POLIST_01_notebookanalyse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROJET 5 : Segmentez des clients d'un site e-commerce\n",
    "*Mathieu GENTHON (juin 2020)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SOMMAIRE<a class=\"anchor\" id=\"sommaire\"></a>\n",
    "### 1 - [Contexte du projet](#1)<a class=\"anchor\" id=\"sommaire\"></a>\n",
    "### 2 - [Importation des librairies et chargement dataframe](#2)<a class=\"anchor\" id=\"sommaire\"></a>\n",
    "### 3 - [Description et nettoyage du Data Set](#3)<a class=\"anchor\" id=\"sommaire\"></a>\n",
    "### 4 - [Consolidation du Data Set](#4)<a class=\"anchor\" id=\"sommaire\"></a>\n",
    "### 5 - [Analyse exploratoire](#5)<a class=\"anchor\" id=\"sommaire\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - [Contexte du projet](#1)<a class=\"anchor\" id=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Olist souhaite que vous fournissiez à ses équipes d'e-commerce une segmentation des clients qu’elles pourront utiliser au quotidien pour leurs campagnes de communication.\n",
    "\n",
    "Votre objectif est de comprendre les différents types d’utilisateurs grâce à leur comportement et à leurs données personnelles.\n",
    "\n",
    "Vous devrez fournir à l’équipe marketing une description actionable de votre segmentation et de sa logique sous-jacente pour une utilisation optimale, ainsi qu’une proposition de contrat de maintenance basée sur une analyse de la stabilité des segments au cours du temps.\n",
    "\n",
    "Pour cette mission, Olist vous fournit une base de données anonymisée comportant des informations sur l’historique de commandes, les produits achetés, les commentaires de satisfaction, et la localisation des clients depuis janvier 2017.\n",
    "\n",
    "Votre mission est d’aider les équipes d’Olist à comprendre les différents types d'utilisateurs. Vous utiliserez donc des méthodes non supervisées pour regrouper ensemble des clients de profils similaires. Ces catégories pourront être utilisées par l’équipe marketing pour mieux communiquer.\n",
    "\n",
    "Pour des raisons de confidentialité, Olist ne fournit pas beaucoup de données, à vous de fouiller dans celles dont vous disposez et de créer les meilleures features pour les exploiter.\n",
    "\n",
    "Enfin, votre client, Olist, a spécifié sa demande ainsi :\n",
    "\n",
    "    - La segmentation proposée doit être exploitable et facile d’utilisation pour l’équipe marketing.\n",
    "    - Vous évaluerez la fréquence à laquelle la segmentation doit être mise à jour, afin de pouvoir effectuer un devis de contrat de maintenance.\n",
    "    - Le code fourni doit respecter la convention PEP8, pour être utilisable par Olist.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - [Importation des librairies et chargement dataframe](#2)<a class=\"anchor\" id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ligne pour vérifier compatibilité PEP8.\n",
    "# %load_ext pycodestyle_magic\n",
    "# %pycodestyle_on\n",
    "# On importe les librairies dont on devrait avoir besoin pour le projet.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn import preprocessing\n",
    "from datetime import date, time, datetime\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "pd.set_option('display.max_columns', None)  # On affiche tte les col des df\n",
    "pd.options.display.max_rows = 100  # On affiche jusqu'à 100 lignes d'un df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On charge les données depuis des fichiers en local\n",
    "# et on crée des data frame pour chaque fichier.\n",
    "path = 'C:/Users/GENTHON-M/Documents/formation/Open Classrooms/\\\n",
    "5-Segmentation clients/'\n",
    "\n",
    "df_Cust = pd.read_csv(path+\"olist_customers_dataset.csv\",\n",
    "                      low_memory=False)\n",
    "df_GeoLoc = pd.read_csv(path+\"olist_geolocation_dataset.csv\",\n",
    "                        low_memory=False)\n",
    "df_OrdItem = pd.read_csv(path+\"olist_order_items_dataset.csv\",\n",
    "                         low_memory=False)\n",
    "df_OrdPaymt = pd.read_csv(path+\"olist_order_payments_dataset.csv\",\n",
    "                          low_memory=False)\n",
    "df_OrdRvews = pd.read_csv(path+\"olist_order_reviews_dataset.csv\",\n",
    "                          low_memory=False)\n",
    "df_OrdDtset = pd.read_csv(path+\"olist_orders_dataset.csv\",\n",
    "                          low_memory=False)\n",
    "df_PdctDtset = pd.read_csv(path+\"olist_products_dataset.csv\",\n",
    "                           low_memory=False)\n",
    "df_SelrsDtset = pd.read_csv(path+\"olist_sellers_dataset.csv\",\n",
    "                            low_memory=False)\n",
    "df_PdctCat = pd.read_csv(path+\"product_category_name_translation.csv\",\n",
    "                         low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - [Description et nettoyage du data set](#3)<a class=\"anchor\" id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 - [Customers](#31)<a class=\"anchor\" id=\"31\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  \n",
       "0                     14409                 franca             SP  \n",
       "1                      9790  sao bernardo do campo             SP  \n",
       "2                      1151              sao paulo             SP  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Cust.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99441 96096\n",
      "(99441, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_Cust['customer_id'].nunique(),\n",
    "      df_Cust['customer_unique_id'].nunique())\n",
    "print(df_Cust.shape)\n",
    "cle = ['customer_id']\n",
    "df_Cust[cle].drop_duplicates().shape[0] == df_Cust.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il y a beaucoup de client qui n'achètent qu'une seule fois.\n",
    "Features possible : Encodage par état du Brésil (27 au total). Un client a un seul customer_unique_id et plusieurs customer_id s'il a passé plusieurs commandes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    0.,  5000., 10000., 15000., 20000., 25000., 30000., 35000.,\n",
       "        40000., 45000.]),\n",
       " <a list of 10 Text yticklabel objects>)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nombre de clients par état\n",
    "ClientEtat = df_Cust.groupby('customer_state')['customer_unique_id'].nunique()\n",
    "\n",
    "# Liste des états\n",
    "Etats = df_Cust['customer_state'].unique()\n",
    "Etats.sort()\n",
    "\n",
    "# Répartition des clients par état\n",
    "fig31 = plt.figure(figsize=(15, 7))\n",
    "axes = plt.axes()\n",
    "axes.set_xlabel('Etats', fontsize=12)\n",
    "axes.set_ylabel('Nombre de clients', fontsize=12)\n",
    "axes.set_ylim(0, 45000)\n",
    "plt.title('Nombre de clients par état du Brésil', fontsize=16)\n",
    "plt.bar(Etats, ClientEtat)\n",
    "plt.xticks(Etats, fontsize=12)\n",
    "plt.yticks(fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les deux tiers des clients sont situés dans 3 états (Sao Paulo, Rio de Janeiro et Minas Gerais)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 - [Geo Location](#32)<a class=\"anchor\" id=\"32\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>-23.545621</td>\n",
       "      <td>-46.639292</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546081</td>\n",
       "      <td>-46.644820</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546129</td>\n",
       "      <td>-46.642951</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "0                         1037       -23.545621       -46.639292   \n",
       "1                         1046       -23.546081       -46.644820   \n",
       "2                         1046       -23.546129       -46.642951   \n",
       "\n",
       "  geolocation_city geolocation_state  \n",
       "0        sao paulo                SP  \n",
       "1        sao paulo                SP  \n",
       "2        sao paulo                SP  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_GeoLoc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000163, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_GeoLoc.shape)\n",
    "cle = ['geolocation_zip_code_prefix']\n",
    "df_GeoLoc[cle].drop_duplicates().shape[0] == df_GeoLoc.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour faire une variable distance client - vendeur. Attention Zip code à 4 chiffres sur les\n",
    "zip code commencant par 0. Pas de clé unique simple ici\n",
    "Feature possible : Calcul distance vendeur-clients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 - [Order Data set](#33)<a class=\"anchor\" id=\"33\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "\n",
       "  order_status order_purchase_timestamp    order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33  2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37  2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49  2018-08-08 08:55:23   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0           2017-10-18 00:00:00  \n",
       "1           2018-08-13 00:00:00  \n",
       "2           2018-09-04 00:00:00  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdDtset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb Customer Id: 99441\n",
      "Nb Order Id: 99441\n",
      "Taille: (99441, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Nb Customer Id:', df_OrdDtset['customer_id'].nunique())\n",
    "print('Nb Order Id:', df_OrdDtset['order_id'].nunique())\n",
    "print('Taille:', df_OrdDtset.shape)\n",
    "cle = ['customer_id']\n",
    "df_OrdDtset[cle].drop_duplicates().shape[0] == df_OrdDtset.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On s'assure que order_id est une variable de string\n",
    "df_OrdDtset['order_id'] = df_OrdDtset['order_id'].astype('str', copy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La clé unique est Customer_Id. \n",
    "Il ne manque pas de lignes dans l'un ou l'autre\n",
    "Feature possible : Temps entre livraison et commande, temps entre estimation date livraison et date réélle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([     0.,  20000.,  40000.,  60000.,  80000., 100000.]),\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution des statuts\n",
    "Dist_Statut = df_OrdDtset.groupby('order_status')['customer_id'].nunique()\n",
    "\n",
    "# Liste des scores\n",
    "Statut = df_OrdDtset['order_status'].unique()\n",
    "Statut.sort()\n",
    "\n",
    "# Répartition des scores\n",
    "fig31 = plt.figure(figsize=(15, 7))\n",
    "axes = plt.axes()\n",
    "axes.set_xlabel('Statut', fontsize=12)\n",
    "axes.set_ylabel('Distribution', fontsize=12)\n",
    "axes.set_ylim(0, 100000)\n",
    "plt.title('Distribution des statuts de distribution', fontsize=16)\n",
    "plt.bar(Statut, Dist_Statut)\n",
    "plt.xticks(Statut, fontsize=12)\n",
    "plt.yticks(fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La grande majorité des commandes a été réceptionnée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On transforme au format date pour pouvoir faire des opérations de calcul.\n",
    "df_OrdDtset['order_delivered_customer_date'] = pd.to_datetime(\n",
    "    df_OrdDtset['order_delivered_customer_date'], yearfirst=True)\n",
    "df_OrdDtset['order_purchase_timestamp'] = pd.to_datetime(\n",
    "    df_OrdDtset['order_purchase_timestamp'], yearfirst=True)\n",
    "df_OrdDtset['order_estimated_delivery_date'] = pd.to_datetime(\n",
    "    df_OrdDtset['order_estimated_delivery_date'], yearfirst=True)\n",
    "\n",
    "# On calcule un temps de livraison par commande.\n",
    "df_OrdDtset['delivery_time'] = df_OrdDtset['order_delivered_customer_date'] - \\\n",
    "                               df_OrdDtset['order_purchase_timestamp']\n",
    "\n",
    "# On calcule la précision de la livraison / estimation par commande.\n",
    "df_OrdDtset['delivery_accuracy'] = \\\n",
    " df_OrdDtset['order_delivered_customer_date'] - \\\n",
    " df_OrdDtset['order_estimated_delivery_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vXq52r8ORNBu8CcoqPNz51/e4ZtwZnPtf2YmujohIp2IYBg8/PJ9///tf9OrVi3A4zDnnDOHll//Js8/+g3DYoH//Adx0029wOBzR1z3xxGMAdOvWnV27vmHatFsBeOih+bjdbgoKfsy8eX/kyy+/IBwOc+mlRVx88WhWrVrJyy//k8rKCr73vfNYvXoVS5cuJy0tnb179zB9+g089dSzDdb1739/krKyUqZPv4GFCxfxy19ezkMPPcYHH2xi/fp/cfBgJWVlpYwbV8i+fft4//336NatO3PnLsDhcMQ8p5ZSWDdB5aHaWXuf76pUWItIp/Pvj/fyr8174/Lewwb24ftnHfsWpDfeeI3//GcbTz21lKqqKq64YiIej4fXXlvLI4/8Lw6Hg0cffZh//OPvXHHF1Ue9/gc/yOOqqy5l6tSbMZvNvPnm6zz22F/5f//vCfr3/y/uuOMuDh2q5pprruK73z0TgNLSEp566lmsVitVVQdZt+418vPHsXr1S4wePbbRul5++RWsWPEcf/rTg3TvnlmvbOvWT/nb356hqqqKCRMKuP/+h7jhhpu5/vopvPvuBvr27cfKlcubdE7NpbBuAq+/dlGBHXsPJrgmIiKdzwcfbGLEiAuxWq1kZWWRm/t9wGDXrp1MmXIlAMFggO98Z0CDr8/KyuK0007n/fc3YrPZOOGEE+nZsxcbN76Lz+flpZdeBMDr9fLVV18C8J3vDMBqrY24/PxxLFr0KPn543jlldUsWPBoi87jrLPOJi0tnbS0dACGDPkeAL1796GqqooPPtjY5HNqLoV1E3h8QQB2llQTDhuYzbpvUUQ6j++fFbv3G08mk4nDp/xYLBZCoTAXXfQDbrxxOgA1NTWEQo2vtpaXN4bXX38Fq9XGqFE/BCAcDvHb395D//61gVhevp9u3bqzdu3L9YaezzlnMKWlpbz55uv06dO3xdegbTZbvceRfwxENPecmkMTzJog0rP2B8PsO1CT4NqIiHQuOTnn8vrrr+D3+zl48CDvvLMBgP/7vzc4cKAcwzC4//45LF36dKPvccEFI/jww/d57723GT78QgAGD/5edLZ4WVkZkyb9guLifUe91mQy8cMfjuWBB+YyZkx+zPrW/mOi+SF7zjlDmnVOzaGedRN4/MHo3zv2HuS4ng3vNyoiIke74IKRbN26haKin9OjR09OOukU0tPTufLKyUydeg2GYXDaad/hssuuaPQ9HI4UzjrrbAKBAE6nE4CrrprM/ff/kcsv/xnhcJj/+Z+p9O17PB999MFRr//BD/L4xz+e4oILRsas79ChF3DLLTcwb95DzTrP00//TrPOqTlMhtFx70fav786bnuQulwZlJZWNenYlf/+ihfe+gqrxcSIQcdx6cX941KnZNGctpe2pbZPnPZs+337vqZ37xPb5bM6A7MZli17lm++2REdok60hr4js9lEz57pDR6vnnUTeP0hbFYz7qxUduzT/9GJiHQmM2bcwr59e7n//ocB8Pm8TJlyVYPHXn31FIYNG9Ge1WsShXUTePwhHDYL2VlOPvlqvyaZiYh0IvfdN6/erlsORwpPPtk215LbiyaYNYHXFyTVYaF3Dyf+QJjiA55EV0lERJJIk3rWK1eu5JFHHiEYDDJp0iQuvfTSeuVbt25l5syZHDp0iJycHO666y4qKyu56qpvhxmqqqo4cOAAH3xw9IX/js7rD+GwWendIxWAHfsO0qenM8G1EhFpjAnDCGvnrQ6qJVPFYn6TxcXFzJ8/n6effprly5ezZMkStm/fXu+Y6dOnM2vWLNasWYNhGCxdupSePXuyYsUKVqxYwQsvvEDfvn25++67m13BjsDjC5Jit9AjIwWrxcRXe7Q4ioh0XHZ7ChUVZQSDgRYFg8SPYRgcOnQQq9XerNfF7FmvX7+e3NxcMjNrl13Ly8tj9erVXHfddQDs3r0br9fLoEGDACgsLGTBggVccskl0fd47rnnSE1NpaCgoFmV6yi8/hDpThtmswl3piaZiUjHlpXlorq6kvLyYsLhtlmUozMzm82Ew+HYB7YTq9VOVlbzFmaJGdYlJSW4XN++qdvtZvPmzY2Wu1wuiouLo49DoRCPPvoof/7zn5tVsY7E4w/Ss3sKANk9nGzZcYCwYWBuwQbiIiLxZjKZyMjIJCMjM/bBSaAr3LIYM6zD4TCmw0LJMIx6j2OVv/XWW5x00kn079/8e5Mbu9+srbhcGU06zh8Ik5FmJzPTySnHZ/LB52UEMHF8E18vR2tq20vbU9snjto+cTp728cM6969e7Nx48bo49LSUtxud73y0tJvN+YuKyurV/7qq68yZsyYFlWuoyyKUuMNAFBRUUOGo7bJPti6D4c61i3SFf6V21mp7RNHbZ84naXtj7UoSswJZkOHDmXDhg2Ul5fj8XhYu3Ytw4cPj5b37dsXh8PBpk2bAFixYkW98g8//JCcnJzWnkPChMJh/MEwdmttU/XqnoLFbOJrXbcWEZF2EjOss7OzmTZtGkVFRYwfP578/HwGDhzI5MmT+fjjjwGYO3cuc+bMYfTo0dTU1FBUVBR9/c6dO+ndu3f8ziDOIpt4OGy1TWU2m0h32jhQ5UtktUREJIk06T7rgoKCo2ZyL1q0KPr3gAEDWLZsWYOv/eijj1pRvcSLbI9ps1qiz6Wn2KioVliLiEj70B3zMXh9tT1ru+3bpkpPtVFZ7U9UlUREJMkorGOIDIPbLEeE9SGFtYiItA+FdQyRvawPD+u0VCtefwh/QIsNiIhI/CmsY4j0rO22w65Zp9oAqNBQuIiItAOFdQzfTjCrPwwOUHlIk8xERCT+FNYxeOvC2mE7OqwPaEa4iIi0A4V1DN9OMDt6GLz8oMJaRETiT2Edg8cfxG4zc/i2sA6bBYvZpIVRRESkXSisY/D4QqTYrRy+JazJVLuKWaWGwUVEpB0orGPw+oOk2C1HbeCuVcxERKS9KKxj8PpDpNgtRz2vVcxERKS9KKxj8PoiPev6z2sVMxERaS8K6xg8/hB229H7nWgVMxERaS8K6xg8dT3rI2kVMxERaS8K6xi8/hAOW+NhrVXMREQk3hTWMXjr7rM+klYxExGR9qKwPoZAMEwwZGC3Nh7WWsVMRETiTWF9DN667THtDQyDaxUzERFpLwrrY/BE1gVvoGetVcxERKS9KKyPIbLjlt16dM8atIqZiIi0D4X1MUR33LKaGizXKmYiItIeFNbH4KnrWdssDTeTVjETEZH2oLA+hkjPuqEJZqBVzEREpH0orI/BUzcb3NbAfdagVcxERKR9KKyPweur7TE7GptgplXMRESkHSisjyFyn/WxrlmDVjETEZH4alJYr1y5kjFjxjBq1CgWL158VPnWrVspLCwkLy+PmTNnEgzWhlxJSQm/+tWvGD9+PBMnTmTXrl1tW/s48/ga3ss6QquYiYhIe4gZ1sXFxcyfP5+nn36a5cuXs2TJErZv317vmOnTpzNr1izWrFmDYRgsXboUgFtvvZULL7yQ5cuXM27cOObOnRufs4gTr79uL+tGyrWKmYiItIeYYb1+/Xpyc3PJzMzE6XSSl5fH6tWro+W7d+/G6/UyaNAgAAoLC1m9ejXl5eV89tlnTJw4EYCf/OQn3HjjjXE6jfjw+EM47FYMo+G41ipmIiLSHmKGdUlJCS6XK/rY7XZTXFzcaLnL5aK4uJidO3dy3HHHce+99/KTn/yEqVOnYrPZ2rj68RXtWTfWtaZ2FTP1rEVEJJ6ssQ4Ih8OYTN+u4GUYRr3HjZUHg0G2bNnC9ddfz2233cazzz7LjBkz+Pvf/97kyvXsmd7kY1vC5co4ZnkoDOlOO5mZzkaPyeqeQtkBT8z3kvrUXomjtk8ctX3idPa2jxnWvXv3ZuPGjdHHpaWluN3ueuWlpaXRx2VlZbjdblwuF2lpaVx44YUA5OfnM3v27GZVbv/+asLhY3RrW8HlyqC0tOqYxxw85KNntxQqKmoaPcZhNVNe5aWk5GC9f7RI45rS9hIfavvEUdsnTmdpe7PZ1GgnNeYw+NChQ9mwYQPl5eV4PB7Wrl3L8OHDo+V9+/bF4XCwadMmAFasWMHw4cM54YQT6N27N2+++SYA69at44wzzmiL82k3Xl8IxzFmgwNkpNrwB8LR1c5ERETaWsywzs7OZtq0aRQVFTF+/Hjy8/MZOHAgkydP5uOPPwZg7ty5zJkzh9GjR1NTU0NRUREADz30EI8//jj5+fn87W9/4w9/+EN8z6aNef3BRnfcitC91iIiEm8xh8EBCgoKKCgoqPfcokWLon8PGDCAZcuWHfW6U045pVnXqDsSwzDw+kM4GllqNCLDaQegotrHcT3T2qNqIiKSZLSCWSMCwTChsBFzGFwLo4iISLwprBvhiexl3chSoxHfhrU37nUSEZHkpLBuRHRd8Ea2x4ywWc2k2C3qWYuISNworBsR2XHLbo3dRBlOGxWaYCYiInGisG6Ex1fbs7ZaYt87nZ6qVcxERCR+FNaNiNw37YgxDA6QnmrX+uAiIhI3CutGeCLXrGPcZw21w+BVNQFC4XC8qyUiIklIYd0Ib90weFOuWaen2jCAykOBONdKRESSkcK6EZFbt+xNGAbPqLt9S5PMREQkHhTWjfD4gpjNJprQsSbdWbfkqO61FhGROFBYN8LrC5FqtwCxZ4NnaBUzERGJI4V1Izz+ICl2K03ZoDPVYcViNlGu27dERCQOFNaN8PiCpNgtGEbsuDaZTHX3WmsYXERE2p7CuhHfhnXTjtfCKCIiEi8K60Z4/CFSHE3aQRSonWRWUe2PY41ERCRZKawb4fUFm7R6WURGqo3Kal+Ths1FRESaQ2HdCI8/FHMv68Olp9rwB8PR3bpERETaisK6EZ5m9qwj+1ofqNJQuIiItC2FdQOCoTCBYBi7renNk+G0A1rFTERE2p7CugGRHbeasi54RKRnvV+3b4mISBtTWDcgspe1w9aM2eCRVcwq1bMWEZG2pbBuQCSsbc3oWdusZlLsFt1rLSIibU5h3YDIMLjN0rzmyXBqYRQREWl7CusG1ET2sm7GBDOoHQrXBDMREWlrCusGeCNhbW36rVsA6al2hbWIiLQ5hXUDPJFh8GZcswbonmanqiZAIBiKR7VERCRJKawb4I3OBm9+WAPs177WIiLShpqURitXrmTMmDGMGjWKxYsXH1W+detWCgsLycvLY+bMmQSDtWH3wgsvMGzYMMaNG8e4ceOYP39+29Y+Tjz+IGaTCavF1KzXdU+vDeuySk88qiUiIkkq5o3ExcXFzJ8/n+effx673c7EiRM577zzOO2006LHTJ8+ndmzZzNo0CBuv/12li5dyiWXXMInn3zCjBkzyM/Pj+tJtDWPL0SKwwKYgKZvzBHpWRcf8HDmyfGpm4iIJJ+YPev169eTm5tLZmYmTqeTvLw8Vq9eHS3fvXs3Xq+XQYMGAVBYWBgt//jjj3nhhRcoKCjglltuobKyMk6n0baie1k383XpqTYsZhOlB9SzFhGRthOzZ11SUoLL5Yo+drvdbN68udFyl8tFcXFx9O+rrrqKwYMHM2/ePO6++27uv//+JleuZ8/0Jh/bEi5XRoPPh4G0VBvdu6U2O7CzujmoqAk0+t5SS+2TOGr7xFHbJ05nb/uYYR0OhzGZvr12axhGvcfHKl+4cGH0+auvvpqLL764WZXbv7+acDg++0O7XBmUllY1WFZZ5cNmMXOgoqbZ75uRYmNvaXWj7y3HbnuJL7V94qjtE6eztL3ZbGq0kxpzGLx3796UlpZGH5eWluJ2uxstLysrw+12U1VVxZNPPhl93jAMLJbm3becKJFh8Jbonu5g/0Ft5iEiIm0nZlgPHTqUDRs2UF5ejsfjYe3atQwfPjxa3rdvXxwOB5s2bQJgxYoVDB8+HKfTyeOPP85HH30EwFNPPdXsnnWiePwhUuxN38TjcN3T7NR4g3j9wTaulYiIJKuYiZSdnc20adMoKioiEAgwYcIEBg4cyOTJk5k6dSpnnXUWc+fO5Y477qC6upozzjiDoqIiLBYLDzzwAHfeeSder5eTTjqJ++67rz3OqdW8viAOW0t71pHbt7wc74rvNXcREUkOTeo+FhQUUFBQUO+5RRxJ7asAACAASURBVIsWRf8eMGAAy5YtO+p1OTk5vPDCC62sYvvz+ILNXhc8InL7VmmFR2EtIiJtQiuYHSEYCuMPhrG3uGftAKD0gK5bi4hI21BYHyGyPWZzlxqNSLVbsFnNlLRgJrmIiEhDFNZHaOmOWxEmk4nMNDulFepZi4hI21BYH+HbHbdafptZt3S7bt8SEZE2o7A+gifSs7Y1bxOPw2WmOdhf6cUw4rOgi4iIJBeF9REiYW2ztLxpuqfZ8QVCHPIG2qpaIiKSxBTWR/D4Iz3rlg+Df3uvtfa1FhGR1lNYH8Hrq71mbbe2rmcNUKoZ4SIi0gYU1keI9KxbuoIZfHuvdUmFtsoUEZHWU1gfweMLYTKB1dLyCWYOm4UUu0ULo4iISJtQWB/B6wuSarcCLQ9rgMx0O2WV6lmLiEjrKayPENkes7U3XXVLc1BWqZ61iIi0nsL6CJHtMVt7j3T3NDvlB72Eda+1iIi0ksL6CB5fkBSHhdZmbGa6nWDI4OAhf9tUTEREkpbC+ghefxCHveUzwSPSU20AVCqsRUSklRTWR/D4QqS04ratiEhYV1RpYRQREWkdhfURPP5gq+6xjkhLqQtr9axFRKSVFNZH8PjaZhjcmWIFoKJKM8JFRKR1FNaHCYXD+ANh7LbWN4vVYibFbqGiWj1rERFpHYX1Ybx1e1k7bNY2eb+0VJvCWkREWk1hfZjo9pit2MTjcOkpVt26JSIiraawPkxb7Lh1uLQUm8JaRERaTWF9mMiOW23Vs05LtVF5yN/q1dBERCS5KawPExkGb7uetZVgKBy9Fi4iItISCuvDeKLD4K2/dQtqe9agVcxERKR1FNaHiQ6D29tqgpnCWkREWq9JqbRy5UrGjBnDqFGjWLx48VHlW7dupbCwkLy8PGbOnEkwGKxXvmXLFs4888y2qXEcRSaYOdqsZ117C1hltZYcFRGRlosZ1sXFxcyfP5+nn36a5cuXs2TJErZv317vmOnTpzNr1izWrFmDYRgsXbo0WubxeLjnnnsIBAJtX/s25vEFMQE2S9vNBgco1/rgIiLSCjFTaf369eTm5pKZmYnT6SQvL4/Vq1dHy3fv3o3X62XQoEEAFBYW1iu/9957mTRpUhyq3vY8bbTjVkSK3YLFbNLCKCIi0ioxl+oqKSnB5XJFH7vdbjZv3txoucvlori4GIDXXnsNr9fL6NGjW1S5nj3TW/S6pnK5Muo9tlgtpNitdOue2ur9rCMynHY8/tBRn5Xs1B6Jo7ZPHLV94nT2to8Z1uFwGJPJFH1sGEa9x42Vl5aW8sgjj/Dkk0+2uHL791cTDsfnHmWXK4PS0qp6z1VWebFaTFRWeAi3UVqnOiyUHqg56rOSWUNtL+1DbZ84avvE6SxtbzabGu2kxhwG7927N6WlpdHHpaWluN3uRsvLyspwu9288cYbVFRUcOmllzJu3DgAxo0bR3V1dYtPJN78gTB2qwWDtvsHglYxExGR1ooZ1kOHDmXDhg2Ul5fj8XhYu3Ytw4cPj5b37dsXh8PBpk2bAFixYgXDhw/npz/9Ka+++iorVqxgxYoV0bL09PgObbeGLxDCbjO32RA41M4I12xwERFpjZhhnZ2dzbRp0ygqKmL8+PHk5+czcOBAJk+ezMcffwzA3LlzmTNnDqNHj6ampoaioqK4Vzwe/HVh3ZbSU2wc8gYJhcNt+r4iIpI8mrQXZEFBAQUFBfWeW7RoUfTvAQMGsGzZsmO+x7Zt21pQvfblD4RJd9ra9D0jq5hV1QTITHe06XuLiEhy0Apmh/EFQm22LnhEmlYxExGRVlJYH8YfDLXZjlsR6XWrmFVoYRQREWkhhfVh/IEw1jZaajQi0rM+oElmIiLSQgrrw/gCbd+zdqZofXAREWkdhXWdsGEQCIaxWUyxD24Gq8VMit2iJUdFRKTFFNZ1AoHaW6vaahOPw6Wn2hTWIiLSYgrrOr5A7faY1jYeBgdIS7FqFTMREWkxhXUdfySs49CzTkuxUXlI16xFRKRlFNZ1fMHaYfC4hHWqjYOHAhhtuY6piIgkDYV1nW971m07wQxqh8GDoTBef6jN31tERLo+hXWdeIZ1eqpWMRMRkZZTWNfxBeI4DK4lR0VEpBUU1nXiOcEs0rM+UOVt8/cWEZGuT2FdJ3LrVluvYAZEd/IqP6gZ4SIi0nwK6zr+yGxwc9s3icNmwWEzU35QPWsREWk+hXUdfxx71gAZTjvl2nlLRERaQGFdxxfHa9ZQe936gMJaRERaQGFdxx8IYzGbiMMoOAAZTpv2tBYRkRZRWNfxB0LYbWaI0yJjGU47Bw/5CYXD8fkAERHpshTWdXyBEHabJV5ZTUaqDQOoPBSI0yeIiEhXpbCu4w+GsVstcVu/O3L7lobCRUSkuRTWdfyBEPY4zQQHyEi1A+j2LRERaTaFdZ3oMHjcrlnX9qzLFNYiItJMCus6/kA4rj3rFLsFq8WkVcxERKTZFNZ1/IEQNlv8msNkMpHhtHNAPWsREWkmhXUdXyCE3WqJ62dkpNo4UK2etYiINE+TwnrlypWMGTOGUaNGsXjx4qPKt27dSmFhIXl5ecycOZNgMAjAxo0bKSwspKCggGuuuYbKysq2rX0b8gfDcVtqNEILo4iISEvETKfi4mLmz5/P008/zfLly1myZAnbt2+vd8z06dOZNWsWa9aswTAMli5dCsBtt93Gfffdx8qVKznttNN44okn4nMWbcAfCMVtqdGIDKedikN+wvGaxSYiIl1SzHRav349ubm5ZGZm4nQ6ycvLY/Xq1dHy3bt34/V6GTRoEACFhYXR8lWrVnHaaacRCAQoLi6mW7ducTqN1vPF+dYtqO1Zh8MG1TX+uH6OiIh0LdZYB5SUlOByuaKP3W43mzdvbrTc5XJRXFwMgM1mY9u2bVx55ZVYrVZuuummZlWuZ8/0Zh3fXC5XBgChUJhgyCA9zU5mpjNun9e7V+35hC2W6Gcnq2Q//0RS2yeO2j5xOnvbxwzrcDiMyWSKPjYMo97jWOX9+/dn/fr1PPPMM0ybNo1nnnmmyZXbv7+acDg+Q8YuVwalpVUAeHy119hDwTAVFTVx+TwAU93w91c7D5CZErPpu6zD217al9o+cdT2idNZ2t5sNjXaSY057tu7d29KS0ujj0tLS3G73Y2Wl5WV4Xa78fl8vPrqq9Hnf/SjH7Ft27YWnUC8+aPbY5piHNk6kYVR9ldqkpmIiDRdzLAeOnQoGzZsoLy8HI/Hw9q1axk+fHi0vG/fvjgcDjZt2gTAihUrGD58OFarlbvuuotPPvkEgJdffpnBgwfH6TRaxxes3QnLGudr1k6HFYvZRHmV7rUWEZGmizkWm52dzbRp0ygqKiIQCDBhwgQGDhzI5MmTmTp1KmeddRZz587ljjvuoLq6mjPOOIOioiIsFgvz589n1qxZhEIhsrOz+f3vf98e59Rs3/as4xvWJpOJdKdN64OLiEizNOnCaUFBAQUFBfWeW7RoUfTvAQMGsGzZsqNel5OTw/PPP9/KKsafP1DXs45zWEPdwii611pERJpBK5hRe9sWQJxHwQFIV1iLiEgzKaxpv2FwqFsYpdoXt32zRUSk61FYU7vUKIDVEt+1waF2RngwZHDIG4j7Z4mISNegsAZ8/tqedbzXBofanjXAgSqtYiYiIk2jsAb8wfa5zxpqJ5gB2n1LRESaTGHNt7PB26dnHVkYRbdviYhI0yisOXw2ePybIy3FhtkEpRWeuH+WiIh0DQprIttjmjC1Q2uYzSZ6dk9hV0l1/D9MRES6BIU1tcPgdpsF2uluKndmKt8orEVEpIkU1tQOgzuslvbKatxZTg4e8nNQ+1qLiEgTKKypnQ1us5nbbaESd1YqADvVuxYRkSZQWFM3DG6N/4IoEdl1Yb1jb8ffX1VERBJPYU3tMLjdZqa9VgBNsVvpnmbn6+KD7fOBIiLSqSmsqZ0Nbm+PXTwO485K5ZtiDYOLiEhsCmvAFwhja8dhcKidEV56wIPXH2zXzxURkc5HYU1dz9rW3j1rJwawu7SmXT9XREQ6H4U14AuGsLXD9piHi0wy03VrERGJRWFN7Wzw9lgX/HAZThspdgs79mlGuIiIHJvCmshyo+3bFCaTieysVL4pVliLiMixJX1YB0NhQmEDWztfswZwZaayp+wQoXC43T9bREQ6j6QP6+j2mO3cswbIznISDBnsK9cOXCIi0jiFdbBue8wEhHVk2VENhYuIyLEkfVhH97K2mNr9s3t2S8FiNrFjr2aEi4hI45I+rCPD4FZL+y6KArV7W7szU/lSYS0iIsegsE5gzxrgeHc6O/ZWRXv4IiIiR0r6sE7kMDjACdnphMIGX+xW71pERBrWpLBeuXIlY8aMYdSoUSxevPio8q1bt1JYWEheXh4zZ84kGKxd73rTpk1MmDCBcePGMWnSJHbv3t22tW8D3w6DJ+bfLce70jGZYOvX5Qn5fBER6fhiJlRxcTHz58/n6aefZvny5SxZsoTt27fXO2b69OnMmjWLNWvWYBgGS5cujT4/e/ZsVqxYQUFBAbNnz47PWbRCImeDAzhsFvr0cLJ1x4GEfL6IiHR8MRNq/fr15ObmkpmZidPpJC8vj9WrV0fLd+/ejdfrZdCgQQAUFhayevVq/H4/N9xwAwMGDACgf//+7N27N06n0XI+f21Y26yJGQYH6OfOYEdxlXbgEhGRBlljHVBSUoLL5Yo+drvdbN68udFyl8tFcXExdrudcePGARAOh3n44Yf5wQ9+0KzK9eyZ3qzjm8vlysCeYgOgR1YaKfaYzREX3z21J+9sLaakys+QAVkJqUN7c7kyEl2FpKW2Txy1feJ09raPmU7hcBiT6dtep2EY9R7HKvf7/cyYMYNgMMiUKVOaVbn9+6sJh41mvaapXK4MSkurKD9Qu0VlzSEf3hp/XD4rlu6pVsxmE29v3sMJPZ0JqUN7irS9tD+1feKo7ROns7S92WxqtJMacxi8d+/elJaWRh+XlpbidrsbLS8rK4uWHzp0iKuvvppgMMgjjzyCzWZr8UnES2Q2uC1Bs8EB7FYLx/V0svVrXbcWEZGjxQzroUOHsmHDBsrLy/F4PKxdu5bhw4dHy/v27YvD4WDTpk0ArFixIlo+ffp0TjzxRB544AHsdnucTqF1ottjmhIX1lB73fqb4ipqvLpuLSIi9cUM6+zsbKZNm0ZRURHjx48nPz+fgQMHMnnyZD7++GMA5s6dy5w5cxg9ejQ1NTUUFRWxZcsWXnvtNd5//31+/OMfM27cOCZPnhz3E2ouXzCE3WbGiM9oe5OdkJ2OYcD23ZWJrYiIiHQ4TZpRVVBQQEFBQb3nFi1aFP17wIABLFu2rF75d7/7XbZt29YGVYwvvz+Ew9r+S40eqW+vNCxmE5/uKGfgqT0TXR0REelAtIJZMIzNZsFIcNfaajHTt1can+m6tYiIHCHpw9ofCGG3doxm6Jedzq6Sal23FhGRejpGSiVQJKwTfc0aoJ8rHQP4Yo+uW4uIyLeSPqw9/hAOe+KvWQP06ZmG2WzSLVwiIlJP0oe1twOFtc1qpk9PJ9u+UViLiMi3FNb+YIeYDR7Rz5XO18XV0TXLRUREFNa+EHZbxwnr413phMMGX+3V/tYiIlIrqcM6bBj4ArWLonQUfV1pdftbayhcRERqdZyUSoDIUHNH6lk7bBays5x8trMi0VUREZEOIqnD2hsJ6w5yn3XE8a40duw5SCAYTnRVRESkA+hYKdXOPL7axUdsHSys+7nTCYTCfF3c8bd0ExGR+OtYKdXOIj1rq6VjNUNfV+1+pp/pFi4RESHpw7q2Z93RhsGdDiuu7il89rWuW4uISNKHdW3P2taB7rOOON6dzhd7KgmFdd1aRCTZJXVYR65Zd7SeNdTeb+3zh/im+FCiqyIiIgnW8VKqHX3bs+54zXCCO3LdujzBNRERkUTreCnVjqLXrDvQfdYRaak2enZLYctXCmsRkWSX5GEdwmyCDtixBuCE7HQ+311JMKTr1iIiyayDxlT78PpCpNitgCnRVWnQCe50/IEwX++rTnRVREQkgZI7rP1BHHYLRqIr0oh+2RkAbP1aQ+EiIsksycM6RIrdgmF0zLh2Oqy4MlPYok09RESSWpKHdbAurBNdk8ad4M7gi92VWidcRCSJJXVYe/whHB1wJvjhTshOJxAMs2Of9rcWEUlWSR3WXn8Ih71jh3W/uvutt+zQULiISLJK8rAOdviedYrdSnZWKlt13VpEJGkld1j7Qh1yQZQj9XOn8+WeSgLBUKKrIiIiCdCksF65ciVjxoxh1KhRLF68+KjyrVu3UlhYSF5eHjNnziQYDNYrf+CBB3jooYfapsZtxDAMvP5Qh1wX/EgnZGcQDBls+6Yy0VUREZEEiJlUxcXFzJ8/n6effprly5ezZMkStm/fXu+Y6dOnM2vWLNasWYNhGCxduhSAqqoqbr/9dv7617/Gp/at4AuECBtGp+hZn5idQVqKldXvfp3oqoiISALEDOv169eTm5tLZmYmTqeTvLw8Vq9eHS3fvXs3Xq+XQYMGAVBYWBgtf+211zjppJO48sor41T9luvIO24dyWY1k9PfzZYdBzQrXEQkCVljHVBSUoLL5Yo+drvdbN68udFyl8tFcXExAOPHjwdo8RB4z57pLXpdU+wpq13Cs1tGCpmZzrh9TlsZkdOPd7YWs3bjLmZeeV6iq9NqLldGoquQtNT2iaO2T5zO3vYxwzocDmMyfbt2tmEY9R7HKm+N/furCYfjs2KJx1c7WSsYCFFRUROXz2hr55zei7c/2cdHW/dxXK+0RFenxVyuDEpLqxJdjaSktk8ctX3idJa2N5tNjXZSY44B9+7dm9LS0ujj0tJS3G53o+VlZWX1yjuqmrphcKulY27i0ZAh33FhsZhZ9bauXYuIJJOYYT106FA2bNhAeXk5Ho+HtWvXMnz48Gh53759cTgcbNq0CYAVK1bUK++oItesO/p91odzptg4+7SevL2lmLJKT6KrIyIi7SRmWGdnZzNt2jSKiooYP348+fn5DBw4kMmTJ/Pxxx8DMHfuXObMmcPo0aOpqamhqKgo7hVvLY+3NqxtnWCC2eG+17921OKfG9S7FhFJFiajo245RXyvWW/avp+Fyz7i+glnkWqLeem+Q3nt/V28/59S7vnleZ3y2nVnuX7UFantE0dtnzidpe1bdc26q6qp61k7LJ1nGDzi/DN6Y7daWPbGF4muioiItIOkDevINevONgwOtftcn/dfbj7cXsa2b7RmuIhIV9f5kqqNeHx1m3h0nsng9Qzp7ybDaePZdV/Qga9kiIhIG0jqsE6xW+isOWezmhl2Vh++3HuQTdtKY79AREQ6raQN6xpvAIfd0ql7pWec1AN3ZipPv/ofDnkDia6OiIjESdKGdaRn3ZmZzSZGn3cCBw8FeGrtfxJdHRERiZOkD+tO3LEGoHcPJ0PP7M07W4p5d2txoqsjIiJxkNRhbe9k91c3Jve72RzX08nf1mzjQJUv0dUREZE2lrRhXeMN4rB1jdM3m02MyT2RQCDMX1dt7dTX4UVE5GhdI61aIHrrVhfRo1sKIwYdxydflfPGh7sTXR0REWlDSR3W9i4U1lC7hebJfTJY8tp2iss7x7afIiISW1KGdTAUJhAMY+8iw+ARJpOJ0eeegNlsYtE/txAKhxNdJRERaQNdK62ayOsPAWDvhEuNxpLhtHNxTj++3HOQVW9/k+jqiIhIG+ga06GbyVu3Lrjd2rWGwSP+68Qstu+uZMVbX2K11Pa2TaZOuq6qiIgkaVjX9axtXWwY/HCjvtcPA3h23Rd8sbuSq8b8F84UW6KrJSIiLdB10+oYomFt6bqn77BZKDj/RP57yPF8uH0/dz35Hl/srkx0tUREpAW6blodg8dfOwxu7cJhDbUTzoZ8x8Uv/vs0AsEwf3hqE8+9+QWBoCaeiYh0Jkk9DN5Vr1kfqW+vdCaNHsCbH+7hpQ1fs/mL/eSd248BJ2TRo1tKoqsnIiIxJGdYRyaYdeFr1kdy2CyM+l4/TuvbnbUbd/L4P7cC4M5K5b8HH88Pco7XJDQRkQ4qOcO6C9+6Fcspx3VjSsF32V/lY1dpNdt3VfKP1z7nm5IqivIGYEvCNhER6eiSNKxre9Y2a+fez7qlTCYTvbql0KtbCmef0pO3txTz1ua9lBzwcO2Pz6Jbmj3RVRQRkcMkZTfK4w9hs5oxJ+XZ12cymTj/jN6MG3YyX+2t4o7H3+Gf63dQ4w0kumoiIlInSXvWIVId1k6/l3Vb6t8vk6wMO//+ZB/P/9+XvPzO11ww8DhOP747J2Rn0Kt7iq5pi4gkSJKGdZAUuwUU1vW4M538eNgplFR4eO+zEl7duJO17+0EIMVuIcVuwWoxY7dZ6H9CJsPO6sNJvTMU4iIicZacYe0LkeKwYiitG+TOTGVs7onkfa8f5VU+yio8lFf5CIUNgqEwHl+Itz7aw7r3d9PXlcapx3XDZrVgt5rplZnK4NN70T3dkejTEBHpMpIzrP1BUu0aBo/FajHjzkzFnZl6VJkvpy+f76rk068O8NH2/fiDYQLBEMGQwVNrtnF6v0wGndaLE7PT6etK16Q1EZFWaFJYr1y5kkceeYRgMMikSZO49NJL65Vv3bqVmTNncujQIXJycrjrrruwWq3s2bOH6dOns3//fk4++WTmzp1LWlpaXE6kOTz+kHp+reSwWTnz5J6ceXJPTKbaiWomoKzSy+e7K/ns6wMsXbc9enx6qg1XZgo9uqVwfHY3nDYzPbql0LO7gx7dUshItWk4XUSkESYjxr1LxcXF/OIXv+D555/HbrczceJE5s2bx2mnnRY9Jj8/n9mzZzNo0CBuv/12zjzzTC655BKmTJnCj370I8aOHcvChQupqalh+vTpTa7c/v3VhMNt0/399Ktyuqfb6dsrjdsXvcOJvTMYefZxbfLe0jCPL8j+gz7Kq7yUH/RRechHRbWPA1U+/IH6S57arbXhnZ5qi4a/zWomM81OZoaDbk47VqsZi9mE1WIiLcVGtzQ73Zx2Uh1W7DZzl18+ti24XBmUllYluhpJSW2fOJ2l7c1mEz17pjdYFrNnvX79enJzc8nMzAQgLy+P1atXc9111wGwe/duvF4vgwYNAqCwsJAFCxbw05/+lPfee4+FCxdGn7/sssuaFdZmc9v0tIKhMM++8QVef5CsDAcWiwlXVioOe3IsN5ooDruFzAwHp9Kt3vPduqWwv9zDIa+fQ74QNd4AVTV+qmoC+AK1C9YYBgSCYUoqvXyx92CT/tFmMZtqr53bzNisZmwWM2HDIBw2MAwIG9/+d9ig9j3r/jaM2hkMVrOJ1BQraSk2UmxmzBYzVrMZk4noNftQKIzNZsFR9x+LxQRG3XxF49uZECbAYjZjtpgwm0x1n2tAmOjf4bCB2WzCWnecxWyqfWwxYcZU916Hza6o+5zD/41tGNQrP/z5I6Wk2vB6jn1bXnSAw/Ttf0WeM5lMtf+Yqis5vKzDakEFW3RKMV6U5rRzqMbfwMvapwFbPEenfV/W8A+3uUyRX2ittLTatjdhwsAgHDIIHfa/P4vJhNliIhgK4w+ECQTDmEwm7FYzdquZQCjMwUN+Kmv8BAJhMpx2uqfb6dPDyeD+Lsxt9D+CY2VezLAuKSnB5XJFH7vdbjZv3txoucvlori4mAMHDpCeno7Vaq33fHNkZbXdkPnCWy9qs/eSNnBSoisgItJ5xBw3DIfD9a4lGoZR73Fj5UceB+iapIiISAvEDOvevXtTWloafVxaWorb7W60vKysDLfbTY8ePaiqqiIUCjX4OhEREWmamGE9dOhQNmzYQHl5OR6Ph7Vr1zJ8+PBoed++fXE4HGzatAmAFStWMHz4cGw2Gzk5OaxatQqA5cuX13udiIiINE3M2eBQe+vWY489RiAQYMKECUyePJnJkyczdepUzjrrLD777DPuuOMOqqurOeOMM5gzZw52u53du3czY8YM9u/fT58+fZg3bx7du3dvj/MSERHpMpoU1iIiIpI4ujFVRESkg1NYi4iIdHAKaxERkQ5OYS0iItLBKaxFREQ6uKQL65UrVzJmzBhGjRrF4sWLE12dLu/yyy9n7NixjBs3jnHjxvHRRx/pO4iz6upq8vPz2bVrF1C7vn9BQQGjRo1i/vz50eO2bt1KYWEheXl5zJw5k2AwmKgqdxlHtv1tt93GqFGjor//V155BWj8O5GWefjhhxk7dixjx47lvvvuA7rg795IIvv27TMuvPBC48CBA8ahQ4eMgoIC4/PPP090tbqscDhsDBs2zAgEAtHn9B3E14cffmjk5+cbZ5xxhrFz507D4/EYI0aMML755hsjEAgYV111lfHGG28YhmEYY8eONT744APDMAzjtttuMxYvXpzIqnd6R7a9YRhGfn6+UVxcXO+4Y30n0nz//ve/jZ///OeGz+cz/H6/UVRUZKxcubLL/e6Tqmd9+A5iTqczuoOYxMeXX34JwFVXXcWPfvQjnnrqKX0HcbZ06VJ+97vfRZf23bx5MyeeeCL9+vXDarVSUFDA6tWrG9wtT99D6xzZ9h6Phz179nD77bdTUFDAggULCIfDjX4n0jIul4sZM2Zgt9ux2Wyceuqp7Nixo8v97mPuutWVxNpBTNrWwYMHOf/88/ntb39LIBCgqKiIH/7wh/oO4uj3v/99vccN/eaLi4sb3S1PWu7Iti8rKyM3N5ff/e53ZGRkMGXKFJYtW4bT6WzwO5GWOf3006N/79ixg5dffpnLLrusy/3uk6pnHWsHMWlb55xzDvfddx8ZGRn06NGDCRMmsGDBAn0H7aix37z+txB//fr1Y+HChbjdblJTU7n88st588031fZx8vnnn3PVVVdx66230q9fvy73u0+qsI61g5i0rY0bN7Jhw4boY8Mw6Nu3r76DdtTYb76x3AA5AwAAA2lJREFU3fKk7Wzbto01a9ZEHxuGgdVq1f8PxcGmTZu44ooruPnmm/nxj3/cJX/3SRXWsXYQk7ZVVVXFfffdh8/no7q6mhdeeIE//elP+g7a0dlnn81XX33F119/TSgU4p///CfDhw9vdLc8aTuGYfCHP/yByspKAoEAS5Ys4eKLL270O5GW2bt3L9deey1z585l7NixQNf83SfVNevs7GymTZtGUVFRdAexgQMHJrpaXdaFF17IRx99xPjx4wmHw1xyySUMGTJE30E7cjgc3HvvvVx//fX4fD5GjBjB6NGjAZg7d2693fKKiooSXNuuZcCAAfzqV7/iF7/4BcFgkFGjRpGfnw/Q6HcizffEE0/g8/m49957o89NnDixy/3uteuWiIhIB5dUw+AiIiKdkcJaRESkg1NYi4iIdHAKaxERkQ5OYS0iItLBKaxFkkT//v0pLy9PdDVEpAUU1iIiIh1cUi2KIiKwZMkS/v73v2M2m+nVqxe//e1vOfnkk5kxYwann346v/zlLwHqPb7ooosYOHAg27Zt46abbqK0tJRnnnkGm82Gw+Hg7rvv5rTTTkvwmYl0XQprkSTy9ttv8/jjj7NkyRJ69OjB888/z7XXXstLL70U87Wnn346DzzwAKFQiLPPPpvXX38dt9vN8uXL2bRpk8JaJI40DC6SRN566y3GjBlDjx49gNr9fIuLi9m1a1fM1+bk5ABgsVgYPXo0EydO5O6776Zbt25MmDAhrvUWSXYKa5EuasuWLWzbtg2o3VQCICMj46jjDMMgGAxiMpk4fPXhQCBQ7zin0xn9e+7cuTz66KOccMIJ/OUvf+Gmm26KxymISB2FtUgXtW7dOh5//HEAPv30U3r06MGIESNYtWpVdFb4c889R2ZmJieeeCJZWVl88sknABQXF/Puu+82+L7l5eWMGDGCzMxMrrjiCm688UY+/vjj9jkpkSSla9YiXdRll13GzTffTH5+PoFAgNmzZ/P973+fK664gkmTJhEOh+nRowePPfYYZrOZyy+/nFtuuYW8vDyOP/54cnNzG3zfHj168Otf/5orrriClJQULBYLs2fPbuezE0ku2nVLRESkg9MwuIiISAensBYREengFNYiIiIdnMJaRESkg1NYi4iIdHAKaxERkQ5OYS0iItLB/X+UfJU06lSg+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Density plot délai de livraison\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.kdeplot(df_OrdDtset['delivery_time'].dt.total_seconds()/(3600 * 24),\n",
    "            shade=True, ax=ax)\n",
    "ax.set_title('Délai de livraison, skewness: ' +\n",
    "             str(round(df_OrdDtset['delivery_time']\n",
    "                 .astype('timedelta64[D]').skew(), 1)), fontsize=13)\n",
    "ax.set_xlabel('Jours', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La majorité des commandes arrivent dans les 30 jours suivant la commande."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Density plot précision délai livraion\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.kdeplot(df_OrdDtset['delivery_accuracy'].dt.total_seconds() /\n",
    "            (3600 * 24), shade=True, ax=ax)\n",
    "ax.set_title('Précision du délai de livraison annoncé, skewness : '\n",
    "             + str(round(df_OrdDtset['delivery_accuracy']\n",
    "                         .astype('timedelta64[D]').skew(), 1)), fontsize=13)\n",
    "ax.set_xlabel('Jours', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La majorité des commandes ont été reçues avant la date estimée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4541</th>\n",
       "      <td>2e7a8482f6fb09756ca50c10d7bfc047</td>\n",
       "      <td>08c5351a6aca1c1589a38f244edeee9d</td>\n",
       "      <td>shipped</td>\n",
       "      <td>2016-09-04 21:15:19</td>\n",
       "      <td>2016-10-07 13:18:03</td>\n",
       "      <td>2016-10-18 13:14:51</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4396</th>\n",
       "      <td>e5fa5a7210941f7d56d0208e4e071d35</td>\n",
       "      <td>683c54fc24d40ee9f8a6fc179fd9856c</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2016-09-05 00:15:34</td>\n",
       "      <td>2016-10-07 13:17:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-10-28</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10071</th>\n",
       "      <td>809a282bbd5dbcabb6f2f724fca862ec</td>\n",
       "      <td>622e13439d6b5a0b486c435618b2679e</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2016-09-13 15:24:19</td>\n",
       "      <td>2016-10-07 13:16:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30710</th>\n",
       "      <td>bfbd0f9bdef84302105ad712db648a6c</td>\n",
       "      <td>86dc2ffce2dfff336de2f386a786e574</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2016-09-15 12:16:38</td>\n",
       "      <td>2016-09-15 12:16:38</td>\n",
       "      <td>2016-11-07 17:11:53</td>\n",
       "      <td>2016-11-09 07:47:38</td>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>54 days 19:31:00</td>\n",
       "      <td>36 days 07:47:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83078</th>\n",
       "      <td>71303d7e93b399f5bcd537d124c0bcfa</td>\n",
       "      <td>b106b360fe2ef8849fbbd056f777b4d5</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2016-10-02 22:07:52</td>\n",
       "      <td>2016-10-06 15:50:56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50387</th>\n",
       "      <td>392ed9afd714e3c74767d0c4d3e3f477</td>\n",
       "      <td>2823ffda607a2316375088e0d00005ec</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-09-29 09:13:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-10-15</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88500</th>\n",
       "      <td>616fa7d4871b87832197b2a137a115d2</td>\n",
       "      <td>bf6181a85bbb4115736c0a8db1a53be3</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-10-01 15:30:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31891</th>\n",
       "      <td>a2ac6dad85cf8af5b0afb510a240fe8c</td>\n",
       "      <td>4c2ec60c29d10c34bd49cb88aa85cfc4</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-10-03 18:55:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68373</th>\n",
       "      <td>b059ee4de278302d550a3035c4cdb740</td>\n",
       "      <td>856336203359aa6a61bf3826f7d84c49</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-10-16 20:16:02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-11-12</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60938</th>\n",
       "      <td>10a045cdf6a5650c21e9cfeb60384c16</td>\n",
       "      <td>a4b417188addbc05b26b72d5e44837a1</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-10-17 17:30:18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99441 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id                       customer_id  \\\n",
       "4541   2e7a8482f6fb09756ca50c10d7bfc047  08c5351a6aca1c1589a38f244edeee9d   \n",
       "4396   e5fa5a7210941f7d56d0208e4e071d35  683c54fc24d40ee9f8a6fc179fd9856c   \n",
       "10071  809a282bbd5dbcabb6f2f724fca862ec  622e13439d6b5a0b486c435618b2679e   \n",
       "30710  bfbd0f9bdef84302105ad712db648a6c  86dc2ffce2dfff336de2f386a786e574   \n",
       "83078  71303d7e93b399f5bcd537d124c0bcfa  b106b360fe2ef8849fbbd056f777b4d5   \n",
       "...                                 ...                               ...   \n",
       "50387  392ed9afd714e3c74767d0c4d3e3f477  2823ffda607a2316375088e0d00005ec   \n",
       "88500  616fa7d4871b87832197b2a137a115d2  bf6181a85bbb4115736c0a8db1a53be3   \n",
       "31891  a2ac6dad85cf8af5b0afb510a240fe8c  4c2ec60c29d10c34bd49cb88aa85cfc4   \n",
       "68373  b059ee4de278302d550a3035c4cdb740  856336203359aa6a61bf3826f7d84c49   \n",
       "60938  10a045cdf6a5650c21e9cfeb60384c16  a4b417188addbc05b26b72d5e44837a1   \n",
       "\n",
       "      order_status order_purchase_timestamp    order_approved_at  \\\n",
       "4541       shipped      2016-09-04 21:15:19  2016-10-07 13:18:03   \n",
       "4396      canceled      2016-09-05 00:15:34  2016-10-07 13:17:15   \n",
       "10071     canceled      2016-09-13 15:24:19  2016-10-07 13:16:46   \n",
       "30710    delivered      2016-09-15 12:16:38  2016-09-15 12:16:38   \n",
       "83078     canceled      2016-10-02 22:07:52  2016-10-06 15:50:56   \n",
       "...            ...                      ...                  ...   \n",
       "50387     canceled      2018-09-29 09:13:03                  NaN   \n",
       "88500     canceled      2018-10-01 15:30:09                  NaN   \n",
       "31891     canceled      2018-10-03 18:55:29                  NaN   \n",
       "68373     canceled      2018-10-16 20:16:02                  NaN   \n",
       "60938     canceled      2018-10-17 17:30:18                  NaN   \n",
       "\n",
       "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "4541           2016-10-18 13:14:51                           NaT   \n",
       "4396                           NaN                           NaT   \n",
       "10071                          NaN                           NaT   \n",
       "30710          2016-11-07 17:11:53           2016-11-09 07:47:38   \n",
       "83078                          NaN                           NaT   \n",
       "...                            ...                           ...   \n",
       "50387                          NaN                           NaT   \n",
       "88500                          NaN                           NaT   \n",
       "31891                          NaN                           NaT   \n",
       "68373                          NaN                           NaT   \n",
       "60938                          NaN                           NaT   \n",
       "\n",
       "      order_estimated_delivery_date    delivery_time delivery_accuracy  \n",
       "4541                     2016-10-20              NaT               NaT  \n",
       "4396                     2016-10-28              NaT               NaT  \n",
       "10071                    2016-09-30              NaT               NaT  \n",
       "30710                    2016-10-04 54 days 19:31:00  36 days 07:47:38  \n",
       "83078                    2016-10-25              NaT               NaT  \n",
       "...                             ...              ...               ...  \n",
       "50387                    2018-10-15              NaT               NaT  \n",
       "88500                    2018-10-23              NaT               NaT  \n",
       "31891                    2018-10-16              NaT               NaT  \n",
       "68373                    2018-11-12              NaT               NaT  \n",
       "60938                    2018-10-30              NaT               NaT  \n",
       "\n",
       "[99441 rows x 10 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tri par date.\n",
    "df_OrdDtset.sort_values(by=\"order_purchase_timestamp\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orders from 2016-09-04 21:15:19 to 2018-10-17 17:30:18\n"
     ]
    }
   ],
   "source": [
    "print('Orders from {} to {}'.format(\n",
    "    df_OrdDtset['order_purchase_timestamp'].min(),\n",
    "    df_OrdDtset['order_purchase_timestamp'].max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On enlève hh:mm:ss de la date et on calcule le nb de commande par jour.\n",
    "df_OrdDtset[\"order_purchase_date\"] = \\\n",
    "    df_OrdDtset[\"order_purchase_timestamp\"].dt.date\n",
    "Order_day = df_OrdDtset.groupby('order_purchase_date')['customer_id'].nunique()\n",
    "Period = df_OrdDtset['order_purchase_date'].unique()\n",
    "Period.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "ax.plot(Period, Order_day, label=\"Commande quotidienne\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons des commandes du 4 septembre 2016 au  17 octobre 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taille avant filtre:  (99441, 11)\n",
      "Commande supprimée : -2963\n"
     ]
    },
    {
     "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_accuracy</th>\n",
       "      <th>order_purchase_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30710</th>\n",
       "      <td>bfbd0f9bdef84302105ad712db648a6c</td>\n",
       "      <td>86dc2ffce2dfff336de2f386a786e574</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2016-09-15 12:16:38</td>\n",
       "      <td>2016-09-15 12:16:38</td>\n",
       "      <td>2016-11-07 17:11:53</td>\n",
       "      <td>2016-11-09 07:47:38</td>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>54 days 19:31:00</td>\n",
       "      <td>36 days 07:47:38</td>\n",
       "      <td>2016-09-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93285</th>\n",
       "      <td>3b697a20d9e427646d92567910af6d57</td>\n",
       "      <td>355077684019f7f60a031656bd7262b8</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2016-10-03 09:44:50</td>\n",
       "      <td>2016-10-06 15:50:54</td>\n",
       "      <td>2016-10-23 14:02:13</td>\n",
       "      <td>2016-10-26 14:02:13</td>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>23 days 04:17:23</td>\n",
       "      <td>-1 days +14:02:13</td>\n",
       "      <td>2016-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28424</th>\n",
       "      <td>be5bc2f0da14d8071e2d45451ad119d9</td>\n",
       "      <td>7ec40b22510fdbea1b08921dd39e63d8</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2016-10-03 16:56:50</td>\n",
       "      <td>2016-10-06 16:03:44</td>\n",
       "      <td>2016-10-21 16:33:46</td>\n",
       "      <td>2016-10-27 18:19:38</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>24 days 01:22:48</td>\n",
       "      <td>-11 days +18:19:38</td>\n",
       "      <td>2016-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97979</th>\n",
       "      <td>a41c8759fbe7aab36ea07e038b2d4465</td>\n",
       "      <td>6f989332712d3222b6571b1cf5b835ce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2016-10-03 21:13:36</td>\n",
       "      <td>2016-10-05 03:11:49</td>\n",
       "      <td>2016-10-25 11:57:59</td>\n",
       "      <td>2016-11-03 10:58:07</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>30 days 13:44:31</td>\n",
       "      <td>-26 days +10:58:07</td>\n",
       "      <td>2016-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88472</th>\n",
