3. Visualizations and EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import sqlite3 \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import itertools\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
    "from sklearn.metrics import make_scorer, accuracy_score #Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn.tree import export_graphviz\n",
    "# from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "import pydotplus\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV  # Randomized search on hyper parameters.\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from scipy.stats import randint\n",
    "import seaborn as sns # used for plot interactive graph. \n",
    "from pandas import set_option\n",
    "plt.style.use('ggplot') # nice plots\n",
    "\n",
    "from sklearn.model_selection import train_test_split # to split the data into two parts\n",
    "from sklearn.linear_model import LogisticRegression # to apply the Logistic regression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.model_selection import KFold # for cross validation\n",
    "from sklearn.model_selection import RandomizedSearchCV  # Randomized search on hyper parameters.\n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "import warnings\n",
    "from sklearn.utils import resample\n",
    "pd.set_option('display.max_columns', 1000)\n",
    "%matplotlib inline \n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('clean_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>hdi</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",
       "      <td>0.939</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",
       "      <td>0.939</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",
       "      <td>0.961</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",
       "      <td>0.667</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",
       "      <td>0.939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id  appointment_id gender         scheduled_day  \\\n",
       "0  2.987250e+13         5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14         5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12         5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11         5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12         5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "        appointment_day  age      neighbourhood  scholarship  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    hdi  \n",
       "0         0           0        0             0      No  0.939  \n",
       "1         0           0        0             0      No  0.939  \n",
       "2         0           0        0             0      No  0.961  \n",
       "3         0           0        0             0      No  0.667  \n",
       "4         1           0        0             0      No  0.939  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting the target varibale to binary\n",
    "df['no_show']=np.where(df['no_show']=='No',0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_id', 'appointment_id', 'gender', 'scheduled_day',\n",
       "       'appointment_day', 'age', 'neighbourhood', 'scholarship',\n",
       "       'hipertension', 'diabetes', 'alcoholism', 'handcap', 'sms_received',\n",
       "       'no_show', 'hdi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_patient = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          29872499824296.0\n",
       "1         558997776694438.0\n",
       "2           4262962299951.0\n",
       "3            867951213174.0\n",
       "4           8841186448183.0\n",
       "                ...        \n",
       "110522      2572134369293.0\n",
       "110523      3596266328735.0\n",
       "110524     15576631729893.0\n",
       "110525     92134931435557.0\n",
       "110526    377511518121127.0\n",
       "Name: patient_id, Length: 110527, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_patient['patient_id'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting the date information in string to datetime type:\n",
    "df['scheduled_day'] = pd.to_datetime(df.scheduled_day)\n",
    "df['appointment_day'] = pd.to_datetime(df.appointment_day)\n",
    "## Creating a new column (attribute) containing just the scheduling time:\n",
    "#df['schedule_time'] = df.scheduled_day.dt.time\n",
    "## Normalizing the \"Day\" columns to keep just the date information (dropping the time info)\n",
    "df['scheduled_day'] = df.scheduled_day.dt.normalize()\n",
    "df['day_of_week']= df.appointment_day.dt.day_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WAITING TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#waiting days\n",
    "df['waiting_days'] = df['appointment_day'] - df['scheduled_day']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['waiting_days'] = df.waiting_days.dt.days  #Extract just the day value from the full \"timedelta\" object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>hdi</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27033</th>\n",
       "      <td>7.839273e+12</td>\n",
       "      <td>5679978</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10 00:00:00+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>1</td>\n",
       "      <td>0.642</td>\n",
       "      <td>Monday</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>7.896294e+12</td>\n",
       "      <td>5715660</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18 00:00:00+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>1</td>\n",
       "      <td>0.645</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>2.425226e+13</td>\n",
       "      <td>5664962</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05 00:00:00+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>1</td>\n",
       "      <td>0.706</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>9.982316e+14</td>\n",
       "      <td>5686628</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11 00:00:00+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>1</td>\n",
       "      <td>0.645</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>3.787482e+12</td>\n",
       "      <td>5655637</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04 00:00:00+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>1</td>\n",
       "      <td>0.953</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patient_id  appointment_id gender             scheduled_day  \\\n",
       "27033  7.839273e+12         5679978      M 2016-05-10 00:00:00+00:00   \n",
       "55226  7.896294e+12         5715660      F 2016-05-18 00:00:00+00:00   \n",
       "64175  2.425226e+13         5664962      F 2016-05-05 00:00:00+00:00   \n",
       "71533  9.982316e+14         5686628      F 2016-05-11 00:00:00+00:00   \n",
       "72362  3.787482e+12         5655637      M 2016-05-04 00:00:00+00:00   \n",
       "\n",
       "                appointment_day  age  neighbourhood  scholarship  \\\n",
       "27033 2016-05-09 00:00:00+00:00   38    RESISTÊNCIA            0   \n",
       "55226 2016-05-17 00:00:00+00:00   19  SANTO ANTÔNIO            0   \n",
       "64175 2016-05-04 00:00:00+00:00   22     CONSOLAÇÃO            0   \n",
       "71533 2016-05-05 00:00:00+00:00   81  SANTO ANTÔNIO            0   \n",
       "72362 2016-05-03 00:00:00+00:00    7     TABUAZEIRO            0   \n",
       "\n",
       "       hipertension  diabetes  alcoholism  handcap  sms_received  no_show  \\\n",
       "27033             0         0           0        1             0        1   \n",
       "55226             0         0           0        1             0        1   \n",
       "64175             0         0           0        0             0        1   \n",
       "71533             0         0           0        0             0        1   \n",
       "72362             0         0           0        0             0        1   \n",
       "\n",
       "         hdi day_of_week  waiting_days  \n",
       "27033  0.642      Monday            -1  \n",
       "55226  0.645     Tuesday            -1  \n",
       "64175  0.706   Wednesday            -1  \n",
       "71533  0.645    Thursday            -6  \n",
       "72362  0.953     Tuesday            -1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking which instances were scheduled after the appointment:\n",
    "df[df['waiting_days'] <0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Recording the inconsistent instances index \n",
    "dropIx = df[df['waiting_days'] < 0].index\n",
    "## Dropping these instances from the dataset:\n",
    "df.drop(dropIx, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Grouping by the 'WaitingDays' and 'No_show' values:\n",
    "waitingdays = df.groupby(by=['waiting_days','no_show'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "waitingdays = waitingdays.count()['patient_id'].unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 129 entries, 0 to 128\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   waiting_days  129 non-null    int64  \n",
      " 1   0             129 non-null    float64\n",
      " 2   1             129 non-null    float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 3.1 KB\n"
     ]
    }
   ],
   "source": [
    "waitingdays.fillna(value=0, inplace=True)\n",
    "waitingdays.reset_index(drop=False, inplace=True)\n",
    "waitingdays.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>no_show</th>\n",
       "      <th>waiting_days</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>36771.0</td>\n",
       "      <td>1792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>1113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5123.0</td>\n",
       "      <td>1602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4059.0</td>\n",
       "      <td>1231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>155</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>162</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>169</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>176</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>179</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "no_show  waiting_days        0       1\n",
       "0                   0  36771.0  1792.0\n",
       "1                   1   4100.0  1113.0\n",
       "2                   2   5123.0  1602.0\n",
       "3                   3   2093.0   644.0\n",
       "4                   4   4059.0  1231.0\n",
       "..                ...      ...     ...\n",
       "124               155      4.0     6.0\n",
       "125               162      9.0     2.0\n",
       "126               169      7.0     1.0\n",
       "127               176     10.0     6.0\n",
       "128               179      8.0     2.0\n",
       "\n",
       "[129 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waitingdays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WAITING CATEGORIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Defining the categories label:\n",
    "categories = pd.Series(['same day: 0', 'short: 1-3', 'week: 4-7', 'fortnight: 8-15', 'month: 16-30', 'quarter: 31-90', 'semester: 91-180', 'long: 181-10000'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Applying these categories both to the auxiliary and to the working datasets:\n",
    "waitingdays['waiting_days'] = pd.cut(waitingdays.waiting_days, bins = [-1,0,3,7,15,30,90,180, 10000], labels=categories)\n",
    "df['waiting_categories'] = pd.cut(df.waiting_days, bins = [-1,0,3,7,15,30,90,180, 10000], labels=categories)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Grouping the dataset by the waiting categories, returning the sum of all instances:\n",
    "waitingdays = waitingdays.groupby('waiting_days').sum()\n",
    "## Creating a new attribute, \"No-showing rate\", relating how many patients did not show up against those who did.\n",
    "waitingdays['no_showing_rate'] = (waitingdays[1]/ waitingdays[0])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>no_show</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>no_showing_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waiting_days</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>same day: 0</th>\n",
       "      <td>36771.0</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>4.873406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short: 1-3</th>\n",
       "      <td>11316.0</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>29.683634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week: 4-7</th>\n",
       "      <td>13097.0</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>33.694739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fortnight: 8-15</th>\n",
       "      <td>9362.0</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>44.499039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month: 16-30</th>\n",
       "      <td>10709.0</td>\n",
       "      <td>5159.0</td>\n",
       "      <td>48.174433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarter: 31-90</th>\n",
       "      <td>6792.0</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>49.602473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semester: 91-180</th>\n",
       "      <td>161.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>34.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long: 181-10000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "no_show                 0       1  no_showing_rate\n",
       "waiting_days                                      \n",
       "same day: 0       36771.0  1792.0         4.873406\n",
       "short: 1-3        11316.0  3359.0        29.683634\n",
       "week: 4-7         13097.0  4413.0        33.694739\n",
       "fortnight: 8-15    9362.0  4166.0        44.499039\n",
       "month: 16-30      10709.0  5159.0        48.174433\n",
       "quarter: 31-90     6792.0  3369.0        49.602473\n",
       "semester: 91-180    161.0    56.0        34.782609\n",
       "long: 181-10000       0.0     0.0              NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waitingdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>no_show</th>\n",
       "      <th>waiting_days</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>no_showing_rate</th>\n",
       "      <th>no_show_percentual</th>\n",
       "      <th>no_showing_rate_(500x)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>same day: 0</td>\n",
       "      <td>36771.0</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>4.873406</td>\n",
       "      <td>4.87%</td>\n",
       "      <td>2436.702836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>11316.0</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>29.683634</td>\n",
       "      <td>29.68%</td>\n",
       "      <td>14841.816896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>week: 4-7</td>\n",
       "      <td>13097.0</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>33.694739</td>\n",
       "      <td>33.69%</td>\n",
       "      <td>16847.369627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fortnight: 8-15</td>\n",
       "      <td>9362.0</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>44.499039</td>\n",
       "      <td>44.50%</td>\n",
       "      <td>22249.519333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>month: 16-30</td>\n",
       "      <td>10709.0</td>\n",
       "      <td>5159.0</td>\n",
       "      <td>48.174433</td>\n",
       "      <td>48.17%</td>\n",
       "      <td>24087.216360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>quarter: 31-90</td>\n",
       "      <td>6792.0</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>49.602473</td>\n",
       "      <td>49.60%</td>\n",
       "      <td>24801.236749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>semester: 91-180</td>\n",
       "      <td>161.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>34.782609</td>\n",
