o8YzMvk1qOuavCravig0a7sLyrA4G1hz2Z9Tje70XWDY8XgmL+Mj9XDEV1s/l/B2IDWyH/V4qKTH3+ZgzsCoGTwKf6YP9lRRvc167NnA0pmx7Rw82bB6uZ1/CwnrfH13PNq/Yx8pklK6xlfai9k80rs19/m6Wy9m2VvicfoGJSj0JvHZQ58uo/FGTaj2W+ta4nrwdU//eGTgGuGjI73lJYK/MthTl6MOwBaTLCZoEYfvqwM0djl2o8JxptyUW3fA/wLY9vt+Ua/vCWETlU9gC2Zzw2ziejBp+lc+MLpSYR/0Pc3T7XonDV3IrICLXYeput2pxjcaW0ElJEH8RE6gqRLsMQRGRD6nq2eHx5hrl/ojIvqp6Ujf9Ok4WaRUSOQa7+O8TwvvnaJc5gqG/5WiufAhWvuvbmhMKLolCVXUgIptiokk3quoTYuqphwD/qEMMW5YctWKpTzDsLorr066pqjM6vH5dbHVgV42Kw4/aSkYnUqJ0hh1qXxUR+YR2Wbs94RgzgPdiq7mrYBFUp6qpkPaj/9wVKBFZRINQk1hu+6zw9zfMudxIMylIice9FfiUqt6Z2f5m4HuqunHFfrLn1UDqZUqJmFRO241Cu12Ax1V1s37YMA5IgphUYr+11zWvYENKCHLS6qwk5iZXDdOViiJViZ9DcgSmWM766th98CHNCAc2+qHDZyYiH1PV76eEjo8DUoNImDu5FRCRf8dWho4D1tX2/NtFsdqpa0Tb+iL+Ij2oSdY1gHWcLBIJ4YjIzcBxqnpZeD5xY+6i35VUtU2Br6R9klBVvxGR47DB81zsZvYj4NPAV7FB7F/qPH6OPZ3UiiduKtkbTC83nH5d/3L6HWiuW6+IyBOYjQ12i59rFKbWoZ9eSsmNFSJyBpb6cTWmX3F3zcebyGcUkZ9gOaxnYbV6zw82PCgij2qPYYCSkMZU0keb0nX4zB4GjtFoUCcih2OTSpVKGRYcr5KYVMnrF8Emq87u1oZxJNVhq9hnX0J6eyExBLkuZz8lv76ySFWiDZWv7YkTA119Zp1Cx0cdEdkaOFwTyj1VwevkVkBVDwzx82cCF4dV0McAxISkvoXdFOPXFA7ighOweWZb4eoPFi7dDbXVLnWcDPNF5Hjg15hzdw2AmCBKL1yGDawQkYs1UpgtQPJW81T1aZGBnPLvBTZQy/dbBgsnW09VHxzEwWFi0J5VK15V89WKsyJDRfuSSHFipZkH2raLdhGaadH3OxM4WU2p+WKxOoGjRrZu5JzcVgEpETIBei4lNyb8MyZKtCbw2eh327MokZh2wmeAhojXfcBJ2oyWaigfP4mVAVsOGyA/SA+/h1YTZJmclaZlsXq48baU2rd59TI3wMI+9+7B2FhM6iSaYlKzc9ouha2kLY+tvP8kPD8ImAdMKSdXq9dbTaGWuuaJVL4Gq+pXRORams5+43xeCDtnuyWlXvDzwJ+xiIKdoU1voFunKuXa/j1shZUwMXAszYmBk4NtZlCXn5l2qC08Kkh+lNey2Fip68m4ItzJrUiYifuyiOwL3CgmtAP2AzpeVU9M6C4rux+v/hwsIvHqTy9y2rUMYB0nh48B+2Ghhe+KwnDWxvJOuiW+IVURMHlWRNbXfKGq5wpe009eaKzWquofxVRxB+bgBp6gXa14p4K2K4jIt7DPufGY8LwwFLETKY6rqi4Zva7T6vE0EZkeQrG2wQY7DUbufqYFqSZh1n2HzLb9iYRMRCQWMtkw07ZI6O2X2uc6g4NGVRfq3CodEXkv5qgdjeXBNoQjTw0T1/+lqo0ct5ukGZr7peBELC0ib1PV23sw4xvANSJyEJZfCfbdfj3si8leN5WC2req+izwQWmtl3mwqj7cg61gK+p/xCYD7lfVv5c4E2eFtv8LfBRzAhYBdtSo/q/TPTU6jSkkXYNrcvZfSzNM9wQxBeXFIrviY72jqBMR6aq0Yeg3JY0waXK26mfWaYU4wb5Bsn3muQJP1xWp5OHKCYjIBxoDiDBTgmak/Cv2ky2hcS8WktDX1R9p5gULsBrNHOG2vGDHGUXKQu4L2m8BnAOchs2sKvBWYC/gQ6p6U832PoOlNjR4e/xce6zxWdGGymrFUpMeQLd0+o7rCn8bBNKaP/Vu4H8yoX2VS8mJyI2Ywu6D4b3fjp33awO3q+q/1v+OxguxsiP75UyArQecqKpblrx2OWxwuhuW79Z1br2IbI9pBrwpbLoHS++4sqB917Vvw+THQar6sR7sXQuLCpmJTaCthaVt/S7TLtZlmEb4jXYzRnJGl1G7BqeG6Yr0p5ZsTqSFYp/J9ZoJza8rzDwldHyq4k5uAlUG2VHblPpyc1R1w+h5XwQEpKa8OMfJIjXVopNmPcW4liKUhC5KglBVvxGrM1qIdlnjs0tbVsUcqt2ANYCjsJzcXmfRa6PiREZRrtvimhH0GQXC4GN3LJT9dmwgtqpmREey771s8CM1Cr1NVkTkfi0Wjizcl9N25UHcOyWh9m1w1I/HQtovw5Ttv4OVD/p3Vc2uEle1YZN4kkVMTGoWluffIiaVc/667sckpeQa3HW+cZ/sKqwXLH2uJVtwr18WSyV4UFUPidpOWiGyUced3AQSndzTyvZra325ZxjQ6o+YEM3T6l+800ekplp0XdiRJFQ1VZACteKwby8s1DzOU/yWqp45INviCcHjsRy+CTqF34rpJewEzFLV9/bfwu4RkccxcZTvApep6nNSIGAkaUImtQi9TWayk8ll+0TkirK+ur0ni8iR5d3qMVHbyrVvxeqJfxcLFX4Pdt09FzhCexC7KxrzBDveHk/aZSYkoTkp2XMutePEdArT1VYhp4HWkg1RDHOyDmYdEwN1rRBPJtzJTUDKywJpY9BRoZ/lNCr8XtfqT/hRHYvVLD0Gy5l5FZa/sWdjFthx+kk4n4/Aalx+VVWvHuCx4/DmKkJV/T7++4EVVPXb4fltmHANwBdU9aJB2tOJMDg4AKvH+nOYyFM8DvjmIBzdDhOCqqptugRhxXI7zHF/D3AxcElRyOewEMur3RGr/XsuVgPyLu0xdFxEzgZ+hwm9HQK8XlX/L6z+3eBObjs5k8kTu7Aw8WWitk8CC7BB8W3QKtTYwz35wJzNi2PiUP+gqktEbQtL/4QJkc01pDRlV21EZAGwiqr+vRs7o358NdYZOVLCdMNv+RfACTRFqh7Juwb30b5BlXMaqdDxUcSd3ARE5B7shMqlLIRJEurLRa9pq2uZgojcARyKlUI4GSt+fWvIsTlP+1yPypnaSA216Lqw4U4tKIkzoOPfjP1mF4TnczFxjsWB07oN2060obLok1jdzt00U/tTTDX+fFXdpEZTOyIiO6sJdDSev5NmTuv1WL7xiaq6ynAs7ExY9doKs3s77Hq8N3CVqv65wusXxXLHfhhtWwxbfX8dVj92Xti+GbCaqp6V29kUJmUyOazGNM619YCrsHvmPX20Z0nsO9wbuBALK34i2l+59m1YxZlF0xk/BxtrSHhv3a4UPUP+xACh3/dFbRfFwqhXB+Zj5+XY1ep0Rp+UMF2pqZasmCJ6lmUwheDVVXWPbvtOtGMkQ8dHBXdyE0gdNEsX9eWkQ13LRHsnfuwicp9GNUKH4QA4kxepqRZdF3YkCVXVcPyfxaHZInKSqu4bHt86aKex0+9c+lC3s06kXaTvZaxsxodV9dGwrdZZ+X4ipub5Huz6/k6NtBky7UpFqpz+0WkyWURmYN/DcVjocEolhbz+lsUiJ/YAzsAiJtryAiWh9q2YJkIRql3WnhSRBzGl5KKO44mBC4CXsN/ntpjS937dHNdxyug2TFf6WEtWRB7FJpMbE0sN9fPrgS+rKZ47Q2bkSi6MODdXbShp9eVS6lqmEDvSL2T2+eyG00/iWnTZwXgvtehSWV9EnsVuPIuFxzC4vLBl4icNBzeQ69DUTKffefa6UHXfoMgWN94Qy1X9qYg8goWpTRu4VRUpWFVvvKeXwkr6Yap6bWifJ1L1em0XqcrWGpxQ9sRK2nWdhzkVyJtMzmkzA/seZmGl0b4F9FSeSaxc4AewyKp1O6zkV659q6pblRyz6zIpwJ8TQrPX1qYY2inY+es4ddBVvWDtYy1ZrSmn1+kvvpKbQMiZKvzA4vw1EZmHDWbOBC5Q1QVFKw4i8gLtdS17Xp2QcmXaRVW1l5uf4zgZwuTWbFX9fmb7J4B3qOqsAdvTqSRPmc7A0MuMZVdyM/s2xxyQnTH12UtV9eQBmtcTYbV2HeAcVV1H0kSq8pTzl8VKZS2uPZSMmawUTCbPzJtMDquo6wBXY2H7d/fJhpeBF4G/0TqWKFOLj2vf3qMVat9GIfK9lkm5RFUrOQLZa43n8zp1UjVMVxJEqhKPvxSwnDbz4j+IjbMB/lsj3R1neLiTm4CI5IUpCbADsLyqTs+0r1pfrnJdS8cZRUTkC6r6b+HxBzP5g19V1UOHZ93gEJHXYCU8XsSEnMBWH2cAOw7ixicJasUFzlLcdhClUrKrkhO7sLDMGR1evxCWY7Wb5ohUjToi8glV/Z4kiFR16M9TUXJImUwOzujz4WklZ3RQSEntW6mnTErZxP6NUdvGpDq0TqwP/TNzpi5SUy1ZETkZuEVVTw/PH8ImxRYD/qaqn+yD+U6PuJPbJWGmdA/gYOBeTGhnfkn7wvpyUZuxq2vpOFCeCzsVZ/RFZGusTi/Y6st1Azx2slrxMElxtMXEzZbUjEq1iOyBlar6ST1WDoZoBa4rkarQh5cQymHcJpMlofat1FQmRUTy1MoVWB9TkR/ZVAHHgdbrofSxlqyI3ElQM24816bo5U2qukU/7Hd6w53cRERkOvBh4ECstMDXVPUXOe3uxRQOz4/Di8IgpqW+XMFxCutaOs6oISWqxr6yNDpIRq143Ag5rDuo6pOZ7a/FJgQ3HY5l/adMpEpE8iaNlgE+hOVRfmYwVo4f4zKZLAm1b2VAZVJEZAvgMOxc+4qOWMkux8nSrUhVhX7vauSgh+frNNIaeunX6S/u5CYgIvtgkv/XAseWhfKJyPrYTXRXTBDkPOBCVf1NxWO9Cnha/QtyxgBfyR0PynJch0WBOBPkhDmKyHwtqEdetm8cKPgcJkSqgAcIIlXSrqbbUPacDZysqi/VaetkIUwmz8Jyc0dqMjm7yiQltW+lpjIpUf/bYOXhFKt9PtYRE87UQWqqJRt0d96dk364PHD1ON+LJhPu5CYQ8nSeAJ4kP0+naPC1CZaXuzMm8nKeRsI0Yf+xwB+AY4CzgFcBCwF7quqP+/9uHKd/uMjZeCAiC1R1xQrteqrRXRci8gCm4vq3zPaFgXs1qh06mciKVA3bnsmEiLwL+IKq/tOwbYmRLmvfSrNMyixgC3ork/JebOX2T1hZlMoVJhxnVKgqUpXY54ewRa8DMcVzsFKhxwPfUq9VPhK4k5tAryItIvIO4BvYIG1GtP0O4FAs/+pkYFtVvTUIV53noZ6O4/SDspVc6WON7roQkWOB5YB9o8HK4lh5l6dU9eBh2lc3kUjVDsD8xj1HRI6kqRi6n4Yawk6TkCf/nzRzXL+KVT8QzIFrKyM0THJW62NUo9q3wbH9JLA6MB84NYRnLgXspKpndGnDy8DjwDxyoi1U9X3d9Os4kwEReQ82dn8T9vu4B4vyvHqohjkTuJPbB8RKWeyuqvvk7HsrzTIXj2HKbj9U1aeiNhNhSSJyn6q+Mdrn+YyO41QmRa1YEsqqjAJBE+HLwEcxh06AFbF6okdMlTBdEZkPbKKq/yci2wP/gd1nNgA+qKrvHqqBI0gQijkAy3HdFnNwj1DVbw7VsC4QkYXjc11ELsBC2v8He2+Pqer+fTjOlmX7O2mLOI7jDJPpnZs4eYjImwnCUMCjZIrEi8hXsRDlP2KO7eaq+nhBdy9Hj1/I7PNZCMdxUtg+oe0TtJdV2akes3onhCkfIiJfwlatAB5S1ex1c7KjqtpICfgAcIqqzgHmiMinh2jXKKOqOjs8vkxEnhwnBzdS3t4dK1sY175duyGCIyKnYL/pnmk4sWGleHVsPPJwVvjKcaYaInKhqu4aHn89jiISkWtU9V3Ds85p4E5uAiGGfzdsxvxprASBqOpWOc1fxMKOqyg2ri8izxLyGcNjwvNFe7fccZypQqe0iQyHYte07wLnhhWhkUVaawA3WMPG/601gCc5IiJLYLnv22ClZRr4PSOfpTPnj8TPR/Xckfzat5/PNJtY1Q1hyv069nQsrPsjWOTEQsAKYmXKDpsqkROOk0Os//BOrJxog1fjjAQerpxAyE/5H2BvVX0obCuU6ReRdbCbUSNe/16svl1hPV3HcZxeSFErjl4zLmVVxqoGcF2IyEewCYpnsfrA7wnbNwCOV9VthmnfKDJu544k1L6NhP+gVfyv8Ddf0YZvAEsCB6jqc2HbUpi4zguqul83/TrOuOMVJcYDd3ITCGF8uwGbAT/GwpB/UHDTeT92I/gacAd2s9kQ+FfgIFW9fFB2O47jVEW8RvdYEEpVvAaYp6ovh22vAxZW1V8N1TinZwZV+7aDDQ9iefya2T4NuH+yqpk7Tici9fOFgLNpKp8LcHasreMMD3dyuyCoee5IsybdGdiqxzVRm3nA+1X1scxrVwEuV9X1B2Wv4zjOZEBE9izZrVOlbIOIfEhVzw6PN49Lu4jIvqp60vCsG10KoquOV9W7hmpYDnXXvq1owwOqumbqPseZ7IjIbEo0cwrSGJ0B405uj4jIsljZjZkZSf97VXXtgtcU7nMcxxkU3YQ2DxMROTFvMybEs7yqTgmdCQ+VS2eco6v6Wfs28biXAZeo6pmZ7R/CIj28hJDjOCOLO7k1EVZyd8iGjYVau1eq6nrDscxxHKedcStXFtRm98AEP+4FvjJV9A7i7yr7vY3b9zgoxi26qq7at4k2LI9VjngBmINNiL0Vy/ndSVV/XbcNjjOKhPKgC1T1d+H5njRrlX9RVf8wTPscY0rMeg+Jo4CfhlJC8c3hEFpV2BzHcUaBsZjxDIqvHwYOBG4DdlHVXwzVqMGjBY/znjvGwlkHF0BVHxORhYdgTyfOoFn7djtgbWB/VX027Kud4MRuLCJbYyHeAlytqtcO4viOM8J8D0sfQETeDhwLfAZ4M3AysMvQLHMmcCe3JlT1MhF5FBuIfQa7OdyDhfjMG6pxjuM4Y4iI7APsB1wLvCexXNJk4o0iMh+7r6wWHhOeD0yYaMx4SURWKoiuGkiOayK11L5NIbOafBdWj3kUPyvHGTTTotXamcDJqnoxcLGIzB2eWU6MO7k1EpzZMqEUx3GcoZGpG5qtIzqKtUNPBJ7A8hKvjOqBNnKIp0oayFr4im0q4xZdVUvt20Ti1eRtgTcC+w/DEMcZMaaJyPQw6bMN8PFon/tWI4Ln5NaEiFxJufKaCzY4jjNUxrB26Mpl+6fKym6JYBjAi8DDwGEeVtqKiKyPRVc1Qm/vwdSVRy66qq7at4k23BWtJk8HbndRM8cBETkMSyN4ClgJeIuqqoisDpyhqpsP1UAHcCe3NkRky7L9qnrDoGxxHMdJRUR2DuFXI4+IbA7srqr7DNuWYRNKz6wDnKOq6wzbHmd8ceVuxylGRDYBXgdco6rPh21rAkuo6s+HapwDuJM7FLJ1DR3HcUYNEfmVqq40bDuKEJE3A7sDuwKPYqVO8koMTUlE5BOq+r1h2zEqiMgVZfs9uqqdUVhNdhzH6RaPG6+JMJu+K7A88GNVvVtEtgcOxW4UXuLBcZxRZihJgHmEGfPlMfGb3bB6oU8DF2CTtVsN0byRxB3cNjYFFgDnYarcI3N+jyqqOm3YNjiO43SLr+TWhIicDqyIKSJujNXO2hQ4RFUvG55ljuM4nRmllVwRuQX4R5oiOHur6kNh3yOq6orCTilh4vmd2ATJesBVwHmqes9QDXMcx3FqwZ3cmhCRu4H1VPXlIMP/FLB6o3C04zjOsBGRu8gXMBJgTVWdMWCTchGRj2Bhky9iTspmwI+B84EfqOrrh2ieM2aIyAzsPDoOONrD3B3HcSYf7uTWhAs2OI4z6oyrWrGILA7siDkqW2OlTi5V1WuGaZcz2gTn9r3YebMKcAVwqqr+eph2OY7jOP3HndyaEJH/Ax5qPAVWC8+nWj1Hx3Gc2hCRZYEPAjNVdeth2+OMJiJyBqY6fTVwvqrePWSTHMdxnBpxJ7cmxnWFxHGcqUNJvdWRVE8Vka1V9brw+PWq+mi0b2xKHjmDR0RepqkUHJ/zI3muO47jOL3hTu6A8XqOjuM43RGnfXhKiOM4juM4RXgJoQGQV89xqAY5juOMJ1LwOO+54ziO4zhTFHdya0JE1sTrOTqO4/QTLXic99xxHMdxnCmKhyvXRMj/8XqOjuM4fUJEngFuxFZt/zE8JjzfQlWXGZJpjuM4juOMEO7k1oSI7ISt5Ho9R8dxnD4gIluW7VfVGwZli+M4juM4o4s7uTXj9Rwdx3H6g4i8Gni1qt6b2f4m4AlVfXI4ljmO4ziOM0osNGwDJjuq+ryqnqOq2wMrAHOBQ4ZrleM4zlhyIvDqnO0rAN8csC2O4ziO44wovpLrOI7jjAUico+qvqlg392qus6gbXIcx3EcZ/TwlVzHcRxnXFi4y32O