       "      <td>d207cc272675637bfed0062edffd0818</td>\n",
       "      <td>b8cf418e97ae795672d326288dfab7a7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2016-10-03 22:06:03</td>\n",
       "      <td>2016-10-04 10:28:07</td>\n",
       "      <td>2016-10-21 14:23:37</td>\n",
       "      <td>2016-10-31 11:07:42</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>27 days 13:01:39</td>\n",
       "      <td>-23 days +11:07:42</td>\n",
       "      <td>2016-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99369</th>\n",
       "      <td>52018484704db3661b98ce838612b507</td>\n",
       "      <td>e450a297a7bc6839ceb0cf1a2377fa02</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-29 12:25:59</td>\n",
       "      <td>2018-08-29 12:35:17</td>\n",
       "      <td>2018-08-29 13:38:00</td>\n",
       "      <td>2018-08-30 22:48:27</td>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>1 days 10:22:28</td>\n",
       "      <td>-4 days +22:48:27</td>\n",
       "      <td>2018-08-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30077</th>\n",
       "      <td>0b223d92c27432930dfe407c6aea3041</td>\n",
       "      <td>e60df9449653a95af4549bbfcb18a6eb</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-29 14:18:23</td>\n",
       "      <td>2018-08-29 14:31:07</td>\n",
       "      <td>2018-08-29 15:29:00</td>\n",
       "      <td>2018-08-30 16:24:55</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>1 days 02:06:32</td>\n",
       "      <td>-5 days +16:24:55</td>\n",
       "      <td>2018-08-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31486</th>\n",
       "      <td>168626408cb32af0ffaf76711caae1dc</td>\n",
       "      <td>6e353700bc7bcdf6ebc15d6de16d7002</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-29 14:18:28</td>\n",
       "      <td>2018-08-29 14:30:23</td>\n",
       "      <td>2018-08-29 18:51:00</td>\n",
       "      <td>2018-08-30 16:52:31</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>1 days 02:34:03</td>\n",
       "      <td>-12 days +16:52:31</td>\n",
       "      <td>2018-08-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69678</th>\n",
       "      <td>03ef5dedbe7492bdae72eec50764c43f</td>\n",
       "      <td>496630b6740bcca28fce9ba50d8a26ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-29 14:52:00</td>\n",
       "      <td>2018-08-29 15:05:22</td>\n",
       "      <td>2018-08-29 20:01:00</td>\n",
       "      <td>2018-08-30 16:36:59</td>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>1 days 01:44:59</td>\n",
       "      <td>-4 days +16:36:59</td>\n",
       "      <td>2018-08-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88097</th>\n",
       "      <td>35a972d7f8436f405b56e36add1a7140</td>\n",
       "      <td>898b7fee99c4e42170ab69ba59be0a8b</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-29 15:00:37</td>\n",
       "      <td>2018-08-29 15:10:26</td>\n",
       "      <td>2018-08-29 16:57:00</td>\n",
       "      <td>2018-08-30 16:23:36</td>\n",
       "      <td>2018-09-05</td>\n",
       "      <td>1 days 01:22:59</td>\n",
       "      <td>-6 days +16:23:36</td>\n",
       "      <td>2018-08-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96478 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id                       customer_id  \\\n",
       "30710  bfbd0f9bdef84302105ad712db648a6c  86dc2ffce2dfff336de2f386a786e574   \n",
       "93285  3b697a20d9e427646d92567910af6d57  355077684019f7f60a031656bd7262b8   \n",
       "28424  be5bc2f0da14d8071e2d45451ad119d9  7ec40b22510fdbea1b08921dd39e63d8   \n",
       "97979  a41c8759fbe7aab36ea07e038b2d4465  6f989332712d3222b6571b1cf5b835ce   \n",
       "88472  d207cc272675637bfed0062edffd0818  b8cf418e97ae795672d326288dfab7a7   \n",
       "...                                 ...                               ...   \n",
       "99369  52018484704db3661b98ce838612b507  e450a297a7bc6839ceb0cf1a2377fa02   \n",
       "30077  0b223d92c27432930dfe407c6aea3041  e60df9449653a95af4549bbfcb18a6eb   \n",
       "31486  168626408cb32af0ffaf76711caae1dc  6e353700bc7bcdf6ebc15d6de16d7002   \n",
       "69678  03ef5dedbe7492bdae72eec50764c43f  496630b6740bcca28fce9ba50d8a26ef   \n",
       "88097  35a972d7f8436f405b56e36add1a7140  898b7fee99c4e42170ab69ba59be0a8b   \n",
       "\n",
       "      order_status order_purchase_timestamp    order_approved_at  \\\n",
       "30710    delivered      2016-09-15 12:16:38  2016-09-15 12:16:38   \n",
       "93285    delivered      2016-10-03 09:44:50  2016-10-06 15:50:54   \n",
       "28424    delivered      2016-10-03 16:56:50  2016-10-06 16:03:44   \n",
       "97979    delivered      2016-10-03 21:13:36  2016-10-05 03:11:49   \n",
       "88472    delivered      2016-10-03 22:06:03  2016-10-04 10:28:07   \n",
       "...            ...                      ...                  ...   \n",
       "99369    delivered      2018-08-29 12:25:59  2018-08-29 12:35:17   \n",
       "30077    delivered      2018-08-29 14:18:23  2018-08-29 14:31:07   \n",
       "31486    delivered      2018-08-29 14:18:28  2018-08-29 14:30:23   \n",
       "69678    delivered      2018-08-29 14:52:00  2018-08-29 15:05:22   \n",
       "88097    delivered      2018-08-29 15:00:37  2018-08-29 15:10:26   \n",
       "\n",
       "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "30710          2016-11-07 17:11:53           2016-11-09 07:47:38   \n",
       "93285          2016-10-23 14:02:13           2016-10-26 14:02:13   \n",
       "28424          2016-10-21 16:33:46           2016-10-27 18:19:38   \n",
       "97979          2016-10-25 11:57:59           2016-11-03 10:58:07   \n",
       "88472          2016-10-21 14:23:37           2016-10-31 11:07:42   \n",
       "...                            ...                           ...   \n",
       "99369          2018-08-29 13:38:00           2018-08-30 22:48:27   \n",
       "30077          2018-08-29 15:29:00           2018-08-30 16:24:55   \n",
       "31486          2018-08-29 18:51:00           2018-08-30 16:52:31   \n",
       "69678          2018-08-29 20:01:00           2018-08-30 16:36:59   \n",
       "88097          2018-08-29 16:57:00           2018-08-30 16:23:36   \n",
       "\n",
       "      order_estimated_delivery_date    delivery_time  delivery_accuracy  \\\n",
       "30710                    2016-10-04 54 days 19:31:00   36 days 07:47:38   \n",
       "93285                    2016-10-27 23 days 04:17:23  -1 days +14:02:13   \n",
       "28424                    2016-11-07 24 days 01:22:48 -11 days +18:19:38   \n",
       "97979                    2016-11-29 30 days 13:44:31 -26 days +10:58:07   \n",
       "88472                    2016-11-23 27 days 13:01:39 -23 days +11:07:42   \n",
       "...                             ...              ...                ...   \n",
       "99369                    2018-09-03  1 days 10:22:28  -4 days +22:48:27   \n",
       "30077                    2018-09-04  1 days 02:06:32  -5 days +16:24:55   \n",
       "31486                    2018-09-11  1 days 02:34:03 -12 days +16:52:31   \n",
       "69678                    2018-09-03  1 days 01:44:59  -4 days +16:36:59   \n",
       "88097                    2018-09-05  1 days 01:22:59  -6 days +16:23:36   \n",
       "\n",
       "      order_purchase_date  \n",
       "30710          2016-09-15  \n",
       "93285          2016-10-03  \n",
       "28424          2016-10-03  \n",
       "97979          2016-10-03  \n",
       "88472          2016-10-03  \n",
       "...                   ...  \n",
       "99369          2018-08-29  \n",
       "30077          2018-08-29  \n",
       "31486          2018-08-29  \n",
       "69678          2018-08-29  \n",
       "88097          2018-08-29  \n",
       "\n",
       "[96478 rows x 11 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On garde uniquement les commandes qui ont été livrées.\n",
    "# des commandes passé en 2016 n'ont pas été livrées\n",
    "Delta1 = df_OrdDtset.shape[0]\n",
    "print('Taille avant filtre: ',df_OrdDtset.shape)\n",
    "Delivered_Order = df_OrdDtset['order_status'] == 'delivered'\n",
    "df_OrdDtset = df_OrdDtset[Delivered_Order]\n",
    "Delta2 = df_OrdDtset.shape[0]\n",
    "print('Commande supprimée :', Delta2 - Delta1)\n",
    "df_OrdDtset.sort_values(by=\"order_purchase_timestamp\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "commande supprimée : -267 \n",
      "Taille finale:  (96211, 11)\n"
     ]
    }
   ],
   "source": [
    "# Filtre sur période. On garde les deux années 2017 et 2018 jusqu'à fin août.\n",
    "Purchase_date = (df_OrdDtset[\"order_purchase_date\"] > date(2016, 12, 31)) & \\\n",
    "                 (df_OrdDtset[\"order_purchase_date\"] < date(2018, 9, 1))\n",
    "df_OrdDtset = df_OrdDtset[Purchase_date]\n",
    "Delta3 = df_OrdDtset.shape[0]\n",
    "print('commande supprimée :', Delta3 - Delta2,\n",
    "      '\\nTaille finale: ', df_OrdDtset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On calcule la récence du dernier achat.\n",
    "from datetime import timedelta\n",
    "NOW = df_OrdDtset[\"order_purchase_date\"].max() + timedelta(days=1)\n",
    "df_OrdDtset['DaysSinceOrder'] = df_OrdDtset[\"order_purchase_date\"].apply(\n",
    "                                 lambda x: (NOW - x).days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 - [Order Reviews Data set](#34)<a class=\"anchor\" id=\"34\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7bc2406110b926393aa56f80a40eba40</td>\n",
       "      <td>73fc7af87114b39712e6da79b0a377eb</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-18 00:00:00</td>\n",
       "      <td>2018-01-18 21:46:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
       "      <td>a548910a1c6147796b98fdf73dbeba33</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-10 00:00:00</td>\n",
       "      <td>2018-03-11 03:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-17 00:00:00</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          review_id                          order_id  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1  80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2  228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "\n",
       "   review_score review_comment_title review_comment_message  \\\n",
       "0             4                  NaN                    NaN   \n",
       "1             5                  NaN                    NaN   \n",
       "2             5                  NaN                    NaN   \n",
       "\n",
       "  review_creation_date review_answer_timestamp  \n",
       "0  2018-01-18 00:00:00     2018-01-18 21:46:59  \n",
       "1  2018-03-10 00:00:00     2018-03-11 03:05:13  \n",
       "2  2018-02-17 00:00:00     2018-02-18 14:36:24  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdRvews.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb Order Id: 99441\n",
      "Nb Review Id: 99173\n",
      "Taille: (100000, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Nb Order Id:', df_OrdRvews['order_id'].nunique())\n",
    "print('Nb Review Id:', df_OrdRvews['review_id'].nunique())\n",
    "print('Taille:', df_OrdRvews.shape)\n",
    "cle = ['order_id']\n",
    "df_OrdRvews[cle].drop_duplicates().shape[0] == df_OrdRvews.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On transforme en string.\n",
    "df_OrdRvews['order_id'] = df_OrdRvews['order_id'].astype('str', copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "03c939fd7fd3b38f8485a0f95798f1f6    3\n",
       "df56136b8031ecd28e200bb18e6ddb2e    3\n",
       "8e17072ec97ce29f0e1f111e598b0c85    3\n",
       "c88b1d1b157a9999ce368f218a407141    3\n",
       "c86cd0844d36479f7e7adb1edd5e59d3    2\n",
       "                                   ..\n",
       "f909a30204df41fa3c27e9490f536aa8    1\n",
       "9aad6dbdcca5469f24d0ae9abf0977e9    1\n",
       "04da4dc0ed95cc93f9f25375fa08ad47    1\n",
       "3d6809371021cc510de4743748a1c235    1\n",
       "7b324b5737fa42a7fb6df7c3769c3c9f    1\n",
       "Name: order_id, Length: 99441, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdRvews['order_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La clé unique n'est pas order_id. Il existe plusieurs lignes avec le même order_id. il ne manque pas de commande.\n",
    "Feature : Review score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    0., 10000., 20000., 30000., 40000., 50000., 60000.]),\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution des scores\n",
    "Dist_Score = df_OrdRvews['review_score'].value_counts()\n",
    "\n",
    "# Liste des scores\n",
    "Score = df_OrdRvews['review_score'].unique()\n",
    "Score = sorted(Score, reverse=True)\n",
    "\n",
    "# Répartition des scores\n",
    "fig31 = plt.figure(figsize=(15, 7))\n",
    "axes = plt.axes()\n",
    "axes.set_xlabel('Score', fontsize=12)\n",
    "axes.set_ylabel('Distribution', fontsize=12)\n",
    "axes.set_ylim(0, 60000)\n",
    "plt.title('Distribution des scores de satisfaction', fontsize=16)\n",
    "plt.bar(Score, Dist_Score)\n",
    "plt.xticks(Score, fontsize=12)\n",
    "plt.yticks(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7bc2406110b926393aa56f80a40eba40</td>\n",
       "      <td>73fc7af87114b39712e6da79b0a377eb</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-18 00:00:00</td>\n",
       "      <td>2018-01-18 21:46:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
       "      <td>a548910a1c6147796b98fdf73dbeba33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-10 00:00:00</td>\n",
       "      <td>2018-03-11 03:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-17 00:00:00</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recebi bem antes do prazo estipulado.</td>\n",
       "      <td>2017-04-21 00:00:00</td>\n",
       "      <td>2017-04-21 22:02:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f7c4243c7fe1938f181bec41a392bdeb</td>\n",
       "      <td>8e6bfb81e283fa7e4f11123a3fb894f1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Parabéns lojas lannister adorei comprar pela I...</td>\n",