       "      <td>34.78%</td>\n",
       "      <td>17391.304348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "no_show      waiting_days        0       1  no_showing_rate  \\\n",
       "0             same day: 0  36771.0  1792.0         4.873406   \n",
       "1              short: 1-3  11316.0  3359.0        29.683634   \n",
       "2               week: 4-7  13097.0  4413.0        33.694739   \n",
       "3         fortnight: 8-15   9362.0  4166.0        44.499039   \n",
       "4            month: 16-30  10709.0  5159.0        48.174433   \n",
       "5          quarter: 31-90   6792.0  3369.0        49.602473   \n",
       "6        semester: 91-180    161.0    56.0        34.782609   \n",
       "\n",
       "no_show no_show_percentual  no_showing_rate_(500x)  \n",
       "0                    4.87%             2436.702836  \n",
       "1                   29.68%            14841.816896  \n",
       "2                   33.69%            16847.369627  \n",
       "3                   44.50%            22249.519333  \n",
       "4                   48.17%            24087.216360  \n",
       "5                   49.60%            24801.236749  \n",
       "6                   34.78%            17391.304348  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda_waitingDays = waitingdays.copy()  #Copying the dataframe from Section 2.3.3\n",
    "eda_waitingDays.reset_index(drop=False, inplace=True)  #Making the index as a column in order to be plotted.\n",
    "eda_waitingDays.drop(7, inplace=True)  #Droppping the last row, since it's empty.\n",
    "\n",
    "## Adding new columns:\n",
    "#Transforming the 'No-showing rate' into strings with the percentual values:\n",
    "eda_waitingDays['no_show_percentual'] = eda_waitingDays['no_showing_rate'].apply(lambda x: '{0:.2f}%'.format(x))\n",
    "#Multiplying the rate values by 500 times in order to be plotted in the same scale:\n",
    "eda_waitingDays['no_showing_rate_(500x)'] = eda_waitingDays['no_showing_rate']*500\n",
    "\n",
    "## Showing the adjusting dataframe:\n",
    "eda_waitingDays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Setting the graph parameters:\n",
    "fig1, ax = plt.subplots(figsize=[12,6])  #Defines the graph window size\n",
    "fig1.subplots_adjust(top=0.92)\n",
    "plt.suptitle('Appointments and NO-SHOW distribution by waiting time categories', fontsize=14, fontweight='bold')\n",
    "\n",
    "colors = ['tab:blue', 'tab:green', 'tab:red']  #Defines the colors to be used\n",
    "\n",
    "ax.set_ylabel('Number of occurences', color=colors[0], fontsize=12)  #Set the y-axis color and label\n",
    "ax.tick_params(axis='y', labelcolor=colors[0])\n",
    "\n",
    "## Plotting the line chart:\n",
    "eda_waitingDays[['waiting_days', 'no_showing_rate_(500x)']].plot(x='waiting_days', linestyle='-', marker='o', ax=ax, color=colors[2])\n",
    "#Setting the line chart marker labels\n",
    "x = ax.get_xticks()  #Getting the x-axis ticks to plot the label\n",
    "for a,b,c in zip(x,eda_waitingDays['no_showing_rate_(500x)'], eda_waitingDays['no_show_percentual']):\n",
    "    plt.text(a,b+1500,c, color='red', fontsize=14)\n",
    "    \n",
    "## Plotting the bar chart:\n",
    "eda_waitingDays[['waiting_days', 1, 0]].plot(x='waiting_days', kind='bar', ax=ax, color=colors[0:2])\n",
    "\n",
    "ax.set_xlabel('Waiting time categories', fontsize=12)  #Set the y-axis color and label\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#made a count column to help with my groupby\n",
    "df['cnt']=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at the target variable (no_show/show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoShow and Show Count of Patients\n",
      "\n",
      "no_show\n",
      "0    88208\n",
      "1    22314\n",
      "dtype: int64\n",
      "\n",
      "NoShow and Show '%' of Patients\n",
      "\n",
      "Percent of Patients who `Showed Up` => 79.81%\n",
      "Percent of Patients who Did `Not Showed Up` => 20.19%\n"
     ]
    }
   ],
   "source": [
    "print(\"NoShow and Show Count of Patients\\n\")\n",
    "print(df.groupby(['no_show']).size())\n",
    "\n",
    "print(\"\\nNoShow and Show '%' of Patients\\n\")\n",
    "show = df.groupby(['no_show']).size()[0]/(df.groupby(['no_show']).size()[0]+df.groupby(['no_show']).size()[1])\n",
    "print(\"Percent of Patients who `Showed Up` => {:.2f}%\".format(show*100))\n",
    "noshow = df.groupby(['no_show']).size()[1]/(df.groupby(['no_show']).size()[0]+df.groupby(['no_show']).size()[1])\n",
    "print(\"Percent of Patients who Did `Not Showed Up` => {:.2f}%\".format(noshow*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEaCAYAAAA2f6EIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAaiElEQVR4nO3deZgdVZ3/8fcxKKKCLHEhCQJCXOIOCigzyKIYkJm4jEfQkUUZHh0XfuqgMOMMzIgriOCC82QACf5Q/I4r8wNEJIobq7hCFDPIEhLEsIkLS6B+f5zTcmm6k15O36a736/nuc+9depU1anOTX3uOVW3buq6DkmSxuthk90ASdL0YKBIkpowUCRJTRgokqQmDBRJUhMGiiSpCQNFktSEgaJJkVI6NaX0rclux2RIKR2VUlo+2e2YSCmlrVJKXUrprya7LeofA0XNpZQ2SCm9P6X065TSn1NKN6eULk0pvWOy2zYgpXTBQHvqge/elNLzBtU5MKW0ZpTrfVhK6Z9SSr9IKf0xpXRbSumnKaWjW7a/hZ6D/sDj9pTSxSmlRaNcz/KU0lGDiq8HNgcubtXeuq15ta27tlyv2lhvshugaekzwG7AocBPgY2A5wFPmsxGDUgpPQ7YGXhDT/FdwHGUdo/Hv1H2++3AhcAjgWcCO41zvRNpEXAJsAnwXuArKaWdu667aKwr7LruXuDGRu3TVNF1nQ8fTR/AbcDb1lHnVOBbwCHAtcDvga8DjxtU7wDgSsoBfwVwNLBenfeSWv6oOv1I4E7g+z3L7wasATbqKTsYuKxnugM+VOu9sqf8QGDNoPbsDfyobvcm4ETg0T3zfwIcu459PwpYTjmQ/xL4I/BtYJuRbgvYtrZ725761wIreqa3qnUWDNOOgfl/1VP2cOBPwIfq9HbAOXX7fwAuBRb21P9OXUfvY6th1v2E+u/+O+AO4AfALj3zd63LvBT4bm3HlcDLBv1b9T6uqeXzgC8Dq4E/A1cDh032/4WZ9nDISxNhFbAwpbTpOuq9gHLAfzmwEHgucOzAzJTSy4FTgM8BzwLeDbwVOLJW+QHloPLXdXpnyoFqh5TSY2rZ7pTw+H3Pdl8JfHVQW64ATgI+mlJ6xFCNTSk9GziTcrB7LiXs9gH+c9C+vzilNHcd+7458Bbg9cCLgI3rvo5oW13XLQeuA/ao9bcBHg88NqX01LqaPYAbu667ch1t6bUGuIcSLFB6l2dQDvbbAecCZ6aUnlLnvwq4BvhY3afNKcNdD5BS2oASmhsCe1F6rGcD56WUnj6o+rHAB4HnAJcBX0wpbVznbVefX1239YI6fSLwWMqHjKcDb6J8AFE/TXai+Zh+D8qB/VrgXuBnwGLKp/HUU+dUyifV9XvKDgdW9Ux/D4hB6z6U8gn0EXX6O8BH6+sPACdTPtXuXct+AHywZ/kNKb2YBT1lHfD3lAPy7cC7avmB9PRQKMF2yaD2LALuA7as008DflHLfgUsoYTGej3LHEU5cD+up2zfuswjR7GtUwf+PsA/AOdTDtL/WMtOBz6/ln+nrejpRVB6eEfVsoVrWe6nwL/0TC8HjlrHug+kHODXG1RvKXB8fb1rXeZVPfOfWMteVqfn1eldh2jTUcO12Ud/HvZQ1FzXdT8AtqH0HJZQhjq+TPlkm3qqLuu67q6e6Rtq3QHPoHxC73UB5cC3TZ1eSumFUJ/Pp3wS3r32Ul5Q6wx4OXBtN8Sn9q7rbqJ8Mv7XlNJmQ+zacO1JwIK6jl9SelPbA58CHkHp+VxUP6UPWNl13e8G7XuihNqItlX3a7f6N33Avtf5uw3a9+F8M6X0B8rQ29uAd3Zd9w0o55tSSiemlH5ZLzD4Q23bliNYb68XUMLhtpTSHwYelPfI/EF1fzLwouu6GykfTJ7A2h0P/HO9qOAjKaVdRtk+NWCgaEJ0Xbem67ofdl33sa7rFlE+oe4D9P5Hv3vwYpQD5uCyXmlQ+VLgeSmlJ1EO4kvrYw/Kweo+Si9lwFDDXb2OB26lfFIfynC/9/CX8q74cdd1n+y6bj/KOYHtgdxTf6h9hwf+n1zXts4HZgPP5v7wGAiZZ1CGhEYSKAdRhtWe2HXd7K7rju+Zdyrl7/ie+vxcygF/yGHBtXgYsKwu3/t4OqV31Wvw32Zg+WF1XfdZSsj9J2W/z0kp/d9RtlHjZKCoX5bV58evtdYDXQG8eFDZLtx/0hXKZal/plxd9ev6ifbblF7Ca4CLuq77M0BKaX3K+P2wgVJ7TIcDb6YMX62rPS+mHODXdp6i1b4/YFtd190A/JpyRdkGlPMNP6aE7jspPbGrWbcbuq5bPqjHNGAX4MSu687suu7nlHNETx5U525g1jq2cVld7vd1W72PlSNoY++2GGp7Xdet6rrus13X7U85h/L6lNJGo1i3xslAUXP1Ox5vTik9P6W0ZUppD8pJ09soB/uR+hDw6pTS4Smlp6SUMqXn8LGu6+4G6LruHuD7lJPWS2vZLcDPKZcF935CfynlpP0la9to13VBCarB35s5BtgupXRcSulpKaWFwCeB07uuu67u+5dTSu9OKb2w7vuLKOdD7gHOGsW+r3Nb1dK679+tvcL7KENjf/l7jNOvKAfmZ6WUngt8gQcfzH8D7JxSelJKaXZKaajjyum13lkppT3rd2B2TCkdkVJ6xSjas5pytdmeKaUnppQ2AUgpfSqltHdKaZvaO3sV5eKAO0a3uxoPA0UT4RzKieizKQekz1I+Se/cdd3qka6k67qzgTdSDo6/AD5OCaZ/H1T1fMp3qnoPoEuHKHsl8LWu60byM6XvpJyr6W3Pz4C/pfQUfkoJirMovZkB36BcsfYV4Crgvymfql881Hmb4YxwWzDyfR+rgyjHiUuAr1H279JBdY6kXGH1K8qFFg/6vlHXdXdS9uUyyvvhKsrfaAfKBRwjUgPzrZThw+spPTIovbLjKe+T7wKPBvYa4b+1Gkn+vTUTpJRmUb5ot1/XdTPyli/SRLOHopliM8pVV9+Z5HZI05Y9FElSE/ZQJElNzPSbQ9o9k6TRG/x9McBAYeXK0VwCL0kz25w5c4ad55CXJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKmJGf9N+fFYddjBk90EPQRtfsxJk90EaVLYQ5EkNWGgSJKaMFAkSU0YKJKkJgwUSVITBookqQkDRZLUhIEiSWrCQJEkNWGgSJKaMFAkSU0YKJKkJgwUSVITBookqQkDRZLURN9+DyXn/E7gYKADfg4cBGwOnAFsClwOvCEi7s45rw+cBmwP3Ay8NiKuqes5AngTcC/wjog4t5YvBE4AZgEnRcSH+7VvkqQ+9VByznOBdwDPj4hnUg76+wIfAT4eEfOBWylBQX2+NSK2BT5e65FzXlCXewawEDgx5zwr5zwL+DSwF7AA2K/WlST1ST+HvNYDNsg5rwc8ClgF7A58qc5fAryivl5Up6nz98g5p1p+RkTcFRG/AZYDO9TH8oi4OiLupvR6FvVhnyRJVV+GvCLihpzzscB1wJ+BbwI/Am6LiDW12gpgbn09F7i+Lrsm53w7sFktv6hn1b3LXD+ofMeh2pJzPgQ4pK6b2bNnj3m/Vo15SU1n43lPSVNZXwIl57wJpcewNXAb8N+U4anBuvqchpk3XPlQPa1uiDIiYjGweKDO6tWrh2+4NAa+pzSdzZkzZ9h5/Rryegnwm4j4XUTcA3wFeBGwcR0CA5gHrKyvVwBbANT5jwVu6S0ftMxw5ZKkPunXVV7XATvlnB9FGfLaA7gM+Dbwd5RzHgcAX6/1z6zTF9b5SyOiyzmfCXw+53wcMAeYD1xC6bnMzzlvDdxAOXH/uj7tmySJPvVQIuJiysn1yymXDD+MMuz0XuBdOefllHMkJ9dFTgY2q+XvAg6v67kCCOBK4BvAWyPi3noe5m3AucCyUjWu6Me+SZKK1HVDnmqYKbqVK8c+MrbqsIMbNkXTxebHnDTZTZAmTD2HMtT5bL8pL0lqw0CRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSE+v1a0M5542Bk4BnAh3wRuBXwBeBrYBrgBwRt+acE3ACsDfwJ+DAiLi8rucA4H11tUdHxJJavj1wKrABcDZwaER0/dg3SVJ/eygnAN+IiKcBzwGWAYcD50fEfOD8Og2wFzC/Pg4BPgOQc94UOBLYEdgBODLnvEld5jO17sByC/uwT5Kkqi+BknPeCNgFOBkgIu6OiNuARcCSWm0J8Ir6ehFwWkR0EXERsHHOeXPgZcB5EXFLRNwKnAcsrPM2iogLa6/ktJ51SZL6oF9DXk8Gfgd8Nuf8HOBHwKHAEyJiFUBErMo5P77Wnwtc37P8ilq2tvIVQ5Q/SM75EEpPhohg9uzZY96pVWNeUtPZeN5T0lTWr0BZD9gOeHtEXJxzPoH7h7eGkoYo68ZQ/iARsRhYPFBn9erVa2mGNHq+pzSdzZkzZ9h5/TqHsgJYEREX1+kvUQLmt3W4ivp8U0/9LXqWnwesXEf5vCHKJUl90pdAiYgbgetzzk+tRXsAVwJnAgfUsgOAr9fXZwL755xTznkn4PY6NHYusGfOeZN6Mn5P4Nw6746c8071CrH9e9YlSeqDvl02DLwdOD3n/AjgauAgSqBFzvlNwHXAa2rdsymXDC+nXDZ8EEBE3JJzfj9waa33HxFxS339Fu6/bPic+pAk9Unquhn9VY1u5cqxj4ytOuzghk3RdLH5MSdNdhOkCVPPoQx13tpvykuS2jBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEyMOlJzzPw1T/q52zZEkTVWj6aH82zDl72vREEnS1LbO30PJOe9eX87KOe/GA29b/GTgjolomCRpahnJD2ydXJ8fCZzSU94BN1J+OEuSNMOtM1AiYmuAnPNpEbH/xDdJkjQVjfgngHvDJOf8sEHz7mvZKEnS1DPiQMk5bwd8Gng2ZfgLyvmUDpjVvmmSpKlkxIECLAH+B3gj8KeJaY4kaaoaTaBsCfxLRHQT1RhJ0tQ1mu+hfBXYc6IaIkma2kbTQ3kk8NWc8/cplwv/hVd/SZJGEyhX1ockSQ8ymsuG/30iGyJJmtpGc9nw7sPNi4ilbZojSZqqRjPkdfKg6ccBjwBWUO7pJUmawUYz5LV173TOeRblTsPeHFKSNPYf2IqIe4EPAO9p1xxJ0lQ13l9sfCngfbwkSaM6KX895b5dAx5F+W7KP7ZulCRp6hnNSfm/HzT9R+CqiPh9w/ZIkqao0ZyUvwD+cuv6JwC/9bb1kqQBoxny2pBy+/rXAg8H7sk5nwG8IyJun6D2SZKmiNGclP8k8GjgWcAG9flRwCcmoF2SpClmNOdQFgJPjoiB30K5Kud8EPC/7ZslSZpqRtNDuZPy7fhes4G72jVHkjRVjaaHchJwXs75OOBayg9uvRP4r4lomCRpahlNoHwAuAF4PTAHWAl8NCIG3+NrWPV2LZcBN0TEPjnnrYEzgE2By4E3RMTdOef1gdOA7YGbgddGxDV1HUcAbwLupVwQcG4tXwicQPl9+5Mi4sOj2DdJ0jiNZsjrBOBXEfGSiFgQES8BluWcjx/FOg4FlvVMfwT4eETMB26lBAX1+daI2Bb4eK1HznkBsC/wDMo5nRNzzrNqUH0a2AtYAOxX60qS+mQ0gbIfpXfR60fA60aycM55HvByytAZOecE7A58qVZZAryivl5Up6nz96j1FwFnRMRdEfEbYDmwQ30sj4irI+JuSq9n0Sj2TZI0TqMZ8uoow0m9ZjHyUDqeciPJDev0ZsBtEbGmTq8A5tbXc4HrASJiTc759lp/LnBRzzp7l7l+UPmOQzUi53wIcEhdN7Nnzx5h8x9s1ZiX1HQ2nveUNJWNJlC+B7w/5/yeiLivfmP+qFq+VjnnfYCbIuJHOedda3Eaomq3jnnDlQ8Vat0QZUTEYmDxQJ3Vq1cP12xpTHxPaTqbM2fOsPNGM+R1KPASYFXO+RLKSfmXAm8fwbI7A3+bc76GMhy1O6XHsnHOeSDU5tV1QulhbAFQ5z8WuKW3fNAyw5VLkvpkxIESESuA7SjnJo6hnO/Yvpava9kjImJeRGxFOam+NCJeD3wb+Lta7QDg6/X1mXWaOn9pRHS1fN+c8/r1CrH5wCXApcD8nPPWOedH1G2cOdJ9kySN32iGvKg3g7yIB57HGI/3AmfknI8Gfsz9PzN8MvC5nPNySs9k37r9K3LOAVwJrAHeWn/oi5zz24BzKed1TomIKxq1UZI0AqnrhjzVMFN0K1eOfWRs1WEHN2yKpovNjzlpspsgTZh6DmWo89nj/sVGSZIAA0WS1IiBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqwkCRJDVhoEiSmjBQJElNGCiSpCYMFElSEwaKJKkJA0WS1ISBIklqYr3JboCkiXHgkgsnuwl6CDr1gBdO2LrtoUiSmjBQJElN9GXIK+e8BXAa8ETgPmBxRJyQc94U+CKwFXANkCPi1pxzAk4A9gb+BBwYEZfXdR0AvK+u+uiIWFLLtwdOBTYAzgYOjYiuH/snSepfD2UN8O6IeDqwE/DWnPMC4HDg/IiYD5xfpwH2AubXxyHAZwBqAB0J7AjsAByZc96kLvOZWndguYV92C9JUtWXQImIVQM9jIi4A1gGzAUWAUtqtSXAK+rrRcBpEdFFxEXAxjnnzYGXAedFxC0RcStwHrCwztsoIi6svZLTetYlSeqDvl/llXPeCngecDHwhIhYBSV0cs6Pr9XmAtf3LLailq2tfMUQ5UNt/xBKT4aIYPbs2WPel1VjXlLT2XjeU9JEm8j3Z18DJef8GODLwP+JiN/nnIermoYo68ZQ/iARsRhYPFBn9erVa22zNFq+p/RQNt7355w5c4ad17ervHLOD6eEyekR8ZVa/Ns6XEV9vqmWrwC26Fl8HrByHeXzhiiXJPVJXwKlXrV1MrAsIo7rmXUmcEB9fQDw9Z7y/XPOKee8E3B7HRo7F9gz57xJPRm/J3BunXdHznmnuq39e9YlSeqDfg157Qy8Afh5zvknteyfgQ8DkXN+E3Ad8Jo672zKJcPLKZcNHwQQEbfknN8PXFrr/UdE3FJfv4X7Lxs+pz4kSX2Sum5Gf1WjW7ly7CNjqw47uGFTNF1sfsxJk90EwFuvaGjjvfVKPYcy1HlrvykvSWrDQJEkNWGgSJKaMFAkSU0YKJKkJgwUSVITBookqQkDRZLUhIEiSWrCQJEkNWGgSJKaMFAkSU0YKJKkJgwUSVITBookqQkDRZLUhIEiSWrCQJEkNWGgSJKaMFAkSU0YKJKkJgwUSVITBookqQkDRZLUhIEiSWrCQJEkNWGgSJKaMFAkSU0YKJKkJgwUSVITBookqQkDRZLUhIEiSWrCQJEkNWGgSJKaMFAkSU0YKJKkJgwUSVIT6012A1rKOS8ETgBmASdFxIcnuUmSNGNMmx5KznkW8GlgL2ABsF/OecHktkqSZo5pEyjADsDyiLg6Iu4GzgAWTXKbJGnGmE5DXnOB63umVwA7Dq6Ucz4EOAQgIpgzZ86YNzjn9LPHvKw00b55xKsnuwmaYaZToKQhyrrBBRGxGFg88c2ZWXLOl0XE8ye7HdJQfH/2x3Qa8loBbNEzPQ9YOUltkaQZZzr1UC4F5uectwZuAPYFXje5TZKkmWPa9FAiYg3wNuBcYFkpiismt1UzisOIeijz/dkHqesedJpBkqRRmzY9FEnS5DJQJElNTKeT8pok3vJGD1U551OAfYCbIuKZk92e6c4eisbFW97oIe5UYOFkN2KmMFA0Xt7yRg9ZEfFd4JbJbsdMYaBovIa65c3cSWqLpElkoGi8RnTLG0nTn4Gi8fKWN5IAr/LS+HnLG0mA35RXAznnvYHjKZcNnxIRH5jkJkkA5Jy/AOwKzAZ+CxwZESdPaqOmMQNFktSE51AkSU0YKJKkJgwUSVITBookqQkDRZLUhIEiSWrCQJEeonLOp+acj57sdkgjZaBIkpowUCRJTXgvL2mUcs7XAJ8C9ge2BL4BHBARd+ac/wF4L7Ap8H3gzREx7M0yc84JOA54PbA+cC3wuoj4Ra2ySc75LGAX4Mo673/rsi+i/FLmU4CrgEMj4oc5592AT0TEs2q9bwEbRcQOdfr7wLER8bVWfxMJDBRprDLllwDvBH4AHJhzvgr4ELAncAVwLOUHx3ZZy3r2rPOfAtwOPA24rWf+fnU7lwNLgA8A++acNwXOAt4BfAF4DXBWznlb4EJg25zz7LquZwL35Zw3BNYA2wPfG+f+Sw/ikJc0Np+IiJURcQvwP8BzKb2MUyLi8oi4CzgCeGHOeau1rOceYENKkKSIWBYRq3rmfyUiLomINcDpdTsALwd+HRGfi4g1EfEF4JfA30TEncBllKB6PvAzSm9pZ2CnutzNLf4IUi8DRRqbG3te/wl4DDCHMmQFQET8AbiZtfyCZUQspQyffRr4bc55cc55o3Vsh8Hbqq7t2dYFlLvs7lJffwd4cX1csK6dk8bCQJHaWUk5pwJAzvnRwGaU34kZVkR8IiK2B55BGfo6bLTbqp7Us63BgXIBBoommOdQpHY+D5yRc/48sAz4IHBxRFwz3AI55xdQPthdDvyRck7m3hFs62zgkznn1wEBvBpYAPy/Ov+HwFOBJwKXRMTdOectgU2A145+16R1s4ciNRIR5wP/CnwZWAVsQ/kFy7XZCPgv4FbKkNXNlJP569rWzcA+wLvrMu8B9omI1XX+HykhdUVE3F0XuxC4NiJuGt2eSSPjD2xJkpqwhyJJasJzKNIEyzn/NXDOUPMi4jFDlUtTkUNekqQmHPKSJDVhoEiSmjBQJElNGCiSpCb+P0o7YGmdDqzrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow Patients\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GENDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "#a plot of no show and show count of both males and females\n",
    "\n",
    "ax = sns.countplot(x=df.gender, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for Females and Males\")\n",
    "x_ticks_labels=['Female', 'Male']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age distribution plot\n",
    "plt.figure(figsize=(16,2))\n",
    "plt.xticks(rotation=90)\n",
    "_ = sns.boxplot(x=df.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "# appointments by age plot\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=df.age)\n",