4ziO40wh3Ml1HMdxxoUHRWS77EYR2RZ4ZAj2OI7jOI4zgni4suM4jjMWhPrjPwJuAeaEzRsBmwLbq+oDw7LNcRzHcZzRwZ1cx3EcZ2wQkRnA7kAj//Ye4FxV/cvwrHIcx3EcZ5SYPmwDHMdxHKcqqvqiiMwGngQUuM8dXMdxHMdxYnwl13EcxxkLRGQp4AfAhlg5toWA9bHQ5b1V9dnhWec4juM4zqjgTq7jOI4zFojI6cBjwNGq+nLYJsARwOqquufwrHMcx3EcZ1RwJ9dxHMcZC0TkQVVdI3Wf4ziO4zhTCy8h5DiO44wLMmwDHMdxHMcZfdzJdRzHccaFm0XkyBCiPIGIHAHcOiSbHMdxHMcZMTxc2XEcxxkLgvDUKcBbMOEpBTYA7sSEp/40POscx3EcxxkV3Ml1HMdxxgoRWQ1YGwtfvkdVHx6ySY7jOI7jjBDu5DqO4ziO4ziO4ziTBs/JdRzHcRzHcRzHcSYN7uQ6juM4juM4juM4k4bpwzbAcRzHcaogIq8AXlLVl8LzNwDbAb9U1UuGapzjOI7jOCODr+Q6juM448KPgVUARGR14H+BVYF9RORrQ7TLcRzHcZwRwoWnHMdxnLFARO5S1XXD42OAZVV1HxFZBJjT2Oc4juM4ztTGV3Idx3GccSGeld0a+AmAqv4VeHkoFjmO4ziOM3J4Tq7jOI4zLswXkeOBXwOrA9cAiMjSwzTKcRzHcZzRwldyHcdxnHHhY8BTWF7uu1T1/8L2tYHjh2WU4ziO4zijha/kOo7jOGOBqr4AHJuzawGw+YDNcRzHcRxnRPGVXMdxHGfsEJFXicinRORGYDaw3JBNchzHcRxnRPCVXMdxHGcsEJElgZ2A3YE1gUuBVVV1haEa5jiO4zjOSOElhBzHcZyxQEReAG4HDgduUlUVkUdUddUhm+Y4juM4zgjh4cqO4zjOuHAosCjwXeBfRWS1IdvjOI7jOM4I4iu5juM4zlghIqsCs4DdgDWAo4BLVfWBoRrmOI7jOM5I4E6u4ziOM7aIyLpYju6uquoru47jOI7juJPrOI7jOI7jOI7jTB5cXdlxHMcZC0TkOSBvZlYAVdWlBmyS4ziO4zgjiK/kOo7jOGOHiNypqhsM2w7HcRzHcUYPV1d2HMdxxhGfoXUcx3EcJxd3ch3HcRzHcRzHcZxJg+fkOo7jOGOBiHwgerp05jmqesmATXIcx3EcZwTxnFzHcRxnLBCR00p2q6p+ZGDGOI7jOI4zsriT6ziO44w9IrKzql48bDscx3Ecxxk+7uQ6juM4Y4+I/EpVVxq2HY7jOI7jDB8XnnIcx3EmAzJsAxzHcRzHGQ3cyXUcx3EmAx6W5DiO4zgO4OrKjuM4zpggIneR78wKsNyAzXEcx3EcZ0TxnFzHcRxnLBCRlcv2q+ovB2WL4ziO4zijizu5juM4juM4juM4zqTBw5Udx3GcsUBEnqM4XFlVdakBm+Q4juM4zgjiK7mO4ziO4ziO4zjOpMHVlR3HcRzHcRzHcZxJgzu5juM4juM4juM4zqTBnVzHcRzHcRzHcRxn0uBOruM4juM4juM4jjNpcCfXcRzHcRzHcRzHmTS4k+s4juM4juM4juNMGv4/hOM+p+UZIdgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=np.sort(df.Neighbourhood))\n",
    "ax.set_title(\"No of Appointments based on Neighbourhood\")\n",
    "plt.show()\n",
    "\n",
    "# No of appointments by Neighboorhod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the two charts above, the Neighbourhood with the most patients at the hospital was `JARDIM CAMBURI`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### No of Appointments based on Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=np.sort(df.Age))\n",
    "ax.set_title(\"No of Appointments based on Age\")\n",
    "plt.show()\n",
    "\n",
    "# No of appointments by Neighboorhod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart above showed that most patients visiting the hospital for appointments are in pediatric age group with a gradual decline in visits by older age patients from 60 years and above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Patient Hypertensive Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": [
    "fig = sns.countplot(x=df.Hypertension, data=df)\n",
    "fig.set_title(\"Patient Hypertensive Status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart above showed most of the patients visiting the hospital are non-hypertensive. This is to be expected since essential hypertension risk increases with increasing age. Moreso from the chart of the age distribution above, majority of the patienst belong to the pediatric age group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gender Based Distribution of the Patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": [
    "fig = sns.countplot(x=df.Gender, data=df)\n",
    "fig.set_title(\"Patient Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows the distribution of the patients based on Gender. From the chart, it is obvious that the femaile patients are more than the males, (almost twice the number of males)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of Patients based on Diabetic status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": [
    "fig = sns.countplot(x=df.Diabetes, data=df)\n",
    "fig.set_title(\"Patient distribution based on Diabetis status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows the distribution of the patients based on Diabetes. From the chart, it is obvious that majority of patients are the  non-diabetics. The non-diabetics make up a very high percentage of the total patients. This is similar to\n",
    "what was observed for the Hypertensive distribution of the patients in the previously shown chart above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution based on Handicap Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": [
    "fig = sns.countplot(x=df.Handicapped, data=df)\n",
    "fig.set_title(\"Patient Distribution Based on Handicap Status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above chart, the patients without handicap are more than those with handicap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Research Question 1 Does having scholarship affect the hospital visits and showing up of patients for appointments?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Scholarship Vs Showing Up for Appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Scholarship  Showed_up_for_appointment\n",
       "0            Yes                          0.723150\n",
       "             No                           0.178569\n",
       "1            Yes                          0.074923\n",
       "             No                           0.023358\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Scholarship\",\"Showed_up_for_appointment\"]].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Insights\n",
    "\n",
    "From the above results, 72% of those without scholarships showed up for their appointments while only 7.5% of those with scholarships showed up for appointments.\n",
    "\n",
    "Amongst those with scholarship, 7.5% showed up for appointment while 2.3% did not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": [
    "df[[\"Scholarship\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\", \\\n",
    "title=\"Counts of those who showed up for appointment based on scholarship status\")\n",
    "plt.xlabel(\"Scholarship, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.Scholarship, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on Scholarship Status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The charts above further confirms that those without scholarship constitute majority of those who showed up for appointments. A small percentage of those with scholarships actually showed up for their appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2: Does Gender of the patient have any effect on the showing up for hospital appointments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gender Vs Showing up for Appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Identify the number of males and females who showed up for appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  Showed_up_for_appointment\n",
       "F       Yes                          57162\n",
       "M       Yes                          30887\n",
       "F       No                           14568\n",
       "M       No                            7710\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Gender\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above result, females showed up for appointments more than males did. In addition, more females than males didn't show up for appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "df[[\"Gender\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\", \\\n",
    "title=\"Counts of those who showed up for appointment based on Gender\")\n",
    "plt.xlabel(\"Gender, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.Gender, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on Gender Status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two charts above show that more females showed up for appointments compared to males.Generally more people showed up for appointment than those who didn't. More females failed to show up for appointment compared to females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  Does having any of the chronic diseases/conditions listed in the description above (hypertension, diabetes,alcoholism, handicap) affect the hospital visits and showing up of patients for appointments?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hypertension Vs Showed up for Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hypertension  Showed_up_for_appointment\n",
       "0             Yes                          70081\n",
       "              No                           18530\n",
       "1             Yes                          17968\n",
       "              No                            3748\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Hypertension\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "63.5% (70, 178) of the non-hypertensive patients showed up for their appointments. While only 16.3% (18, 029) of the hypertensive patients showed up.\n",