       "      <td>2018-03-01 00:00:00</td>\n",
       "      <td>2018-03-02 10:26:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>f3897127253a9592a73be9bdfdf4ed7a</td>\n",
       "      <td>22ec9f0669f784db00fa86d035cf8602</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-09 00:00:00</td>\n",
       "      <td>2017-12-11 20:06:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>b3de70c89b1510c4cd3d0649fd302472</td>\n",
       "      <td>55d4004744368f5571d1f590031933e4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Excelente mochila, entrega super rápida. Super...</td>\n",
       "      <td>2018-03-22 00:00:00</td>\n",
       "      <td>2018-03-23 09:10:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1adeb9d84d72fe4e337617733eb85149</td>\n",
       "      <td>7725825d039fc1f0ceb7635e3f7d9206</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-01 00:00:00</td>\n",
       "      <td>2018-07-02 12:59:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>be360f18f5df1e0541061c87021e6d93</td>\n",
       "      <td>f8bd3f2000c28c5342fedeb5e50f2e75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Solicitei a compra de uma capa de retrovisor c...</td>\n",
       "      <td>2017-12-15 00:00:00</td>\n",
       "      <td>2017-12-16 01:29:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>efe49f1d6f951dd88b51e6ccd4cc548f</td>\n",
       "      <td>90531360ecb1eec2a1fbb265a0db0508</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meu produto chegou e ja tenho que devolver, po...</td>\n",
       "      <td>2017-07-03 00:00:00</td>\n",
       "      <td>2017-07-03 21:01:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99441 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              review_id                          order_id  \\\n",
       "0      7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1      80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2      228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3      e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4      f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "...                                 ...                               ...   \n",
       "99995  f3897127253a9592a73be9bdfdf4ed7a  22ec9f0669f784db00fa86d035cf8602   \n",
       "99996  b3de70c89b1510c4cd3d0649fd302472  55d4004744368f5571d1f590031933e4   \n",
       "99997  1adeb9d84d72fe4e337617733eb85149  7725825d039fc1f0ceb7635e3f7d9206   \n",
       "99998  be360f18f5df1e0541061c87021e6d93  f8bd3f2000c28c5342fedeb5e50f2e75   \n",
       "99999  efe49f1d6f951dd88b51e6ccd4cc548f  90531360ecb1eec2a1fbb265a0db0508   \n",
       "\n",
       "       review_score review_comment_title  \\\n",
       "0               1.0                  NaN   \n",
       "1               5.0                  NaN   \n",
       "2               5.0                  NaN   \n",
       "3               5.0                  NaN   \n",
       "4               5.0                  NaN   \n",
       "...             ...                  ...   \n",
       "99995           5.0                  NaN   \n",
       "99996           5.0                  NaN   \n",
       "99997           5.0                  NaN   \n",
       "99998           5.0                  NaN   \n",
       "99999           5.0                  NaN   \n",
       "\n",
       "                                  review_comment_message review_creation_date  \\\n",
       "0                                                    NaN  2018-01-18 00:00:00   \n",
       "1                                                    NaN  2018-03-10 00:00:00   \n",
       "2                                                    NaN  2018-02-17 00:00:00   \n",
       "3                  Recebi bem antes do prazo estipulado.  2017-04-21 00:00:00   \n",
       "4      Parabéns lojas lannister adorei comprar pela I...  2018-03-01 00:00:00   \n",
       "...                                                  ...                  ...   \n",
       "99995                                                NaN  2017-12-09 00:00:00   \n",
       "99996  Excelente mochila, entrega super rápida. Super...  2018-03-22 00:00:00   \n",
       "99997                                                NaN  2018-07-01 00:00:00   \n",
       "99998  Solicitei a compra de uma capa de retrovisor c...  2017-12-15 00:00:00   \n",
       "99999  meu produto chegou e ja tenho que devolver, po...  2017-07-03 00:00:00   \n",
       "\n",
       "      review_answer_timestamp  \n",
       "0         2018-01-18 21:46:59  \n",
       "1         2018-03-11 03:05:13  \n",
       "2         2018-02-18 14:36:24  \n",
       "3         2017-04-21 22:02:06  \n",
       "4         2018-03-02 10:26:53  \n",
       "...                       ...  \n",
       "99995     2017-12-11 20:06:42  \n",
       "99996     2018-03-23 09:10:43  \n",
       "99997     2018-07-02 12:59:13  \n",
       "99998     2017-12-16 01:29:43  \n",
       "99999     2017-07-03 21:01:49  \n",
       "\n",
       "[99441 rows x 7 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On garde la moyenne des scores par commande.\n",
    "rev_score_mean = df_OrdRvews.groupby('order_id')['review_score'].mean()\n",
    "df_OrdRvews['review_score'] = df_OrdPaymt['order_id'].map(rev_score_mean)\n",
    "df_OrdRvews.drop_duplicates(subset=['order_id'], keep='last', inplace=True)\n",
    "df_OrdRvews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 - [Order Payments Data set](#35)<a class=\"anchor\" id=\"35\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>99.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a9810da82917af2d9aefd1278f1dcfa0</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42fdf880ba16b47b59251dd489d4441a</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>128.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>298fcdf1f73eb413e4d26d01b25bc1cd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>96.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>771ee386b001f06208a7419e4fc1bbd7</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>81.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3d7239c394a212faae122962df514ac7</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>3</td>\n",
       "      <td>51.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1f78449c87a54faf9e96e88ba1491fa9</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>6</td>\n",
       "      <td>341.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0573b5e23cbd798006520e1d5b4c6714</td>\n",
       "      <td>1</td>\n",
       "      <td>boleto</td>\n",
       "      <td>1</td>\n",
       "      <td>51.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  payment_sequential payment_type  \\\n",
       "0  b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1  a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2  25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4  42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "5  298fcdf1f73eb413e4d26d01b25bc1cd                   1  credit_card   \n",
       "6  771ee386b001f06208a7419e4fc1bbd7                   1  credit_card   \n",
       "7  3d7239c394a212faae122962df514ac7                   1  credit_card   \n",
       "8  1f78449c87a54faf9e96e88ba1491fa9                   1  credit_card   \n",
       "9  0573b5e23cbd798006520e1d5b4c6714                   1       boleto   \n",
       "\n",
       "   payment_installments  payment_value  \n",
       "0                     8          99.33  \n",
       "1                     1          24.39  \n",
       "2                     1          65.71  \n",
       "3                     8         107.78  \n",
       "4                     2         128.45  \n",
       "5                     2          96.12  \n",
       "6                     1          81.16  \n",
       "7                     3          51.84  \n",
       "8                     6         341.09  \n",
       "9                     1          51.95  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdPaymt.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb Order Id: 99440\n",
      "Taille: (103886, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Nb Order Id:', df_OrdPaymt['order_id'].nunique())\n",
    "print('Taille:', df_OrdPaymt.shape)\n",
    "cle = ['order_id', 'payment_sequential']\n",
    "df_OrdPaymt[cle].drop_duplicates().shape[0] == df_OrdPaymt.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  4  5  3  8  6  7 10 11 17 19 27 12  9 15 13 14 16 25 22 26 29 28\n",
      " 18 21 24 23 20] \n",
      " ['credit_card' 'boleto' 'voucher' 'debit_card' 'not_defined'] \n",
      " [ 8  1  2  3  6  5  4 10  7 12  9 13 15 24 11 18 14 20 21 17 22  0 16 23]\n"
     ]
    }
   ],
   "source": [
    "print(df_OrdPaymt['payment_sequential'].unique(), '\\n',\n",
    "      df_OrdPaymt['payment_type'].unique(), '\\n',\n",
    "      df_OrdPaymt['payment_installments'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On s'assure que order_id est une variable de string\n",
    "df_OrdPaymt['order_id'] = df_OrdPaymt['order_id'].astype('str', copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>Pymt_Sq_Nb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5cfd514482e22bc992e7693f0e3e8df7</td>\n",
       "      <td>2</td>\n",
       "      <td>voucher</td>\n",
       "      <td>1</td>\n",
       "      <td>45.17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>b2bb080b6bc860118a246fd9b6fad6da</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>173.84</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>3689194c14ad4e2e7361ebd1df0e77b0</td>\n",
       "      <td>2</td>\n",
       "      <td>voucher</td>\n",
       "      <td>1</td>\n",
       "      <td>57.53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>723e462ce1ee50e024887c0b403130f3</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>13.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>21b8b46679ea6482cbf911d960490048</td>\n",
       "      <td>2</td>\n",
       "      <td>voucher</td>\n",
       "      <td>1</td>\n",
       "      <td>43.12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103778</th>\n",
       "      <td>fd86c80924b4be8fb7f58c4ecc680dae</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>76.10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103817</th>\n",
       "      <td>6d4616de4341417e17978fe57aec1c46</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>19.18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103860</th>\n",
       "      <td>31bc09fdbd701a7a4f9b55b5955b8687</td>\n",
       "      <td>6</td>\n",
       "      <td>voucher</td>\n",
       "      <td>1</td>\n",
       "      <td>77.99</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103869</th>\n",
       "      <td>c9b01bef18eb84888f0fd071b8413b38</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>6</td>\n",
       "      <td>238.16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103874</th>\n",
       "      <td>d41f1036712e18ee525939d08b7a1c98</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>3.57</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7407 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  payment_sequential payment_type  \\\n",
       "25      5cfd514482e22bc992e7693f0e3e8df7                   2      voucher   \n",
       "35      b2bb080b6bc860118a246fd9b6fad6da                   1  credit_card   \n",
       "75      3689194c14ad4e2e7361ebd1df0e77b0                   2      voucher   \n",
       "84      723e462ce1ee50e024887c0b403130f3                   1  credit_card   \n",
       "102     21b8b46679ea6482cbf911d960490048                   2      voucher   \n",
       "...                                  ...                 ...          ...   \n",
       "103778  fd86c80924b4be8fb7f58c4ecc680dae                   1  credit_card   \n",
       "103817  6d4616de4341417e17978fe57aec1c46                   1  credit_card   \n",
       "103860  31bc09fdbd701a7a4f9b55b5955b8687                   6      voucher   \n",
       "103869  c9b01bef18eb84888f0fd071b8413b38                   1  credit_card   \n",
       "103874  d41f1036712e18ee525939d08b7a1c98                   1  credit_card   \n",
       "\n",
       "        payment_installments  payment_value  Pymt_Sq_Nb  \n",
       "25                         1          45.17           2  \n",
       "35                         1         173.84           2  \n",
       "75                         1          57.53           2  \n",
       "84                         1          13.80           2  \n",
       "102                        1          43.12           2  \n",
       "...                      ...            ...         ...  \n",
       "103778                     1          76.10           3  \n",
       "103817                     1          19.18           2  \n",
       "103860                     1          77.99           7  \n",
       "103869                     6         238.16           2  \n",
       "103874                     1           3.57           6  \n",
       "\n",
       "[7407 rows x 6 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On calcule le nombre de paiement par commande\n",
    "paymt_sq_nb = df_OrdPaymt.groupby('order_id')['payment_sequential'].count()\n",
    "df_OrdPaymt['Pymt_Sq_Nb'] = df_OrdPaymt['order_id'].map(paymt_sq_nb)\n",
    "df_OrdPaymt[df_OrdPaymt['Pymt_Sq_Nb'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée une variable pour chaque type de paiement par commande\n",
    "df_OrdPaymt['order_and_payment'] = df_OrdPaymt['order_id'] + \\\n",
    "                                    df_OrdPaymt['payment_type']\n",
    "# On calcule la somme par type de paiement par commande\n",
    "paymt_tp_amounnt = df_OrdPaymt.groupby('order_and_payment')['payment_value']\\\n",
    "                    .sum()\n",
    "df_OrdPaymt['Type_Paid'] = df_OrdPaymt['order_and_payment']\\\n",
    "                            .map(paymt_tp_amounnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée un data frame pour avoir la somme par type de paiement par commande\n",