    "ax.set_title(\"No of Appointments by Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e2gV525zzusAZwFOHSTZ0/+1w/bGbUZa3b+tk4OSh9YsWLRp7gSVJkiRJk16n7s19n6c3Iv6cc74E2AVYN+c8s7b2bgwM3Xw7H9gEmJ9zngmsA9zVsnxI62skSZpUmqbhgQceYMmSJaQ04vgaU17TNMyYMYM11lhjoPdTkjQ19Wv05scCD9WAd01gT8rgVBcDL6WM4HwYcFZ9ydn1+eV1/UUR0eSczwa+lnM+njKQ1ebAT/qxD5IkjdUDDzzAqquuysyZfb/G3HeLFy/mgQceYM0115zookiStIx+zdM7G7g45/xL4Erg/Ij4HvAu4O0555so9+x+vqb/PPCYuvztwLsBIuI6IIDrgXOBN3QauVmSpImyZMmSaRHwAsycOZMlS5ZMdDEkSVpOapqBn8a2ccoiSdJEuP/++1lrrbUmuhh9M932V5I0OdR7eke8v6ZfLb2SJEmSJPWdQa8kSZIkaWBNjxuNurDwnUcufTz7uFMmsCSSJK28t771rcyePZt3vetdE10USZImlC29kiRJkqSBZdArSZIkSRpYBr2SJPXRzjvvzGc/+1n23HNPttxyS17/+tfzwAMPAPDVr36V3Xbbja222orDDz+cO+64Y9S8mqbhmGOOYZtttmHLLbdkzz335Fe/+tXS9ffccw+HHnooW2yxBS984Qu59dZbl6678sor2W+//dhyyy3Zb7/9uPLKKwH48Y9/zB577LE03UEHHcQLXvCCpc8POOAAzj333F5UhSRJfWHQK0lSn333u9/lK1/5Cpdffjk33HADEcFll13Gf/3Xf/HZz36Wq666io033pijjjpq1HwuvfRSrrjiCn70ox9xww03cNJJJ7HeeustXX/WWWfx9re/neuvv55NN92Uj370owDcfffdHHbYYRxxxBFce+21zJ07l8MOO4y77rqLHXbYgVtvvZW77rqLxYsX8+tf/5qFCxdy33338be//Y1rrrmGnXbaaVzrR5KkXjLolSSpz4444gg23HBD1ltvPfbaay+uu+46zjjjDA4++GC23nprVl99dd7znvfws5/9jNtuu23EfGbOnMl9993HTTfdRNM0bL755mywwQZL1++7775st912zJw5kwMPPJDrrrsOgAsvvJDNNtuMl770pcycOZMDDjiAJz3pSZx//vmsscYabLPNNsybN49f/OIXPPWpT+WZz3wmV155JVdddRWbbbYZ66+//rjXkSRJveLozZIk9dljH/vYpY/XXHNN7rzzTu6++2623nrrpcvXXntt1ltvPe644w422WSTYfN51rOexatf/WqOPvpobr/9dvbdd1/+4z/+g0c/+tHDbuevf/0rAHfeeScbbbTRMnltvPHGS7tT77LLLlx++eXMnj2bXXbZhXXWWYd58+ax2mqrscsuu/SmEiRJ6hNbeiVJmgQ22GAD5s+fv/T5/fffz913382GG2446ute85rXcO6553LxxRdzyy23cNJJJ3W1rdtvv32ZZbfffvvSbe26665cfvnlzJs3j1133XXp88svv9ygV5I05Rj0SpI0CRxwwAF84xvf4Nprr+XBBx/k2GOPZbvtthuxlRfg6quv5qqrruKhhx5irbXWYvXVV2eVVVbpuK3nPe953HLLLZxxxhksXryYs846i9/85jfstddeAOy4447cfPPNXH311Wy77bY85SlP4fbbb+fnP/+5Qa8kacqxe7MkSZPAs5/9bN75zncyd+5c7rnnHnbYYQdOPPHEUV9z77338oEPfIDf/e53rL766uy+++68/vWv77it9ddfn9NOO433ve99vOc972HTTTfltNNOW3qv7lprrcXTn/50Vl99dVZbbTUAtt9+e2688UZmzZq18jsrSVIfpaZpJroM461ZsGBBx0QL33nk0sezjztlPMsjSZom7r//ftZaa62JLkbfTLf9lSRNDnPmzAFII623e7MkSZIkaWDZvVmSpEnsiiuu4JWvfOWw637zm9/0uTSSJE09Br2SJE1iO++8s8GtJEkrwe7NkiRJkqSBZdArSZIkSRpYBr2SJEmSpIHlPb2SJE0irVPo9UI30/BdfPHFvO9972PJkiUccsghvPGNb+xpGSRJmki29EqSNI09/PDDHH300XzlK1/h4osv5swzz+TGG2+c6GJJktQzBr2SJE1jP//5z9l00015whOewGqrrcb+++/PeeedN9HFkiSpZwx6JUmaxu644w7mzJmz9Pns2bO54447JrBEkiT1lkGvJEnTWNM0yy1LKU1ASSRJGh8GvZIkTWOzZ89mwYIFS58vXLiQDTbYYAJLJElSbxn0SpI0jW277bb89re/5fe//z1///vfOeuss9h7770nuliSJPWMUxZJkjSJdDPFUC/NnDmTD3/4w7z85S9nyZIlHHTQQTzlKU/paxkkSRpPfQl6c86bAF8CNgSWACdHxAk55/cDrwX+WJP+e0ScU1/zHuA1wMPAmyPivLp8H+AEYBXglIg4th/7IEnSoNpjjz3YY489JroYkiSNi3619C4G/jUirso5Pxr4Wc75/LruExHxsdbEOeenAQcDWwFzgAtyzlvU1Z8B9gLmA1fmnM+OiOv7sheSJEmSpCmlL0FvRCwEFtbH9+acbwA2GuUl+wOnR8SDwG9zzjcBO9V1N0XELQA559NrWoNeSZIkSdJy+n5Pb855U2A74ApgN+CNOedXAT+ltAbfTQmI57W8bD6PBMm3tS3feZhtzAXmAkQEs2bN6liuhS2Pu0kvSVInd955JzNnTp/hM1ZffXW/QyVJk05fv4lzzo8Cvg28NSL+knM+CfgQ0NT/HweOAIabILBh+NGml5tgMCJOBk4eWr9o0aIxlXOs6SVJGs6DDz7IKqusMtHF6JsHH3zQ71BJUt/NmTNn1PV9C3pzzqtSAt6vRsR3ACLizpb1nwO+V5/OBzZpefnGwNAkgiMtlyRJkiRpGf0avTkBnwduiIjjW5bPrvf7AhwIXFsfnw18Led8PGUgq82Bn1BagDfPOW8G3E4Z7Orl/dgHSZIkSdLU06+W3t2AQ4Frcs5X12X/DhySc96W0kX5VuB1ABFxXc45KANULQbeEBEPA+Sc3wicR5my6AsRcV2f9kGSpHF3+GmX9zS/Uw/btWOat7/97VxwwQXMmjWLiy66qKfblyRpoqWmWe6W2EHTLFjQuQf0wnceufTx7ONOGc/ySJKmifvvv5+11lprTK+ZiKB33rx5rL322rzlLW9ZqaB3RfZXkqSVVe/pHW5cKGD4gaEkSdI0sssuu7DuuutOdDEkSRoXBr2SJEmSpIFl0CtJkiRJGlgGvZIkSZKkgWXQK0mSJEkaWP2askiSJHWhm9GWe+2oo47i8ssv56677mKHHXbgHe94B4ccckjfyyFJ0ngw6JUkaZo78cQTJ7oIkiSNG7s3S5IkSZIGlkGvJEmSJGlgGfRKkjROmqaZ6CL01XTbX0nS1GDQK0nSOJkxYwaLFy+e6GL0xeLFi5kxw58VkqTJx4GsJEkaJ2ussQYPPPAADz74ICmliS7OuGmahhkzZrDGGmtMdFEkSVqOQa8kSeMkpcSaa6450cWQJGlasx+SJEmSJGlgGfRKkiRJkgaWQa8kSZIkaWAZ9EqSJEmSBpZBryRJkiRpYBn0SpIkSZIGlkGvJEmSJGlgGfRKkiRJkgaWQa8kSZIkaWAZ9EqSJEmSBpZBryRJkiRpYBn0SpIkSZIGlkGvJEmSJGlgGfRKkiRJkgbWzH5sJOe8CfAlYENgCXByRJyQc14f+AawKXArkCPi7pxzAk4A9gPuBw6PiKtqXocB761ZfzgiTuvHPkiSJEmSpp5+tfQuBv41Ip4K7AK8Ief8NODdwIURsTlwYX0OsC+wef2bC5wEUIPkY4CdgZ2AY3LO6/VpHyRJkiRJU0xfgt6IWDjUUhsR9wI3ABsB+wNDLbWnAQfUx/sDX4qIJiLmAevmnGcDzwfOj4i7IuJu4Hxgn37sgyRJkiRp6ulL9+ZWOedNge2AK4ANImIhlMA45/y4mmwj4LaWl82vy0Za3r6NuZQWYiKCWbNmdSzXwpbH3aSXJEmSJE1+fQ16c86PAr4NvDUi/pJzHilpGmZZM8ryZUTEycDJQ+sXLVo0pnKONb0kSZIkaWLMmTNn1PV9G70557wqJeD9akR8py6+s3Zbpv7/Q10+H9ik5eUbAwtGWS5JkiRJ0nL6EvTW0Zg/D9wQEce3rDobOKw+Pgw4q2X5q3LOKee8C3BP7QZ9HrB3znm9OoDV3nWZJEmSJEnL6Vf35t2AQ4Frcs5X12X/DhwLRM75NcDvgZfVdedQpiu6iTJl0asBIuKunPOHgCtrug9GxF392QVJkiRJ0lSTmma5W2IHTbNgQece0AvfeeTSx7OPO2U8yyNJkiRJ6pF6T+9w4z8BfbynV5IkSZKkfjPolSRJkiQNLINeSZIkSdLAMuiVJEmSJA2sfo3ePOW1DnQFDnYlSZIkSVOBLb2SJEmSpIFl0CtJkiRJGlgGvZIkSZKkgWXQK0mSJEkaWAa9kiRJkqSBZdArSZIkSRpYBr2SJEmSpIFl0CtJkiRJGlgGvZIkSZKkgdV10JtzfscIy9/eu+JIkiRJktQ7Y2npfd8Iy9/bi4JIkiRJktRrMzslyDk/rz5cJef8XCC1rH4icO94FEySJEmSpJXVMegFPl//rwF8oWV5A9wBvKnXhZIkSZIkqRc6Br0RsRlAzvlLEfGq8S+SJEmSJEm90U1LLwCtAW/OeUbbuiW9LJQkSZIkSb3QddCbc94e+AywDaWrM5T7extgld4XTZIkSZKkldN10AucBnwXOAK4f3yKI0mSJElS74wl6H0CcHRENONVGEmSJEmSemks8/SeAew9XgWRJEmSJKnXxtLSuwZwRs75MspURUs5qrMkSZIkaTIaS9B7ff2TJEmSJGlKGMuURR8Yz4JIkiRJktRrY5my6HkjrYuIizq89gvAC4E/RMTT67L3A68F/liT/XtEnFPXvQd4DfAw8OaIOK8u3wc4gTJF0ikRcWy35ZckSZIkTT9j6d78+bbnjwVWA+YDT+zw2lOBTwNfalv+iYj4WOuCnPPTgIOBrYA5wAU55y3q6s8Ae9VtXplzPjsi7HItSZIkSRrWWLo3b9b6POe8CvBe4N4uXvvDnPOmXW5qf+D0iHgQ+G3O+SZgp7rupoi4pW7/9JrWoFeSJEmSNKyxtPQuIyIezjl/hNLqevwKZvPGnPOrgJ8C/xoRdwMbAfNa0syvywBua1u+83CZ5pznAnNrOZk1a1bHgixseTxc+oVtz7vJU5IkSZI0sVY46K32Apas4GtPAj4ENPX/x4EjgDRM2obh5xRuhss4Ik4GTh5Ks2jRojEVrJv0Y81TkiRJktR7c+bMGXX9WAayuo1lg8y1KHP3HrUiBYuIO1vy/hzwvfp0PrBJS9KNgQX18UjLJUmSJElazlhael/Z9vyvwI0R8ZcV2XDOeXZEDPUaPhC4tj4+G/hazvl4ykBWmwM/obQAb55z3gy4nTLY1ctXZNuSJE13C9955DLPZx93ygSVRJKk8TWWgawuBcg5zwA2AO6MiK66Nuecvw7sDszKOc8HjgF2zzlvS2k9vhV4Xd3OdTnnoAxQtRh4Q0Q8XPN5I3AeZcqiL0TEdd2WX5IkSZI0/aSmGfa22OXknB9NmTLoIGBV4CHgdMo8uveMWwlXXrNgQede0K1XvIe72u0VcUnSIPF7TZI0KOo9vcONDQUMPzjUSD4FrA1sDaxZ/68F/M9KlE+SJEmSpHEzlnt69wGeGBH31+c35pxfDdzc+2JJkiRJkrTyxtLS+wDw2LZls4AHe1ccSZIkSZJ6ZywtvacA59dRlX8HPAF4G/C58SjYVNTpvmBJkrQ8vz8lSeNpLEHvRyhTBb2CMpXQAuC/I+Lz41EwSZKmK4PA3nPgLkmavsbSvfkE4NcRsWdEPC0i9gRuyDl/cpzKJkmSJEnSShlLS+8hwDvalv0MOBN4a89KJEmSRtWvVsvJ0uJsK60kaWWMJehtgFXalq3C2FqLJUlThIGGJEkaBGMJWH8EfCjnPAOg/n9/XS5JkiRJ0qQzlpbetwDfAxbmnH8HPB5YCLxoPAomSZJW3GTpmixJ0hGPp+cAACAASURBVETruqU3IuYD2wP7A8cBBwA71OWSJEmSJE06Y2npJSKWAPPqnyRJmsa871uSNBWMKejVyrO7maR+8FwjSZJUOPKyJEmSJGlg2dIrSdOUrcEaJB7PkqSRGPQOIO+xkqa2qfTjfSqVtRPPneqXQfrcSNJUYPdmSZIkSdLAsqV3CvIKsTR5dfp82po4tXn+HbupVGdTqaySpO4Z9EqTjD+6JobB6ODzsyVJ0vRk0Cv1kYGVJE1t/bh44neFJPWWQe80ZYuHJPVeN8GK519JkvrLoFfqIX/MTox+tYr4/kqSJE09Br0akT/wpy67xmmy8FjUdOMxL0mTj0GvNAV5QWJ680e1JElS9wx6JU0LXiiQJEmanmZMdAEkSZIkSRovfWnpzTl/AXgh8IeIeHpdtj7wDWBT4FYgR8TdOecEnADsB9wPHB4RV9XXHAa8t2b74Yg4rR/ll8AupRPJVlpJkiStqH619J4K7NO27N3AhRGxOXBhfQ6wL7B5/ZsLnARLg+RjgJ2BnYBjcs7rjXvJpQG18J1HLv2TJlIvjkWPZ0mSNJK+tPRGxA9zzpu2Ld4f2L0+Pg24BHhXXf6liGiAeTnndXPOs2va8yPiLoCc8/mUQPrr413+frI1UZIkSZJ6ZyIHstogIhYCRMTCnPPj6vKNgNta0s2vy0Zavpyc81xKKzERwaxZszoWZmHL4+HSL2x73inNSNvsx3b6VdZrXn3AMs+3/uKZw+YzKKbSe9OL7Uym93eQPjeWdXyOxUHa335tZ7Lk0a/tDFpZO5lM53BJmmiTcfTmNMyyZpTly4mIk4GTh9IsWrRoTAXoJn2nNL3Io1/b6VdZB80gvTdT6f2dLPs7ld6b6VbW6ba//drOZMmjX9uZSmXtxmQ5h0vSeJgzZ86o6ycy6L0z5zy7tvLOBv5Ql88HNmlJtzGwoC7fvW35JX0op1aCAxCp0zFgl35JkiSNp4mcsuhs4LD6+DDgrJblr8o5p5zzLsA9tRv0ecDeOef16gBWe9dlkiRJkiQNq19TFn2d0ko7K+c8nzIK87FA5JxfA/weeFlNfg5luqKbKFMWvRogIu7KOX8IuLKm++DQoFaaumzlkyRJkjSe+jV68yEjrNpjmLQN8IYR8vkC8IUeFk2SJGnK8fYhSereZBzIStIU4Y8uSZIkTXYTeU+vJEmSJEnjypZeqbLVUpIkSRo8tvRKkiRJkgaWQa8kSZIkaWDZvVmSJEnD8tYfSYPAll5JkiRJ0sCypVeTnleZJUnqPb9fJU0XBr2aFlq/2MEvd0mSJGm6MOjVlGdAK0mSJGkk3tMrSZIkSRpYBr2SJEmSpIFl0CtJkiRJGlgGvZIkSZKkgWXQK0mSJEkaWI7eLEmSpBXiDAqSpgJbeiVJkiRJA8ugV5IkSZI0sAx6JUmSJEkDy6BXkiRJkjSwHMhqGIefdvkyz089bNcJKokkSZIkaWUY9EqSJGnctI7wPJ6jO/drO5KmHrs3S5IkSZIGlkGvJEmSJGlgGfRKkiRJkgaW9/RKkiRpwrTeiwvejyup92zplSRJkiQNrAlv6c053wrcCzwMLI6IHXPO6wPfADYFbgVyRNydc07ACcB+wP3A4RFx1USUW5IkSZI0+U2Wlt7nRsS2EbFjff5u4MKI2By4sD4H2BfYvP7NBU7qe0klSZIkSVPGhLf0jmB/YPf6+DTgEuBddfmXIqIB5uWc1805z46IhRNSSkmSJI27XszB673D0vQ1GYLeBvhBzrkB/jciTgY2GApkI2JhzvlxNe1GwG0tr51fly0T9Oac51JagokIZs2a1bEQo0XNs2bNWm79cHku7LC+mzS92M50K+tY8+jXdnxv+redyZJHv7Yz3co63fa3X9uZLHn0azvTrazTbX97tR1Jg2kyBL27RcSCGtien3P+1Shp0zDLmvYFNXA+eWj9okWLVqqAw72+U57dbLMXaSZLHv3aznQr63Tb335tZ7Lk0a/tTLeyTrf97dd2Jkse/drOdCvrdNvfbtO0s7VYmpzmzJkz6voJv6c3IhbU/38AzgB2Au7MOc8GqP//UJPPBzZpefnGwIL+lVaSJEmSNJVMaEtvznltYEZE3Fsf7w18EDgbOAw4tv4/q77kbOCNOefTgZ2Be7yfV5IkSb3Qi3uHx7KN8dyOpEdMdPfmDYAzcs5DZflaRJybc74SiJzza4DfAy+r6c+hTFd0E2XKolf3v8iSJEnS8PoROEsamwkNeiPiFuAZwyz/E7DHMMsb4A19KJokSZI0aRlcS92b6JbeKevw0y5f+vjUw3adwJJIkiRJkkZi0CuNk9YLI+DFEUmSJGkiGPRKk5iBsyRJkrRyDHolaRjewiBJmsocJVp6hEGvema6BQmTZX+7KcdkKaskSZLUbwa90gSaLMFoN92oe1HWybK/k0k/6qRf3eTtji9JkiYjg15JU0ovAvSJCAINACVJ3epX12SnPdJ0YdA7xdlCt2ImS1A0SHpVZ1PlWBu01tPp1k1+kPalXzwvSpKmKoNeTcsf75ImRj8uOA1acNavc5rnTknSoDLolaQpzmBlYkym4NreK5IkjcygdwL1q2vyIPGHm6RB4jltxfjdKE0e3hesqcCgV5I0pUyl7r7TLTjrRYvzVHp/JUlTg0HvOPHq/YrxR4gkaWX5HSxJamXQK0mS1MbAWZIGh0GvJEmSpOX0ar7gTvf99mteYk1fBr2SJEkrYLrNby1JU9WMiS6AJEmSJEnjxZZeSZKkCdKvEa1tcZY0nRn0SpIkTWH9GnTLwb00kZwPWCvDoFeSJGmaM6DVdGDgPH15T68kSZIkaWDZ0itJkqSe8P5jSZORQa8kSZKmDLtiazjO9avRGPRKkiSpLyZi0K3xnEN5suQhaXQGvZIkSVKbXgTojqw99TjY1WAy6JUkSZImsX60KPdrzujpwMB58jHolSRJktQXUz1w9t7hqWlKBr05532AE4BVgFMi4tgJLpIkSZI0rfWqm/VUD4w7MXDuvykX9OacVwE+A+wFzAeuzDmfHRHXT2zJJEmSJI23ft1v3etu5WPJp1MXaQPnsZlyQS+wE3BTRNwCkHM+HdgfMOiVJEmS1LfW4sl8v3U39xb3I7gejwB9rPWemqZZ6Y32U875pcA+EXFkfX4osHNEvLElzVxgLkBE7DAhBZUkSZIk9UsaacWMfpaiR4bbmWUi94g4OSJ2jIgda/pl/nLOPxtuebfr+5WHZbWs021/p1JZp9v+TqWyTrf9nUplnW77O5XKOt3217JO3jws6+TNYwqUdURTMeidD2zS8nxjYMEElUWSJEmSNIlNxXt6rwQ2zzlvBtwOHAy8fGKLJEmSJEmajKZcS29ELAbeCJwH3FAWxXVjzObklVzfrzz6tR3LOth59Gs7062s021/+7WdyZJHv7Yz3co63fa3X9uZLHn0azuWdbDz6Nd2pltZp9v+LmPKDWQlSZIkSVK3plxLryRJkiRJ3TLolSRJkiQNLINeSZIkSdLAmoqjN/dcznlLYH9gI8qcvwuAsyPihjHmsRFwRUTc17J8n4g4tz7eCWgi4sqc89OAfYBfRcQ5I+T5pYh41SjbfBawE3BtRPygLtsZuCEi/pJzXhN4N7A9cD3wn8BhwBkRcdso+a5GGRV7QURckHN+OfCPlIHDTo6Ih3LOTwIOpEwftRj4DfD1iLinc21pEOWcHxcRf1jJPB4TEX/qVZkk9Zfngd7rRZ3WfKzXFtarphLPrStv2g5klXN+VETcl3N+F3AIcDplDmAoc/8eDJweEcd2yOfVwKOBN1CCwm2Bt0TEWXX9VRGxfc75GGBfyoWG84GdgUuAPSkjUe/clnUCngtcBBARL845/yQidqr5vrZu8wxgb+C7EXFszvk64BkRsTjnfDJwP/AtYA/gGfX/X4Gbga8D34yIP7bt01drOdcC/gw8CvhOfW0Cfga8CLgU2A+4GribEgQfFRGXjFZnk8FUPXnknNcB3gMcADy2Lv4DcBZwbET8eZTXfj8i9s05/0PNY2Pg+xHxtZY0J0bEUTnnDYFjgCXA+4A3Af9MOcbfAjzYlv3QcbEdkCLirrYLPusAxwPPBK4F3lb/PhYRi3LOOwJRt7cq8KqIuDTnfBXl2Pt6RNw8zD7tCBxHmb7sPcAXKBeCbgTmRsTPc86PAv6tln9j4O+U4/+zEXFqzWcm8BrKMTyHRy5+nQV8PiIeGqVeT46IuTnnVYAj6zbOjYgft6R5b93/N9a8P0U5x7wE+BXwwdaLZW353xgRW7Q83yYiflkfrwq8q+7ztcCHgSMo565FOecn1zrZBvg1cGREXJNz/k6t1zOH227O+YnAe2sdHAt8AtiV8v6/MyJuzTnPAA5vqdehi1+fjYhLVqZOh+oV+JfR6jQiPpxzXmus9TrWOo2I+3PObxytXoEPjFan3dQr8PvR6rTmscLngPr6rs4DwAcZ5RwQEQtzzuu3Zb/MeQDYabRzQETcmXM+llHOA7WORjwH1LxX+jzQi3oFDhqtTvt1bu1XvVKOzU7nVut1fOrV76xlt7nS31k1n3H/jcUkObf28zfWZDGdW3qvBx5POWls1X6CyDkfD1xH+fCM5gPAPcAONYjeFPhWznnTiDiBcnACvJQSEK8O3AFsXFtjjwOuoHz4rgdOoZxgErAj8PGWba3a8ngusFdE/DHn/DFgXi3rjDqtE8COEbF9fXxZzvlq4BZgB0qwfRDwgZzzzygB8Hci4l5g64jYpp5UbwfmRMTDOeevAL+gtBxvW5cdD5wTEbvnnP+XcmLYbrJ80Y1y8vhJznmlTx71BN63H2Z1uxcBu0fEHTXfDSkt+N+sF3GGkyjHH8AXKSf6bwNH5Jz/GXh5RDwI7FLTnAr8P2Bt4GLgq8ALKD0iPku56PG7tm1sBFxFOX6fSOlZcG5d93FgYX3dS4D/BZ4UEe+u648DDorSC2IL4GuU4389YF3g4pzzHZTj9BsRsaC+7kTK+78u8H+U92uvnPMedd2utexnAM8Hct2n04H35py3iIh/B75MucDzfpa9+HUY8JWc87+MUq/71cf/S7lQ9BPgf3LOl0bE2+u6l1C+xG8D1qx1ewPwsVonJwGH5pzvrfU3lDfAWkPLI+IfKO/N0Of6WOAxtX4PoLw3O0TEp+v6E4BPRMQZOefd6/rdKBfZltRyXlDr9f9FxN/r606ty9ahnFu+SPmi3pty3D4P+DzlGPgvyvntL8CPar1uTekdMmKdAgcN89lsr9dOdfrhWtYR6zXnfEAP6vRVwL90qNdNO9RpN/X6u9HqNCI+RYdzALBXznl7hjeW88CpjH4O2B9YxOjngT8z+jngAOAFHc4Dnc4B0IPzAOV7cWXrdbKcW/tVr38YrU7rubUXx6v16nfWuH9ndXN+7dFvrFOZHOfWfv7GotblBrT0qo2IO0eoz47q7+UtgFs6xRRDBjrozTm/fYRVidJ6CeVDNIflD67ZdR0551+Oks8GwH1DV57q1aTdKYHvE3jkJLA4Ih4G7s853xwRf6np/5ZzXkI5+N4CHE25KnV1zvlvEXFpy/Zm5JzXo9yLnaK20EbEX3POQ4HutTnnV0fEF4Ff5Jx3jIif1oP8IWCViFgC/AD4QQ3a9qW0dn+MEqDOyKWL89qUE+I6wF2UgH1VSoA+E3i4Lnt0Lcfva34web7oBu2H2aYR8dHWHan1+9Gc8xHAlZQW+MTy1q3/nxQR/1wfn5lzPhq4KOf84pa0G9QvAHLOR7Vs81M559dQAvM9KcfqNTXdbyNis2G2C+UCzNAXwidyzocBq+acZ9aLNGtGxJV1f27MOa9e094dEe8A3pFzfjblOL0q53xDreNVI+L7dfsfjYhv1TwurBeDqHV2an18fM75yoj4UC69NK4H/h3YPiKe0lbm+cC8nPONwB8px1BrvQ5dnHpcfb5TRGxTy/Jp4MRcrk4fUtNtERE555wox9ieEdHknH9EuZgE5Xhep9brnSPUa2sZ9gCeGeWWgx/WfFrP64+LiDNqnVySc350Xf6HiHhpfX4A8Frg5Jzz92q9PjoiTqrbPyoihi6+fT6XFk8oP1ReXR9flnOeFxHvq+W4mnIBbrQ6pYt67VSndFGvvahTuqjXUes0yi0onep1Voc6/RSdzwHQm/NAp3MAdDgP5NKKMGS4cwB0Pg+Meg6IiJPpzXkg9aBeJ8u5tV/12s25tRfHq/Xqd1Y/vrO6Ob8O0rkV+vQbK+e8LeU3+TqUhh+AjXPOfwaOosQnn6P8Nv8+8K6IuLtu9ycRsVOujV112bMov71vBp6cc35djHCraKuBDnopV+2OowRp7YYG8XorcGHO+TeUK1pQWoCfTOnWASWwfT6lC2+rRAle7sg5bxsRVwNEafF9IeXq0tY17d9zzmtFxP2UK8rA0pbFJVEC0U/knL9Z/9/J8u/POpTuDQlocs4bRsQduVztGPoAHgmckEvXlEXA5Tnn2+q+HUk5QS0VpYX7bODsXO4BhnI17FfAKpQg/Js551sogebplCDxypzzPOCfgI/WfXksJTiGyfNFB4P1w+x3Oed/A05r+YLZgNJl5zbKieN1EfGb9gqtxwHA6jnnGfWYIyI+knOeD/yQRy4GtQ5y96W2rFaJiI/lnE+vdXQbJZhvv1ficblceErAP+ScU0QMpZkBfAY4J5dW9HNzzp/kkW70V7eXPyJ+BPwo5/wmYC9KT4AHcs57Uz4bTc75gIg4M+f8HMpFGYC/5pyfFRGX5ZxfRD1GI2JJ/TIHuDvn/DLg20P1kktXqJdRPve3AHtExO9HqdfVWsq6GJiby20NF9V6/Wtd1+Sczxmqi/p86PGbcs47AF/POZ8JfHqYel0n5/ySWq+r189waz7fyjmfSrnKfUbO+a0t9TpU/qHt3UtpMfhyLq2umTIOwJJcLrKsS7lqP3Tx7MmU8wLAQznnJ0XEzblcwPp7zfPBWo5OdUoX9dqpTmlZP2y9jqFOD6Qcl8PVKV3U65wOdfqDlnpdZ4R67VSn0PkcAKVFZmXPA61XztvPATPqazqdBzqdA6DzeWCrocxGOAecTG/OA72o15U9t87o0bm1m3p9eg/q9W9dnFut1/GpV7+zuvvO2pzuv7OgP7+xxnpunU/pydjrc+syevQb68UMf7yeWuvtirY624XSwHUvpdfCPEqsclnO+cVRek4ONabt0vLSDwEHRMRVuXRtD2DaB71XUfr//6x9Rc75SICIOLd+UHaiXGFIlKtlV0ZpmQX4HvCooaC2LZ9LKEHVMoF1PYG8KpcuvwD/FKWFkqEPQrUqpfVz6HXzgZflnF9A6XrRmuemI+znEsp9HUQZSOrwXK6GPZHyHs9v+fAeNEIeRMTf6v9P5Jy/UR8vyDl/iRI0fi4iflLzuQB4KnB8RPyqpv0jJQiGyfFFt0p93SD9MDuIcoK/tNZnA9xJuXCRKV14RhqV/U31/3drugtaynxaLhdaPlUXnZXrfe8R8d6hdLn8OP91fc3Qsfoiyn3qa7Vt73PUXgDAacAs4I+5tPhfHRGfyjlfQ7lvcwvKsboFcCal2yqUrt/LqJ/LcynvwzOA/6Z8Bp4P/Ev98rydciWYmv/n6uf8WsotDUMXaT5T0xxMuXjzmVyuPEJtua/r9qO05i/3A6JuH+CnueWesFrWD+Scb6d0BTutpU6HLvyQy6Bw97a85mc55z0pF90uBdZo296llJ4IUK7qbxClC/6GwKKIODrnfDjlKu2TKL0x5lLq9RX1dcvdExURd1GuxH42l94H36XU6wHAe3LO21CO3aF6fSelZ8MDlPPYwS31+j1K74WPUloP7qZ8vtZpqVOAT3ao15071CmUeh+1Xruo0x8CQxfTlqvTmsfRuVx8GqlevztandZF/zZKvc6lnPMvzjk/SPk8HNJWp9D5HADlx8PKngdGOwfc2PKa0c4Do54D6us7nQe+3L4DreeAuuhfKMda63ngi5T7++a2pGk9DxxR92foPPBlVr5e/7FDnUIX9bqy59aaRy/q9fUsf35trdd7gVNGqVPozfE62er12lo3vazXU3nke+s+Sr0+BbiG4et1qn1nvZDyHTCZv7PgkeP1knq8Qu9/Y/X63Hoqnc8Bm9d93oJym+HK/MYa67kVYO32gLdua17OeW1KV/ih4/Bjudx2eW7O+VCWv4gC8A8RcVXN45Zc7k/vaKAHsqonjD9FxKKWZUOtoxvESvQl1+hy6Yb9bkr34qGuNEMnjmMj4u6c80uBayLi18O8/gDKF90PIuKCtnX7AJ+KiM1zzh8E/jvaBjaoJ49jI+KlbctfRGm93jQiNqzLjmnb/IlR7pXesOb9qppud5b9AXEb5aT8BeArEXEwo2g7ebyt5nUY9YsuIv6vnqRPqdu4FjgiSmvyY4FDIuJ/chkpfGNgXgwzUnjubiTxkdLsG4+0Ro+aT+t6StD+pIi4tsvt9KKsQ3k8ldLKNloeT615DFtn9fHOlJPrzZQLOrsA10ftMpO7GH29U5oR1v+acl98M0yaZ1MGtPtp23Z2pvQQ6WY7W9X1N4yxrK3b2IpyG8T1bXnsSrl1Y9QR6XPOj6H84PlkRLySUeTOo9aPur41TV72AtbQutmUEe8fszLlqGm+HBGHrmQe3wNeHI+01iTgMUPfW13Ux7MpF22viTqK/zBplhvpf6xpuszj2cBzgJ+sZB4j7k8v8hgun3q8/yoi7sllcLShmQ+uA/6zLm+fHeE9lIFlhmZH2LIlj+XWj5BH+wwLW7asX4sSEG5P6eXVmkd7WUfbznL707ad0cravj/tebSW412t+1KXv5nOM0aMmmY88qj786SIuHY8tzOOeaxGuSi2gNLAsy/lN9N1lIvsMygB3GizcKw+SprPUc7ZnfJon+3jUMpYNx+jNJQMbedg4Pbh8qnbac3jFXX99V2WdSiPQ1q28QrKvcDXtZX1FZRbEr853P7Uun0yy85OciMts5PkLmYv6ZRmBfJ4GPgt8KWWPIbKuXGHPF4y0r50Wdb27dwEfK0tj9Y6e2iYPP6HckHjSzzS8LUJZbyM3wLPpjQOtua5DeX2xvUj4jE55/vrthNlDI3HR4klZgC/jIilvSxGMtBB73ByHU15ossxneVH7jle4TQrk0frl914bqeXeQylocNI4ZQrfp1GEn8T5YrsCqfpxXZ6mMdRlO74I+Xx5i7SHMOyo6vvRLk6PTS6+kxGGX09Si+E9jzaR2hfkTyWKUc3aYbZTjd5dCrripSjfUR6KFe/W0ekP7ttfaJl1PphLLN+BfPophxjTrOCZe2Ux3Dl+Ek8Mor/kZTP0JksO4p/a5qRRvofNQ3wkhXI46jWsrTlcSTlszxaHsvtTy/KMcJ2likLcCjLznzwV8oPrj3q8pfkzrMjPGW09SuYx5jLMUKaZfJZwbKuSB738MiMEV+jzBixtCGivh+taZabVaLT+hXMI8ZajmHSLLc/41DWrwHfGiaPoVk21qQMpro25Xjeg3Lumckos3BExGF59Jk6oLQMjrg+Ig4fJo9lyrGC2xlrHq37O2x9dFOOuj9vprRK/5BhZiehDO414voo9yH3Ko8RZ0npMo+3UMa4Ge/tjLq/8cjI2PvyyPSwQ71qz46Ic+oFiFsiYh4tcs6PB/4jIl6byzhJrRbUixmzKAHzd+hg0Ls3D2e4+0fVXx+g9OFfmTQrnEeUbtzXjpamF9vpcR5DaTqNFP7aDuuhdElZ2TS92E6v8tixQx7dpOk0unrqsP4jfcqjX9vpRR7DjUj/TJYdkX4TypX4kUat/2SH9d3k8YkVLEd7mo075LOiZe2UR3s5Wkfxfx2wdyw/in83I/13SrN4BfJoL0trHq/rIo/h9qcX5RhuO+35dJr5gC7STJY8JlNZ22eM+GBefsaIUWeV6LS+X3mMkOaDfSjrcHXWaZYNOq3vVx5TrKyvZfTZSWZ2WL/dJMrjyElS1u3q+/N9yiBVy4mW2Vnalv++5k9E/G6ENIson5uOpmPQ+7mJLsB0kDuPeN0xTS/y6Nd2+lVWOo8UvkqH9fQozSDlAZ1HV286rO9XHlOprN2MSL/DaGlyGSBlZfNY6XJUo+bTo7J2k0c3o/j3Is0g5dFNml/l0Wc+gM6zI/xmkuQxmcraROcZIzqluW2S5DGZytpplo2HO6ynT3n0azu9KiuMPjtJ02H9VMqjb9vJj0xj2nrL49JpTOt2Vnr++dHSwMg3Yw+siDhxosswTWxA6av/omH+/tRlml7k0a/t9Kusd+Qy9DsANZh7IWUQg627WE+P0gxSHlBHV6+PlxtdvYv1/cpjypQ1IpZExCeAVwNH5zIlxjIXWjulmSx5TKay8sgo/j8F1s9l7AHysqP49yLNIOXRTZojgefknG8GnkaZ+eAWyoXyI2sendJMljwmU1mX6V0XEQ9FxNkRcQhlpoxu0kyWPCZTWYdm2biaR2bZ+BxlVozTu1jfrzymWllPocxOcjJwOWU0avIjs5N0Wj+V8ujndoLS5fm5EfGYKGNqPJfSzfybLet3b1t/d11Pznn7Ef524JE5kkc17e7pVX/knD8PfDEiLhtm3dci4uWd0gB/W9k8+rWdPpb13ygtbHcMs343yrx8I66PiB/nnDde2TS92M5kyaOmWT3q6Opt62dR5uy+cbT1EXFNP/Lo13Z6Vda25S8AdouWieqHee2oaSZLHpOprC1p16JM4fbb8UwzSHkMlyaPMPNB22tGTTNZ8pgMZc05bxERy40Q2/b6UdNMljwmU1lruqFp0hbknNeldIf+fTwyy8ao6/uVxxQs61aUwSyvjTo7SVu9j7p+KuXRx7L+OpafV3rpOoDR1kfEU3LODzPyNKe7RMSawyxfhkGvJEmSJKnncs4/oEzhNNw0pntRepCNuD4i9sxlqrADY4RpTiNik07lmI739EqSJEmSxl/rfN3t05i+rD7vtP79dJ4jeVS29EqSbWwyWgAAArRJREFUJEmS+ir3adpPmIYDWUmSJEmSJtwHVnJ9t2ns3ixJkiRJ6r3cp2k/OzHolSRJkiSNhw2A51OmIGqVgP/rYn03eXRk0CtJkiRJGg/fAx4VEVe3r8g5X0KZ9nO09d3k0ZEDWUmSJEmSBpYDWUmSJEmSBpZBryRJkiRpYBn0SpIkSZIGlkGvJEmSJGlgGfRKkiRJkgaWUxZJkjRF5JzfDbwWeBxwG3B0RJyRc14F+G/gMOBe4OPAp4BVI2Jxznkd4HhgP2AJ8EXgmIh4eAJ2Q5KkvjLo1f9v745ZsorCAI7/DQoihyAhCuM1cBKkvkCDJIFTkw+0vGM0CEF7hd9CcQpaehSipaGmhnBLcbBFIXQwp5ZCAgWHc1988QPc+97L/zcdzjkXnjs+POc8R5LUHvvAI+A3sAi8j4hp4CmwADwE/gHrl757BxwD08ANypuHh8BqPWFLktQc3+mVJKmlImIbeAu8BD5k5mo1Pw98Ba4Ct4AD4GZmnlTrz4DnmTnXSOCSJNXISq8kSS0REX3gFTBVTY0DE8BdSuV2YHjcoyS/RxExmLtyaY8kSZ1l0itJUgtERA9YAx4Dm5l5VlV6x4AjYHJo+72h8SHwH5jIzNO64pUkaVR4vFmSpBaIiBngB/AA2AP6lCT4BaWSuwQ84eJO7zwXjaw+Ab+A18Bf4D4wmZnfav4NSZJq55NFkiS1QGbuUroyb1KaUs0C36vlNeALsANsAZ+BU2DQnbkPXAN2gT/ABnCnrtglSWqSlV5JkjomIhaAlczsNR2LJElN806vJEktFxHXgTlKtfc2paPzx0aDkiRpRHi8WZKk9hsDlilHl7eAn8CbRiOSJGlEeLxZkiRJktRZVnolSZIkSZ1l0itJkiRJ6iyTXkmSJElSZ5n0SpIkSZI6y6RXkiRJktRZ5/Y1URpuY9umAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#no show and show accross age vs appointment count\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=df.age, hue=df.no_show)\n",