    "\n",
    "Also majority of those with hypertension (17, 968) showed up for appointment compare to those who didnt show up (3, 748)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": [
    "df[[\"Hypertension\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\", \\\n",
    "title=\"Counts of those who showed up for appointment based on Hypertension\")\n",
    "plt.xlabel(\"Hypertension, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.Hypertension, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on  Hypertensive status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two charts shows that many of those who showed up for appointments didn't have hypertension issues. From the charts it is obvious that number of those without hypertension who didn't show up for appointment and those with hypertension who showed up for appointment is about thesame. Generally however, more hypertensives showed up for appointments compared to thos hypertensives who didn't show up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diabetes Vs Showed up for Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diabetes  Showed_up_for_appointment\n",
       "0         Yes                          81571\n",
       "          No                           20856\n",
       "1         Yes                           6478\n",
       "          No                            1422\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Diabetes\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "73.9% (81, 695) patients without diabetes showed up for appointment while only 5.9% (6. 513) patients with diabetes showed up for appointment.\n",
    "\n",
    "Amongst the diabetics, those who showed up (6, 478) are more than those who didn't show up (1, 422) for appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sz2mj8nB9epcyEbEBWAfsVl8XSdMlLZa0ePXq1X2zgGZmNmCVeVpyV1LPaj9gb2BHScd0V6RBWnST3l2ZrgkR50fEhIiYMHJkxzyxwczMNlOZpyXfBjwQEasj4jngx8DrgEfzqUby+6qcfzmwT6H8aNJpzOV5uD69S5l86nM4sKaUpTEzs45RZnB7GDhU0g75OthE4G5gDjA155kKXJmH5wBT8h2Q+5FuHFmUT12ul3Rons6xdWVq0zoKuD5flzMzs61Yab9zi4iFki4HfgtsAG4Bzgd2AmZLmkYKgEfn/EskzQbuyvlPiIiNeXLHAxcCQ4Fr8gvgAmCWpKWkHtuUspbHzMw6R6k/4o6IU4FT65KfJfXiGuWfAcxokL4YOLBB+jPk4GhmZlbjv98yM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKcXAzM7PKKS24SXqZpFsLryclfULSCElzJd2X33ctlDlF0lJJ90o6vJA+XtIdedzZkpTTt5N0WU5fKGlMWctjZmado7TgFhH3RsRBEXEQMB74I3AFcDIwLyLGAvPyZySNA6YABwCTgHMlDcqTOw+YDozNr0k5fRqwNiL2B84EzihreczMrHP012nJicD/RcRDwGRgZk6fCRyZhycDl0bEsxHxALAUOFjSXsCwiFgQEQFcVFemNq3LgYm1Xp2ZmW29+iu4TQEuycN7RsRKgPy+R04fBSwrlFme00bl4fr0LmUiYgOwDtithPqbmVkHKT24SdoWeDfww56yNkiLbtK7K1Nfh+mSFktavHr16h6qYWZmna4/em5HAL+NiEfz50fzqUby+6qcvhzYp1BuNLAip49ukN6ljKTBwHBgTX0FIuL8iJgQERNGjhzZJwtlZmYDV38Etw/wwilJgDnA1Dw8FbiykD4l3wG5H+nGkUX51OV6SYfm62nH1pWpTeso4Pp8Xc7MzLZig8ucuKQdgLcD/1hIPh2YLWka8DBwNEBELJE0G7gL2ACcEBEbc5njgQuBocA1+QVwATBL0lJSj21KmctjZmadodTgFhF/pO4Gj4h4nHT3ZKP8M4AZDdIXAwc2SH+GHBzNzMxq/A8lZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOQ5uZmZWOaUGN0m7SLpc0j2S7pb0l5JGSJor6b78vmsh/ymSlkq6V9LhhfTxku7I486WpJy+naTLcvpCSWPKXB4zM+sMZffcvgb8PCJeDrwauBs4GZgXEWOBefkzksYBU4ADgEnAuZIG5emcB0wHxubXpJw+DVgbEfsDZwJnlLw8ZmbWAUoLbpKGAW8CLgCIiD9FxBPAZGBmzjYTODIPTwYujYhnI+IBYClwsKS9gGERsSAiAriorkxtWpcDE2u9OjMz23qV2XN7MbAa+J6kWyR9R9KOwJ4RsRIgv++R848ClhXKL89po/JwfXqXMhGxAVgH7FbO4piZWacoM7gNBl4DnBcRfwH8gXwKsolGPa7oJr27Ml0nLE2XtFjS4tWrV3dfazMz63hlBrflwPKIWJg/X04Kdo/mU43k91WF/PsUyo8GVuT00Q3Su5SRNBgYDqypr0hEnB8REyJiwsiRI/tg0czMbCArLbhFxCPAMkkvy0kTgbuAOcDUnDYVuDIPzwGm5Dsg9yPdOLIon7pcL+nQfD3t2LoytWkdBVyfr8uZmdlWbHDJ0/8YcLGkbYH7gb8nBdTZkqYBDwNHA0TEEkmzSQFwA3BCRGzM0zkeuBAYClyTX5BuVpklaSmpxzal5OUxM7MOUGpwi4hbgQkNRk1skn8GMKNB+mLgwAbpz5CDo5mZWY3/ocTMzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCrHwc3MzCqn1OAm6UFJd0i6VdLinDZC0lxJ9+X3XQv5T5G0VNK9kg4vpI/P01kq6WxJyunbSbospy+UNKbM5TEzs87QHz23t0TEQRExIX8+GZgXEWOBefkzksYBU4ADgEnAuZIG5TLnAdOBsfk1KadPA9ZGxP7AmcAZ/bA8ZmY2wLXjtORkYGYengkcWUi/NCKejYgHgKXAwZL2AoZFxIKICOCiujK1aV0OTKz16szMbOtVdnAL4DpJN0uantP2jIiVAPl9j5w+ClhWKLs8p43Kw/XpXcpExAZgHbBbCcthZmYdZHDJ0399RKyQtAcwV9I93eRt1OOKbtK7K9N1wimwTgfYd999u6+xmZl1vFJ7bhGxIr+vAq4ADgYezacaye+rcvblwD6F4qOBFTl9dIP0LmUkDQaGA2sa1OP8iJgQERNGjhzZNwtnZmYDVq+Dm6RdJb2qhXw7Stq5Ngz8FXAnMAeYmrNNBa7Mw3OAKfkOyP1IN44syqcu10s6NF9PO7auTG1aRwHX5+tyZma2FWvptKSk+cC7c/5bgdWSboiIE7sptidwRb6/YzDwg4j4uaSbgNmSpgEPA0cDRMQSSbOBu4ANwAkRsTFP63jgQmAocE1+AVwAzJK0lNRjm9LK8piZWbW1es1teEQ8KelDwPci4lRJt3dXICLuB17dIP1xYGKTMjOAGQ3SFwMHNkh/hhwczczMalo9LTk4Xx97H3BVifUxMzPbYq0Gty8A1wJLI+ImSS8G7iuvWmZmZpuv1dOSKyPi+ZtIIuJ+SV8tqU5mZmZbpNWe29dbTDMzM2u7bntukv4SeB0wUlLxzshhwKDGpczMzNqrp9OS2wI75Xw7F9KfJP2uzMzMbMDpNrhFxA3ADZIujIiH+qlOZmZmW6TVG0q2k3Q+MKZYJiLeWkalzMzMtkSrwe2HwDeB7wAbe8hrZmbWVq0Gtw0RcV6pNTEzM+sjrf4U4KeSPiJpL0kjaq9Sa2ZmZraZWu251f55/9OFtABe3LfVMTMz23ItBbeI2K/sipiZmfWVVh95c2yj9Ii4qG+rY2ZmtuVaPS352sLw9qRH1vwWcHAzM7MBp9XTkh8rfpY0HJhVSo3MzMy2UKt3S9b7IzC2lYySBkm6RdJV+fMISXMl3Zffdy3kPUXSUkn3Sjq8kD5e0h153NnKj/eWtJ2ky3L6QkljNnN5zMysQloKbpJ+KmlOfl0N3Atc2eI8Pg7cXfh8MjAvIsYC8/JnJI0DpgAHAJOAcyXV/pz5PGA6KaCOzeMBpgFrI2J/4EzgjBbrZGZmFdbqNbevFIY3AA9FxPKeCkkaDbwTmAHUniowGTgsD88E5gOfzemXRsSzwAOSlgIHS3oQGBYRC/I0LwKOBK7JZU7L07ocOEeSIiJaXC4zM6uglnpu+Q+U7yE9GWBX4E8tTv8s4DPAnwtpe0bEyjzdlcAeOX0UsKyQb3lOG5WH69O7lImIDcA6YLcW62ZmZhXV6mnJ9wGLgKOB9wELJXX7yBtJ7wJWRcTNLdZFDdKim/TuytTXZbqkxZIWr169usXqmJlZp2r1tOTngddGxCoASSOBX5BOBTbzeuDdkt5B+vnAMEnfBx6VtFdErJS0F7Aq518O7FMoPxpYkdNHN0gvllkuaTAwHFhTX5GIOB84H2DChAk+ZWlmVnGt3i25TS2wZY/3VDYiTomI0RExhnSjyPURcQwwhxf+zmsqL9yYMgeYku+A3I9048iifOpyvaRD812Sx9aVqU3rqDwPBy8zs61cqz23n0u6Frgkf34/8LPNnOfpwGxJ04CHSac6iYglkmYDd5FuWjkhImqP1zkeuBAYSrqR5JqcfgEwK998soYURM3MbCvXbXCTtD/pBpBPS/ob4A2k61wLgItbnUlEzCfdFUlEPE76h5NG+WaQ7qysT18MHNgg/RlycDQzM6vp6bTkWcB6gIj4cUScGBGfJPXaziq3amZmZpunp+A2JiJur0/MPakxpdTIzMxsC/UU3LbvZtzQvqyImZlZX+kpuN0k6cP1iflmkFZ/v2ZmZtaverpb8hPAFZI+yAvBbAKwLfCeEutlZma22boNbhHxKPA6SW/hhbsVr46I60uvmZmZ2WZq9XluvwR+WXJdzMzM+sTmPs/NzMxswHJwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzyiktuEnaXtIiSbdJWiLpCzl9hKS5ku7L77sWypwiaamkeyUdXkgfL+mOPO5sScrp20m6LKcvlDSmrOUxM7POUWbP7VngrRHxauAgYJKkQ4GTgXkRMRaYlz8jaRwwBTgAmAScK2lQntZ5wHRgbH5NyunTgLURsT9wJnBGictjZmYdorTgFslT+eOQ/ApgMjAzp88EjszDk4FLI+LZiHgAWAocLGkvYFhELIiIAC6qK1Ob1uXAxFqvzszMtl6lXnOTNEjSrcAqYG5ELAT2jIiVAPl9j5x9FLCsUHx5ThuVh+vTu5SJiA3AOmC3UhbGzMw6RqnBLSI2RsRBwGhSL+zAbrI36nFFN+ndlek6YWm6pMWSFq9evbqHWpuZWafrl7slI+IJYD7pWtmj+VQj+X1VzrYc2KdQbDSwIqePbpDepYykwcBwYE2D+Z8fERMiYsLIkSP7ZqHMzGzAKvNuyZGSdsnDQ4G3AfcAc4CpOdtU4Mo8PAeYku+A3I9048iifOpyvaRD8/W0Y+vK1KZ1FHB9vi5nZmZbsZYeVrqZ9gJm5jsetwFmR8RVkhYAsyVNAx4GjgaIiCWSZgN3ARuAEyJiY57W8cCFwFDgmvwCuACYJWkpqcc2pcTlMTOzDlFacIuI24G/aJD+ODCxSZkZwIwG6YuBTa7XRcQz5OBoZmZW438oMTOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzynFwMzOzyiktuEnaR9IvJd0taYmkj+f0EZLmSrovv+9aKHOKpKWS7pV0eCF9vKQ78rizJSmnbyfpspy+UNKYspbHzMw6R5k9tw3ASRHxCuBQ4ARJ44CTgXkRMRaYlz+Tx00BDgAmAedKGpSndR4wHRibX5Ny+jRgbUTsD5wJnFHi8piZWYcYXNaEI2IlsDIPr5d0NzAKmAwclrPNBOYDn83pl0bEs8ADkpYCB0t6EBgWEQsAJF0EHAlck8uclqd1OXCOJEVElLVcVr4xJ1/d7iq05MHT39nuKphZE/1yzS2fLvwLYCGwZw58tQC4R842ClhWKLY8p43Kw/XpXcpExAZgHbBbKQthZmYdo/TgJmkn4EfAJyLiye6yNkiLbtK7K1Nfh+mSFktavHr16p6qbGZmHa7U4CZpCCmwXRwRP87Jj0raK4/fC1iV05cD+xSKjwZW5PTRDdK7lJE0GBgOrKmvR0ScHxETImLCyJEj+2LRzMxsACvzbkkBFwB3R8RXC6PmAFPz8FTgykL6lHwH5H6kG0cW5VOX6yUdmqd5bF2Z2rSOAq739TYzMyvthhLg9cDfAXdIujWnfQ44HZgtaRrwMHA0QEQskTQbuIt0p+UJEbExlzseuBAYSrqR5JqcfgEwK998soZ0t6WZmW3lyrxb8n9ofE0MYGKTMjOAGQ3SFwMHNkh/hhwczczMavwPJWZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjkObmZmVjmlBTdJ35W0StKdhbQRkuZKui+/71oYd4qkpZLulXR4IX28pDvyuLMlKadvJ+mynL5Q0piylsXMzDpLmT23C4FJdWknA/MiYiwwL39G0jhgCnBALnOupEG5zHnAdGBsftWmOQ1YGxH7A2cCZ5S2JGZm1lFKC24RcSOwpi55MjAzD88EjiykXxoRz0bEA8BS4GBJewHDImJBRARwUV2Z2rQuBybWenVmZrZ16+9rbntGxEqA/L5HTh8FLCvkW57TRuXh+vQuZSJiA7AO2K20mpuZWccYKDeUNOpxRTfp3ZXZdOLSdEmLJS1evXr1ZlbRzMw6RX8Ht0fzqUby+6qcvhzYp5BvNLAip49ukN6ljKTBwHA2PQ0KQEScHxETImLCyJEj+2hRzMxsoOrv4DYHmJqHpwJXFtKn5Dsg9yPdOLIon7pcL+nQfD3t2LoytWkdBVyfr8uZmdlWbnBZE5Z0CXAYsLuk5cCpwOnAbEnTgIeBowEiYomk2cBdwAbghIjYmCd1POnOy6HANfkFcAEwS9JSUo9tSlnLYmZmnaW04BYRH2gyamKT/DOAGQ3SFwMHNkh/hhwczczMigbKDSVmZmZ9xsHNzMwqx8HNzMwqx8HNzMwqp7QbSsys/cacfHW7q9CSB09/Z7urYBXjnpuZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVWOg5uZmVVOx/9xsqRJwNeAQcB3IuL0NlfJzCrKf0TdOTq65yZpEPAN4AhgHPABSePaWyszM2u3jg5uwMHA0oi4PyL+BFwKTG5znczMrM06PbiNApYVPi/PaWZmthXr9GtuapAWm2SSpgPT88enJN1baq36xu7AY305QZ3Rl1PrOF6ffcfrsm91yvp8USlTLUmnB7flwD6Fz6OBFfWZIuJ84Pz+qlRfkLQ4Iia0ux5V4fXZd7wu+5bXZzk6/bTkTcBYSftJ2haYAsxpc53MzKzNOrrnFhEbJH0UuJb0U4DvRsSSNlfLzMzarKODG0BE/Az4WbvrUYKOOo3aAbw++47XZd/y+iyBIja5/8LMzKyjdfo1NzMzs004uJmZWeV0/DW3qpA0AXgjsDfwNHAn8IuIWNPWinU4STsCz0TExnbXpZO5fZbD7bM87rm1maTjJP0WOAUYCtwLrALeAMyVNFPSvu2sYyeRtI2kv5V0taRVwD3ASklLJP2XpLHtrmMncfvsW26f/cc9t/bbEXh9RDzdaKSkg4CxwMP9WakO9kvgF6SD8Z0R8WcASSOAtwCnS7oiIr7fxjp2ErfPvuX22U98t6RViqQhEfHcluYxK4PbZ//xackBQtKXJQ2TNETSPEmPSTqm3fXqNMWDgqRXS/pofr26UR5rjdtn33D77D8ObgPHX0XEk8C7SP+Z+VLg0+2tUueS9HHgYmCP/Pq+pI+1t1Ydze2zD7l9ls/X3AaOIfn9HcAlEbFGavTQA2vRNOCQiPgDgKQzgAXA19taq87l9tm33D5L5uA2cPxU0j2k26w/Imkk8Eyb69TJBBRvr95I40ckWWvcPvuW22fJfEPJACJpV+DJiNiYf/+yc0Q80u56dSJJJwJTgSty0pHAhRFxVrvq1OncPvuO22f5HNwGCEk7ACcC+0bE9Px7l5dFxFVtrlrHkjQeeD3pG/GNEXFLm6vUsdw++57bZ7kc3AYISZcBNwPHRsSBkoYCCyLioPbWzMzt0zqPr7kNHC+JiPdL+gBARDwtX7HvNUnrgdo3NhWGBwPbRoTb/OZx++wDbp/9xyty4PhT/jYcAJJeAjzb3ip1nojYufhZ0s7AR4B/5IXrG9Z7bp99wO2z//h3bm0m6dA8eBrwc2AfSRcD84DPtKtenU7SLpJOA24DdgZeGxEntbdWncftsxxun+XzNbc2k3QLsAj4LDAIOJR0uuI3EfFYO+vWiSTtDpwEvB/4LvD1iFjX3lp1LrfPvuX22X8c3NpM0jbAP5NOTXwxIma1uUodTdIfgNXA94D19eMj4qv9XqkO5vbZt9w++4+D2wAhaRzpHwq2IV3XEBARMaytFesw+VRP00YdEV/ov9pUh9tn33D77D8ObgOApGnAycDXgG+EN4oNIG6f1ol8t2SbSfo18CDwRv/bgw00bp/WqdxzazNJb4+Iue2uh1kjbp/WqRzczMyscvw7N9sqSJos6ZB218OsEbfPvudrbra1OAR4paTBEXFEuytjVsfts4/5tOQAJWky8EhELGx3XczquX3aQOee28Dlb3KbSdJwYBIwivSbohXAtRHxRDvrVTFun33MN+/0LffcrFIkHQucClwH/D4njwbeDnwhIi5qV93MuiPp4YjYt931qAoHtwHAPY2+I+le4JD6dZefIr0wIl7alopVkHsavSdpTrNRwFsjYsf+rE+V+bRkmzXpabwF+E9J7mn0XvEZWUV/zuOs71wAuKfRO28EjgGeqksXcHD/V6e6HNza7/PA+GY9DcDBrXdmAL+VdB2wLKftSzot+cW21apD9dDT2K0/61IRvwH+GBE31I/IZx2sj/i0ZJtJ+h3pWU7r6tKHA4sjYmx7ata58heDw0mneQUsJ53mXdvWinUgSWtp3tO4LCL27P9amfXMPbf2c0+jD0lSDmKX9pDH3+pa455GH2ql7bl99g333AYA9zT6jqT5wI+AKyPi4UL6tsAbgKnALyPiwrZU0LZqbp/9x8GtzfxNrm9J2h74B+CDwH7AE8BQ0l/NXUd6ZMut7apfp3H77FtN2uf2pKecu332IQe3NvM3ufJIGgLsDjztn1VsHrfP8rh9lsvBrc3c07CBzD0N61QObgOIv8nZQOb2aZ3Ewc3MzCrHz3MzM7PKcXAzM7PKcXAzM7PKcXCz50naKOlWSUsk3SbpREnb5HETJJ3dQ/njJJ3Ty3l+bkvq3GSae0q6Ki/DXZJ+ltMPk3RVX8+vxTrV/31VGfMYKWmhpFskvbHs+W0JSXtLuryFfH3ePto5H+s/vqHEnifpqYjYKQ/vAfwA+N+IOLXF8scBEyLio5szz74i6VvAXRHxtfz5VRFxu6TDgE9FxLv6cn4t1qnPl7PBPKYAR0TE1F6UGRQRG0us1hbpj/XWn/Ox/uOemzUUEauA6cBHlTzf65F0sKRf5x7CryW9rFB0H0k/l3SvpOeDoqRjJC3KPcNvSRok6XRgaE67uJt8gyRdKOlOSXdI+mQP1d+L9BdmtWW5vTBuJ0mXS7pH0sWSlOc7MS/PHZK+K2m7vJw/zuMnS3pa0raStpd0f05/SV7emyX9StLLc/p+khZIuklSt/8RWt+jlHRO/qKApAclnZHXySJJ+zeZxkHAl4F35HU3VNIH8vLcKemMQt6nJP27pIXAXzaZ3r/lut8p6fzCepov6ay83e+UdHBOP03SLEnXS7pP0odzuiT9V2HbvT+nj5F0Zx4+TtKP83q8T9KXc3qX9pHL3CPpO3l6F0t6m6T/zeVqddkxb8Ob8jad3Jv5dLetrINEhF9+EREATzVIWwvsCRwGXJXThgGD8/DbgB/l4eOAlaRHoQwF7gQmAK8AfgoMyfnOBY6tn2ezfMB4YG4h3y49LMfhpB8b/5L0SKG9c/phwDrSk7m3ARaQ/mVje9KfVr8057sI+ATpj8UfyGlfAW4CXg+8Gbgkp88DxubhQ4Dr8/CcwjKe0GjdFur7/LrNn88BjsvDDwKfz8PHFvM1mM5xwDl5eG/gYWBkXo7rgSPzuADe18M6HFEYngX8dR6eD3w7D78JuDMPnwbclrf77nl97g28F5hL+tH3nrlOewFjCmWPA+4Hhudt8RCwT4P2MQbYALwyb7+bge+S/o91MvCTnO8/gWNqbQX4HbBjq/PxqxovPxXAetLoAZ/DgZmSxpIOlEMK4+ZGxOMAudfzBtIBaTxwU+4ADAVWNZjuxCb5fgq8WNLXgatJ/4zRVERcK+nFpKebHwHcIunAPHpRRCzP9buVdMBcTwpiv8t5ZgInRMRZkpZKegXpQZJfJR3QBwG/krQT8Drgh7m+ANvl99eTDuyQgsPzPafNcEnh/cwWy7wWmB8RqwFyj+RNwE+AjaS/1OrOWyR9BtgBGAEsIW2H5+sTETdKGiZpl5x+ZUQ8DTwt6ZekdfYG0heBjcCjkm7IdSv2pgHmRX7sk6S7gBfxwlMyih6IiDtyviW5XEi6g7QtAf4KeLekT+XP2/PCQ1VbnY91OAc3ayoHiI2kAPOKwqgvkv5P8D2SxpC+zdfUX8QNUoCcGRGn9DTLZvkkvZrUIzsBeB/pL6Gaiog1pGuGP8in/N4EPA48W8i2kbQPdPeE7l+RAuRzwC+AC0nB7VOk3sMTEXFQs2p0V8eCDXS9RLB9N9NpdZrdLdMz0c11NqW/3DqXdP10maTT6urUaBs3S2/16eeNtktP+f5c+PznQhkB742ILo/kkXRIL+ZjHc7X3KwhSSOBb5JOc9UftIYDv8/Dx9WNe7ukEZKGAkcC/0s6dXeU0k0q5PEvyvmfU/pbJ5rlk7Q7sE1E/Aj4V+A1efxHJW1y84qkt0raIQ/vDLyEdDqsmXuAMYXrWX8H1J5fdiPpFOWC3AvaDXg5sCQingQekHR0npdyECYv95Q8/MFu5g3p9Ng4pet8w0k92KL3F94X9DCtmoXAmyXtLmkQ8IHCMvWkFsgey73ToxrVR9IbgHXxwoN2Jytdj9yNdKr1JtL6e7/SddORpC8Zi1qsB3RtH626FvhY4TrhX5Q0HxvA/K3FiobmU3VDSL2JWaRTcfW+TDoteSLpWk7R/+Ry+wM/iIjFAJL+BbhO6acFz5F6YA8B5wO3S/ptRHywSb6nge/lNIBaz+7lpCBSbzxwjqRaj+g7EXGT0t2Sm4iIZyT9Pen04mDSQfmbefRC0rWiG/Pn24FVhYD/QeC8XO8hpIek3gZ8nNRr/Dg9nALMvaPZedr3AbfUZdlO6eaPbUhBqkcRsVLSKaTrjgJ+FhFXtlj2CUnfBu4gXfO7qS7LWkm/Jl17LfagF5FOG+8LfDEiVki6gnTTym2kntxnIuKR3ONvxfPtg3T9tBVfBM7K5ZSXoac7ZLu0wxbnYwOYfwpgHSufbvybiPhTu+tSFkkPkk4PPtbuusDzj8D5VO1LSyH9NNJNGV9pR73M6rnnZh0r2vB7NTPrDO65mfUTSa8knbItejYiDunldD4PHF2X/MOImLEZdbqC9Jy2os9GxLW9nZbZQOLgZmZmleO7Jc3MrHIc3MzMrHIc3MzMrHIc3MzMrHIc3MzMrHL+P0C9C0JKC2T1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"Diabetes\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\", \\\n",
    "title=\"Counts of those who showed up for appointment based on Diabetes\")\n",
    "plt.xlabel(\"Diabetes, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.Diabetes, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on  Diabetes status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the charts above, the number of non-diabeteics who showed up are more that those diabetics who didn't show up for appointments.\n",
    "\n",
    "Amongst the diabetics, those who showed up are a little more than those who did not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Alcoholism Vs Show up for Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alcoholism  Showed_up_for_appointment\n",
       "0           Yes                          85372\n",
       "            No                           21603\n",
       "1           Yes                           2677\n",
       "            No                             675\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Alcoholism\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "77.4% (85, 525) patients without alcoholism problem showed up for appointment while only 2.4% (2, 683) patients with alcoholism showed up for appointment.\n",
    "\n",
    "Amongst those with alcoholism, those who showed up for appointment (2, 677) are more than those who did not (675)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": [
    "df[[\"Alcoholism\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\",\\\n",
    "title=\"Counts of those who showed up for appointment based on Alcoholism\")\n",
    "plt.xlabel(\"Alcoholism, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QIVx77bUAzJo1iyOPPJLBgwdz7rnnsm7duo15rr32Wt75zncyePDgjbWsVatWMWrUKIYMGcIxxxzDE088sdmyJkyYwHe+8x0ArrnmGgYNGsSQIUMYM2bMxvHjxo3j5JNPpl+/fvziF7/gK1/5CoMHD2bEiBGsX7++zbfP1nAgMjPbAr/85S8ZMWIEhxxyCN27d+fRRx9tNP7GG29k0aJFPPbYYzzxxBOceeaZrF27lrPPPptbbrmFefPmsWHDBq6//vqNeXr27Mmjjz7KhRdeuDGwXHbZZRx55JE88cQTfPOb32Ts2JZbH6688sqNy7zhhhs2pv/5z3/m9ttv57bbbuOss87ixBNPZN68eXTr1o3bb7+9DbfM1nMgMjPbAlOnTt1Y4xgzZgxTp05tNP53v/sdF1xwAZ07pysf3bt3Z8GCBfTv359DDjkEgHHjxnHvvfduzPPRj34UgKFDh7J48WIA7rvvPj75yU8CcNJJJ7Fy5UpWr17dbLmGDBnCmWeeyc0337xx2QAf/OAH6dKlC4MHD6ahoYERI0YAMHjw4I3LKpqvEZmZVWnlypXcfffdPPnkk0iioaEBSXzmM5/ZOE1EbNbzrLUHkO62224AdOrUiQ0bNjSbp6Uebbfffjv33nsvM2fO5PLLL2f+/PmN5r3LLrvQpUuXjfPYZZddNi6raK4RmZlVacaMGYwdO5a//OUvLF68mCVLltC/f3/q6+s3TnPyySdzww03bPyRX7VqFW9729tYvHgxCxcuBGDKlCkcf/zxLS7ruOOO4yc/+QkAs2fPpmfPnuy5554Vp33zzTdZsmQJJ554It/61rd49dVXWbNmTVuscrtwjcjMrEpTp07lkksuaZT2sY99jG9+85sbP3/qU5/iT3/6E0OGDKFLly6cf/75XHzxxfz4xz9m9OjRbNiwgaOOOqrVHm8TJkzgnHPOYciQIey+++5MmjSp2WkbGho466yzWL16NRHBF77wBfbee+9tWtf2pNaqjDuaYcOGxbY+j8jdtzdx923bkT399NO8/e1vL7oY7a7Sekt6JCKG1WJ5bpozM7NCORCZmVmhahqIJH1B0nxJT0qaKqmrpO6S7pL0bH7fp2z6SyUtlLRA0ill6UMlzcvjrlHu9iFpN0m35PQ5kvrVcn3MzKzt1SwQSeoDfBYYFhGHA52AMcAlwKyIGAjMyp+RNCiPPwwYAVwnqVOe3fXAeGBgfo3I6ecBr0TEAOBq4KparY+ZmdVGrZvmOgPdJHUGdgdeBEYCpe4fk4BReXgkMC0i1kXEImAhcLSk/YE9I+LBSD0rJjfJU5rXDGB4qbZkZmYdQ80CUUS8AHwHeB5YCqyOiDuB/SJiaZ5mKbBvztIHWFI2i/qc1icPN01vlCciNgCrgR5NyyJpvKS5kuYuX768bVbQzMzaRC2b5vYh1Vj6A72Bt0g6q6UsFdKihfSW8jROiLgxIoZFxLBevXq1XHAzsw7oN7/5DYceeigDBgzgyiuvLLo4W6SWf2h9P7AoIpYDSPoF8B7gZUn7R8TS3Oy2LE9fD/Qty19Hasqrz8NN08vz1Ofmv72AVTVaHzOzqrT1fw1b+79eQ0MDF110EXfddRd1dXUcddRRnHbaaQwaNKhNy1ErtbxG9DxwjKTd83Wb4cDTwExgXJ5mHHBbHp4JjMk94fqTOiU8nJvvXpd0TJ7P2CZ5SvM6Hbg7drZ/6JrZTu/hhx9mwIABHHTQQey6666MGTOG2267rfWM24ma1YgiYo6kGcCjwAbgMeBG4K3AdEnnkYLV6Dz9fEnTgafy9BdFREOe3YXARKAbcEd+AdwETJG0kFQTGlOr9TEz21698MIL9O27qUGprq6OOXPmFFiiLVPTe81FxGXAZU2S15FqR5WmvwK4okL6XGCz5/BGxFpyIDMz21lt6Z26tze+s4KZWQdXV1fHkiWbOh3X19fTu3fvAku0ZRyIzMw6uKOOOopnn32WRYsW8cYbbzBt2jROO+20ootVNT8Gwsysg+vcuTM/+MEPOOWUU2hoaODcc8/lsMMOK7pYVXMgMjNrY0U8HuXUU0/l1FNPbffltgU3zZmZWaEciMzMrFAORGZmVigHIjMzK5QDkZmZFcqByMzMCuVAZGa2Azj33HPZd999Ofzwze6Gtt3z/4jMzNrY818f3KbzO+Cr81qd5uyzz+biiy9m7Nj2/w/TtnKNyMxsB3DcccfRvXv3oouxVRyIzMysUA5EZmZWqJoFIkmHSnq87PWapM9L6i7pLknP5vd9yvJcKmmhpAWSTilLHyppXh53TX5SK/lprrfk9DmS+tVqfczMrDZqFogiYkFEHBERRwBDgb8BtwKXALMiYiAwK39G0iDSE1YPA0YA10nqlGd3PTCe9PjwgXk8wHnAKxExALgauKpW62NmZrXRXk1zw4E/R8RfgJHApJw+CRiVh0cC0yJiXUQsAhYCR0vaH9gzIh6M9BjCyU3ylOY1AxiujvRYQjOzNnLGGWfw7ne/mwULFlBXV8dNN91UdJGq1l7dt8cAU/PwfhGxFCAilkraN6f