    "df_temp2 = df_OrdPaymt[['order_id', 'payment_type',\n",
    "                        'order_and_payment', 'Type_Paid']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df_temp2.drop_duplicates(subset=['order_and_payment'],\n",
    "                         keep='last', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>order_id</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>00010242fe8c5a6d1ba2dd792cb16214</th>\n",
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       "    <tr>\n",
       "      <th>00018f77f2f0320c557190d7a144bdd3</th>\n",
       "      <td>0.00</td>\n",
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       "      <th>000229ec398224ef6ca0657da4fc703e</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00024acbcdf0a6daa1e931b038114c75</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00042b26cf59d7ce69dfabb4e55b4fd9</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00048cc3ae777c65dbb7d2a0634bc1ea</th>\n",
       "      <td>34.59</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>00054e8431b9d7675808bcb819fb4a32</th>\n",
       "      <td>0.00</td>\n",
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       "      <th>000576fe39319847cbb9d288c5617fa6</th>\n",
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       "      <th>0005a1a1728c9d785b8e2b08b904576c</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0005f50442cb953dcd1d21e1fb923495</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00061f2a7bc09da83e415a52dc8a4af1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>68.87</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00063b381e2406b52ad429470734ebd5</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0006ec9db01a64e59a68b2c340bf65a7</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0008288aa423d2a3f00fcb17cd7d8719</th>\n",
       "      <td>126.54</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0009792311464db532ff765bf7b182ae</th>\n",
       "      <td>127.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0009c9a17f916a706d71784483a5d643</th>\n",
       "      <td>0.00</td>\n",
       "      <td>650.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>000aed2e25dbad2f9ddb70584c5a2ded</th>\n",
       "      <td>0.00</td>\n",
       "      <td>152.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>000c3e6612759851cc3cbb4b83257986</th>\n",
       "      <td>112.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>000e562887b1f2006d75e0be9558292e</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>000e63d38ae8c00bbcb5a30573b99628</th>\n",
       "      <td>0.00</td>\n",
       "      <td>56.78</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>000e906b789b55f64edcb1f84030f90d</th>\n",
       "      <td>0.00</td>\n",
       "      <td>33.84</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>000f25f4d72195062c040b12dce9a18a</th>\n",
       "      <td>0.00</td>\n",
       "      <td>164.39</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>001021efaa8636c29475e7734483457d</th>\n",
       "      <td>0.00</td>\n",
       "      <td>64.10</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0010b2e5201cc5f1ae7e9c6cc8f5bd00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>65.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0010dedd556712d7bb69a19cb7bbd37a</th>\n",
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       "      <th>00119ff934e539cf26f92b9ef0cdfed8</th>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0011d82c4b53e22e84023405fb467e57</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00125cb692d04887809806618a2a145f</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00130c0eee84a3d909e75bc08c5c3ca1</th>\n",
       "      <td>35.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0013503b13da1eac686219390b7d641b</th>\n",
       "      <td>137.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00137e170939bba5a3134e2386413108</th>\n",
       "      <td>421.65</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>001427c0ec99cf8af737bd88e92fd444</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00143d0f86d6fbd9f9b38ab440ac16f5</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0014ae671de39511f7575066200733b7</th>\n",
       "      <td>30.60</td>\n",
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       "      <th>0015ebb40fb17286bea51d4607c4733c</th>\n",
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       "    <tr>\n",
       "      <th>00169e31ef4b29deaae414f9a5e95929</th>\n",
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       "    <tr>\n",
       "      <th>0016dfedd97fc2950e388d2971d718c7</th>\n",
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       "      <th>0017afd5076e074a48f1f1a4c7bac9c5</th>\n",
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       "    <tr>\n",
       "      <th>001862358bf858722e1e2ae000cfed8b</th>\n",
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       "      <th>0019c29108428acffd089c36103c9440</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>001d8f0e34a38c37f7dba2a37d4eba8b</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>0025081dcf9330f9a5052ae82c6ce396</th>\n",
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       "    <tr>\n",
       "      <th>00254baeb6c932b0a8aeead91fbd02b5</th>\n",
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       "    <tr>\n",
       "      <th>00259a44fcad3fc0474329e925d14fc3</th>\n",
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       "    <tr>\n",
       "      <th>0025c5d1a8ca53a240ec2634bb4492ea</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>002691433f09002ac9ca0c4e8dbb8ead</th>\n",
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       "    <tr>\n",
       "      <th>0026a368634b6e6f34f33b1499773a30</th>\n",
       "      <td>0.00</td>\n",
       "      <td>164.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00275bce676303c3bfd7292aefdfa223</th>\n",
       "      <td>293.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>00276d5c3491fbf55305e26891040df9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>68.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>002834535f7a609a5c68266f173fa59e</th>\n",
       "      <td>57.17</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0028de0ca693a1bb26448916a81105cc</th>\n",
       "      <td>45.30</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>002955b0acc63d04f6724a31967e82bd</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>0029c5db35ef291ce99d1262012e7daa</th>\n",
       "      <td>514.37</td>\n",
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       "      <th>0029f17cf0e7640c5cb6825af681303f</th>\n",
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       "    <tr>\n",
       "      <th>002b430ff89b3a24c31a1170acbbedea</th>\n",
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       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>002d040018d12a3853c059f7f23ab5b1</th>\n",
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       "      <th>002f16b7bc4530031b7d90f791b12d8a</th>\n",
       "      <td>0.00</td>\n",
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       "      <th>002f19a65a2ddd70a090297872e6d64e</th>\n",
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       "      <th>002f98c0f7efd42638ed6100ca699b42</th>\n",
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       "      <th>0030d783f979fbc5981e75613b057344</th>\n",
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       "    <tr>\n",
       "      <th>0030ff924c38549807645976adeef2c0</th>\n",
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       "      <th>00310b0c75bb13015ec4d82d341865a4</th>\n",
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       "    <tr>\n",
       "      <th>00324b3eda39ba5ecce3945823e3594c</th>\n",
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       "      <th>0032d07457ae9c806c79368d7d9ce96b</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "payment_type                      boleto  credit_card  debit_card  \\\n",
       "order_id                                                            \n",
       "00010242fe8c5a6d1ba2dd792cb16214    0.00        72.19        0.00   \n",
       "00018f77f2f0320c557190d7a144bdd3    0.00       259.83        0.00   \n",
       "000229ec398224ef6ca0657da4fc703e    0.00       216.87        0.00   \n",
       "00024acbcdf0a6daa1e931b038114c75    0.00        25.78        0.00   \n",
       "00042b26cf59d7ce69dfabb4e55b4fd9    0.00       218.04        0.00   \n",
       "00048cc3ae777c65dbb7d2a0634bc1ea   34.59         0.00        0.00   \n",
       "00054e8431b9d7675808bcb819fb4a32    0.00        31.75        0.00   \n",
       "000576fe39319847cbb9d288c5617fa6    0.00       880.75        0.00   \n",
       "0005a1a1728c9d785b8e2b08b904576c    0.00       157.60        0.00   \n",
       "0005f50442cb953dcd1d21e1fb923495    0.00        65.39        0.00   \n",
       "00061f2a7bc09da83e415a52dc8a4af1    0.00        68.87        0.00   \n",
       "00063b381e2406b52ad429470734ebd5    0.00        57.98        0.00   \n",
       "0006ec9db01a64e59a68b2c340bf65a7    0.00        97.32        0.00   \n",
       "0008288aa423d2a3f00fcb17cd7d8719  126.54         0.00        0.00   \n",
       "0009792311464db532ff765bf7b182ae  127.55         0.00        0.00   \n",
       "0009c9a17f916a706d71784483a5d643    0.00       650.34        0.00   \n",
       "000aed2e25dbad2f9ddb70584c5a2ded    0.00       152.77        0.00   \n",
       "000c3e6612759851cc3cbb4b83257986  112.71         0.00        0.00   \n",
       "000e562887b1f2006d75e0be9558292e    0.00        41.11        0.00   \n",
       "000e63d38ae8c00bbcb5a30573b99628    0.00        56.78        0.00   \n",
       "000e906b789b55f64edcb1f84030f90d    0.00        33.84        0.00   \n",
       "000f25f4d72195062c040b12dce9a18a    0.00       164.39        0.00   \n",
       "001021efaa8636c29475e7734483457d    0.00        64.10        0.00   \n",
       "0010b2e5201cc5f1ae7e9c6cc8f5bd00    0.00        65.50        0.00   \n",
       "0010dedd556712d7bb69a19cb7bbd37a  111.12         0.00        0.00   \n",
       "00119ff934e539cf26f92b9ef0cdfed8    0.00       236.88        0.00   \n",
       "0011d82c4b53e22e84023405fb467e57    0.00       315.33        0.00   \n",
       "00125cb692d04887809806618a2a145f    0.00       135.41        0.00   \n",
       "00130c0eee84a3d909e75bc08c5c3ca1   35.84         0.00        0.00   \n",
       "0013503b13da1eac686219390b7d641b  137.22         0.00        0.00   \n",
       "00137e170939bba5a3134e2386413108  421.65         0.00        0.00   \n",
       "001427c0ec99cf8af737bd88e92fd444    0.00        83.25        0.00   \n",
       "00143d0f86d6fbd9f9b38ab440ac16f5    0.00       109.29        0.00   \n",
       "0014ae671de39511f7575066200733b7   30.60         0.00        0.00   \n",
       "0015ebb40fb17286bea51d4607c4733c    0.00        37.00        0.00   \n",
       "00169e31ef4b29deaae414f9a5e95929   55.11         0.00        0.00   \n",
       "0016dfedd97fc2950e388d2971d718c7    0.00        52.63        0.00   \n",
       "0017afd5076e074a48f1f1a4c7bac9c5    0.00       853.39        0.00   \n",
       "001862358bf858722e1e2ae000cfed8b    0.00       113.71        0.00   \n",
       "0019c29108428acffd089c36103c9440    0.00        79.85        0.00   \n",
       "001ab0a7578dd66cd4b0a71f5b6e1e41  127.56         0.00        0.00   \n",
       "001ac194d4a326a6fa99b581e9a3d963   62.54         0.00        0.00   \n",
       "001b76dd48a5b1eee3e87778daa40df8    0.00        95.00        0.00   \n",
       "001c85b5f68d2be0cb0797afc9e8ce9a    0.00       112.71        0.00   \n",
       "001d8f0e34a38c37f7dba2a37d4eba8b    0.00        53.54        0.00   \n",
       "001daeb0eddc45b999bad0801ad9d273   55.12         0.00        0.00   \n",
       "001dbc16dc51075e987543d23a0507c7    0.00        87.90        0.00   \n",
       "001e7ba991be1b19605ca0316e7130f9    0.00       213.21        0.00   \n",
       "001e7cf2ad6bef3ade12ebc56ceaf0f3    0.00        51.10        0.00   \n",
       "0020262c8a370bd5a174ea6a2a267321    0.00       100.55        0.00   \n",
       "0020a222f55eb79a372d0efee3cca688    0.00        45.09        0.00   \n",
       "002175704e8b209f61b9ad5cfd92b60e    0.00       123.11        0.00   \n",
       "00229e4e43f7a7e0b9dd819ad43268d3    0.00        91.39        0.00   \n",
       "0025081dcf9330f9a5052ae82c6ce396    0.00        33.18        0.00   \n",
       "00254baeb6c932b0a8aeead91fbd02b5    0.00       193.01        0.00   \n",
       "00259a44fcad3fc0474329e925d14fc3    0.00        34.09        0.00   \n",
       "0025c5d1a8ca53a240ec2634bb4492ea    0.00       419.39        0.00   \n",
       "002611a77fe03d076285fd4ca95db77c  156.75         0.00        0.00   \n",
       "002691433f09002ac9ca0c4e8dbb8ead    0.00       120.43        0.00   \n",
       "0026a368634b6e6f34f33b1499773a30    0.00       164.80        0.00   \n",
       "00275bce676303c3bfd7292aefdfa223  293.36         0.00        0.00   \n",
       "00276d5c3491fbf55305e26891040df9    0.00        68.12        0.00   \n",
       "002834535f7a609a5c68266f173fa59e   57.17         0.00        0.00   \n",
       "0028de0ca693a1bb26448916a81105cc   45.30         0.00        0.00   \n",
       "002955b0acc63d04f6724a31967e82bd    0.00       135.59        0.00   \n",
       "0029c5db35ef291ce99d1262012e7daa  514.37         0.00        0.00   \n",
       "0029f17cf0e7640c5cb6825af681303f    0.00       113.44        0.00   \n",
       "002af7fa9eb99d1456bceb6a13d6fc0b    0.00       109.45        0.00   \n",
       "002b430ff89b3a24c31a1170acbbedea    0.00       265.55        0.00   \n",
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       "\n",
       "payment_type                      not_defined  voucher  \n",
       "order_id                                                \n",
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       "0035e6b7ade84b3f5b86bd49814793df          0.0     0.00  \n",