    "ax.set_title(\"Show/NoShow of Appointments by Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age_ratio = df[df.no_show == 1].groupby(['age']).size()/df.groupby(['age']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "\n",
    "#rate of no shows accross the age distribution\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.barplot(x=df_age_ratio.index, y=df_age_ratio)\n",
    "ax.set_title(\"Percentage of Patients that NO-Showed  by Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings: No show rate seems to rise as one becomes a young adult but falls later as an older adult possibly becaue older adults have more need for medical checks hence don't miss out too much but later as the age increase further there is another rise possible because they are dependets again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "\n",
    "#appointment distrubtion across hoods\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.countplot(x=np.sort(df.neighbourhood))\n",
    "ax.set_title(\"Number of Appointments by Neighbourhood\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "#ratio of no shows acccording to neighbourhood\n",
    "df_hood_ratio = df[df.no_show == 1].groupby(['neighbourhood']).size()/df.groupby(['neighbourhood']).size()\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.barplot(x=df_hood_ratio.index, y=df_hood_ratio)\n",
    "ax.set_title(\"Percentage of Patients that NO-SHOWED by Neighbourhoods\")\n",
    "plt.savefig('hoods.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings:  The RATE OF NO SHOWS  accroos the neighbourhood are consistently about 20% except for a few outliers which are easily explained with the lack in volume of data in the area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scholarship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of no show and show \n",
    "\n",
    "ax = sns.countplot(x=df.scholarship, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for Scholarship\")\n",
    "x_ticks_labels=['No Scholarship', 'Scholarship']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('scho_1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "# The rate of no show of the two populations with and without Scholarship\n",
    "df_s_ratio = df[df.no_show == 1].groupby(['scholarship']).size()/df.groupby(['scholarship']).size()\n",
    "ax = sns.barplot(x=df_s_ratio.index, y=df_s_ratio, palette=\"RdBu_r\")\n",
    "ax.set_title(\"NO-SHOW Percentage for Scholarship\")\n",
    "x_ticks_labels=['No Scholarship', 'Scholarship']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('scho_2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STATS TEST for scholarship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NUll hypotesis: That the rate of noshows of at risk population = the rate of no show for not at risk population\n",
    "\n",
    "Alternate Hypotesis: The rate of no show of people at risk is not equal to the rate of no shows for people at risk "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-9.70069768859156, pvalue=3.0562861231303697e-22)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=df[df['no_show']==0]\n",
    "noshow=df[df['no_show']==1]\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "stats.ttest_ind(show['scholarship'], noshow['scholarship'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can rejct the null hypotesis, there is a significant difference in the rate of noshows for people at risk vs the rate of noshows for people not at risk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings: We find that this infact is an important feature, and we can see that people who are at risk tend to make their appointments more often"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DAYS OF THE WEEK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_key=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
    "ax = sns.countplot(x=df.day_of_week, order=week_key)\n",
    "ax.set_title(\"Appointment Count for Scheduled Day of Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[16,5])\n",
    "ax = sns.countplot(x=df.day_of_week, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for different Days of the week\")\n",
    "x_ticks_labels=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('DOW1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,5])\n",
    "df_w_ratio = df[df.no_show == 1].groupby(['day_of_week']).size()/df.groupby(['day_of_week']).size()\n",
    "ax = sns.barplot(x=df_w_ratio.index, y=df_w_ratio, palette=\"Set2\")\n",
    "ax.set_title(\"NO-SHOW Percentage for Days of the week\")\n",
    "x_ticks_labels=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('DOW2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STATS TEST for days of the week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null hypotesis : That the no show rates across the different days are equal\n",
    "Alternate hypotesis : The no show rates across the different days are not equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dow1 = df[df['day_of_week'] == 'Monday']\n",
    "df_dow2 = df[df['day_of_week'] == 'Tuesday']\n",
    "df_dow3 = df[df['day_of_week'] == 'Wednesday']\n",
    "df_dow4 = df[df['day_of_week'] == 'Thursday']\n",
    "df_dow5 = df[df['day_of_week'] == 'Friday']\n",
    "df_dow6 = df[df['day_of_week'] == 'Saturday']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_key=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18025</td>\n",
       "      <td>20488</td>\n",
       "      <td>20774</td>\n",
       "      <td>13909</td>\n",
       "      <td>14982</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4689</td>\n",
       "      <td>5150</td>\n",
       "      <td>5092</td>\n",
       "      <td>3337</td>\n",
       "      <td>4037</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Monday  Tuesday  Wednesday  Thursday  Friday  Saturday\n",
       "no_show                                                        \n",
       "0         18025    20488      20774     13909   14982        30\n",
       "1          4689     5150       5092      3337    4037         9"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_dow = pd.DataFrame(columns=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'])\n",
    "\n",
    "\n",
    "df_dow['Monday'] = df_dow1.groupby('no_show')['no_show'].count()\n",
    "df_dow['Tuesday'] = df_dow2.groupby('no_show')['no_show'].count()\n",
    "df_dow['Wednesday'] = df_dow3.groupby('no_show')['no_show'].count()\n",
    "df_dow['Thursday'] = df_dow4.groupby('no_show')['no_show'].count()\n",
    "df_dow['Friday'] = df_dow5.groupby('no_show')['no_show'].count()\n",
    "df_dow['Saturday'] = df_dow6.groupby('no_show')['no_show'].count()\n",
    "df_dow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   no_show    2 non-null      int64\n",
      " 1   Monday     2 non-null      int64\n",
      " 2   Tuesday    2 non-null      int64\n",
      " 3   Wednesday  2 non-null      int64\n",
      " 4   Thursday   2 non-null      int64\n",
      " 5   Friday     2 non-null      int64\n",
      " 6   Saturday   2 non-null      int64\n",
      "dtypes: int64(7)\n",
      "memory usage: 240.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df_dow.fillna(value=0, inplace=True)\n",
    "df_dow.reset_index(drop=False, inplace=True)\n",
    "df_dow.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>no_show</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18025</td>\n",
       "      <td>20488</td>\n",
       "      <td>20774</td>\n",
       "      <td>13909</td>\n",
       "      <td>14982</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4689</td>\n",
       "      <td>5150</td>\n",
       "      <td>5092</td>\n",
       "      <td>3337</td>\n",
       "      <td>4037</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no_show  Monday  Tuesday  Wednesday  Thursday  Friday  Saturday\n",
       "0        0   18025    20488      20774     13909   14982        30\n",
       "1        1    4689     5150       5092      3337    4037         9"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dof=6\n",
      "[[7.98096324e-01 1.81279599e+04 2.04615936e+04 2.06435595e+04\n",
      "  1.37639692e+04 1.51789940e+04 3.11257566e+01]\n",
      " [2.01903676e-01 4.58604010e+03 5.17640645e+03 5.22244049e+03\n",
      "  3.48203080e+03 3.84000602e+03 7.87424337e+00]]\n",
      "probability=0.950, critical=12.592, stat=31.533)\n",
      "Dependent (regject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import  chi2_contingency\n",
    "from scipy.stats import chisquare\n",
    "from scipy.stats import chi2\n",
    "\n",
    "stat, p, dof, expected = chi2_contingency(df_dow)\n",
    "print('dof=%d' % dof)\n",
    "print(expected)\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (regject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reject the null hypotesis and say there is a significant differents in the rate of noshows across the different days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HUMAN DEVELOPMENT INDEX (HDI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdi=df.groupby(by=['hdi', 'no_show'])\n",
    "hdi = hdi.count()['appointment_id'].unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54 entries, 0 to 53\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   hdi     54 non-null     float64\n",
      " 1   0       54 non-null     float64\n",
      " 2   1       54 non-null     float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 1.4 KB\n"
     ]
    }
   ],
   "source": [
    "hdi.fillna(value=0, inplace=True)\n",
    "hdi.reset_index(drop=False, inplace=True)\n",
    "hdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Grouping the dataset by the waiting categories, returning the sum of all instances:\n",
    "# hdi =hdi.groupby('hdi').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "## Creating a new attribute, \"No-showing rate\", relating how many patients did not show up against those who did.\n",
    "hdi['no_show_rate'] = (hdi[1]/ hdi[0])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54 entries, 0 to 53\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   hdi           54 non-null     float64\n",
      " 1   0             54 non-null     float64\n",
      " 2   1             54 non-null     float64\n",
      " 3   no_show_rate  54 non-null     float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.8 KB\n"
     ]
    }
   ],
   "source": [
    "hdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "## Adding new columns:\n",
    "#Transforming the 'No-showing rate' into strings with the percentual values:\n",
    "hdi['No-show percentual'] = hdi['no_show_rate'].apply(lambda x: '{0:.2f}%'.format(x))\n",
    "#Multiplying the rate values by 500 times in order to be plotted in the same scale:\n",
    "hdi['No-showing rate (500x)'] = hdi['no_show_rate']*1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>no_show</th>\n",
       "      <th>hdi</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>no_show_rate</th>\n",
       "      <th>No-show percentual</th>\n",
       "      <th>No-showing rate (500x)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.615</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>23.207444</td>\n",
       "      <td>23.21%</td>\n",
       "      <td>23.207444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.618</td>\n",
       "      <td>907.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>40.683572</td>\n",
       "      <td>40.68%</td>\n",
       "      <td>40.683572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.642</td>\n",
       "      <td>5506.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>27.188522</td>\n",
       "      <td>27.19%</td>\n",
       "      <td>27.188522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.645</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>21.308576</td>\n",
       "      <td>21.31%</td>\n",
       "      <td>21.308576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.646</td>\n",
       "      <td>210.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>22.86%</td>\n",
       "      <td>22.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.647</td>\n",
       "      <td>1682.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>24.137931</td>\n",
       "      <td>24.14%</td>\n",
       "      <td>24.137931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.648</td>\n",
       "      <td>3336.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>21.762590</td>\n",
       "      <td>21.76%</td>\n",
       "      <td>21.762590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.651</td>\n",
       "      <td>325.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>37.846154</td>\n",
       "      <td>37.85%</td>\n",
       "      <td>37.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.658</td>\n",
       "      <td>431.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>21.577726</td>\n",
       "      <td>21.58%</td>\n",
       "      <td>21.577726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.659</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>21.589689</td>\n",
       "      <td>21.59%</td>\n",
       "      <td>21.589689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.667</td>\n",
       "      <td>57.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.052632</td>\n",
       "      <td>21.05%</td>\n",
       "      <td>21.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.678</td>\n",
       "      <td>7312.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>23.755470</td>\n",
       "      <td>23.76%</td>\n",
       "      <td>23.755470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.681</td>\n",
       "      <td>2631.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>26.719878</td>\n",
       "      <td>26.72%</td>\n",
       "      <td>26.719878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.684</td>\n",
       "      <td>4958.0</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>27.289230</td>\n",
       "      <td>27.29%</td>\n",
       "      <td>27.289230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.691</td>\n",
       "      <td>692.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>20.664740</td>\n",
       "      <td>20.66%</td>\n",
       "      <td>20.664740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.706</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>23.848101</td>\n",
       "      <td>23.85%</td>\n",
       "      <td>23.848101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.711</td>\n",
       "      <td>936.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>25.213675</td>\n",
       "      <td>25.21%</td>\n",
       "      <td>25.213675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.712</td>\n",
       "      <td>2223.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>24.741341</td>\n",
       "      <td>24.74%</td>\n",
       "      <td>24.741341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.719</td>\n",
       "      <td>689.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>23.222061</td>\n",
       "      <td>23.22%</td>\n",
       "      <td>23.222061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.720</td>\n",