3AR4qy1Of09bn4abppTxL8rw2SFoN9ABWlC9c0nhSjYoDDjigjVbJzKyyarpbt7WpU6e2PtF2quY1Ikm7AqcBP2tt0gpp0UJ6S3kaJ0TcGBHDImJYr169WimGmZm1p/Zomvsg8GhEvJw/v5yb28jvy3J6PdC3LF8d8GJOr6uQ3iiPpM7AXsCqGqyDmZnVSHsEojPY1CwHMBMYl4fHAbeVpY/JPeH6kzolPJyb8V6XdEy+/jO2SZ7SvE4H7s7XkczMrIOo6TUiSbsDHwA+XZZ8JTBd0nnA88BogIiYL2k68BSwAbgoIhpynguBiUA34I78ArgJmCJpIakmNKaW62NmO5+IYGfqA1XEuXxNA1FE/I3UeaA8bSWpF12l6a8ArqiQPhfY7E5+EbGWHMjMzNpa165dWblyJT169NgpglFEsHLlSrp27dquy/VNT83MmlFXV0d9fT3Lly8vuijtpmvXrtTV1bU+YRtyIDIza0aXLl3o379/0cXY4flec2ZmVigHIjMzK5QDkZmZFcqByMzMCuVAZGZmhXIgMjOzQjkQmZlZoRyIzMysUA5EZmZWKAciMzMrlAORmZkVyoHIzMwK5UBkZmaFqmkgkrS3pBmSnpH0tKR3S+ou6S5Jz+b3fcqmv1TSQkkLJJ1Slj5U0rw87pr8pFby01xvyelzJPWr5fqYmVnbq3WN6PvAbyLibcA7gKeBS4BZETEQmJU/I2kQ6QmrhwEjgOskdcrzuR4YT3p8+MA8HuA84JWIGABcDVxV4/UxM7M2VrNAJGlP4DjS47yJiDci4lVgJDApTzYJGJWHRwLTImJdRCwCFgJHS9of2DMiHoz0DNvJTfKU5jUDGK6d4TGKZmY7kFrWiA4ClgM/lvSYpB9KeguwX0QsBcjv++bp+wBLyvLX57Q+ebhpeqM8EbEBWE2TR5MDSBovaa6kuTvTkxbNzDqCWgaizsA7gesj4kjgr+RmuGZUqslEC+kt5WmcEHFjRAyLiGG9evVqudRmZtauahmI6oH6iJiTP88gBaaXc3Mb+X1Z2fR9y/LXAS/m9LoK6Y3ySOoM7AWsavM1MTOzmqlZIIqIl4Alkg7NScOBp4CZwLicNg64LQ/PBMbknnD9SZ0SHs7Nd69LOiZf/xnbJE9pXqcDd+frSGZm1kF0rvH8/wn4iaRdgeeAc0jBb7qk84DngdEAETFf0nRSsNoAXBQRDXk+FwITgW7AHfkFqSPEFEkLSTWhMTVeHzMza2M1DUQR8TgwrMKo4c1MfwVwRYX0ucDhFdLXkgOZmZl1TL6zgpmZFcqByMzMCuVAZGZmhXIgMjOzQjkQmZlZoRyIzMysUA5EZmZWKAciMzMrlAORmZkVyoHIzMwK5UBkZmaFciAyM7NCORCZmVmhHIjMzKxQDkRmZlaomgYiSYslzZP0uKS5Oa27pLskPZvf9ymb/lJJCyUtkHRKWfrQPJ+Fkq7JT2olP831lpw+R1K/Wq6PmZm1vfaoEZ0YEUdEROkBeZcAsyJiIDArf0bSINITVg8DRgDXSeqU81wPjCc9PnxgHg9wHvBKRAwArgauaof1MTOzNlRE09xIYFIengSMKkufFhHrImIRsBA4WtL+wJ4R8WBEBDC5SZ7SvGYAw0u1JTMz6xhqHYgCuFPSI5LG57T9ImIpQH7fN6f3AZaU5a3PaX3ycNP0RnkiYgOwGuhRg/UwM7Ma6Vzj+b83Il6UtC9wl6RnWpi2Uk0mWkhvKU/jGacgOB7ggAMOaLnEZmbWrqqqEUmaVU1aUxHxYn5fBtwKHA28nJvbyO/L8uT1QN+y7HXAizm9rkJ6ozySOgN7AasqlOPGiBgWEcN69erVWrHNzKwdtRiIJHWV1B3oKWmf3OOte+6d1ruVvG+RtEdpGDgZeBKYCYzLk40DbsvDM4ExuSdcf1KnhIdz893rko7J13/GNslTmtfpwN35OpKZmXUQrTXNfRr4PCnoPMKmprDXgP9sJe9+wK2570Bn4KcR8RtJfwCmSzoPeB4YDRAR8yVNB54CNgAXRURDnteFwESgG3BHfgHcBEyRtJBUExrTSpnMzGw702IgiojvA9+X9E8Rce2WzDgingPeUSF9JTC8mTxXAFdUSJ8LHF4hfS05kJmZWcdUVWeFiLhW0nuAfuV5ImJyjcplZmY7iaoCkaQpwMHA40Cpuaz0nx4zM7OtVm337WHAIHcEMDOztlbtH1qfBP6hlgUxM7OdU7U1op7AU5IeBtaVEiPitJqUyszMdhrVBqIJtSyEmZntvKrtNXdPrQtiZmY7p2p7zb3Opnu47Qp0Af4aEXvWqmBmZrZzqLZGtEf5Z0mjSPeNMzMz2yZb9RiIiPglcFLbFsXMzHZG1TbNfbTs4y6k/xX5P0VmZrbNqu0195Gy4Q3AYtLTUc3MzLZJtdeIzql1QczMbOdU7YPx6iTdKmmZpJcl/VxSXes5zczMWlZtZ4Ufkx5C1xvoA/xPTjMzM9sm1QaiXhHx44jYkF8TAT9z28zMtlm1gWiFpLMkdcqvs4CV1WTM0z8m6Vf5c3dJd0l6Nr/vUzbtpZIWSlog6ZSy9KGS5uVx1+RHhpMfK35LTp+TH2FuZmYdSLWB6Fzg48BLwFLgdKDaDgyfA54u+3wJMCsiBgKz8mckDSI96vswYARwnaROOc/1wHhgYH6NyOnnAa9ExADgauCqKstkZmbbiWoD0eXAuIjoFRH7kgLThNYy5Q4NHwJ+WJY8EpiUhycBo8rSp0XEuohYBCwEjpa0P7BnRDyYn4c0uUme0rxmAMNLtSUzM+sYqg1EQyLildKHiFgFHFlFvu8BXwHeLEvbLyKW5vksBfbN6X2AJWXT1ee0Pnm4aXqjPBGxAVgN9GhaCEnjJc2VNHf58uVVFNvMzNpLtYFolybXcrrTyn+QJH0YWBYRj1S5jEo1mWghvaU8jRMiboyIYRExrFcv97EwM9ueVHtnhf8HPCBpBumH/uPAFa3keS9wmqRTga7AnpJuBl6WtH9ELM3Nbsvy9PVA37L8dcCLOb2uQnp5nnpJnYG9gFVVrpOZmW0HqqoRRcRk4GPAy8By4KMRMaWVPJdGRF1E9CN1Qrg7Is4i/R9pXJ5sHHBbHp4JjMk94fqTOiU8nJvvXpd0TL7+M7ZJntK8Ts/L8D3wzMw6kGprRETEU8BTbbDMK4Hpks4DngdG5/nPlzQ9L2MDcFFENOQ8FwITgW7AHfkFcBMwRdJCUk1oTBuUz8zM2lHVgWhbRMRsYHYeXgkMb2a6K6jQ5BcRc4HDK6SvJQcyMzPrmLbqeURmZmZtxYHIzMwK5UBkZmaFciAyM7NCORCZmVmhHIjMzKxQDkRmZlYoByIzMyuUA5GZmRXKgcjMzArlQGRmZoVyIDIzs0I5EJmZWaEciMzMrFAORGZmVqiaBSJJXSU9LOmPkuZL+lpO7y7pLknP5vd9yvJcKmmhpAWSTilLHyppXh53TX5SK/lprrfk9DmS+tVqfczMrDZqWSNaB5wUEe8AjgBGSDoGuASYFREDgVn5M5IGkZ6wehgwArhOUqc8r+uB8aTHhw/M4wHOA16JiAHA1cBVNVwfMzOrgZoFokjW5I9d8iuAkcCknD4JGJWHRwLTImJdRCwCFgJHS9of2DMiHoyIACY3yVOa1wxgeKm2ZGZmHUNNrxFJ6iTpcWAZcFdEzAH2i4ilAPl93zx5H2BJWfb6nNYnDzdNb5QnIjYAq4EeFcoxXtJcSXOXL1/eRmtnZmZtoaaBKCIaIuIIoI5Uuzm8hckr1WSihfSW8jQtx40RMSwihvXq1auVUpuZWXtql15zEfEqMJt0befl3NxGfl+WJ6sH+pZlqwNezOl1FdIb5ZHUGdgLWFWLdTAzs9qoZa+5XpL2zsPdgPcDzwAzgXF5snHAbXl4JjAm94TrT+qU8HBuvntd0jH5+s/YJnlK8zoduDtfRzIzsw6icw3nvT8wKfd82wWYHhG/kvQgMF3SecDzwGiAiJgvaTrwFLABuCgiGvK8LgQmAt2AO/IL4CZgiqSFpJrQmBquj5mZ1UDNAlFEPAEcWSF9JTC8mTxXAFdUSJ8LbHZ9KSLWkgOZmZl1TL6zgpmZFcqByMzMCuVAZGZmhXIgMjOzQjkQmZlZoRyIzMysUA5EZmZWKAciMzMrlAORmZkVyoHIzMwK5UBkZmaFciAyM7NCORCZmVmhHIjMzKxQDkRmZlaomj2PSFJfYDLwD8CbwI0R8X1J3YFbgH7AYuDjEfFKznMpcB7QAHw2In6b04ey6cF4vwY+FxEhabe8jKHASuATEbG4Vutktr0b+uXJRRdhu/HIt8cWXQSrUi1rRBuAL0bE24FjgIskDQIuAWZFxEBgVv5MHjcGOAwYAVyXn+4KcD0wnvT48IF5PKSg9UpEDACuBq6q4fqYmVkN1CwQRcTSiHg0D78OPA30AUYCk/Jkk4BReXgkMC0i1kXEImAhcLSk/YE9I+LBiAhSDag8T2leM4DhklSrdTIzs7bXLteIJPUjPTZ8DrBfRCyFFKyAffNkfYAlZdnqc1qfPNw0vVGeiNgArAZ6VFj+eElzJc1dvnx5G62VmZm1hZoHIklvBX4OfD4iXmtp0gpp0UJ6S3kaJ0TcGBHDImJYr169WiuymZm1o5oGIkldSEHoJxHxi5z8cm5uI78vy+n1QN+y7HXAizm9rkJ6ozySOgN7Aavafk3MzKxWahaI8rWam4CnI+K7ZaNmAuPy8DjgtrL0MZJ2k9Sf1Cnh4dx897qkY/I8xzbJU5rX6cDd+TqSmZl1EDXrvg28F/gkME/S4zntX4ErgemSzgOeB0YDRMR8SdOBp0g97i6KiIac70I2dd++I78gBbopkhaSakJjarg+ZmZWAzULRBFxH5Wv4QAMbybPFcAVFdLnAodXSF9LDmRmZtYx+c4KZmZWKAciMzMrlAORmZkVyoHIzMwK5UBkZmaFciAyM7NCORCZmVmhHIjMzKxQDkRmZlYoByIzMyuUA5GZmRXKgcjMzArlQGRmZoVyIDIzs0I5EJmZWaFq+YTWH0laJunJsrTuku6S9Gx+36ds3KWSFkpaIOmUsvShkublcdfkp7SSn+R6S06fI6lfrdbFzMxqp5Y1oonAiCZplwCzImIgMCt/RtIg0tNVD8t5rpPUKee5HhhPenT4wLJ5nge8EhEDgKuBq2q2JmZmVjM1C0QRcS/p8d3lRgKT8vAkYFRZ+rSIWBcRi4CFwNGS9gf2jIgHIyKAyU3ylOY1Axheqi2ZmVnH0d7XiPaLiKUA+X3fnN4HWFI2XX1O65OHm6Y3yhMRG4DVQI+aldzMzGpie+msUKkmEy2kt5Rn85lL4yXNlTR3+fLlW1lEMzOrhfYORC/n5jby+7KcXg/0LZuuDngxp9dVSG+UR1JnYC82bwoEICJujIhhETGsV69ebbQqZmbWFto7EM0ExuXhccBtZeljck+4/qROCQ/n5rvXJR2Tr/+MbZKnNK/TgbvzdSQzM+tAOtdqxpKmAicAPSXVA5cBVwLTJZ0HPA+MBoiI+ZKmA08BG4CLIqIhz+pCUg+8bsAd+QVwEzBF0kJSTWhMrdbFzMxqp2aBKCLOaGbU8GamvwK4okL6XODwCulryYHMzMw6ru2ls4KZme2kHIjMzKxQDkRmZlYoByIzMyuUA5GZmRXKgcjMzArlQGRmZoVyIDIzs0I5EJmZWaEciMzMrFAORGZmVigHIjMzK1TNbnpqO4fnvz646CJsNw746ryii2DWIblGZGZmhXIgMjOzQjkQmZlZoTp8IJI0QtICSQslXVJ0eczMbMt06M4KkjoB/wl8AKgH/iBpZkQ8VWzJzKxo7kizyfbekaaj14iOBhZGxHMR8QYwDRhZcJnMzGwLdOgaEdAHWFL2uR54V9OJJI0HxuePayQtaIey7RQOhJ7AiqLLsV24TEWXwMp43yzTNvvmgW0xk0o6eiCqtHVjs4SIG4Eba1+cnY+kuRExrOhymDXlfbPj6OhNc/VA37LPdcCLBZXFzMy2QkcPRH8ABkrqL2lXYAwws+AymZnZFujQTXMRsUHSxcBvgU7AjyJifsHF2tm4ydO2V943OwhFbHZJxczMrN109KY5MzPr4ByIzMysUA5EZrbVJDVIelzSfEl/lPTPknbJ44ZJuqaZfIsl9WyD5Y+SNGgLpj9b0g+2dbnWthyIdkCS/i3/MDyRfyTe1VYH/laUZaKk09t7udZu/h4RR0TEYaRbbZ0KXAYQEXMj4rM1Xv4ooOpAZNsnB6IdjKR3Ax8G3hkRQ4D30/juE2Y1ERHLSHcwuVjJCZJ+BSCph6Q7JT0m6b+o/Gd0JK2RdEWuXT0kab+cfqCkWfnkapakAyS9BzgN+HY+4Tq4ybxGS3oyz+veslG9Jf1G0rOSvlU2/RmS5uU8V+W0j0v6bh7+nKTn8vDBku6rUP7Zkobl4Z6SFufhsyXdlpe7QNJlW7WRd1AORDue/YEVEbEOICJWRETpT77/JOnRfLC9DUBSd0m/zAf4Q5KG5PR5kvbOPygrJY3N6VMkvV9SJ0nflvSHnPfTebwk/UDSU5JuB/ZtqbDlNbXclDM7D0/Iy7o7/2Cc3/abytpaRDxH+l1p+r1fBtwXEUeS/ut3QDOzeAvwUES8A7gXKH3vPwAm55OrnwDXRMQDeV5fzrWyPzeZ11eBU/K8TitLPwL4BDAY+ISkvpJ6A1cBJ+XxR0kalctwbM53LLBSUh/gfcDvW98ijRwNnJnnP7oUsMyBaEd0J9BX0p8kXSfp+LJxKyLincD1wJdy2teAx/IB/q/A5Jx+P/Be4DDgOTYdjMcADwHnAasj4ijgKOB8Sf2B/wMcSjrIzwfesw3rMgT4EPBu4Kv5x8K2f5VqO8cBNwNExO3AK83kfQP4VR5+BOiXh98N/DQPTyEFgtbcD0zMJzGdytJnRcTqiFgLPEW6h9pRwOyIWB4RG0jB7riIeAl4q6Q9SHdx+Wlel2PZ8kB0V0SsjIi/A7+och12Cg5EO5iIWAMMJTWRLAdukXR2Hv2L/F5+gL+PdGATEXcDPSTtRTrIjsuv64HB+UxwVV7GycBYSY8Dc4AewMA8/dSIaMg1sbu3YXVui4i/R8QK4H9JZ5S2HZN0ENAALKswupo/La6PTX9ubKD5P923Oq+IuAD4d1IAeVxSjzxqXdlkpWW0dFfQB4FzgAWk4+JYUmC8v8K0G9j0u9q1lTL7T5yZA9EOKAeB2RFxGXAx8LE8qnQAlh/gzd04ttQkcSwwmxTUTmfTWaCAf8pNIkdERP+IuLMsf7V84O4gJPUCbgB+UBZMSu4lNUsh6YPAPls4+wdIt/Aiz6d0feZ1YI9mynNwRMyJiK+S7sLdt9J02Rzg+HxdpxNwBnBPWdm/lN8fA04E1kXE6grzWUw6EYR0vJT7QG4K70bqZFEpkO2UHIh2MJIOlTSwLOkI4C8tZCn/gTiB1Hz3WkQsId1Gf2Bu97+PdDCWAtFvgQsldcl5D5H0ljy/Mfka0v6kg7Yli9l04H6sybiRkrrmM9kTSPcWtO1Lt9xRYD7wO1LT8NcqTPc14DhJj5Jq089v4XI+C5wj6Qngk8Dncvo04Mu5E8TBTfJ8u9T5gLRf/rG5mUfEUuBSUs37j8CjEXFbHv17UhC7NyIaSJ1/NuuokH2HdFw8QDp+yt1Han14HPh5RMxtZZ13Gr7Fzw5G0lDgWmBvUm1jIamZbi4wLCJW5Iuk34mIEyR1B34M9Af+BoyPiCfyvKYAnSLiH3MPpfuAXhGxUum/It8APkKqHS0nneW9lpd/EvCnXKybI2JGM+U9FrgJeJl0Vjosl2sC0Bs4mHRh+1sR8d9ts5XM2lduHh8WERcXXZbtkQORbZdyIFoTEd8puixm28qBqGUd+u7bZmYdQURMBCYWXIztlmtE1i4k3Upq/iv3LxHx2yLKY2bbDwciMzMrlHvNmZlZoRyIzMysUA5E1u60E94dPN+Xb76kb9d6WdtK0g/VyqMVtIWPX9iGsrTLcqxY7jVn7UqN7w6+LgefXQsuVnv4NOk/WOtanRKQ1Dnf86zdRcSnqphsFOmecE/VtjTtthwrkGtE1t52uruDS5pJuqv0HEmfUIVHGuTpJkr6rqT/Jd0JutK8jpb0QL6TwAOSDs3pFR8zIKmfpGckTcrLmyFp9zxueJ7PPEk/krRbTi9/lMFmj2VQhccv5DxXS7pX0tOSjpL0i7xtvlFW/rMkPZzz/ZfS7XSqXk5L35V1YBHhl1/t9gLeSrrFyZ+A64Djc/pi0r3rAD4D/DAPXwtclodPAh7PwzeQ7sx9OOnWP/+d05/NyxgP/HtO2410Z4n+wEeBu0h3Y+4NvAqc3kJ5FwM98/Aw0h2aASaQbgXTjXQrlyVA7xbms6Zs+H+AcXn4XOCXeXgi6ey/Uwvz2RPonIffT7pVDMDZwFLSzWe7AU/m8vYj3aPvvXm6H5Fu1dQ1l/mQnD4Z+Hwenk368yU570fy8LfKtunE8u2W81yVhz8HvEg66dgNqM/lente9y55uuuAsVuyHL92zJdrRNauwncHh5YfafCzSPcza85ewM+U7p92NekxHSXNPWZgSUSUbrB5c04/FFgUEaXbME0ibZummnssQyUz8/s8YH5ELI1U832OdK+24aTv/g/5exkOHLQVy7EdjK8RWbvLP7SzgdmS5gHj8qgtvTv4RaT70P0b6TlIle4O3ugPs5JOZfu7O3h5vr+2Mu3lwP9GxP+R1I+0HVsrT6X0lh57UK7axzLApu/vTRo/auFNNj1qYVJEXLqNy7EdjGtE1q7ku4ND8480qMZewAt5+Owm45p7zMABSp1EID3e4D7gGaCfpAE5/ZNseuxBNZp9/EILZgGnS9oXNl7/O7AGy7EOxoHI2ttbgUm5s8ATwCDS9ZbmTACG5WmvZFPtCVKTW6lp6fdAHzb9qP+Q1NPq0dyM9V+ks+xbSdeR5pGa9Fr78f0a8H1JvyedqZd7GLid9MTay2NTp4vWNPdIg2p8C/gPSffT+Kmj0PxjBp4GxuXldQeuj/R00nNIzXzzSLWWG7agHC09fqGiiHiK9KC6O3NZ7iJdR2rT5VjH41v8mG0FbWd3B1czd3fOzXe/iojDiyiXWTVcIzIzs0K5RmRG29wdXNJgcg+/Musi4l1bUZ5z2LzJ7v6IuGhL52W2vXMgMjOzQrlpzszMCuVAZGZmhXIgMjOzQjkQmZlZoRyIzMysUP8fKSGRfwY7B9oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.Alcoholism, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on  Alcoholism status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two charts above indicate that those without alcoholism turned up for their appointments more than those with alcoholism. Comparatively, very few of those with alcoholism came for their appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Handicapped Vs Show up for Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Handicapped  Showed_up_for_appointment\n",