       "0036757472ece3dde52fd4bfd929c90e          0.0     0.00  \n",
       "0036887767dea4bd43b1a88cd0d9477a          0.0     0.00  \n",
       "00378c6c981f234634c0b9d6128df6dd          0.0     0.00  \n",
       "003822434f91204da0a51fe4cf2aba18          0.0     0.00  \n",
       "0039500db36d50d7d0bfaf23723550f3          0.0     0.00  \n",
       "003a7f59d7e08a9c61d9e2881fe6459c          0.0     0.00  \n",
       "003a94f778ef8cfd50247c8c1b582257          0.0     0.00  \n",
       "003cc6161d7a2593f2525cce0c330d32          0.0     0.00  \n",
       "003d0634280ff3d1d3a54459349a6899          0.0     0.00  \n",
       "003d804eef0e1b856881cd18e0cc0d4c          0.0     0.00  \n",
       "003d9fc84ad902adf2265248b5ffe1a4          0.0     0.00  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On crée un data frame ou a la somme par type de paiement par commande \n",
    "# (plusieurs types possibles par commande)\n",
    "df_OrdPaymt2 = df_temp2.pivot(index = 'order_id', columns = 'payment_type', values = 'Type_Paid')\n",
    "df_OrdPaymt2.fillna(0, inplace=True)\n",
    "df_OrdPaymt2.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99440, 5)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdPaymt2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>Pymt_Sq_Nb</th>\n",
       "      <th>order_and_payment</th>\n",
       "      <th>Type_Paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
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       "      <td>99.33</td>\n",
       "      <td>1</td>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17credit_card</td>\n",
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       "      <td>42fdf880ba16b47b59251dd489d4441acredit_card</td>\n",
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       "    <tr>\n",
       "      <th>103881</th>\n",
       "      <td>0406037ad97740d563a178ecc7a2075c</td>\n",
       "      <td>1</td>\n",
       "      <td>boleto</td>\n",
       "      <td>1</td>\n",
       "      <td>363.31</td>\n",
       "      <td>1</td>\n",
       "      <td>0406037ad97740d563a178ecc7a2075cboleto</td>\n",
       "      <td>363.31</td>\n",
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       "    <tr>\n",
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       "      <td>7b905861d7c825891d6347454ea7863f</td>\n",
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       "      <td>credit_card</td>\n",
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       "      <td>96.80</td>\n",
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       "      <td>96.80</td>\n",
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       "    <tr>\n",
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       "      <td>191.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  payment_sequential payment_type  \\\n",
       "0       b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1       a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2       25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3       ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4       42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "...                                  ...                 ...          ...   \n",
       "103881  0406037ad97740d563a178ecc7a2075c                   1       boleto   \n",
       "103882  7b905861d7c825891d6347454ea7863f                   1  credit_card   \n",
       "103883  32609bbb3dd69b3c066a6860554a77bf                   1  credit_card   \n",
       "103884  b8b61059626efa996a60be9bb9320e10                   1  credit_card   \n",
       "103885  28bbae6599b09d39ca406b747b6632b1                   1       boleto   \n",
       "\n",
       "        payment_installments  payment_value  Pymt_Sq_Nb  \\\n",
       "0                          8          99.33           1   \n",
       "1                          1          24.39           1   \n",
       "2                          1          65.71           1   \n",
       "3                          8         107.78           1   \n",
       "4                          2         128.45           1   \n",
       "...                      ...            ...         ...   \n",
       "103881                     1         363.31           1   \n",
       "103882                     2          96.80           1   \n",
       "103883                     1          47.77           1   \n",
       "103884                     5         369.54           1   \n",
       "103885                     1         191.58           1   \n",
       "\n",
       "                                  order_and_payment  Type_Paid  \n",
       "0       b81ef226f3fe1789b1e8b2acac839d17credit_card      99.33  \n",
       "1       a9810da82917af2d9aefd1278f1dcfa0credit_card      24.39  \n",
       "2       25e8ea4e93396b6fa0d3dd708e76c1bdcredit_card      65.71  \n",
       "3       ba78997921bbcdc1373bb41e913ab953credit_card     107.78  \n",
       "4       42fdf880ba16b47b59251dd489d4441acredit_card     128.45  \n",
       "...                                             ...        ...  \n",
       "103881       0406037ad97740d563a178ecc7a2075cboleto     363.31  \n",
       "103882  7b905861d7c825891d6347454ea7863fcredit_card      96.80  \n",
       "103883  32609bbb3dd69b3c066a6860554a77bfcredit_card      47.77  \n",
       "103884  b8b61059626efa996a60be9bb9320e10credit_card     369.54  \n",
       "103885       28bbae6599b09d39ca406b747b6632b1boleto     191.58  \n",
       "\n",
       "[99440 rows x 8 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On garde une ligne par commande\n",
    "df_OrdPaymt.drop_duplicates(subset=['order_id'], keep='last', inplace=True)\n",
    "df_OrdPaymt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99440, 13)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On fusionne les 2 df.\n",
    "df_OrdPaymt3 = pd.merge(df_OrdPaymt, df_OrdPaymt2,\n",
    "                        left_on='order_id', right_index=True)\n",
    "df_OrdPaymt3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attention il manque une commande dans ce data set. De plus, il y a plusieurs lignes pur une même commande.\n",
    "Feature possible : type de paiement, panier commande"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 - [Products Data set](#36)<a class=\"anchor\" id=\"36\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>product_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>40.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>artes</td>\n",
       "      <td>44.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>46.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         product_id product_category_name  \\\n",
       "0  1e9e8ef04dbcff4541ed26657ea517e5            perfumaria   \n",
       "1  3aa071139cb16b67ca9e5dea641aaa2f                 artes   \n",
       "2  96bd76ec8810374ed1b65e291975717f         esporte_lazer   \n",
       "\n",
       "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                 40.0                       287.0                 1.0   \n",
       "1                 44.0                       276.0                 1.0   \n",
       "2                 46.0                       250.0                 1.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0             225.0               16.0               10.0              14.0  \n",
       "1            1000.0               30.0               18.0              20.0  \n",
       "2             154.0               18.0                9.0              15.0  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PdctDtset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Id: 32951 \n",
      " Nombre de catégorie : 73\n",
      "Taille: (32951, 9)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Product Id:', df_PdctDtset['product_id'].nunique(), '\\n',\n",
    "      'Nombre de catégorie :',\n",
    "      df_PdctDtset['product_category_name'].nunique(),)\n",
    "print('Taille:', df_PdctDtset.shape)\n",
    "cle = ['product_id']\n",
    "df_PdctDtset[cle].drop_duplicates().shape[0] == df_PdctDtset.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a le même nombre de Product ID entre Product DS et Order Item DS.\n",
    "Les features Product_description_lenght et product_photos_qty peuvent etre intéressante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{nan, 'pc_gamer', 'portateis_cozinha_e_preparadores_de_alimentos'}"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Différence de catégorie entre Data set\n",
    "set(df_PdctDtset['product_category_name'])\\\n",
    "    .symmetric_difference(df_PdctCat['product_category_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>sports_leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>perfumaria</td>\n",
       "      <td>perfumery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>housewares</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>telefonia</td>\n",
       "      <td>telephony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>relogios_presentes</td>\n",
       "      <td>watches_gifts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>alimentos_bebidas</td>\n",
       "      <td>food_drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bebes</td>\n",
       "      <td>baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>papelaria</td>\n",
       "      <td>stationery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tablets_impressao_imagem</td>\n",
       "      <td>tablets_printing_image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>brinquedos</td>\n",
       "      <td>toys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>telefonia_fixa</td>\n",
       "      <td>fixed_telephony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ferramentas_jardim</td>\n",
       "      <td>garden_tools</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fashion_bolsas_e_acessorios</td>\n",
       "      <td>fashion_bags_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>eletroportateis</td>\n",
       "      <td>small_appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>consoles_games</td>\n",
       "      <td>consoles_games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>audio</td>\n",
       "      <td>audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fashion_calcados</td>\n",
       "      <td>fashion_shoes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>cool_stuff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>malas_acessorios</td>\n",
       "      <td>luggage_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>climatizacao</td>\n",
       "      <td>air_conditioning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>construcao_ferramentas_construcao</td>\n",
       "      <td>construction_tools_construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>moveis_cozinha_area_de_servico_jantar_e_jardim</td>\n",
       "      <td>kitchen_dining_laundry_garden_furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>construcao_ferramentas_jardim</td>\n",
       "      <td>costruction_tools_garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fashion_roupa_masculina</td>\n",
       "      <td>fashion_male_clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pet_shop</td>\n",
       "      <td>pet_shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>moveis_escritorio</td>\n",
       "      <td>office_furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>market_place</td>\n",
       "      <td>market_place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>eletronicos</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>eletrodomesticos</td>\n",
       "      <td>home_appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>artigos_de_festas</td>\n",
       "      <td>party_supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>casa_conforto</td>\n",
       "      <td>home_confort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>construcao_ferramentas_ferramentas</td>\n",
       "      <td>costruction_tools_tools</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>agro_industria_e_comercio</td>\n",
       "      <td>agro_industry_and_commerce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>moveis_colchao_e_estofado</td>\n",
       "      <td>furniture_mattress_and_upholstery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>livros_tecnicos</td>\n",
       "      <td>books_technical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>casa_construcao</td>\n",
       "      <td>home_construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>instrumentos_musicais</td>\n",
       "      <td>musical_instruments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>moveis_sala</td>\n",
       "      <td>furniture_living_room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>construcao_ferramentas_iluminacao</td>\n",
       "      <td>construction_tools_lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>industria_comercio_e_negocios</td>\n",
       "      <td>industry_commerce_and_business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>alimentos</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>artes</td>\n",
       "      <td>art</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>moveis_quarto</td>\n",
       "      <td>furniture_bedroom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>livros_interesse_geral</td>\n",
       "      <td>books_general_interest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>construcao_ferramentas_seguranca</td>\n",
       "      <td>construction_tools_safety</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>fashion_underwear_e_moda_praia</td>\n",
       "      <td>fashion_underwear_beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>fashion_esporte</td>\n",
       "      <td>fashion_sport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>sinalizacao_e_seguranca</td>\n",
       "      <td>signaling_and_security</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>pcs</td>\n",
       "      <td>computers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>artigos_de_natal</td>\n",