       "      <td>106.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.358491</td>\n",
       "      <td>27.36%</td>\n",
       "      <td>27.358491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.730</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>33.006856</td>\n",
       "      <td>33.01%</td>\n",
       "      <td>33.006856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.731</td>\n",
       "      <td>435.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>25.747126</td>\n",
       "      <td>25.75%</td>\n",
       "      <td>25.747126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.735</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>29.925330</td>\n",
       "      <td>29.93%</td>\n",
       "      <td>29.925330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.739</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>22.423850</td>\n",
       "      <td>22.42%</td>\n",
       "      <td>22.423850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.740</td>\n",
       "      <td>254.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>22.834646</td>\n",
       "      <td>22.83%</td>\n",
       "      <td>22.834646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.752</td>\n",
       "      <td>732.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>21.857923</td>\n",
       "      <td>21.86%</td>\n",
       "      <td>21.857923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.756</td>\n",
       "      <td>2063.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>24.624333</td>\n",
       "      <td>24.62%</td>\n",
       "      <td>24.624333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.761</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>24.643585</td>\n",
       "      <td>24.64%</td>\n",
       "      <td>24.643585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.764</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>9.38%</td>\n",
       "      <td>9.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.772</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>24.913194</td>\n",
       "      <td>24.91%</td>\n",
       "      <td>24.913194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.783</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>24.734982</td>\n",
       "      <td>24.73%</td>\n",
       "      <td>24.734982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.787</td>\n",
       "      <td>3085.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>27.228525</td>\n",
       "      <td>27.23%</td>\n",
       "      <td>27.228525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.790</td>\n",
       "      <td>944.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>28.707627</td>\n",
       "      <td>28.71%</td>\n",
       "      <td>28.707627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.794</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.00%</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.799</td>\n",
       "      <td>5723.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>21.614538</td>\n",
       "      <td>21.61%</td>\n",
       "      <td>21.614538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.809</td>\n",
       "      <td>1478.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>28.687415</td>\n",
       "      <td>28.69%</td>\n",
       "      <td>28.687415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.812</td>\n",
       "      <td>994.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>29.577465</td>\n",
       "      <td>29.58%</td>\n",
       "      <td>29.577465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.833</td>\n",
       "      <td>221.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.624434</td>\n",
       "      <td>22.62%</td>\n",
       "      <td>22.624434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.841</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>27.225732</td>\n",
       "      <td>27.23%</td>\n",
       "      <td>27.225732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.843</td>\n",
       "      <td>855.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>23.625731</td>\n",
       "      <td>23.63%</td>\n",
       "      <td>23.625731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.856</td>\n",
       "      <td>665.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>29.022556</td>\n",
       "      <td>29.02%</td>\n",
       "      <td>29.022556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.867</td>\n",
       "      <td>317.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>17.034700</td>\n",
       "      <td>17.03%</td>\n",
       "      <td>17.034700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.871</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>27.842655</td>\n",
       "      <td>27.84%</td>\n",
       "      <td>27.842655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.880</td>\n",
       "      <td>432.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>24.537037</td>\n",
       "      <td>24.54%</td>\n",
       "      <td>24.537037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.881</td>\n",
       "      <td>441.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>22.448980</td>\n",
       "      <td>22.45%</td>\n",
       "      <td>22.448980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.896</td>\n",
       "      <td>1851.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>30.253917</td>\n",
       "      <td>30.25%</td>\n",
       "      <td>30.253917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.911</td>\n",
       "      <td>2157.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>32.267038</td>\n",
       "      <td>32.27%</td>\n",
       "      <td>32.267038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.916</td>\n",
       "      <td>1933.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>26.642525</td>\n",
       "      <td>26.64%</td>\n",
       "      <td>26.642525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.939</td>\n",
       "      <td>3246.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>19.439310</td>\n",
       "      <td>19.44%</td>\n",
       "      <td>19.439310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.942</td>\n",
       "      <td>1788.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>23.993289</td>\n",
       "      <td>23.99%</td>\n",
       "      <td>23.993289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.952</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>23.687664</td>\n",
       "      <td>23.69%</td>\n",
       "      <td>23.687664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.953</td>\n",
       "      <td>2813.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>22.396018</td>\n",
       "      <td>22.40%</td>\n",
       "      <td>22.396018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.961</td>\n",
       "      <td>534.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>20.599251</td>\n",
       "      <td>20.60%</td>\n",
       "      <td>20.599251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "no_show    hdi       0       1  no_show_rate No-show percentual  \\\n",
       "0        0.615  1827.0   424.0     23.207444             23.21%   \n",
       "1        0.618   907.0   369.0     40.683572             40.68%   \n",
       "2        0.642  5506.0  1497.0     27.188522             27.19%   \n",
       "3        0.645  2262.0   482.0     21.308576             21.31%   \n",
       "4        0.646   210.0    48.0     22.857143             22.86%   \n",
       "5        0.647  1682.0   406.0     24.137931             24.14%   \n",
       "6        0.648  3336.0   726.0     21.762590             21.76%   \n",
       "7        0.651   325.0   123.0     37.846154             37.85%   \n",
       "8        0.658   431.0    93.0     21.577726             21.58%   \n",
       "9        0.659  1862.0   402.0     21.589689             21.59%   \n",
       "10       0.667    57.0    12.0     21.052632             21.05%   \n",
       "11       0.678  7312.0  1737.0     23.755470             23.76%   \n",
       "12       0.681  2631.0   703.0     26.719878             26.72%   \n",
       "13       0.684  4958.0  1353.0     27.289230             27.29%   \n",
       "14       0.691   692.0   143.0     20.664740             20.66%   \n",
       "15       0.706  1975.0   471.0     23.848101             23.85%   \n",
       "16       0.711   936.0   236.0     25.213675             25.21%   \n",
       "17       0.712  2223.0   550.0     24.741341             24.74%   \n",
       "18       0.719   689.0   160.0     23.222061             23.22%   \n",
       "19       0.720   106.0    29.0     27.358491             27.36%   \n",
       "20       0.730  3063.0  1011.0     33.006856             33.01%   \n",
       "21       0.731   435.0   112.0     25.747126             25.75%   \n",
       "22       0.735  1741.0   521.0     29.925330             29.93%   \n",
       "23       0.739  1543.0   346.0     22.423850             22.42%   \n",
       "24       0.740   254.0    58.0     22.834646             22.83%   \n",
       "25       0.752   732.0   160.0     21.857923             21.86%   \n",
       "26       0.756  2063.0   508.0     24.624333             24.62%   \n",
       "27       0.761  1473.0   363.0     24.643585             24.64%   \n",
       "28       0.764    32.0     3.0      9.375000              9.38%   \n",
       "29       0.772  1152.0   287.0     24.913194             24.91%   \n",
       "30       0.783  1415.0   350.0     24.734982             24.73%   \n",
       "31       0.787  3085.0   840.0     27.228525             27.23%   \n",
       "32       0.790   944.0   271.0     28.707627             28.71%   \n",
       "33       0.794     8.0     2.0     25.000000             25.00%   \n",
       "34       0.799  5723.0  1237.0     21.614538             21.61%   \n",
       "35       0.800     1.0     0.0      0.000000              0.00%   \n",
       "36       0.809  1478.0   424.0     28.687415             28.69%   \n",
       "37       0.812   994.0   294.0     29.577465             29.58%   \n",
       "38       0.833   221.0    50.0     22.624434             22.62%   \n",
       "39       0.841  1741.0   474.0     27.225732             27.23%   \n",
       "40       0.843   855.0   202.0     23.625731             23.63%   \n",
       "41       0.856   665.0   193.0     29.022556             29.02%   \n",
       "42       0.867   317.0    54.0     17.034700             17.03%   \n",
       "43       0.871  1627.0   453.0     27.842655             27.84%   \n",
       "44       0.880   432.0   106.0     24.537037             24.54%   \n",
       "45       0.881   441.0    99.0     22.448980             22.45%   \n",
       "46       0.896  1851.0   560.0     30.253917             30.25%   \n",
       "47       0.911  2157.0   696.0     32.267038             32.27%   \n",
       "48       0.916  1933.0   515.0     26.642525             26.64%   \n",
       "49       0.939  3246.0   631.0     19.439310             19.44%   \n",
       "50       0.942  1788.0   429.0     23.993289             23.99%   \n",
       "51       0.952  1524.0   361.0     23.687664             23.69%   \n",
       "52       0.953  2813.0   630.0     22.396018             22.40%   \n",
       "53       0.961   534.0   110.0     20.599251             20.60%   \n",
       "\n",
       "no_show  No-showing rate (500x)  \n",
       "0                     23.207444  \n",
       "1                     40.683572  \n",
       "2                     27.188522  \n",
       "3                     21.308576  \n",
       "4                     22.857143  \n",
       "5                     24.137931  \n",
       "6                     21.762590  \n",
       "7                     37.846154  \n",
       "8                     21.577726  \n",
       "9                     21.589689  \n",
       "10                    21.052632  \n",
       "11                    23.755470  \n",
       "12                    26.719878  \n",
       "13                    27.289230  \n",
       "14                    20.664740  \n",
       "15                    23.848101  \n",
       "16                    25.213675  \n",
       "17                    24.741341  \n",
       "18                    23.222061  \n",
       "19                    27.358491  \n",
       "20                    33.006856  \n",
       "21                    25.747126  \n",
       "22                    29.925330  \n",
       "23                    22.423850  \n",
       "24                    22.834646  \n",
       "25                    21.857923  \n",
       "26                    24.624333  \n",
       "27                    24.643585  \n",
       "28                     9.375000  \n",
       "29                    24.913194  \n",
       "30                    24.734982  \n",
       "31                    27.228525  \n",
       "32                    28.707627  \n",
       "33                    25.000000  \n",
       "34                    21.614538  \n",
       "35                     0.000000  \n",
       "36                    28.687415  \n",
       "37                    29.577465  \n",
       "38                    22.624434  \n",
       "39                    27.225732  \n",
       "40                    23.625731  \n",
       "41                    29.022556  \n",
       "42                    17.034700  \n",
       "43                    27.842655  \n",
       "44                    24.537037  \n",
       "45                    22.448980  \n",
       "46                    30.253917  \n",
       "47                    32.267038  \n",
       "48                    26.642525  \n",
       "49                    19.439310  \n",
       "50                    23.993289  \n",
       "51                    23.687664  \n",
       "52                    22.396018  \n",
       "53                    20.599251  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hdi' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-58265338fe3e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m## Plotting the bar chart:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mhdi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hdi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hdi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacked\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Human Development index'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#Set the y-axis color and label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'hdi' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Setting the graph parameters:\n",
    "fig1, ax = plt.subplots(figsize=[25,10])  #Defines the graph window size\n",
    "fig1.subplots_adjust(top=0.92)\n",
    "plt.suptitle('Appointments distribution by HDI', fontsize=14, fontweight='bold')\n",
    "\n",
    "colors = ['tab:blue', 'tab:green', 'tab:red']  #Defines the colors to be used\n",
    "\n",
    "ax.set_ylabel('Number of occurences', color=colors[0], fontsize=12)  #Set the y-axis color and label\n",
    "ax.tick_params(axis='y', labelcolor=colors[0])\n",
    "\n",
    "## Plotting the bar chart:\n",
    "hdi[['hdi', 0, 1]].plot(x='hdi', kind='bar', ax=ax, color=colors[0:2], stacked= True)\n",
    "\n",
    "ax.set_xlabel('Human Development index', fontsize=12)  #Set the y-axis color and label\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphic showing the histogram of data\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[10,8])\n",
    "# Plot a histogram of \"Weight\" for mens_rowing\n",
    "ax.hist(df[df['no_show']==1]['hdi'], bins=15, histtype='step', label='True', stacked= True)\n",
    "ax.hist(df[df['no_show']==0]['hdi'], bins=15, histtype='step', label='False', stacked= True)\n",