       "0            Yes                          86373\n",
       "             No                           21912\n",
       "1            Yes                           1676\n",
       "             No                             366\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Handicapped\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that majority of those who came for appointments are non-handicapped (86, 373). A comparatively lower number of patients with handicap came for appointments (1, 676) but are more than those with handicap who did not come (366)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": [
    "df[[\"Handicapped\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\",\\\n",
    "title=\"Counts of those who showed up for appointment based on Handicapped Status\")\n",
    "plt.xlabel(\"Handicapped, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.Handicapped, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on  Handicapped status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two charts above show that those without handicap constitute majority of those who showed up for appointment while very few handicapped showed up for appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Neighbourhood Vs Showed_up_for_appointment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us count the total number of Neighbourhoods the patients come from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Neighbourhood.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result shows that the patients come from a total of 81 neighbourhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighbourhood                Showed_up_for_appointment\n",
       "AEROPORTO                    No                              1\n",
       "PARQUE INDUSTRIAL            Yes                             1\n",
       "ILHAS OCEÂNICAS DE TRINDADE  No                              2\n",
       "ILHA DO FRADE                No                              2\n",
       "ILHA DO BOI                  No                              3\n",
       "                                                          ... \n",
       "CENTRO                       Yes                          2629\n",
       "JARDIM DA PENHA              Yes                          3241\n",
       "RESISTÊNCIA                  Yes                          3517\n",
       "MARIA ORTIZ                  Yes                          4586\n",
       "JARDIM CAMBURI               Yes                          6252\n",
       "Length: 160, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Neighbourhood\",\"Showed_up_for_appointment\"]].value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, it is obvious that patients from the `JARDIM CAMBURI` neighbourhood showed up for hospital appointments the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=np.sort(df.Neighbourhood))\n",
    "ax.set_title(\"No of Appointments based Neighbourhood\")\n",
    "plt.show()\n",
    "\n",
    "# No of appointments by Neighboorhod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,8))\n",
    "sns.countplot( x = df[\"Neighbourhood\"], data = df, palette=\"rainbow\" )\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart also confirms that most patients are from `JARDIM CAMBURI` neighbourhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 6: Does sending out SMS reminder messages to patients influence how they turn up for hospital appointments?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SMS Received Vs Show up for Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMS_received  Showed_up_for_appointment\n",
       "0             Yes                          62389\n",
       "1             Yes                          25660\n",
       "0             No                           12503\n",
       "1             No                            9775\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"SMS_received\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "56.6% (62, 510) patients who didn't recieve SMS turned up for their appointments while 23.3% (25, 698) of pateints who received SMS showed up for appointment. A higher number of people who received an SMS showed up compared to those who received an SMS and didn't show up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0tRwUETev0cI0gbtSzczMCu5KNTMzKzgwmpmZFda6e4wDBw6MYcOGNbsYZmYt5fbbb386/9JOj7fWBcZhw4YxZ86cZhfDzKylSOqKn11sCe5KNTMzKzgwmpmZFRwYzczMCg6MZmZmBQdGMzOzggOjmZlZwYHRzMys4MBoZmZWWOu+4N8Iw064stlFqMv8k9/f7CKYmXV7bjGamZkVHBjNzMwKDoxmZmaFhgZGSZtIuljS/ZLuk7S3pAGSpkl6KL9vWkx/oqR5kh6QdECRvrukuXncaZKU0/tKmpLTZ0ka1sj1MTOznq/RLcafAFdHxBuBXYD7gBOA6RExHJiePyNpBDAG2Ak4EDhdUq88nzOACcDw/Dowp48HnouI7YFTgVMavD5mZtbDNSwwSuoPvAM4CyAi/hkRzwOjgcl5ssnAIXl4NHBhRCyNiEeAecAekgYD/SNiZkQEcE5Vnsq8LgZGVVqTZmZmq6KRLcbtgMXAbyT9RdKvJW0AbBERiwDy++Z5+iHAgiL/wpw2JA9Xp6+QJyKWAS8AmzVmdczMbG3QyMDYG9gNOCMi3gL8ndxt2oZaLb1oJ729PCvOWJogaY6kOYsXL26/1GZmtlZrZGBcCCyMiFn588WkQPlk7h4lvz9VTL9VkX8o8HhOH1ojfYU8knoDGwPPVhckIs6MiJERMXLQoEFdsGpmZtZTNSwwRsQTwAJJO+akUcC9wGXA2Jw2Frg0D18GjMlPmm5Leshmdu5uXSJpr3z/8OiqPJV5HQpcn+9DmpmZrZJG/yTcZ4HzJa0LPAx8lBSMp0oaDzwGHAYQEfdImkoKnsuAYyNieZ7PMcAkoB9wVX5BerDnXEnzSC3FMQ1eHzMz6+EaGhgj4k5gZI1Ro9qYfiIwsUb6HGDnGumvkAOrmZlZV/Av35iZmRUcGM3MzAoOjGZmZgUHRjMzs4IDo5mZWcGB0czMrODAaGZmVnBgNDMzKzgwmpmZFRwYzczMCg6MZmZmBQdGMzOzggOjmZlZwYHRzMys4MBoZmZWcGA0MzMrODCamZkVHBjNzMwKDoxmZmYFB0YzM7OCA6OZmVnBgdHMzKzgwGhmZlZwYDQzMys4MJqZmRUaGhglzZc0V9KdkubktAGSpkl6KL9vWkx/oqR5kh6QdECRvnuezzxJp0lSTu8raUpOnyVpWCPXx8zMer410WJ8V0TsGhEj8+cTgOkRMRyYnj8jaQQwBtgJOBA4XVKvnOcMYAIwPL8OzOnjgeciYnvgVOCUNbA+ZmbWgzWjK3U0MDkPTwYOKdIvjIilEfEIMA/YQ9JgoH9EzIyIAM6pylOZ18XAqEpr0szMbFU0OjAGcK2k2yVNyGlbRMQigPy+eU4fAiwo8i7MaUPycHX6CnkiYhnwArBZA9bDzMzWEr0bPP99IuJxSZsD0yTd3860tVp60U56e3lWnHEKyhMAtt566/ZLbGZma7WGthgj4vH8/hRwCbAH8GTuHiW/P5UnXwhsVWQfCjye04fWSF8hj6TewMbAszXKcWZEjIyIkYMGDeqalTMzsx6pYYFR0gaSNqoMA+8F7gYuA8bmycYCl+bhy4Ax+UnTbUkP2czO3a1LJO2V7x8eXZWnMq9DgevzfUgzM7NV0siu1C2AS/KzML2B30bE1ZJuA6ZKGg88BhwGEBH3SJoK3AssA46NiOV5XscAk4B+wFX5BXAWcK6keaSW4pgGro+Zma0FGhYYI+JhYJca6c8Ao9rIMxGYWCN9DrBzjfRXyIHVzMysK/iXb8zMzAoOjGZmZgUHRjMzs4IDo5mZWcGB0czMrODAaGZmVnBgNDMzKzgwmpmZFRwYzczMCg6MZmZmBQdGMzOzggOjmZlZwYHRzMys4MBoZmZWcGA0MzMrODCamZkVHBjNzMwKDoxmZmYFB0YzM7OCA6OZmVnBgdHMzKzgwGhmZlZwYDQzMys4MJqZmRUaHhgl9ZL0F0lX5M8DJE2T9FB+37SY9kRJ8yQ9IOmAIn13SXPzuNMkKaf3lTQlp8+SNKzR62NmZj3bmmgxfg64r/h8AjA9IoYD0/NnJI0AxgA7AQcCp0vqlfOcAUwAhufXgTl9PPBcRGwPnAqc0thVMTOznq6hgVHSUOD9wK+L5NHA5Dw8GTikSL8wIpZGxCPAPGAPSYOB/hExMyICOKcqT2VeFwOjKq1JMzOzVdHoFuOPgS8DrxVpW0TEIoD8vnlOHwIsKKZbmNOG5OHq9BXyRMQy4AVgsy5dAzMzW6s0LDBKOhh4KiJurzdLjbRoJ729PNVlmSBpjqQ5ixcvrrM4Zma2Nmpki3Ef4AOS5gMXAu+WdB7wZO4eJb8/ladfCGxV5B8KPJ7Th9ZIXyGPpN7AxsCz1QWJiDMjYmREjBw0aFDXrJ2ZmfVIDQuMEXFiRAyNiGGkh2quj4ijgMuAsXmyscClefgyYEx+0nRb0kM2s3N36xJJe+X7h0dX5anM69C8jJVajGZmZvXq3YRlngxMlTQeeAw4DCAi7pE0FbgXWAYcGxHLc55jgElAP+Cq/AI4CzhX0jxSS3HMmloJMzPrmdZIYIyIGcCMPPwMMKqN6SYCE2ukzwF2rpH+CjmwmpmZdQX/8o2ZmVnBgdHMzKzgwGhmZlbodGCUtKmkNzeiMGZmZs1WV2CUNENSf0kDgL8Cv5H0o8YWzczMbM2rt8W4cUS8CHwQ+E1E7A7s37himZmZNUe9gbF3/pWaw4ErGlgeMzOzpqo3MH4DuAaYFxG3SdoOeKhxxTIzM2uOer/gvygi/vXATUQ87HuMZmbWE9XbYvxpnWlmZmYtrd0Wo6S9gbcBgyR9oRjVH+jVyIKZmZk1Q0ddqesCG+bpNirSXyT9m4WZmVmP0m5gjIgbgRslTYqIR9dQmczMzJqm3odv+ko6ExhW5omIdzeiUGZmZs1Sb2C8CPgF8GtgeQfTmpmZtax6A+OyiDijoSUxMzPrBur9usblkj4tabCkAZVXQ0tmZmbWBPW2GMfm9y8VaQFs17XFMTMza666AmNEbNvogpiZmXUHdQVGSUfXSo+Ic7q2OGZmZs1Vb1fqW4vh9YBRwB2AA6OZmfUo9Xalfrb8LGlj4NyGlMjMzKyJ6n0qtdo/gOFdWRAzM7PuoN57jJeTnkKF9OPhbwKmNqpQZmZmzVLvPcYfFMPLgEcjYmF7GSStB9wE9M3LuTgivp6//ziF9PNy84HDI+K5nOdEYDzp13WOi4hrcvruwCSgH/BH4HMREZL6ku5z7g48AxwREfPrXCczM7OV1NWVmn9M/H7SP2xsCvyzjmxLgXdHxC7ArsCBkvYCTgCmR8RwYHr+jKQRwBhgJ+BA4HRJlb+2OgOYQOq+HZ7HQwqiz0XE9sCpwCn1rI+ZmVlb6gqMkg4HZgOHAYcDsyS1+7dTkbyUP/bJrwBGA5Nz+mTgkDw8GrgwIpZGxCPAPGAPSYOB/hExMyKC1EIs81TmdTEwSpLqWSczM7Na6u1K/Srw1oh4CkDSIOA6UjBqU27x3Q5sD/w8ImZJ2iIiFgFExCJJm+fJhwC3FtkX5rRX83B1eiXPgjyvZZJeADYDnq5zvczMzFZQ71Op61SCYvZMPXkjYnlE7AoMJbX+dm5n8lotvWgnvb08K85YmiBpjqQ5ixcv7qDUZma2Nqs3MF4t6RpJ4ySNA64kPQRTl4h4HphBujf4ZO4eJb9XAu5CYKsi21Dg8Zw+tEb6Cnkk9QY2Bp6tsfwzI2JkRIwcNGhQvcU2M7O1ULuBUdL2kvaJiC8BvwTeDOwCzATO7CDvIEmb5OF+wP6kB3gu4/UfJR8LXJqHLwPGSOoraVvSQzazc7frEkl75fuHR1flqczrUOD6fB/SzMxslXR0j/HHwFcAIuL3wO8BJI3M4/6jnbyDgcn5PuM6wNSIuELSTGCqpPHAY6QHeoiIeyRNBe4lfSXk2Iio/CnyMbz+dY2r8gvgLOBcSfNILcUxda21mZlZGzoKjMMi4q7qxIiYI2lYexlzvrfUSH+G9FurtfJMBCbWWh6w0v3JiHiFHFjNzMy6Qkf3GNdrZ1y/riyImZlZd9BRYLxN0ieqE3M36O2NKZKZmVnzdNSV+nngEklH8nogHAmsC/xnA8tlZmbWFO0Gxoh4EnibpHfx+j2+KyPi+oaXzMzMrAnq/T/GG4AbGlwWMzOzplvV/2M0MzPrkRwYzczMCg6MZmZmBQdGMzOzQr1/O2W2xgw74cpmF6Eu809+f7OLYGYN4BajmZlZwYHRzMys4MBoZmZWcGA0MzMrODCamZkVHBjNzMwKDoxmZmYFB0YzM7OCA6OZmVnBgdHMzKzgwGhmZlZwYDQzMys4MJqZmRUcGM3MzAoNC4yStpJ0g6T7JN0j6XM5fYCkaZIeyu+bFnlOlDRP0gOSDijSd5c0N487TZJyel9JU3L6LEnDGrU+Zma2dmhki3EZcHxEvAnYCzhW0gjgBGB6RAwHpufP5HFjgJ2AA4HTJfXK8zoDmAAMz68Dc/p44LmI2B44FTilgetjZmZrgYYFxohYFBF35OElwH3AEGA0MDlPNhk4JA+PBi6MiKUR8QgwD9hD0mCgf0TMjIgAzqnKU5nXxcCoSmvSzMxsVayRe4y5i/MtwCxgi4hYBCl4ApvnyYYAC4psC3PakDxcnb5CnohYBrwAbNaQlTAzs7VCwwOjpA2B3wGfj4gX25u0Rlq0k95enuoyTJA0R9KcxYsXd1RkMzNbizU0MErqQwqK50fE73Pyk7l7lPz+VE5fCGxVZB8KPJ7Th9ZIXyGPpN7AxsCz1eWIiDMjYmREjBw0aFBXrJqZmfVQjXwqVcBZwH0R8aNi1GXA2Dw8Fri0SB+TnzTdlvSQzezc3bpE0l55nkdX5anM61Dg+nwf0szMbJX0buC89wE+AsyVdGdO+wpwMjBV0njgMeAwgIi4R9JU4F7SE63HRsTynO8YYBLQD7gqvyAF3nMlzSO1FMc0cH3MzGwt0LDAGBF/ovY9QIBRbeSZCEyskT4H2LlG+ivkwGpmZtYV/Ms3ZmZmBQdGMzOzggOjmZlZwYHRzMys4MBoZmZWcGA0MzMrODCamZkVHBjNzMwKDoxmZmYFB0YzM7OCA6OZmVnBgdHMzKzgwGhmZlZwYDQzMys4MJqZmRUcGM3MzAoOjGZmZgUHRjMzs4IDo5mZWcGB0czMrNC72QUws8YZdsKVzS5CXeaf/P5mF8HsX9xiNDMzKzgwmpmZFRwYzczMCg0LjJLOlvSUpLuLtAGSpkl6KL9vWow7UdI8SQ9IOqBI313S3DzuNEnK6X0lTcnpsyQNa9S6mJnZ2qORLcZJwIFVaScA0yNiODA9f0bSCGAMsFPOc7qkXjnPGcAEYHh+VeY5HnguIrYHTgVOadiamJnZWqNhgTEibgKerUoeDUzOw5OBQ4r0CyNiaUQ8AswD9pA0GOgfETMjIoBzqvJU5nUxMKrSmjQzM1tVa/oe4xYRsQggv2+e04cAC4rpFua0IXm4On2FPBGxDHgB2KxhJTczs7VCd3n4plZLL9pJby/PyjOXJkiaI2nO4sWLV7GIZma2NljTX/B/UtLgiFiUu0mfyukLga2K6YYCj+f0oTXSyzwLJfUGNmblrlsAIuJM4EyAkSNH1gyeZmYd8Q8mrB3WdIvxMmBsHh4LXFqkj8lPmm5Leshmdu5uXSJpr3z/8OiqPJV5HQpcn+9DmpmZrbKGtRglXQDsBwyUtBD4OnAyMFXSeOAx4DCAiLhH0lTgXmAZcGxELM+zOob0hGs/4Kr8AjgLOFfSPFJLcUyj1sXMzNYeDQuMEfHhNkaNamP6icDEGulzgJ1rpL9CDqxmZmZdpbs8fGNmZtYtODCamZkVHBjNzMwKDoxmZmYFB0YzM7OCA6OZmVnBgdHMzKzgwGhmZlZwYDQzMys4MJqZmRUcGM3MzAoOjGZmZgUHRjMzs4IDo5mZWcGB0czMrODAaGZmVnBgNDMzKzgwmpmZFRwYzczMCg6MZmZmBQdGMzOzggOjmZlZwYHRzMys4MBoZmZWcGA0MzMrtHxglHSgpAckzZN0QrPLY2Zmra2lA6OkXsDPgYOAEcCHJY1obqnMzKyVtXRgBPYA5kXEwxHxT+BCYHSTy2RmZi2s1QPjEGBB8XlhTjMzM1slvZtdgNWkGmmx0kTSBGBC/viSpAcaWqquMRB4uitnqFO6cm4tx/XZdVyXXatV6nObhsy1G2r1wLgQ2Kr4PBR4vHqiiDgTOHNNFaorSJoTESObXY6ewvXZdVyXXcv12f20elfqbcBwSdtKWhcYA1zW5DKZmVkLa+kWY0Qsk/QZ4BqgF3B2RNzT5GKZmVkLa+nACBARfwT+2OxyNEBLdf22ANdn13Fddi3XZzejiJWeVTEzM1trtfo9RjMzsy7lwGhmZlZo+XuMPYWkkcC+wJbAy8DdwHUR8WxTC9aiXJ+NIWkD4JWIWN7ssvQErs/uyS3GJpM0TtIdwIlAP+AB4Cng7cA0SZMlbd3MMrYS12fXkrSOpP+SdKWkp4D7gUWS7pH0fUnDm13GVuL6bA1uMTbfBsA+EfFyrZGSdgWGA4+tyUK1MNdn17oBuI50oXF3RLwGIGkA8C7gZEmXRMR5TSxjK3F9tgA/lWpmbZLUJyJeXd1pLHF9tgZ3pXYTkr4nqb+kPpKmS3pa0lHNLlercn12jfIELWkXSZ/Jr11qTWPtc322BgfG7uO9EfEicDDpN2B3AL7U3CK1NNdnF5L0OeB8YPP8Ok/SZ5tbqtbl+uzefI+x++iT398HXBARz0q1/jzE6uT67FrjgT0j4u8Akk4BZgI/bWqpWpfrsxtzYOw+Lpd0P+mrBZ+WNAh4pcllamWuz64loPxKwXJq/+2b1cf12Y354ZtuRNKmwIsRsTx/v2mjiHii2eVqVa7PriPpC8BY4JKcdAgwKSJ+3KwytTLXZ/fmwNhNSFof+AKwdURMyN9n2jEirmhy0VqS67PrSdod2IfUsrkpIv7S5CK1NNdn9+XA2E1ImgLcDhwdETtL6gfMjIhdm1uy1uT6NLNV5XuM3ccbIuIISR8GiIiX5adFVofrswtIWgJUrp5VDPcG1o0In0M6wfXZGrwRuo9/5lZNAEh6A7C0uUVqaa7PLhARG5WfJW0EfBr4JK/fH7M6uT5bg7/H2GSS9sqDJwFXA1tJOh+YDny5WeVqVa7PxpC0iaSTgL8CGwFvjYjjm1uq1uX67N58j7HJJP0FmA38P6AXsBepi+XWiHi6mWVrRa7PriVpIHA8cARwNvDTiHihuaVqXa7P1uDA2GSS1gGOI3WnfCsizm1ykVqa67NrSfo7sBj4DbCkenxE/GiNF6qFuT5bgwNjNyFpBOmXL9Yh3RcTEBHRv6kFa1Guz66Ru/vaPElExDfWXGlan+uzNTgwdgOSxgMnAD8Bfh7eKKvF9Wlmq8NPpTaZpFuA+cC+/lWW1ef6NLPV5RZjk0l6T0RMa3Y5egrXp5mtLgdGMzOzgr/HaGadJmm0pD2bXY6ewvXZvfgeo5mtij2Bf5fUOyIOanZhegDXZzfirtRuStJo4ImImNXssvQErk8zq5dbjN2XryC7lutzFUnaGDgQGEL6Dt7jwDUR8Xwzy9XT+MGx7sMtRjNrk6Sjga8D1wJ/y8lDgfcA34iIc5pVtp5G0mMRsXWzy2EOjN2Cr8jXDF+Rd56kB4A9q/dFSZsCsyJih6YUrEVJuqytUcC7I2KDNVkeq81dqU3WxhX5u4DvSPIVedc6C/AVeeeU/xlYei2Ps87ZFzgKeKkqXcAea744VosDY/N9Fdi9rStywIGxEzq4It9sTZalh5gI3CHpWmBBTtua1JX6raaVqnXdCvwjIm6sHpFb59YNuCu1ySQ9SPovtheq0jcG5kTE8OaUrDVJeo62r8inRMQWa75UrS1fpB1A6uoXsJDU1f9cUwtm1iBuMTafr8i7lq/Iu5Ak5QB4YQfT+Aq7DvXUleuz+dxi7AZ8RW7dlaQZwO+ASyPisSJ9XeDtwFjghoiY1JQCthjXZ2twYGwyX0F2Lddn15K0HvAx4EhgW+B5oB/p5ySvJf2t153NKl+raaM+1wN64frsNhwYm8xXkF3L9dk4kvoAA4GX/VWi1ef67L4cGJvMV+Rdy1fkZra6HBi7EV9Bdi3Xp5mtCgdGMzOzgv+P0czMrODAaGZmVnBgNDMzKzgw9mCSvirpHkl3SbpT0p45fYakxySpmPYPkl7Kw+tIOk3S3ZLmSrpN0rZruOwjJZ3WRfOaL2lgB9McLOkvkv4q6V5Jn8zpkyQd2hXl6AxJ4yT9bA0s5zBJ90m6odHLWl2SPpV/dL+9aXaV9L41UJY1shxrDv8kXA8laW/gYGC3iFiaA8O6xSTPA/sAf5K0CTC4GHcEsCXw5oh4TdJQ4O91LrdXRCxf3fJHxBxgzurOpx756dUzgT0iYqGkvsCwNbHsbmA88OmIqCsw5j96XtbgMtUUEb+oY7JdgZHAHxtbmjW2HGsCtxh7rsHA0xGxFCAino6Ix4vxFwJj8vAHgd9X5V0UEa/lvAvb+3k6SS9J+qakWcDeko6SNDu3Un8pqVee7kBJd+RW2fSctoGks3Or9C+SRuf0/SRdkVuv83PwrixvnqQtJA2S9Luc9zZJ++Txm0m6Ns/vl3T890gbkS4Sn8nruzQiyt9VfYekWyQ9XGk9Kvl+0ao+IqefLukDefgSSWfn4fGSvp2H26qfj0p6UNKNpIuWNlW3ZIvW/n6SbsrLvlfSLyTVPM4l/S/pRw9+kddlPUm/yevzF0nvytONk3SRpMtJ3wWtNa8NJU3P23dusR2HSbpf0mSlnouLJa2fx82XdEqui9mSts/p2+R53ZXft87pJ0n6Yh6eUeR9UNK+Sj/i8E3giFy3R+Q8k/P+MF/SByV9L5fx6nxRhKTdJd0o6XZJ10ga3JnltLetrAVFhF898AVsCNwJPAicDryzGDcD2BO4i9e/+D4MeCmPHwrMz/l/CLylg2UFcHgefhNwOdAnfz4dOBoYRPqR9G1z+oD8/h3gqDy8SS7vBsB+wBU5/SfAR/PwnsB1efi3wNvz8NbAfXn4NOB/8/D7c/kGdrAOvwaeAi4g/TjAOjl9EnAR6SJyBDAvp38ImJbrbwvgMdIFxRjg+3ma2cCtefg3pN/Dbat+Bud5DCK17P8M/Kyd8k4CDi0+V7bdfsArwHa5bNPK6WrMZwYwMg8fD/wmD78xl2c9YBzp93sHtDOf3kD/PDwQmEe6IBmW63+fPO5s4It5eD7w1Tx8dLG9LwfG5uGPAX/IwycVeWcAP8zD7yv2iXFlveU8fwL6ALsA/wAOyuMuAQ7J424BBuX0I4CzO7Mcv3rWyy3GHioiXgJ2ByYAi4EpksYVkywnnTCOAPpFxPwi70JgR+BE0h/STpc0qp3FLSf9DBvAqLzc2yTdmT9vB+wF3BQRj+RlPJunfy9wQp52BulEXP1nwlNyOSEFnil5eH/gZznvZUB/SRsB7wDOy8u5Eujwx9gj4uO5rLOBL5JO4BV/iIjXIuJeUhCE1NK6ICKWR8STwI3AW4GbgX0ljQDuBZ7MrY+9SSfftupnT2BGRCyOiH8W67gqZkfEw5G6tC/IZa3H24FzASLifuBRYIc8blqxzWoR6c+17wKuI/0gfqWuFkTEn/PweVXluaB43zsP70266CGXp63yV3o5bqf9ru+rIuJVYC7pYuHqnD4359sR2BmYlrfJ10gXh51djvUQvsfYg+UT4wxghqS5pN8JnVRMciHpqvmkGnmXAlcBV0l6knRlPb2NRb0Sr99XFDA5Ik4sJ8jdi7V+TULAh2LFrksklf+bOBPYXtKgXI5v5/R1gL0j4uWqvLSxrHZFxFxgrqRzgUdIrQKApVXlLd+r5/E3pX9LORC4CRgAHE5q0S1RKlyt+jmkk2VeRr4VkudZ3j+unk+9822vy7mje8xHklq7u0fEq5Lmky5yOipPW8NtTV+qbJfltH8uq9xOeE3SqxFRmd9rOZ+AeyJi7/by17Ec6yHcYuyhJO0oqfyT411JLYDSzcB3ef2qvZJ3N0lb5uF1gDfXyNuW6cChkjbP+QdI2oYU3N6p/HSrpAF5+muAz+aTO5LeUj3DfCK7BPgRqbv0mTzqWuAzRbl3zYM3kU7USDoI2LSYZrqkIVXru6Gk/YqkXetY35tI95h65YD9DlJrk7yun8/T3Exqgd6cx7VVP7OA/ZTuj/YBDutg+fNJLU+A0aTuwIo9JG2bt90RpJ6BepT1tgOp5V7vf1huDDyVg+K7gG2KcVsrPQwG8OGq8hxRvM/Mw7fw+v3vIztRfoAlpHvGnfEAMKhSRkl9JO3UgOVYi3Bg7Lk2BCYrPYBxF+n+2EnlBJH8ICKersq7OXC5pLtJ9yGXAXV9dSB3N34NuDYvdxowOCIWk7p1fy/pr7zeVfgt0kn9rry8tv6ceQpwFCt2MR4HjMwPadwLfCqnf4P0wMwdpK7ax+BfQX57oLpLUMCXJT2Qu9K+weutxbZcQqqbvwLXA1+OiCfyuJuB3hExD7iD1Gq8uYP6WUTaPjNJXZF3dLD8X5EuNGaTumHLFt1M4GTgblLL95IO5lVxOtAr9y5MAcblnoN6nE/aFnNIwez+Ytx9wNi8vgOAM4pxfZUe2voc8N857Tjgo3n6j+Rx9boBGNGZh2Jy1/WhwCl537wTeFtXL8dah38r1dYaknYGPhYRX2h2WRolt3y/GBEHN7koQHoqlfRQzc41xs0nPfhTfWFm1lTuL7e1RkTcDfTYoGhmXcMtRqtb7vLqW5X8kfzQijWApK+y8v3GiyJiYifn0yXbTtK/k59cLSyNiD07Mx+z7syB0czMrOCHb8zMzAoOjGZmZgUHRjMzs4IDo5mZWcGB0czMrPD/ARD/XqVV6oi/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"SMS_received\",\"Showed_up_for_appointment\"]].value_counts().plot(kind=\"bar\",\\\n",
    "title=\"Counts of those who showed up for appointment based on SMS_received\")\n",
    "plt.xlabel(\"SMS_received, Showed_up_for_appointment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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": [
    "fig = sns.countplot(x=df.Showed_up_for_appointment, hue=df.SMS_received, data=df)\n",
    "fig.set_title(\"Counts of appointments showup based on  SMS_received status\")\n",
    "x_ticks_labels=[\"Showed_up\", \"Did not show up\"]\n",
    "fig.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two charts above shows that those who didn't receive sms showed up more for appointments, making up about double the number of those recieved sms and showed up. There is also a higher number of people who received an SMS and showed up compared to those who received an SMS and didn't show up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 7 Did Delay time Affect the Showing up for Appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>Delay_time</th>\n",
       "      <th>Showed_up_for_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>-7 days +10:10:40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>-2 days +09:09:19</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>-2 days +10:16:02</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27033</th>\n",
       "      <td>-2 days +13:08:07</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>-2 days +17:09:03</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102786</th>\n",
       "      <td>178 days 13:16:26</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102799</th>\n",
       "      <td>178 days 13:16:43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102796</th>\n",
       "      <td>178 days 13:16:59</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102797</th>\n",
       "      <td>178 days 13:17:18</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102795</th>\n",
       "      <td>178 days 13:19:01</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110327 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Delay_time Showed_up_for_appointment\n",
       "71533  -7 days +10:10:40                        No\n",
       "55226  -2 days +09:09:19                        No\n",
       "64175  -2 days +10:16:02                        No\n",
       "27033  -2 days +13:08:07                        No\n",
       "72362  -2 days +17:09:03                        No\n",
       "...                  ...                       ...\n",
       "102786 178 days 13:16:26                       Yes\n",
       "102799 178 days 13:16:43                       Yes\n",
       "102796 178 days 13:16:59                       Yes\n",
       "102797 178 days 13:17:18                       Yes\n",
       "102795 178 days 13:19:01                        No\n",
       "\n",
       "[110327 rows x 2 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Delay_time\",\"Showed_up_for_appointment\"]].sort_values(by=\"Delay_time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above result shows that the delay time between scheduling for appointments and the actual appointments was between 10 hours tp 178 days, with patinets in both extremes of delay time not shoing up for their appointment. Even amongst those with long delay time, we can still see that some showed up for appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delay_time         Showed_up_for_appointment\n",
       "13 days 06:42:14   No                           21\n",
       "34 days 06:41:33   No                           20\n",
       "-1 days +16:50:07  No                           16\n",
       "6 days 06:42:37    No                           16\n",
       "-1 days +15:41:36  No                           15\n",
       "                                                ..\n",
       "3 days 07:03:17    Yes                           1\n",
       "3 days 07:02:53    No                            1\n",
       "3 days 07:01:25    Yes                           1\n",
       "3 days 07:01:23    Yes                           1\n",
       "178 days 13:19:01  No                            1\n",
       "Length: 91578, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Delay_time\",\"Showed_up_for_appointment\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above result, it is obvious that amongst those who did not show up for appointment, the maximum delay time was 178 days.\n",
    "\n",
    "Likewise amongst those who showed for appointment, the maximum wait time was 3 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above results indicate that amongst those who didnt show up for appointment, the maximum delay time was 13 days and the minimum was 15 hours.\n",
    "\n",
    "\n",
    "While amongst those who showed up for appointment, the maximum delay time was 178 days and the minimum was 3 days. \n",
    "\n",
    "The delay time of 178 days resulted in only no patient showing up for appointment. The delay time seems to negatively influence the showing up for appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "The analysis conducted on this data suggests the following:\n",
    "\n",
    "1. Having no scholarship is likely not to affect showing up at the hospital for appointments.\n",
    "\n",
    "2. Although more females showed up for appointments compared to males, the female gender also didn't show up for appointment more than the males. Hence I can't say with certainty that gender affected the rate of showing up at the hospital. Furthermore, the dataset is skewed in favour of females because there are more males than females. This may affect the outcome of any conclusions one can make from the analysis.\n",
    "\n",
    "3. Amongst the chronic diseases of hypertension and diabetes, the former seemed to affect showing up for appointments more than the later. Amongst those with these chronic diseases who showed up for appointment, hypertension accounted for 17, 968 patients while diabetes accounted for 6, 478 cases. Hypertension can thus be said to have more influence on the rate of show up at the hospital.\n",
    "\n",
    "4. Amongst those with other health related conditions such as alcoholism and handicap, the former also accounted more (2, 677) for show up at the hospital compared to the latter (1, 676) although the difference isn't much. \n",
    "\n",
    "5. The delay time calculated from the difference between Appointment day and scheduled day seem to influence show up at the hospital as those who have longer delay time didn't show up for their appointment at the hospital.\n",
    "\n",
    "6. There is a larger number of patients who receieved SMS and showed up for appointment (25, 660) compared to those who receieved SMS and did not show up (9, 775). SMS receival could thus be said to have some effect on the rate of show up at the hospital.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations\n",
    "\n",
    "1. Most of our data are qualitative data (except for the Age column) and not quantitative. This limits the extent to which we can perform statistical analysis to make inferences from formulated hypothesis.\n",
    "\n",
    "\n",
    "2. Our analysis was thus majorly descriptive and not inferential. We cannot draw any inferences from the dataset.\n",
    "\n",
    "3. We are also constrained by paucity of details regarding the dataset. For example:\n",
    "\n",
    "- The scholarship status doesn't seem to affect the show-up at the hospital. No information was provided about the cost/financial benefits of such scholarships or the scope of care to be covered under it. Therefore, we cannot say if this could have been a likely reason for the observation made above.\n",
    "\n",
    "- The SMS_recieved group is also affected by this limitation. For instance, we can't tell what criteria was used in deciding who to send an SMS to, the content of the SMS (it could be even unrelated to the scheduled patient appointments) and when the messages were sent out (before or after the scheduled or appointment days).\n",
    "\n",
    "4. We couldn't measure for correlation because the dataset contained mainly qualitative data.\n",
    "\n",
    "5. The available data couldn't be used to explain some of the observations made on the dataset. For example, we would expect that those with scholarship would show up more for appointments but the data indicated otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting your Project "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some References\n",
    "\n",
    "https://nbviewer.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb\n",
    "\n",
    "https://github.com/jupyter/jupyter/wiki\n",
    "\n",
    "https://nbviewer.org/github/Tanu-N-Prabhu/Python/blob/master/Top_Python_Libraries_Used_In_Data%C2%A0Science.ipynb\n",
    "\n",
    "https://nbviewer.org/github/Tanu-N-Prabhu/Python/blob/master/Data_Cleaning/Data_Cleaning_using_Python_with_Pandas_Library.ipynb\n",
    "\n",
    "https://nbviewer.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb\n",
    "\n",
    "https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba\n",
    "\n",
    "https://datacamp.com\n",
    "\n",
    "https://udacity.com"
   ]
  }
 ],
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