    "# ax.hist(df[df['_STATE']==36]['PHYSHLTH'], bins=15, density=True,histtype='step', label='NY')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Human development Index')\n",
    "ax.set_ylabel('Pecentage of appointments')\n",
    "ax.set_title(\"Distribution of shows and No show by HDI\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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": [
    "#pecentage of noshw by HDI\n",
    "plt.subplots(figsize=[20,8])\n",
    "df_h_ratio = df[df.no_show == 1].groupby(['hdi']).size()/df.groupby(['hdi']).size()\n",
    "ax = sns.barplot(x=df_h_ratio.index, y=df_h_ratio, palette=\"RdBu_r\")\n",
    "ax.set_title(\"NO-SHOW Percentage by Human Development Index\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.savefig('hdi.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Addititonal Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['current_year'] = 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year_birth'] = df['current_year'] - df['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [1890, 1901, 1925, 1946, 1965, 1981, 1997, 2012, 2020]\n",
    "\n",
    "bins_year_birth = pd.cut(df['year_birth'], bins, include_lowest = False, labels = ('interbellum', 'greatest', 'silent', 'boomer', 'x', 'millennial', 'zoomer', 'alpha'))\n",
    "df['generation'] = bins_year_birth.cat.as_unordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['at_risk']= np.where(df['hipertension']+df['diabetes']+df['alcoholism'] >= 1,1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At Risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Took a closer look at patients we considered at risk, that is two or more diseases and check to see how they relate with our target varible no show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33cSRRx7JgQceyLhx43jppaafYpxlGZdddhkHHXQQ++23H8cccwxPPvnkhvlr1qzhtNNOY9999+XEE09k6dKlG+YtWLCA448/nv3224/jjz+eBQsWAHDfffdx9NFHb8h38sknc8IJJ2yYHj16NLNmzdrqz0EBRUQkB3/4wx+48cYbmTdvHosXL8bduffee/n+97/P9ddfz8KFCxk4cCDnnntuk+u55557eOCBB/jLX/7C4sWLmTx5Mr17994wf8aMGVxwwQU88cQTDBo0iKuuugqA1atXM3bsWM4880wef/xxxo8fz9ixY1m1ahVDhw5l6dKlrFq1ioaGBp566inq6+t5/fXXefPNN/nb3/7G4YcfvtWfgQKKiEgOzjzzTHbffXd69+7Nsccey6JFi7j11ls55ZRTGDJkCN26dWPixIk8/PDDvPjii42up2vXrrz++ussWbKELMsYPHgw/fr12zB/5MiRHHLIIXTt2pWTTjqJRYsWATB37lz22msvPvvZz9K1a1dGjx7N3nvvzZw5c+jevTsHHXQQ8+fP57HHHmP//ffngx/8IAsWLGDhwoXstdde9OnTZ6s/A43yEhHJwa677rrhfY8ePVixYgWrV69myJAhG9J33HFHevfuzUsvvcQee+xRbjV8+MMf5owzzuDiiy9m2bJljBw5km9961vstNNOZbfzr3/9C4AVK1YwYMCmN1kfOHDghia24cOHM2/ePGpqahg+fDi77LIL8+fPZ4cddmD48OG5fAaqoYiItJJ+/fpRV7fxyRpvvPEGq1evZvfdd29yubPOOotZs2Zx11138dxzzzF58uQWbWvZsmWbpC1btmzDto444gjmzZvH/PnzOeKIIzZMz5s3TwFFRGRbN3r0aG655RYef/xx1q1bx6RJkzjkkEMarZ0APProoyxcuJC3336bnj170q1bN7p06dLsto466iiee+45br31VhoaGpgxYwbPPPMMxx57LACHHXYYzz77LI8++igHH3ww73vf+1i2bBmPPPJIbgFFTV4iIq3kIx/5CBMmTGD8+PGsWbOGoUOHct111zW5zGuvvca3v/1tXnjhBbp168bHP/5xzjnnnGa31adPH2pra7n00kuZOHEigwYNora2dkPfSM+ePXn/+99Pt27d2GGHHQA49NBDefrpp3N70KBuDrkVN4fUL+U30i/lpTPQzSF1c0gREakCNXmJiFTZAw88wKmnnlp23jPPPFPl0uRHAUVEpMqGDRvWrgNHY9TkJSIiuVBAERGRXCigiIhILtSHIiLSivL+eUFLh+jfddddXHrppaxfv54xY8Zw3nnn5VqOclRDERHpYN555x0uvvhibrzxRu666y5+//vf8/TTT7f6dqtWQzGzXsBU4P1ABpwJPAXcAgwClgLm7qvNLADXAscDbwDj3H1hWs9Y4JK02ivdvTalDwWmAT2A24Hz3b1T/2pTRDqnRx55hEGDBrHnnnsCMGrUKGbPns2+++7bqtutZg3lWmCWu+8HfABYDFwEzHX3wcDcNA0wEhicXuOByQBm1ge4DBgGHA5cZmaFBwVMTnkLy42owj6JiGxzXnrppcIv2gGoqalp9sFeeahKQDGznYGPAjcAuPtb7v4KMAqoTdlqgdHp/Shgurtn7j4f6GVmNcAngTnuvsrdVwNzgBFp3s7uPi/VSqYXrUtEpFMpd0utEMreLSVX1Wryei/wD+AXZvYB4GHgfKCfu9cDuHu9me2W8g8Aip9AU5fSmkqvK5MuItLp1NTUUHyfwvr6+k0e0tVaqhVQugKHAl9x9wfM7Fo2Nm+VUy6UZluQvhkzG09sGsPdt+oum/VbvGTHk9fdSkW2ZStWrKBr17YdHNuS7R922GE8//zzLFu2jJqaGmbOnMnkyZMrLnu3bt0q+m5X65OpA+rc/YE0/RtiQFlhZjWpdlIDvFyUv/iBAQOB5Sn94yXpd6f0gWXyb8bdpwBT0mS2cuXKLdwlKabPUTqDdevWtejZJMXyvhN3Q0NDi/JdeeWVnHzyyaxfv56TTz6ZffbZp8XLFqxbt26z73Zx30ypqvShuPtLwItm9r6UdDTwBDATGJvSxgIz0vuZwOlmFsxsOLAmNY3NBo4zs96pM/44YHaa95qZDU8jxE4vWpeISKdz9NFHc++993L//fdz/vnnV2Wb1ay7fQW4ycx2AJ4DziAGNDezs4C/A59LeW8nDhleQhw2fAaAu68ysyuABSnfd9x9VXr/JTYOG74jvUREpEr0gC09YCsXesCWdAZ6wJYesCUiIlWggCIiIrlQQBERkVwooIiISC50+3oRkVY0rnZeruubNvaIZvNccMEF/OlPf6Jv377ceeeduW6/KaqhiIh0MGbGTTfdVPXtKqCIiHQww4cPp1evXlXfrgKKiIjkQgFFRERyoYAiIiK5UEAREZFcaNiwiEgraskw37yde+65zJs3j1WrVjF06FAuvPBCxowZ0+rbVUAREelgrrvuujbZrpq8REQkFwooIiKSCwUUEZEW6mzPj6p0fxVQRERaaLvttqv4ueztVUNDA9ttV1mIUKe8iEgLde/enbVr17Ju3TpCKPvQwg4hyzK22247unfvXtFyCigiIi0UQqBHjx5tXYxtlpq8REQkFwooIiKSCwUUERHJRdX6UMxsKfAa8A7Q4O6HmVkf4BZgELAUMHdfbWYBuBY4HngDGOfuC9N6xgKXpNVe6e61KX0oMA3oAdwOnO/unWuMn4hIG6p2DeUT7n6wux+Wpi8C5rr7YGBumgYYCQxOr/HAZIAUgC4DhgGHA5eZWe+0zOSUt7DciNbfHRERKWjrJq9RQG16XwuMLkqf7u6Zu88HeplZDfBJYI67r3L31cAcYESat7O7z0u1kulF6xIRkSqo5rDhDPijmWXA/7j7FKCfu9cDuHu9me2W8g4AXixati6lNZVeVyZ9M2Y2nliTwd3p27fvFu9Q/RYv2fFszecoIh1DNQPKke6+PAWNOWb2ZBN5y/1iKNuC9M2kQDalkGflypVNFENaSp+jSOfQv3//RudVrcnL3Zenvy8DtxL7QFak5irS35dT9jpgj6LFBwLLm0kfWCZdRESqpCoBxcx2NLOdCu+B44DHgZnA2JRtLDAjvZ8JnG5mwcyGA2tS09hs4Dgz6506448DZqd5r5nZ8DRC7PSidYmISBVUq4bSD7jXzB4DHgT+z91nAZOAY83sGeDYNA1x2O9zwBLgZ8C5AO6+CrgCWJBe30lpAF8CpqZlngXuqMJ+iYhIEjrb7ZhLZMuXb3nLWP2Es3MsSvtWc/XUti6CiFRB6kMpe2fMth42LCIiHYQCioiI5EIBRUREcqGAIiIiuVBAERGRXCigiIhILhRQREQkFwooIiKSCwUUERHJhQKKiIjkQgFFRERyoYAiIiK5UEAREZFcKKCIiEguFFBERCQXCigiIpILBRQREcmFAoqIiORCAUVERHKhgCIiIrlQQBERkVwooIiISC66VnNjZtYFeAhY5u4nmtlewM1AH2AhcJq7v2Vm3YDpwFDgn8DJ7r40rWMicBbwDvBVd5+d0kcA1wJdgKnuPqma+yYi0tlVu4ZyPrC4aPoq4Bp3HwysJgYK0t/V7r4PcE3Kh5kdAJwCHAiMAK4zsy4pUP0UGAkcAIxJeUVEpEqqFlDMbCBwAjA1TQfgKOA3KUstMDq9H5WmSfOPTvlHATe7+zp3fx5YAhyeXkvc/Tl3f4tY6xnV+nslIiIFLW7yMrML3f2HZdIvcPcftWAVPwb+A9gpTb8beMXdG9J0HTAgvR8AvAjg7g1mtiblHwDML1pn8TIvlqQPa2Q/xgPj07rp27dvC4peXv0WL9nxbM3nKCIdQyV9KJcCmwUU4BKgyYBiZicCL7v7w2b28ZQcymTNmpnXWHq5mlZWJg13nwJMKeRZuXJlY8WWCuhzFOkc+vfv3+i8ZgOKmR2V3nYxs0+w6Un9vcBrLSjDkcCnzex4oDuwM7HG0svMuqZaykBgecpfB+wB1JlZV2AXYFVRekHxMo2li4hIFbSkhnJD+tsd+HlRega8BHyluRW4+0RgIkCqoVzo7l8ws/8FPkvs8xgLzEiLzEzT89L8O909M7OZwK/M7EdAf2Aw8CAxyA1Oo8aWETvuP9+CfRMRkZw0G1DcfS8AM5vu7qfnvP1vAjeb2ZXAI2wMXjcAvzSzJcSaySmpLIvMzIEngAbgy+7+TirfecBs4rDhn7v7opzLKiIiTQhZVraroUlmtkmfhbuvz61E1ZUtX77lLWP1E87OsSjtW83VU9u6CCJSBakPpVx/dkWjvA4l/tbjIGLzV2GlGbFWICIinVglo7xqgT8AZwJvtE5xRESkvaokoOwJXOzulbeRiYhIh1fJL+VvBY5rrYKIiEj7VkkNpTtwq5ndSxwuvEErjP4SEZF2ppKA8kR6iYiIbKbFAcXdv92aBRERkfatkmHDRzU2z93vzKc4IiLSXlXS5HVDyfSuwA7E+2u9N7cSiYhIu1RJk9dexdPpoVaX0LKbQ4qISAe3xQ/YSvfQ+i7xGSciItLJbe0TG48F2ut9vEREJEeVdMq/yKYPrepJ/G3KuXkXSkRE2p9KOuVPLZn+F/C0u7+aY3lERKSdqqRT/h7YcOv6fsCKdnzbehERyVklTV47EW9ffzKwPfC2md0MfNXd17RS+UREpJ2opFP+J8COwBCgR/rbE/ivViiXiIi0M5X0oYwA3uvuhWehPG1mZwDP5l8sERFpbyqpoawl/jq+WF9gXX7FERGR9qqSGspUYI6Z/Qh4gfjAra8DP2uNgomISPtSSUD5LrAM+ALQH1gO/MDdS+/xJSIinVAlTV7XAk+5+zHufoC7HwMsNrMft1LZRESkHamkhjIGuLAk7WHg98DXmlrQzLoDfwa6pW3+xt0vM7O9gJuBPsBC4DR3f8vMugHTgaHAP4GT3X1pWtdE4CzgHeKQ5dkpfQQx6HUBprr7pAr2TUREtlIlNZSMeLIu1qWF61gHHOXuHwAOBkaY2XDgKuAadx8MrCYGCtLf1e6+D3BNyoeZHQCcAhxIHHV2nZl1SXc+/ikwEjgAGJPyiohIlVQSUP4CXJF+KV/4xfzlKb1J7p65++tpcvv0yoCjgN+k9FpgdHo/Kk2T5h9tZiGl3+zu69z9eWAJcHh6LXH359z9LWKtZ1QF+yYiIlupkiav84HbgHozewF4D1APfKolC6daxMPAPsTaxLPAK+7ekLLUAQPS+wHAiwDu3mBma4B3p/T5RastXubFkvRhjZRjPDA+rZu+ffu2pPhl1W/xkh3P1nyOItIxVHIvrzozO5RYG9iDeAJ/sKX380rPTznYzHoBtwL7l8lWuJtxaGReY+nlalpZmTTcfQowpZBn5cqVTRVbWkifo0jn0L9//0bnVVJDIQWP+WxaS6iIu79iZncDw4FeZtY11VIGEociQ6xh7AHUmVlXYBdgVVF6QfEyjaWLiEgVbO0DtlrEzHZNNRPMrAdwDLAYuAv4bMo2FpiR3s9M06T5d7p7ltJPMbNuaYTYYOBBYAEw2Mz2MrMdiB33M1t/z0REpKAqAQWoAe4ys78ST/5z3P024JvABWa2hNhHUviR5A3Au1P6BcBFAO6+CHDgCWAW8GV3fyfVcM4DZhMDlae8IiJSJSHLynY1dBbZ8uVb3jJWP+HsHIvSvtVcPbWtiyAiVZD6UMr1Z1ethiIiIh2cAoqIiORCAUVERHKhgCIiIrlQQBERkVwooIiISC4UUEREJBcKKCIikgsFFBERyYUCioiI5EIBRUREcqGAIiIiuVBAERGRXCigiIhILhRQREQkFwooIiKSCwUUERHJhQKKiIjkQgFFRERyoYAiIiK5UEAREZFcdK3GRsxsD2A6sDuwHpji7teaWR/gFmAQsBQwd19tZgG4FjgeeAMY5+4L07rGApekVV/p7rUpfSgwDegB3A6c7+5ZNfZPRESqFFCABuAb7r7QzHYCHjazOcA4YK67TzKzi4CLgG8CI4HB6TUMmAwMSwHoMuAwIEvrmenuq1Oe8cB8YkAZAdxRpf0T2ebUTzi7rYuwzai5empbF6FTqEqTl7vXF2oY7v4asBgYAIwCalO2WmB0ej8KmO7umbvPB3qZWRf+6t4AAAt5SURBVA3wSWCOu69KQWQOMCLN29nd56VayfSidYmISBVUq4aygZkNAg4BHgD6uXs9xKBjZrulbAOAF4sWq0tpTaXXlUkvt/3xxJoM7k7fvn23eF/qt3jJjmdrPkdpHTo+N9LxWR1VDShm9i7gt8DX3P1VM2ssayiTlm1B+mbcfQowpZBn5cqVTZZZWkafo2zLdHzmp3///o3Oq9ooLzPbnhhMbnL336XkFam5ivT35ZReB+xRtPhAYHkz6QPLpIuISJVUJaCkUVs3AIvd/UdFs2YCY9P7scCMovTTzSyY2XBgTWoamw0cZ2a9zaw3cBwwO817zcyGp22dXrQuERGpgmo1eR0JnAb8zcweTWn/D5gEuJmdBfwd+FyadztxyPAS4rDhMwDcfZWZXQEsSPm+4+6r0vsvsXHY8B1ohJeISFWFLOvUP9XIli/f8pYxDcvcSMMytz06PjfS8Zmf1IdSrt9av5QXEZF8VH3YsHRM42rntXURthnTxh7R1kUQaROqoYiISC4UUEREJBcKKCIikgsFFBERyYUCioiI5EIBRUREcqGAIiIiuVBAERGRXCigiIhILhRQREQkFwooIiKSCwUUERHJhQKKiIjkQgFFRERyoYAiIiK5UEAREZFcKKCIiEguFFBERCQXCigiIpILBRQREclF12psxMx+DpwIvOzu709pfYBbgEHAUsDcfbWZBeBa4HjgDWCcuy9My4wFLkmrvdLda1P6UGAa0AO4HTjf3bNq7JuIiETVqqFMA0aUpF0EzHX3wcDcNA0wEhicXuOBybAhAF0GDAMOBy4zs95pmckpb2G50m2JiEgrq0pAcfc/A6tKkkcBtel9LTC6KH26u2fuPh/oZWY1wCeBOe6+yt1XA3OAEWnezu4+L9VKphetS0REqqQqTV6N6Ofu9QDuXm9mu6X0AcCLRfnqUlpT6XVl0ssys/HE2gzuTt++fbd4B+q3eEnpyLbmmMqTjs+NtpX/SUfXlgGlMaFMWrYF6WW5+xRgSiHfypUrKy6gSFN0TG179D/JT//+/Rud15ajvFak5irS35dTeh2wR1G+gcDyZtIHlkkXEZEqasuAMhMYm96PBWYUpZ9uZsHMhgNrUtPYbOA4M+udOuOPA2anea+Z2fA0Quz0onWJiEiVVGvY8K+BjwN9zayOOFprEuBmdhbwd+BzKfvtxCHDS4jDhs8AcPdVZnYFsCDl+467Fzr6v8TGYcN3pJeIiFRRyLJO/XONbPnyLW8dq59wdo5Fad8mvv+sti7CNmPa2CPaugiAjs9iNVdPbesidBipD6Vc37V+KS8iIvlQQBERkVwooIiISC4UUEREJBcKKCIikgsFFBERyYUCioiI5EIBRUREcqGAIiIiuVBAERGRXCigiIhILrbF56GIiORqXO28ti7CNqM17zWnGoqIiORCAUVERHKhgCIiIrlQQBERkVwooIiISC4UUEREJBcKKCIikgsFFBERyYUCioiI5EIBRUREctGhbr1iZiOAa4EuwFR3n9TGRRIR6TQ6TA3FzLoAPwVGAgcAY8zsgLYtlYhI59FhAgpwOLDE3Z9z97eAm4FRbVwmEZFOoyM1eQ0AXiyargOGlWYys/HAeAB3p3///lu8wf433b7Fy3Y0f2zrAshmdHxupOOzOjpSQAll0rLSBHefAkxp/eJ0Lmb2kLsf1tblEClHx2d1dKQmrzpgj6LpgcDyNiqLiEin05FqKAuAwWa2F7AMOAX4fNsWSUSk8+gwNRR3bwDOA2YDi2OSL2rbUnUqakaUbZmOzyoIWbZZN4OIiEjFOkwNRURE2pYCioiI5KIjdcpLCTPLgB+5+zfS9IXAu9z98kbyXwx8Lk0OAf6W3v/c3f+rTP4ZwG7ufkRR2mjgaXd/okz+y4F/B/4B7ABc4e6/TvO+A/zZ3f/USNmmAbe5+2+a2W3pgMzsJOB3wP7u/mRKGwR8yN1/VSb/IGJf6lPEY+0h4Cx3f9vMDgNOd/evNrKtjwMXuvuJrbArHZpqKB3bOuDfzKxvSzK7+3fd/WB3Pxh4s/C+kWDSCzgU6JVG1hWMJt76pjHXpPWPAv7HzLZP2760sWAiAowB7iWO3iwYRNMjOZ9Nx9oQ4s8IDMDdH2osmMjWUQ2lY2sgjm75OnBx8Qwz2xP4ObArscZwhrv/vYJ1fwb4A7CC+CX/vpl9CPg08DEzuwT4jLs/W25hd3/GzN4AegMvF9dAzGxSWk8D8Ed3v7Ck7FcQf3N0pruvr6DM0g6Z2buAI4FPADOBy9OsScD+ZvYoUOvu15Rb3t3fMbMHiXfT2KQGYmYfI95QFuIPoT9asu0PEr9Dn3H35/Lcr45INZSO76fAF8xsl5L0/wamu/tBwE3AZrWQZowBfp1eYwDc/X7iF35CqtmUDSYAZnYo8Iy7v1yS3gc4CTgwle3Kkvk/AHYjBkAFk85hNDDL3Z8GVqVjB+Ai4C/pWCsbTADMrDvxNkyzysy+EPhyqsl8BHizaLkPAdcDoxRMWkYBpYNz91eB6UBpFf8IoND2/Evgwy1dp5n1A/YB7k1f8gYze38LF/+6mT0FPMDGK81irwJrgalm9m/AG0XzvgX0cvcvurvGu3ceY4g3eyX9HdPC5fZOtZd/An9397+WyXMf8CMz+yrx2GpI6fsTayafqrDm3qkpoHQOPwbOAnZsIk8lJ+iTiU1Vz5vZUmJb9ilNLVDkGnd/X1rH9HT1uEH6Qh8O/JZ0ZVo0ewEwNNVipBMws3cDRxEvMJYCE4CTzazcvftKFfpQ9gGGm9mnSzOkZyadDfQA5pvZfmlWPfHC5pCt34vOQwGlE3D3VYATg0rB/WwMAl8gdni21BhghLsPcvdBwNCidb0G7NSCMv2OOPJmbHF6ai/fxd1vB74GHFw0exax3fz/zKzZbUiH8Fli0+ye6XjbA3ieWKNu6bFWT2wem1g6z8z2dve/uftVxOOxEFBeAU4Avpf6XKQFFFA6j/8Eikd7fRU4w8z+CpwGnN+SlaThmO8B5hfS3P154FUzG0ZskphgZo+Y2d7NrO47wAVmVnwc7gTclsp1D3FAwQbu/r/Az4CZZtajJWWWdm0McGtJ2m+Jo7v+SmxufczMvr7Zkpv6PdDTzD5Skv41M3vczB4j9p/cUZjh7iuATwE/Tce2NEO3XhERkVyohiIiIrlQQBERkVwooIiISC4UUEREJBcKKCIikgsFFBERyYUCikg7YGbXm9m3WpBvqZkdU40yiZTS3YZFqsDMxgFnu3uL75lWzN3PybdEIvlTDUVkG2dmXdq6DCItoRqKSI7M7CLiUyl3A14kPofmSeJt0Lc3s9eBBnfv1cQ6phFvA7In8DFglJmdCtS5+yXpgWnTiPezWg8sAj5Wejv/dKPDO4CJ7n4zIq1MAUUkX88Sn6vxEvFxyjcS73Z7DpU1eX0eOB44kfgI21OL5n0DqCM+HA1gOCV3i07PDPk9cK6737ZFeyJSIQUUkRylm1cW3GJmE4m346/UDHe/L71fa2bF894GaoA93X0J8JeSZT9CvLP0ae5+1xZsW2SLKKCI5MjMTgcuID4jBuBdxLs8v1Phql5sYt7VxIeT/TEFminpuR4F5wD3KJhItalTXiQnZrYn8db65wHvTv0kjwOByh5gRlP53f01d/+Gu7+XeHv1C8zs6KIs5wDvMbNGH4sr0hoUUETysyMxEPwDwMzOAAqPRl4BDDSzHbZ2I2Z2opntk55a+Cqx9lNcA3oNGAF81MwmlVuHSGtQQBHJibs/QXyQ2TxiABlCfGY5wJ3E0VgvmdnKrdzUYOBPwOtpW9e5+90lZXkFOBYYaWZXbOX2RFpED9gSEZFcqIYiIiK50CgvkTZgZouIP1ws9UV3v6na5RHJg5q8REQkF2ryEhGRXCigiIhILhRQREQkFwooIiKSi/8PkbrUiPEtqFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#at risk show/ no show counts\n",
    "ax = sns.countplot(x=df.at_risk, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for AT RISK patients\")\n",
    "x_ticks_labels=['NoT At Risk', 'At Risk']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('risk1.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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_risk_ratio = df[df.no_show== 1].groupby(['at_risk']).size()/df.groupby(['at_risk']).size()\n",
    "ax = sns.barplot(x=df_risk_ratio.index, y=df_risk_ratio, palette=\"RdBu_r\")\n",
    "ax.set_title(\"NO-SHOW Percentage for At Risk patients\")\n",
    "x_ticks_labels=['Not at Risk', 'At Risk']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('risk2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At Risk STATS Test\n",
    "-T-Test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NUll hypotesis: That the rate of noshows of at risk population = the rate of no show for not at risk population\n",
    "\n",
    "Alternate Hypotesis: The rate of no show of people at risk is not equal to the rate of no shows for people at risk "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "at_risk\n",
       "0    0.209045\n",
       "1    0.177712\n",
       "dtype: float64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_risk_ratio = df[df.no_show== 1].groupby(['at_risk']).size()/df.groupby(['at_risk']).size()\n",
    "df_risk_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "at_risk\n",
       "0    0.209045\n",
       "1    0.177712\n",
       "Name: no_show, dtype: float64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['at_risk']).no_show.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "show=df[df['no_show']==0]\n",
    "noshow=df[df['no_show']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=10.895289911332352, pvalue=1.2537486156589837e-27)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "stats.ttest_ind(show['at_risk'], noshow['at_risk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can rejct the null hypotesis, there is a significant difference in the rate of noshows for people at risk vs the rate of noshows for people not at risk.\n",
    "\n",
    "Findings: We find that this infact is an important feature, and we can see that people who are at risk tend to make their appointments more often"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HANDICAP ( handcap *)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=df.handcap, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for Handicap\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ha_ratio = df[df.no_show == 1].groupby(['handcap']).size()/df.groupby(['handcap']).size()\n",
    "ax = sns.barplot(x=df_ha_ratio.index, y=df_ha_ratio, palette=\"RdBu_r\")\n",
    "ax.set_title(\"Show Percentage for Handicap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>hdi</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>waiting_days</th>\n",
       "      <th>waiting_categories</th>\n",
       "      <th>cnt</th>\n",
       "      <th>current_year</th>\n",
       "      <th>year_birth</th>\n",
       "      <th>generation</th>\n",
       "      <th>at_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91820</th>\n",
       "      <td>9.251878e+12</td>\n",
       "      <td>5789549</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>2016-06-08 00:00:00+00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.942</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td>same day: 0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2002</td>\n",
       "      <td>zoomer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98538</th>\n",
       "      <td>4.972463e+14</td>\n",
       "      <td>5760621</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-01 00:00:00+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>SÃO PEDRO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2</td>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1998</td>\n",
       "      <td>zoomer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104268</th>\n",
       "      <td>7.581895e+14</td>\n",
       "      <td>5697136</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-13 00:00:00+00:00</td>\n",
       "      <td>2016-06-02 00:00:00+00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>ITARARÉ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.730</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>20</td>\n",
       "      <td>month: 16-30</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2008</td>\n",
       "      <td>zoomer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id  appointment_id gender             scheduled_day  \\\n",
       "91820   9.251878e+12         5789549      M 2016-06-08 00:00:00+00:00   \n",
       "98538   4.972463e+14         5760621      M 2016-06-01 00:00:00+00:00   \n",
       "104268  7.581895e+14         5697136      F 2016-05-13 00:00:00+00:00   \n",
       "\n",
       "                 appointment_day  age neighbourhood  scholarship  \\\n",
       "91820  2016-06-08 00:00:00+00:00   15      DA PENHA            0   \n",
       "98538  2016-06-03 00:00:00+00:00   19     SÃO PEDRO            0   \n",
       "104268 2016-06-02 00:00:00+00:00    9       ITARARÉ            0   \n",
       "\n",
       "        hipertension  diabetes  alcoholism  handcap  sms_received  no_show  \\\n",
       "91820              0         0           0        4             0        0   \n",
       "98538              0         0           0        4             0        0   \n",
       "104268             0         0           0        4             1        1   \n",
       "\n",
       "          hdi day_of_week  waiting_days waiting_categories  cnt  current_year  \\\n",
       "91820   0.942   Wednesday             0        same day: 0    1          2017   \n",
       "98538   0.916      Friday             2         short: 1-3    1          2017   \n",
       "104268  0.730    Thursday            20       month: 16-30    1          2017   \n",
       "\n",
       "        year_birth generation  at_risk  \n",
       "91820         2002     zoomer        0  \n",
       "98538         1998     zoomer        0  \n",
       "104268        2008     zoomer        0  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.handcap == 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMS Received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=df.sms_received, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for SMSReceived\")\n",
    "x_ticks_labels=['No SMSReceived', 'SMSReceived']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_s_ratio = df[df.no_show == 1].groupby(['sms_received']).size()/df.groupby(['sms_received']).size()\n",
    "ax = sns.barplot(x=df_s_ratio.index, y=df_s_ratio, palette=\"RdBu_r\")\n",
    "ax.set_title(\"Show Percentage of no shows for SMSReceived\")\n",
    "x_ticks_labels=['No SMSReceived', 'SMSReceived']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-42.39668691823939, pvalue=0.0)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#t_test to check the sms_ received no show rates\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "stats.ttest_ind(show['sms_received'], noshow['sms_received'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "findings: SMS recieved feature was counter intuitive, the rate of no shows where significanty more for those who recived SMS, but we councluded that further investigation has to be done on this feature for what it actually means to recieve SMS, do you sign get to sign up for the service? if sowe can say who didnt sign up or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genereations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+++05/PDDmTVrFgA//OEPOeigg5blO+aYY3jTm960bPmoo47i+uuvH46mMKCTJEnN9fWvf50vfelLzJgxg/vvv5/M5Ac/+AGf+MQnuOSSS7jjjjvYYostePe7373c/Xz3u9/l1ltv5fvf/z73338/F198MZtsssmy9ddeey0f+MAHuO+++5g4ceKyc+oWLVrEpEmTOOmkk7jnnnuYPHkykyZNYuHChey2227Mnj2bhQsXsmTJEh544AHmzZvH73//e/74xz9y9913s8ceewxLO6wZF3SR1HXzTn9HV8sb/6lLu1qepJHhpJNO4iUveQkAhxxyCPfeey8/+clPOPbYY3nVq14FwJlnnskOO+zAo48+ypZbbtlxP2PHjuX3v/89Dz74ILvuuivbbLPNs9a/8Y1vZNdddwXg6KOP5rzzzgPgpptuYuutt+Ytb3kLUPe6XXbZZdx4440cc8wx7LzzzsycOZPNNtuMV77ylTz/+c9n1qxZrLPOOmy99dZsuummw9IOBnSSJKmxXvSiFy17vv766zN//nwWLVq0LJgD2HDDDdlkk014/PHHBwzo9t13X0488UTOOuss5s6dyxvf+EY+8pGP8LznPa9jOX/4wx8AmD9/Pptvvvmz9rXFFlssG+Lda6+9mDFjBuPHj2evvfbiBS94ATNnzmSdddZhr732Gp5GwCFXSZI0ymy22WbMmTNn2fJTTz3FokWLlvXkDeTkk0/m+uuv5+abb+bhhx/m4osvHlRZc+fOfVba3Llzl5W19957M2PGDGbOnMnee++9bHnGjBkGdJIkSQM56qijuPrqq7nnnntYvHgxF154IbvuuuuAvXMAd911F3fccQd//vOf2WCDDVh33XUZM2bMCss68MADefjhh7nmmmtYsmQJ1157Lb/4xS845JBDANh999156KGHuOuuu9hll13YbrvtmDt3LnfeeeewBnQOuUqSpFFlv/324/TTT2fy5Mk88cQT7LbbbnzhC19Y7jZPPvkk5513Ho888gjrrrsu+++/P6eccsoKy9p0002ZNm0aH/3oRznzzDOZOHEi06ZNW3Zu3AYbbMBOO+3EuuuuyzrrrAPAa17zGn7+858zbty4VT/Yomq1WsO2swZqPfbYY72ugzQqOSlC0nB66qmn2GCDDXpdja7pdLwTJkwA6HiTV4dcJUmSGs4hV0mStEa49dZbedvb3tZx3S9+8Ysu12Z4GdBJkqQ1wp577tn4wG0gDrlKkiQ1nAGdJElSwxnQSZIkNZwBnSRJ0jC5+eab2W+//dhnn3343Oc+17VynRQhSZJGpeG+HuaKrnf5zDPPcNZZZ3HllVcyfvx4Dj/8cA499FC23XbbYa1HJ10J6CJiS+AK4CXAUmBKZl4UEZsCVwMTgdlAZOaiiKiAi4DDgaeAEzLzjrKvScDZZdcfz8xpJX03YCqwPnAd8P7MXKOvmixJkrrnzjvvZOLEiWy11VYAHHnkkdxwww1dCei6NeS6BPhgZr4S2At4T0TsAJwB3JSZ2wA3lWWANwLblMdk4GKAEgCeA+wJ7AGcExGblG0uLnn7tjusC8clSZIEwOOPP953NwcAxo8fz+OPP96VsrsS0GXmvL4etsx8Ergf2Bw4EphWsk0DjirPjwSuyMxWZs4ENo6I8cAbgBszc2FmLgJuBA4r656fmTNKr9wVbfuSJEla7TrdTrWqOt6pa9h1fVJEREwEdgVuBTbLzHlQB33Ai0u2zYFH2zabU9KWlz6nQ7okSVJXjB8/nvZ7xM+bN4/NNtusK2V3dVJERGwEfBU4LTP/JyIGytopnG2tRHqnOkymHpolMxk3btyKqi1pJczrcnl+lqXRbf78+Ywd29u5nCsqf/fdd+eXv/wlc+fOZfz48UyfPp2LL754peq97rrrDul7rWstExFrUwdzX87Mr5Xk+RExPjPnlWHTX5f0OcCWbZtvATxW0vfvl35LSd+iQ/7nyMwpwJSy2FqwYMHKHpKkEcTPsjS6LV68mDFjxvS0DkuWLFlhno9//OMcc8wxLF26lGOOOYZXvOIVg9quv8WLFz/ne639/Lz+ujXLtQIuA+7PzM+0rZoOTAIuLH+vbUs/NSKuop4A8UQJ+m4A/rFtIsShwJmZuTAinoyIvaiHco8H/m21H5gkSRqxVnSZkdXhoIMO4qCDDup6ud3qodsHeDtwd0TcVdI+TB3IZUScDPwKeGtZdx31JUsepL5syYkAJXA7H5hV8n0sMxeW5+/iL5ct+WZ5SJIkjXpVpxkZa5BW+8mLkobPcF/Qc0V68Z+4pO556qmn2GCDDXpdja7pdLxlyLXjtFlv/SVJktRwBnSSJEkNZ0AnSZLUcAZ0kiRJDWdAJ0mSNEw+8IEPsPPOO3PggQd2tdzeXnJZkiRpNTlh2oxh3d/USXuvME9EcOKJJ/L+979/WMteEXvoJEmShslee+3Fxhtv3PVyDegkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeGc5SpJXeQ9bqXR7d3vfjczZsxg4cKF7LbbbnzoQx/iuOOOW+3lGtBJkqRRaTCXGRluX/jCF7peJjjkKkmS1HgGdJIkSQ1nQCdJktRwBnSSJGnEa7Vava5CVw31eA3oJEnSiLfWWmuxZMmSXlejK5YsWcJaaw0tRHOWqyRJGvHWW289nn76aRYvXkxVVb2uzmrTarVYa621WG+99Ya0nQGdJEka8aqqYv311+91NUYsh1wlSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4bpyYeGIuBw4Avh1Zu5U0q4GtitZNgZ+l5m7RMRE4H7ggbJuZmaeUrbZDZgKrA9cB7w/M1sRsSlwNTARmA1EZi5a/UcmSZLUe926U8RU4HPAFX0JmXlM3/OI+DTwRFv+hzJzlw77uRiYDMykDugOA74JnAHclJkXRsQZZfkfhvkYJEmSRqSuDLlm5veAhZ3WRUQFBHDl8vYREeOB52fmjMxsUQeHR5XVRwLTyvNpbemSJEmj3ki4l+t+wPzM/EVb2tYRcSfwP8DZmfl9YHNgTlueOSUNYLPMnAeQmfMi4sUDFRYRk6l7+chMxo0bN3xHImmZeV0urymfZdtF0uowEgK643h279w84KWZ+dtyztx/R8SOQNVh29ZQC8vMKcCUvu0XLFgw1F1IGoH8LHdmu0ijx4QJEwZc19NZrhExFvhr6gkNAGTm4sz8bXn+Y+AhYFvqHrkt2jbfAnisPJ9fhmT7hmZ/vfprL0mSNDL0+rIlBwM/y8xlQ6kR8aKIGFOevwzYBni4DKk+GRF7lfPujgeuLZtNByaV55Pa0iVJkka9bl225Epgf2BcRMwBzsnMy4Bjee5kiNcBH4uIJcAzwCmZ2Teh4l385bIl3ywPgAuBjIiTgV8Bb119RyNJGm7zTn9H18oa/6lLu1aW1C1VqzXk09BGk9Zjjz224lyShqybP9DQnB9p26UzAzppxco5dJ3mFPR8yFWSJEmryIBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGm5srysgSZKea97p7+hqeeM/dWlXy9Pw6kpAFxGXA0cAv87MnUraucA7gd+UbB/OzOvKujOBk4FngPdl5g0l/TDgImAMcGlmXljStwauAjYF7gDenpl/6saxSZIk9Vq3hlynAod1SP9sZu5SHn3B3A7AscCOZZsvRMSYiBgDfB54I7ADcFzJC/DJsq9tgEXUwaAkSdIaoSsBXWZ+D1g4yOxHAldl5uLM/CXwILBHeTyYmQ+X3rergCMjogIOBP6rbD8NOGpYD0CSJGkE6/U5dKdGxPHA7cAHM3MRsDkwsy3PnJIG8Gi/9D2BFwK/y8wlHfI/R0RMBiYDZCbjxo0bjuOQ1M+8LpfXlM+y7dJZN9vFNumsKe2iznoZ0F0MnA+0yt9PAycBVYe8LTr3JraWk7+jzJwCTOnLt2DBgiFUWdJI5We5M9vluWyTzmyXkW/ChAkDrutZQJeZ8/ueR8QXgW+UxTnAlm1ZtwAeK887pS8ANo6IsaWXrj2/JEnSqNez69BFxPi2xaOBe8rz6cCxEbFumb26DXAbMAvYJiK2joh1qCdOTM/MFnAz8Jay/STg2m4cgyRJ0kjQrcuWXAnsD4yLiDnAOcD+EbEL9fDobODvADLz3ohI4D5gCfCezHym7OdU4Abqy5Zcnpn3liL+AbgqIj4O3Alc1o3jkiRJGgm6EtBl5nEdkgcMujLzAuCCDunXAdd1SH+YehasJEnSGsdbf0mSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ13NhuFBIRlwNHAL/OzJ1K2qeAvwL+BDwEnJiZv4uIicD9wANl85mZeUrZZjdgKrA+cB3w/sxsRcSmwNXARGA2EJm5qBvHJkmS1Gvd6qGbChzWL+1GYKfM3Bn4OXBm27qHMnOX8jilLf1iYDKwTXn07fMM4KbM3Aa4qSxLkiStEboS0GXm94CF/dK+lZlLyuJMYIvl7SMixgPPz8wZmdkCrgCOKquPBKaV59Pa0iVJkka9kXIO3UnAN9uWt46IOyPiuxGxX0nbHJjTlmdOSQPYLDPnAZS/L17dFZYkSRopunIO3fJExFnAEuDLJWke8NLM/G05Z+6/I2JHoOqweWslyptMPWxLZjJu3LiVq7ik5ZrX5fKa8lm2XTrrZrvYJp01pV3UWU8DuoiYRD1Z4qAyjEpmLgYWl+c/joiHgG2pe+Tah2W3AB4rz+dHxPjMnFeGZn89UJmZOQWYUhZbCxYsGM5DktQjfpY7s12eyzbpzHYZ+SZMmDDgup4NuUbEYcA/AG/OzKfa0l8UEWPK85dRT354uAylPhkRe0VEBRwPXFs2mw5MKs8ntaVLkiSNet26bMmVwP7AuIiYA5xDPat1XeDGiIC/XJ7kdcDHImIJ8AxwSmb2Tah4F3+5bMk3+ct5dxcCGREnA78C3tqFw5IkSRoRuhLQZeZxHZIvGyDvV4GvDrDudmCnDum/BQ5alTpKkiQ11aCHXCPiQwOkf2D4qiNJkqShGso5dB8dIP3s4aiIJEmSVs4Kh1wj4sDydExEHMCzLx/yMuDJ1VExSZIkDc5gzqHrO9dtPeDytvQW8Djw3uGulCRJkgZvhQFdZm4NEBFXZObxq79KkiRJGopBz3JtD+YiYq1+65YOZ6UkSZI0eIMO6CLiNcDngZ2ph1+hPp+uBYwZ/qpJkiRpMIZyHbppwNeBk4CnVpBXkiRJXTKUgG4r4Ky+e65KkiRpZBjKdeiuAQ5dXRWRJEnSyhlKD916wDUR8QPqy5Us4+xXSZKk3hlKQHdfeUiSJGkEGcplS85bnRWRJEnSyhnKZUsOHGhdZn5neKojSZKkoRrKkOtl/ZZfBKwDzKG+p6skSZJ6YChDrlu3L0fEGOBs4MnhrpQkSZIGbyiXLXmWzHwGuAD4P8NXHUmSJA3VSgd0xSGA93GVJEnqoaFMiniU+r6tfTagvjbdu4e7UpIkSRq8oUyKeFu/5T8AP8/M/xnG+kiSJGmIhjIp4rsAEbEWsBkwPzMdbpUkSeqxoQy5Pg/4PHAMsDbw54i4CnhfZj6xmuonSZKkFRjKpIh/AzYEXgWsX/5uAPzraqiXJEmSBmko59AdBrwsM58qyz+PiBOBh4a/WpIkSRqsofTQPU19d4h244DFw1cdSZIkDdVQeuguBW6MiM8AjwBbAX8PfHF1VEySJEmDM5SA7gJgLoU+oKsAABnQSURBVPC3wATgMeCfMrP/PV4lSZLURUMZcr0IeCAzD87MHTLzYOD+iPiX1VQ3SZIkDcJQeuiOAz7UL+3HwH8Dp61o44i4HDgC+HVm7lTSNgWuBiYCs4HIzEURUVEHkIcDTwEnZOYdZZtJwNlltx/PzGklfTdgKvUM3OuA92dm+50tJEmSRqWh9NC1gDH90sYMYR9TqWfKtjsDuCkztwFuKssAbwS2KY/JwMWwLAA8B9gT2AM4JyI2KdtcXPL2bde/LEmSpFFpKAHd94Hzy50i+u4YcW5JX6HM/B6wsF/ykcC08nwacFRb+hWZ2crMmcDGETEeeANwY2YuzMxFwI3AYWXd8zNzRumVu6JtX5IkSaPaUIZc3w98A5gXEY8ALwXmAX+1CuVvlpnzADJzXkS8uKRvDjzalm9OSVte+pwO6c8REZOpe/LITMaNG7cK1Zc0kHldLq8pn2XbpbNutott0llT2kWdDeVernMi4jXUQ51bUgdWt62m+7lWHdJaK5H+HJk5BZjSl2fBggUrVUFJI4uf5c5sl+eyTTqzXUa+CRMmDLhuKD10lOBtZnkMh/kRMb70zo0Hfl3S51AHjX22oL5Myhxg/37pt5T0LTrklyRJGvWGcg7d6jAdmFSeTwKubUs/PiKqiNgLeKIMzd4AHBoRm5TJEIcCN5R1T0bEXmWG7PFt+5IkSRrVhtRDtyoi4krq3rVxETGHerbqhUBGxMnAr4C3luzXUV+y5EHqy5acCJCZCyPifGBWyfexzOybaPEu/nLZkm+WhyRJ0qjXtYAuM48bYNVBHfK2gPcMsJ/Lgcs7pN8O7LQqdZQkSWqiXg+5SpIkaRUZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNdzYXldAkiRpsOad/o6uljf+U5d2tbyVZQ+dJElSwxnQSZIkNZwBnSRJUsMZ0EmSJDWcAZ0kSVLDGdBJkiQ1nAGdJElSwxnQSZIkNVxPLywcEdsBV7clvQz4KLAx8E7gNyX9w5l5XdnmTOBk4BngfZl5Q0k/DLgIGANcmpkXduUgJEmSeqynAV1mPgDsAhARY4C5wDXAicBnM/Of2/NHxA7AscCOwATg2xGxbVn9eeAQYA4wKyKmZ+Z9XTkQSZKkHhpJQ64HAQ9l5iPLyXMkcFVmLs7MXwIPAnuUx4OZ+XBm/gm4quSVJEka9UbSvVyPBa5sWz41Io4Hbgc+mJmLgM2BmW155pQ0gEf7pe+5GusqSZI0YoyIgC4i1gHeDJxZki4Gzgda5e+ngZOAqsPmLTr3NLYGKGsyMBkgMxk3btwq1V1SZ/O6XF5TPsu2S2fdbBfbpDPbpbOmtMuICOiANwJ3ZOZ8gL6/ABHxReAbZXEOsGXbdlsAj5XnA6U/S2ZOAaaUxdaCBQtWufKSes/Pcme2y3PZJp3ZLp2NpHaZMGHCgOtGSkB3HG3DrRExPjP7gvCjgXvK8+nAVyLiM9STIrYBbqPuudsmIramnlhxLPC/u1R3SZKknup5QBcRG1DPTv27tuR/iohdqIdNZ/ety8x7IyKB+4AlwHsy85myn1OBG6gvW3J5Zt7btYOQJEnqoZ4HdJn5FPDCfmlvX07+C4ALOqRfB1w37BWUJEka4UbSZUskSZK0EgzoJEmSGq7nQ65S0807/R1dLW/8py7tanmSpJHPHjpJkqSGM6CTJElqOAM6SZKkhjOgkyRJajgDOkmSpIYzoJMkSWo4AzpJkqSGM6CTJElqOAM6SZKkhjOgkyRJajgDOkmSpIYzoJMkSWo4AzpJkqSGM6CTJElqOAM6SZKkhjOgkyRJajgDOkmSpIYzoJMkSWo4AzpJkqSGM6CTJElqOAM6SZKkhjOgkyRJajgDOkmSpIYb2+sKSJLUTSdMm9HV8qZO2rur5WnNZA+dJElSw42IHrqImA08CTwDLMnM3SNiU+BqYCIwG4jMXBQRFXARcDjwFHBCZt5R9jMJOLvs9uOZOa2bxyFJktQLI6mH7oDM3CUzdy/LZwA3ZeY2wE1lGeCNwDblMRm4GKAEgOcAewJ7AOdExCZdrL8kSVJPjKSArr8jgb4etmnAUW3pV2RmKzNnAhtHxHjgDcCNmbkwMxcBNwKHdbvSkiRJ3TZSAroW8K2I+HFETC5pm2XmPIDy98UlfXPg0bZt55S0gdIlSZJGtRFxDh2wT2Y+FhEvBm6MiJ8tJ2/VIa21nPRnKQHjZIDMZNy4cStTX2mZeV0urynvWdulM9uls263Szet7Gvge6Uz26WzERHQZeZj5e+vI+Ia6nPg5kfE+MycV4ZUf12yzwG2bNt8C+Cxkr5/v/RbOpQ1BZhSFlsLFiwYxiORVj/fs53ZLp3ZLr3XlNfgiE9/vavlNeVyLiPp9ZswYcKA63o+5BoRG0bE8/qeA4cC9wDTgUkl2yTg2vJ8OnB8RFQRsRfwRBmSvQE4NCI2KZMhDi1pkiRJo9pI6KHbDLgmIqCuz1cy8/qImAVkRJwM/Ap4a8l/HfUlSx6kvmzJiQCZuTAizgdmlXwfy8yF3TsMSRp5vIiutGboeUCXmQ8Dr+6Q/lvgoA7pLeA9A+zrcuDy4a6jJEnSSNbzIVdJkiStGgM6SZKkhjOgkyRJajgDOkmSpIYzoJMkSWo4AzpJkqSGM6CTJElqOAM6SZKkhuv5hYUlaTh4RwRJazJ76CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhpubC8Lj4gtgSuAlwBLgSmZeVFEnAu8E/hNyfrhzLyubHMmcDLwDPC+zLyhpB8GXASMAS7NzAu7eSySJEm90tOADlgCfDAz74iI5wE/jogby7rPZuY/t2eOiB2AY4EdgQnAtyNi27L688AhwBxgVkRMz8z7unIUkiRJPdTTgC4z5wHzyvMnI+J+YPPlbHIkcFVmLgZ+GREPAnuUdQ9m5sMAEXFVyWtAJ0mSRr1e99AtExETgV2BW4F9gFMj4njgdupevEXUwd7Mts3m8JcA8NF+6XsOUM5kYDJAZjJu3LhhPAqtieZ1ubymvGe73S7dtrKvg+3S2WhuF9uks6a0S1O+c0dEQBcRGwFfBU7LzP+JiIuB84FW+ftp4CSg6rB5i86TO1qdysrMKcCUvjwLFixYxdpL3eV7dmTwdejMdnku26SzprTLSKrnhAkTBlzX84AuItamDua+nJlfA8jM+W3rvwh8oyzOAbZs23wL4LHyfKB0SZKkUa3Xs1wr4DLg/sz8TFv6+HJ+HcDRwD3l+XTgKxHxGepJEdsAt1H33G0TEVsDc6knTvzv7hyFJElSb/W6h24f4O3A3RFxV0n7MHBcROxCPWw6G/g7gMy8NyKSerLDEuA9mfkMQEScCtxAfdmSyzPz3m4eiCRJUq/0epbrD+h8Xtx1y9nmAuCCDunXLW87SZKk0co7RUiSJDWcAZ0kSVLDGdBJkiQ1XK8nRUiSJI1YJ0yb0dXypk7ae6W2s4dOkiSp4QzoJEmSGs6ATpIkqeEM6CRJkhrOgE6SJKnhDOgkSZIazoBOkiSp4QzoJEmSGs4LC0sN05SLXEqSusceOkmSpIYzoJMkSWo4AzpJkqSGM6CTJElqOAM6SZKkhjOgkyRJajgDOkmSpIYzoJMkSWo4AzpJkqSGM6CTJElqOAM6SZKkhjOgkyRJajgDOkmSpIYzoJMkSWq4sb2uwHCKiMOAi4AxwKWZeWGPqyRJkrTajZqALiLGAJ8HDgHmALMiYnpm3tfbmo0u805/R9fKGv+pS7tWliRJTTaahlz3AB7MzIcz80/AVcCRPa6TJEnSajdqeuiAzYFH25bnAHv2qC4aBidMm9HV8qZO2rur5UmSNFyqVqvV6zoMi4h4K/CGzHxHWX47sEdmvrdfvsnAZIDM3K3rFZUkSVp5VafE0TTkOgfYsm15C+Cx/pkyc0pm7p6Zu1M3yoh4RMSPe12HkfawTWwX28V2sU1sl14/RmC7dDSahlxnAdtExNbAXOBY4H/3tkqSJEmr36jpocvMJcCpwA3A/XVS3tvbWkmSJK1+o6mHjsy8Driu1/VYSVN6XYERyDbpzHbpzHbpzHZ5LtukM9uls0a0y6iZFCFJkrSmGjVDrpIkSWsqA7oBRMSPBpHntIjYYCX2fUtE7D6E/PtHxDfK8xMi4nNDLbPJImKXiDh8JbfdOCLePdx1Wp0i4tKI2KE8nx0R41ZyP0f17Wcki4iJEXFPr+uh5omIN0fEGeX5uRHxofJ8akS8ZTWUd11EbLyCPCv9mVXvDOZ1G+mv7ag6h244Zeb/GkS204AvAU8Ndr/lFmVrrIgYWyawDMUuwO6s3PmRGwPvBr6wEtv2RN+1FIfBUcA3AG9/NwQRUQFVZi7tdV20fJk5HZjexfJW6h/LNUlEjMnMZ3pdjzWRAd0AIuL3mblRROwPnAssAHYCfgy8DXgvMAG4OSIWZOYBEXEocB6wLvAQcGJm/j4iZgOXA4cCfb1rb4uIfwWeD5yUmbdFxIbAvwGvon5tzs3Ma5dTx6nANzLzvzrU+TxgPnUw9DXgbuD9wPrAUZn50Kq3Usc6fQT4W+q7diygbq8jgB8B+wDTI+IK4BLgpWWz0zLzhxGxB/AvpY5/BE4Efgl8DFg/IvYFPkEdpDynnSJiR+D/AutQ9z7/DXA+8PKIuAu4MTNPXx3HvbLKa57U100cQ13fdwEfyszb++V9G/A+6uO7FXh3Zj4TEb8HLqJu5z9S3/Lu5cCbgddHxNnA36yu13yYjI2IacCuwM+B44G9gX+mfo1nAe/KzMURcdAA6bOBrwAHAGtTX0D8E8ArgE9l5iUAEXE6ENSf02sy85yImAh8E7i5lHsU8EgXjnu1iIjXApdR3xJxDHAbcExmNqYntLwm1wM/APYCfkL9+T4PeDH198wOwO6Zeepy9rMb8BlgI+rvpBMyc15E3EL9OTqA+h+/kzPz+xFxAvVnZwPqz9E1mfl/yr5ml/IWRMR/U1/7dD3goszs2YnzEXEKcEpZfAEwm/pE/g9TX7fs/2XmP5S8xw2Q/nvq+6EfDCwqef6J+nv6tMycXjokLgT2p/78fD4z/7385pwDzKP+zRnRIwPLe+3a3ne30vZ9lJl9HTfvjYi/ov6OeWtm/qzTb1dmPtCt4+njkOvg7ErdG7cD8DJgn8z8V+oLFx9QgrlxwNnAwZn5GuB24ANt+3g6M/fNzKvK8oalF/Dd1MEewFnAdzLztdRfMp8qP/gr49XUAdyrgLcD22bmHsCl1MHosCvDyH9D3V5/Td2r1mfjzHx9Zn6aOvj4bDnOvyl1AvgZ8LrM3BX4KPCP5b68HwWuzsxdMvNqBm6nU6g/nH09enOAM4CHyrYjKpgrDgMey8xXZ+ZO1F8kzxERrwSOoX7v7QI8Q/2DBrAhMDMzXw18D3hnZv6Iuufi9HLsIzmYA9gOmJKZOwP/Q/3ZmUodhPQF7u+KiPU6pbft59HM3Bv4fsn3Fupg4GMA5Z+ubagDnV2A3SLidW11uCIzd83MxgZzAJk5i/r1/zj1j/KXmhTMtXkF9ffFzsD21NcW3Rf4EHXAsVwRsTb1P39vKXcGuhy4oC3L2PK9eBp1QNJnF+rP26uAYyKi/aL1fU4q+9wdeF9EvHCIxzZsMvOS8r3wWurvvanAJ4EDqY/lteUUjAmd0stuNgRuKcf0JPV75xDgaMrnBzgZeKJ8974WeGe59ivUn6mzMnNEB3PFil67/t9H7aftLCi/8RdTvw+hw2/Xaq39AAzoBue2zJxThmDuAiZ2yLMXdcD3w9IbNAnYqm391f3yXwmQmd8Dnl/OyzgUOKNsfwv1fw8vZeXMysx5mbmYurfwWyX97gHqPxz2Ba7NzD9m5pPA19vWtR//wcDnynFOpz7+51H/Z/mf5XyqzwI7DlDOQO00A/hwRPwDsFVm/nH4Dm21uRs4OCI+GRH7ZeYTA+Q7CNgNmFWO+yDqfy4A/kTdawl1j+jE1Vjf1eXRzPxhef4l6uP7ZWb+vKRNA15H/UXbKb1P3/Db3cCtmflkZv4GeLrtM3YocCdwB3WQsE3Z5pHMnDn8h9YzH6P+Qd6dOqhrol9m5t3lu/de4KbMbDH477HtqEdWbiyfm7Ope8P7fK387f+5uSkzn8jMp6lPWWj/Lu/zvoj4CTCTurdnmw55uu0i4DvA76iDs9+UU1y+TP05ee0A6VB/j/T9Q3k38N3M/DPPbutDgeNLW94KvJC/HPdtmfnL1Xlww2hFr13/76N929Z1es8M9rdrtXLIdXAWtz1/hs7tVlEP6R03wD7+0G+5//ViWmUff9O/qzYiNhtgn0soQXk572edAeq8tG156QD1Hw4D3pKEZx//WsDe/QOuiPg34ObMPLp0e9+ynHKe007A/RFxK/Am4IaIeAfw8BDq33WZ+fMyJHQ48ImI+NYAWStgWmae2WHdn8uPHAz8/hzpBnv9pOW9x+DZ7/P+n4GxZftPZOa/t29U3m/9P6NNtyn1MOPa1P/0NPH4VvV7rALuLb22y9t//8/Ncr/zyxDjwdTfY0+V4dv1BlGf1aYMFW9FfYH9Nw+QbXmfn/bvkWVtnZlLI6Lv+CvgvZl5Q7+y96ch769Bvnadfp/7dHrPnM/gfrtWK3voVs2TwPPK85nAPhHxCoCI2CAitl3OtseUfPtSd2E/QX2Xi/eW4IyI2HUF5c+m7rWB+ryptVfmIIbRD4C/ioj1ImIj6sCqk29Rf+kA9SzW8vQF1LdtAzihLX97O8MA7RQRLwMeLsPh06mHafpvO6KUIZCnMvNL1OeFvWaArDcBb4mIF5ftNo2ITr0G7Ub0sffz0ojo+9E9Dvg2MLHv80R92sB3qYc2OqUP1g3ASeX9SURs3temo9AU4CPUvTCf7HFdeuUB4EV9762IWLuca7uqXgAsKgHB9tQjND1T/in8EPC20pt5K/X5s+PKeW/HUX9OBkofrBuoT31Yu5S77SqcFtQrg3nt+n8f/WAQ++z029VVBnSrZgrwzYi4uQzrnABcGRE/pQ7wtl/OtouivjTKJdTnJUAd5a8N/LR03Z6/gvK/SP3hvA3Ykx7/h9R23s5PqLulbwc6DSG+D9g9In4aEffxl5N5/4m6l+qH1Cdy97kZ2CEi7oqIYxi4nY4B7inDAdtTnw/1W+ph8Hsi4lPDebzD5FXAbaXOZ1Gft/IcmXkf9XDRt8r760Zg/Ar2fRVwekTcGREvH8Y6rw73A5PKsW1KPWxxIvUwxt3UPQaXlCGw56QPtpDM/Bb1xIkZZfv/ojlB76BFxPHAksz8CvVJ7K+NiAN7XK2uK+fgvgX4ZBliuwsYzBUMVuR66ok8P6X+/un1UP2p1J+bm8t3yfnAmdTfnT8B7sjMazNzXqf0IZRzKfUQ9B3lu/ffad6IwGBeu/7fRxevYJ8D/XZ1lXeK0LCKiI2yntm7AfUJ+pMz845e10uSpBUpQ6bfKJPUGqVpkbVGvilRX8x2PepzvgzmJElazeyhkyRJajjPoZMkSWo4AzpJkqSGM6CTJElqOAM6SRpBIuKSqO+JLEmD5qQISeqRcnX/d2TmvivKK0nLYw+dJK0GbbdLkqTVzh46SaNSRLwGuAx4BfXV4ZcCv8jMsyPiCOq7ckykvvL9KZn507LdbOBzwPHU98a8HphU7lLBILa9GPhb6hvDb0h9S6Z3Ai8GHgXOysxrIuKVwJ3Udz35I/WdHTaOiKnAnMw8u+zzncA/UF+x/gelvMfKuhbwLuCDwDjqu2Cc2nZPTklrCHvoJI06EbEOcA0wlToQuhI4uqx7DXA58HfAC6lvXzQ9ItZt3wVwGLA19T2BTxjCtsdR38d448xcAjwE7Ed9v8fzgC9FxPjMvJ/6tnczMnOjzNy4w3EcCHyi1Gc88Aj1Ld3aHQG8Fnh1yfeGwbeUpNHCIQFJo9Fe1N9v/1p6q75W7nkMdW/Zv2fmrWV5WkR8uGzTd6Pyf23rBfs6sMsQt320ryKZ+Z9t9bo6Is4E9gAGcw/NvwUu77vjStl2UURMzMzZJc+Fmfk74HcRcXOp6/WD2LekUcSATtJoNAGY22/osS/I2or6xtvvbVu3Ttmmz+Ntz59qWzeYbR9te05EHA98gHqIFmAj6uHRwR7Hstvnlfsk/xbYHJg9QF03GuS+JY0iBnSSRqN5wOYRUbUFdVtSD38+ClyQmResxH4Hs+2yIDIitgK+CBxEPbT6TETcBVT98w7gMeogsm9/G1IP9c5dibpLGsUM6CSNRjOAZ4BTI+Ji6nPa9gBuoQ6wromIbwO3ARsA+wPfy8wnV7DfoW67IXXQ9huAiDgR2Klt/Xxgi4hYJzP/1GH7rwBXRcRXgPuBfwRubRtulSTASRGSRqESHP01cDLwO+BtwDeAxZl5O/W5cJ8DFgEPUiY9DGK/Q9o2M+8DPk0dYM4HXgX8sC3Ld4B7gccjYkGH7W8CPgJ8lbrX8eXAsYOpq6Q1i5ctkbRGiIhbgUsy8//2ui6SNNwccpU0KkXE64EHgAXUs0V3xtmfkkYpAzpJo9V2QFLP+nwIeEtmzuttlSRp9XDIVZIkqeGcFCFJktRwBnSSJEkNZ0AnSZLUcAZ0kiRJDWdAJ0mS1HAGdJIkSQ33/wHhW2lNRjuTPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,6])\n",
    "ax = sns.countplot(x=df.generation, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for different generations\")\n",
    "# x_ticks_labels=['No SMSReceived', 'SMSReceived']\n",
    "x_ticks_labels=['interbellum', 'greatest', 'silent', 'boomer', 'x', 'millennial', 'zoomer', 'alpha']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('gen1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,5])\n",
    "df_g_ratio = df[df.no_show == 1].groupby(['generation']).size()/df.groupby(['generation']).size()\n",
    "ax = sns.barplot(x=df_g_ratio.index, y=df_g_ratio, palette=\"Set2\")\n",
    "ax.set_title(\"NO-SHOW Percentage for Generations\")\n",
    "x_ticks_labels=['interbellum', 'greatest', 'silent', 'boomer', 'x', 'millennial', 'zoomer', 'alpha']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.savefig('gen2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chi squared on generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null Hypotesis HO: That there is no significant difference in the rate of no- shows accross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfg1 = df[df['generation'] == 'interbellum']\n",
    "dfg2 = df[df['generation'] == 'greatest']\n",
    "dfg3 = df[df['generation'] == 'silent']\n",
    "dfg4 = df[df['generation'] == 'boomer']\n",
    "dfg5 = df[df['generation'] == 'x']\n",
    "dfg6 = df[df['generation'] == 'millennial']\n",
    "dfg7 = df[df['generation'] == 'zoomer']\n",
    "dfg8 = df[df['generation'] == 'alpha']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "['interbellum', 'greatest', 'silent', 'boomer', 'x', 'millennial', 'zoomer', 'alpha']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>greatest</th>\n",
       "      <th>silent</th>\n",
       "      <th>boomer</th>\n",
       "      <th>x</th>\n",
       "      <th>millennial</th>\n",
       "      <th>zoomer</th>\n",
       "      <th>alpha</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>193</td>\n",
       "      <td>7022</td>\n",
       "      <td>21197</td>\n",
       "      <td>18892</td>\n",
       "      <td>17233</td>\n",
       "      <td>15293</td>\n",
       "      <td>8378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>1308</td>\n",
       "      <td>4086</td>\n",
       "      <td>4774</td>\n",
       "      <td>5358</td>\n",
       "      <td>4874</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         greatest  silent  boomer      x  millennial  zoomer  alpha\n",
       "no_show                                                            \n",
       "0             193    7022   21197  18892       17233   15293   8378\n",
       "1              49    1308    4086   4774        5358    4874   1865"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_g = pd.DataFrame(columns=['greatest', 'silent', 'boomer', 'x', 'millennial', 'zoomer', 'alpha'])\n",
    "\n",
    "#df_g['interbellum'] = dfg1.groupby('no_show')['no_show'].count()\n",
    "df_g['greatest'] = dfg2.groupby('no_show')['no_show'].count()\n",
    "df_g['silent'] = dfg3.groupby('no_show')['no_show'].count()\n",
    "df_g['boomer'] = dfg4.groupby('no_show')['no_show'].count()\n",
    "df_g['x'] = dfg5.groupby('no_show')['no_show'].count()\n",
    "df_g['millennial'] = dfg6.groupby('no_show')['no_show'].count()\n",
    "df_g['zoomer'] = dfg7.groupby('no_show')['no_show'].count()\n",
    "df_g['alpha'] = dfg8.groupby('no_show')['no_show'].count()\n",
    "\n",
    "\n",
    "df_g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   no_show     2 non-null      int64\n",
      " 1   greatest    2 non-null      int64\n",
      " 2   silent      2 non-null      int64\n",
      " 3   boomer      2 non-null      int64\n",
      " 4   x           2 non-null      int64\n",
      " 5   millennial  2 non-null      int64\n",
      " 6   zoomer      2 non-null      int64\n",
      " 7   alpha       2 non-null      int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 256.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df_g.fillna(value=0, inplace=True)\n",
    "df_g.reset_index(drop=False, inplace=True)\n",
    "df_g.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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>no_show</th>\n",
       "      <th>greatest</th>\n",
       "      <th>silent</th>\n",
       "      <th>boomer</th>\n",
       "      <th>x</th>\n",
       "      <th>millennial</th>\n",
       "      <th>zoomer</th>\n",
       "      <th>alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>7022</td>\n",
       "      <td>21197</td>\n",
       "      <td>18892</td>\n",
       "      <td>17233</td>\n",
       "      <td>15293</td>\n",
       "      <td>8378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1308</td>\n",
       "      <td>4086</td>\n",
       "      <td>4774</td>\n",
       "      <td>5358</td>\n",
       "      <td>4874</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no_show  greatest  silent  boomer      x  millennial  zoomer  alpha\n",
       "0        0       193    7022   21197  18892       17233   15293   8378\n",
       "1        1        49    1308    4086   4774        5358    4874   1865"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dof=7\n",
      "[[7.98096324e-01 1.93139310e+02 6.64814238e+03 2.01782694e+04\n",
      "  1.88877476e+04 1.80297941e+04 1.60952086e+04 8.17490065e+03]\n",
      " [2.01903676e-01 4.88606896e+01 1.68185762e+03 5.10473064e+03\n",
      "  4.77825240e+03 4.56120595e+03 4.07179144e+03 2.06809935e+03]]\n",
      "probability=0.950, critical=14.067, stat=760.249)\n",
      "Dependent (regject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import  chi2_contingency\n",
    "from scipy.stats import chisquare\n",
    "from scipy.stats import chi2\n",
    "\n",
    "stat, p, dof, expected = chi2_contingency(df_g)\n",
    "print('dof=%d' % dof)\n",
    "print(expected)\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (regject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "significant difference in the rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NO SHOWS: a look at the no shows data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df[df.no_show == 1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduled_day</th>\n",
       "      <th>appointment_day</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>hdi</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>waiting_days</th>\n",
       "      <th>waiting_categories</th>\n",
       "      <th>cnt</th>\n",
       "      <th>current_year</th>\n",
       "      <th>year_birth</th>\n",
       "      <th>generation</th>\n",
       "      <th>at_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.336882e+14</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 00:00:00+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.647</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2</td>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1994</td>\n",
       "      <td>millennial</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.449833e+12</td>\n",
       "      <td>5630575</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 00:00:00+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.647</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2</td>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1978</td>\n",
       "      <td>x</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.542951e+12</td>\n",
       "      <td>5620163</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-26 00:00:00+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>29</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.659</td>\n",
       "      <td>Friday</td>\n",
       "      <td>3</td>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1988</td>\n",
       "      <td>millennial</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.479497e+13</td>\n",
       "      <td>5633460</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28 00:00:00+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>40</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>1</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>0.719</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1977</td>\n",
       "      <td>x</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.222575e+14</td>\n",
       "      <td>5626083</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27 00:00:00+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>30</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>1</td>\n",
       "      <td>0.659</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2</td>\n",
       "      <td>short: 1-3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1987</td>\n",
       "      <td>millennial</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  appointment_id gender             scheduled_day  \\\n",
       "6   7.336882e+14         5630279      F 2016-04-27 00:00:00+00:00   \n",
       "7   3.449833e+12         5630575      F 2016-04-27 00:00:00+00:00   \n",
       "11  7.542951e+12         5620163      M 2016-04-26 00:00:00+00:00   \n",
       "17  1.479497e+13         5633460      F 2016-04-28 00:00:00+00:00   \n",
       "20  6.222575e+14         5626083      F 2016-04-27 00:00:00+00:00   \n",
       "\n",
       "             appointment_day  age   neighbourhood  scholarship  hipertension  \\\n",
       "6  2016-04-29 00:00:00+00:00   23      GOIABEIRAS            0             0   \n",
       "7  2016-04-29 00:00:00+00:00   39      GOIABEIRAS            0             0   \n",
       "11 2016-04-29 00:00:00+00:00   29  NOVA PALESTINA            0             0   \n",
       "17 2016-04-29 00:00:00+00:00   40       CONQUISTA            1             0   \n",
       "20 2016-04-29 00:00:00+00:00   30  NOVA PALESTINA            0             0   \n",
       "\n",
       "    diabetes  alcoholism  handcap  sms_received  no_show    hdi day_of_week  \\\n",
       "6          0           0        0             0        1  0.647      Friday   \n",
       "7          0           0        0             0        1  0.647      Friday   \n",
       "11         0           0        0             1        1  0.659      Friday   \n",
       "17         0           0        0             0        1  0.719      Friday   \n",
       "20         0           0        0             0        1  0.659      Friday   \n",
       "\n",
       "    waiting_days waiting_categories  cnt  current_year  year_birth  \\\n",
       "6              2         short: 1-3    1          2017        1994   \n",
       "7              2         short: 1-3    1          2017        1978   \n",
       "11             3         short: 1-3    1          2017        1988   \n",
       "17             1         short: 1-3    1          2017        1977   \n",
       "20             2         short: 1-3    1          2017        1987   \n",
       "\n",
       "    generation  at_risk  \n",
       "6   millennial        0  \n",
       "7            x        0  \n",
       "11  millennial        0  \n",
       "17           x        0  \n",
       "20  millennial        0  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3[df3['generation']=='greatest'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1308"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3[df3['generation']=='silent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4774"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3[df3['generation']=='x'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genereations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,8])\n",
    "ax = sns.barplot(x=\"generation\", y=\"cnt\", data=df3, estimator=lambda x: len(x) / len(df3) * 100)\n",
    "ax.set(ylabel=\"Percent\")\n",
    "ax.set_title(\"NoShow by generation\")\n",
    "x_ticks_labels=['interbellum', 'greatest', 'silent', 'boomer', 'x', 'millennial', 'zoomer', 'alpha']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NoShow by Days of the week')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,8])\n",
    "week_key=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "ax = sns.barplot(x=\"day_of_week\", y=\"cnt\", data=df3, estimator=lambda x: len(x) / len(df3) * 100, order=week_key)\n",
    "ax.set(ylabel=\"Percent of total no shows\")\n",
    "ax.set_title(\"NoShow by Days of the week\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of hdi of No show population')"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,8])\n",
    "ax= sns.distplot(df3['hdi'])\n",
    "# ax.set(ylabel=\"Percent of total no shows\")\n",
    "ax.set_title(\"Distribution of hdi of No show population\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of AGE of No show population')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,8])\n",
    "ax= sns.distplot(df3['age'])\n",
    "# ax.set(ylabel=\"Percent of total no shows\")\n",
    "ax.set_title(\"Distribution of AGE of No show population\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No-Shows by Waiting categoies')"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[15,11])\n",
    "# week_key=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "ax = sns.barplot(x=\"waiting_categories\", y=\"cnt\", data=df3, estimator=lambda x: len(x) / len(df3) * 100)\n",
    "ax.set(ylabel=\"Percent of total no shows\")\n",
    "ax.set_title(\"No-Shows by Waiting categoies\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of waiting days of the No show population')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[10,8])\n",
    "ax= sns.distplot(df3['waiting_days'])\n",
    "# ax.set(ylabel=\"Percent of total no shows\")\n",
    "ax.set_title(\"Distribution of waiting days of the No show population\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}