       "      <td>christmas_supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>fashion_roupa_feminina</td>\n",
       "      <td>fashio_female_clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>eletrodomesticos_2</td>\n",
       "      <td>home_appliances_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>livros_importados</td>\n",
       "      <td>books_imported</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>bebidas</td>\n",
       "      <td>drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>cine_foto</td>\n",
       "      <td>cine_photo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>la_cuisine</td>\n",
       "      <td>la_cuisine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>musica</td>\n",
       "      <td>music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>casa_conforto_2</td>\n",
       "      <td>home_comfort_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>portateis_casa_forno_e_cafe</td>\n",
       "      <td>small_appliances_home_oven_and_coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>cds_dvds_musicais</td>\n",
       "      <td>cds_dvds_musicals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>dvds_blu_ray</td>\n",
       "      <td>dvds_blu_ray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>flores</td>\n",
       "      <td>flowers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>artes_e_artesanato</td>\n",
       "      <td>arts_and_craftmanship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>fraldas_higiene</td>\n",
       "      <td>diapers_and_hygiene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>fashion_roupa_infanto_juvenil</td>\n",
       "      <td>fashion_childrens_clothes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>seguros_e_servicos</td>\n",
       "      <td>security_and_services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>pc_gamer</td>\n",
       "      <td>computers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>home_appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             product_category_name  \\\n",
       "0                                     beleza_saude   \n",
       "1                           informatica_acessorios   \n",
       "2                                       automotivo   \n",
       "3                                  cama_mesa_banho   \n",
       "4                                 moveis_decoracao   \n",
       "5                                    esporte_lazer   \n",
       "6                                       perfumaria   \n",
       "7                            utilidades_domesticas   \n",
       "8                                        telefonia   \n",
       "9                               relogios_presentes   \n",
       "10                               alimentos_bebidas   \n",
       "11                                           bebes   \n",
       "12                                       papelaria   \n",
       "13                        tablets_impressao_imagem   \n",
       "14                                      brinquedos   \n",
       "15                                  telefonia_fixa   \n",
       "16                              ferramentas_jardim   \n",
       "17                     fashion_bolsas_e_acessorios   \n",
       "18                                 eletroportateis   \n",
       "19                                  consoles_games   \n",
       "20                                           audio   \n",
       "21                                fashion_calcados   \n",
       "22                                      cool_stuff   \n",
       "23                                malas_acessorios   \n",
       "24                                    climatizacao   \n",
       "25               construcao_ferramentas_construcao   \n",
       "26  moveis_cozinha_area_de_servico_jantar_e_jardim   \n",
       "27                   construcao_ferramentas_jardim   \n",
       "28                         fashion_roupa_masculina   \n",
       "29                                        pet_shop   \n",
       "30                               moveis_escritorio   \n",
       "31                                    market_place   \n",
       "32                                     eletronicos   \n",
       "33                                eletrodomesticos   \n",
       "34                               artigos_de_festas   \n",
       "35                                   casa_conforto   \n",
       "36              construcao_ferramentas_ferramentas   \n",
       "37                       agro_industria_e_comercio   \n",
       "38                       moveis_colchao_e_estofado   \n",
       "39                                 livros_tecnicos   \n",
       "40                                 casa_construcao   \n",
       "41                           instrumentos_musicais   \n",
       "42                                     moveis_sala   \n",
       "43               construcao_ferramentas_iluminacao   \n",
       "44                   industria_comercio_e_negocios   \n",
       "45                                       alimentos   \n",
       "46                                           artes   \n",
       "47                                   moveis_quarto   \n",
       "48                          livros_interesse_geral   \n",
       "49                construcao_ferramentas_seguranca   \n",
       "50                  fashion_underwear_e_moda_praia   \n",
       "51                                 fashion_esporte   \n",
       "52                         sinalizacao_e_seguranca   \n",
       "53                                             pcs   \n",
       "54                                artigos_de_natal   \n",
       "55                          fashion_roupa_feminina   \n",
       "56                              eletrodomesticos_2   \n",
       "57                               livros_importados   \n",
       "58                                         bebidas   \n",
       "59                                       cine_foto   \n",
       "60                                      la_cuisine   \n",
       "61                                          musica   \n",
       "62                                 casa_conforto_2   \n",
       "63                     portateis_casa_forno_e_cafe   \n",
       "64                               cds_dvds_musicais   \n",
       "65                                    dvds_blu_ray   \n",
       "66                                          flores   \n",
       "67                              artes_e_artesanato   \n",
       "68                                 fraldas_higiene   \n",
       "69                   fashion_roupa_infanto_juvenil   \n",
       "70                              seguros_e_servicos   \n",
       "71                                        pc_gamer   \n",
       "72   portateis_cozinha_e_preparadores_de_alimentos   \n",
       "73                                           other   \n",
       "\n",
       "              product_category_name_english  \n",
       "0                             health_beauty  \n",
       "1                     computers_accessories  \n",
       "2                                      auto  \n",
       "3                            bed_bath_table  \n",
       "4                           furniture_decor  \n",
       "5                            sports_leisure  \n",
       "6                                 perfumery  \n",
       "7                                housewares  \n",
       "8                                 telephony  \n",
       "9                             watches_gifts  \n",
       "10                               food_drink  \n",
       "11                                     baby  \n",
       "12                               stationery  \n",
       "13                   tablets_printing_image  \n",
       "14                                     toys  \n",
       "15                          fixed_telephony  \n",
       "16                             garden_tools  \n",
       "17                 fashion_bags_accessories  \n",
       "18                         small_appliances  \n",
       "19                           consoles_games  \n",
       "20                                    audio  \n",
       "21                            fashion_shoes  \n",
       "22                               cool_stuff  \n",
       "23                      luggage_accessories  \n",
       "24                         air_conditioning  \n",
       "25          construction_tools_construction  \n",
       "26  kitchen_dining_laundry_garden_furniture  \n",
       "27                 costruction_tools_garden  \n",
       "28                    fashion_male_clothing  \n",
       "29                                 pet_shop  \n",
       "30                         office_furniture  \n",
       "31                             market_place  \n",
       "32                              electronics  \n",
       "33                          home_appliances  \n",
       "34                           party_supplies  \n",
       "35                             home_confort  \n",
       "36                  costruction_tools_tools  \n",
       "37               agro_industry_and_commerce  \n",
       "38        furniture_mattress_and_upholstery  \n",
       "39                          books_technical  \n",
       "40                        home_construction  \n",
       "41                      musical_instruments  \n",
       "42                    furniture_living_room  \n",
       "43                construction_tools_lights  \n",
       "44           industry_commerce_and_business  \n",
       "45                                     food  \n",
       "46                                      art  \n",
       "47                        furniture_bedroom  \n",
       "48                   books_general_interest  \n",
       "49                construction_tools_safety  \n",
       "50                  fashion_underwear_beach  \n",
       "51                            fashion_sport  \n",
       "52                   signaling_and_security  \n",
       "53                                computers  \n",
       "54                       christmas_supplies  \n",
       "55                   fashio_female_clothing  \n",
       "56                        home_appliances_2  \n",
       "57                           books_imported  \n",
       "58                                   drinks  \n",
       "59                               cine_photo  \n",
       "60                               la_cuisine  \n",
       "61                                    music  \n",
       "62                           home_comfort_2  \n",
       "63    small_appliances_home_oven_and_coffee  \n",
       "64                        cds_dvds_musicals  \n",
       "65                             dvds_blu_ray  \n",
       "66                                  flowers  \n",
       "67                    arts_and_craftmanship  \n",
       "68                      diapers_and_hygiene  \n",
       "69                fashion_childrens_clothes  \n",
       "70                    security_and_services  \n",
       "71                                computers  \n",
       "72                          home_appliances  \n",
       "73                                    other  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On crée les catégories non traduite\n",
    "df_PdctCat.loc[71] = ['pc_gamer', 'computers']\n",
    "df_PdctCat.loc[72] = ['portateis_cozinha_e_preparadores_de_alimentos', 'home_appliances']\n",
    "df_PdctCat.loc[73] = ['other','other']\n",
    "df_PdctCat.head(74)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On remplit les Na.\n",
    "df_PdctDtset['product_category_name'].fillna(\"other\", inplace=True)\n",
    "df_PdctDtset['product_name_lenght'].fillna(0, inplace=True)\n",
    "df_PdctDtset['product_description_lenght'].fillna(0, inplace=True)\n",
    "df_PdctDtset['product_photos_qty'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionnaire pour réduire le nombre de catégorie\n",
    "product_categories_dict = {\n",
    "    'construction_tools_construction': 'construction',\n",
    "    'construction_tools_lights': 'construction',\n",
    "    'construction_tools_safety': 'construction',\n",
    "    'costruction_tools_garden': 'construction',\n",
    "    'costruction_tools_tools': 'construction',\n",
    "    'garden_tools': 'construction',\n",
    "    'home_construction': 'construction',\n",
    "\n",
    "    'fashio_female_clothing': 'fashion',\n",
    "    'fashion_bags_accessories': 'fashion',\n",
    "    'fashion_childrens_clothes': 'fashion',\n",
    "    'fashion_male_clothing': 'fashion',\n",
    "    'fashion_shoes': 'fashion',\n",
    "    'fashion_sport': 'fashion',\n",
    "    'fashion_underwear_beach': 'fashion',\n",
    "    'health_beauty': 'fashion',\n",
    "    'perfumery': 'fashion',\n",
    "    'diapers_and_hygiene': 'fashion',\n",
    "    'baby': 'fashion',\n",
    "    'luggage_accessories': 'fashion',\n",
    "\n",
    "    'furniture_bedroom': 'home',\n",
    "    'furniture_decor': 'home',\n",
    "    'furniture_living_room': 'home',\n",
    "    'furniture_mattress_and_upholstery': 'home',\n",
    "    'bed_bath_table': 'home',\n",
    "    'kitchen_dining_laundry_garden_furniture': 'home',\n",
    "    'office_furniture': 'home',\n",
    "    'home_appliances': 'home',\n",
    "    'home_appliances_2': 'home',\n",
    "    'home_comfort_2': 'home',\n",
    "    'home_confort': 'home',\n",
    "    'air_conditioning': 'home',\n",
    "    'housewares': 'home',\n",
    "    'art': 'home',\n",
    "    'arts_and_craftmanship': 'home',\n",
    "    'flowers': 'home',\n",
    "    'cool_stuff': 'home',\n",
    "\n",
    "    'drinks': 'food',\n",
    "    'food': 'food',\n",
    "    'food_drink': 'food',\n",
    "    'la_cuisine': 'food',\n",
    "\n",
    "    'electronics': 'electronics',\n",
    "    'audio': 'electronics',\n",
    "    'tablets_printing_image': 'electronics',\n",
    "    'telephony': 'electronics',\n",
    "    'fixed_telephony': 'electronics',\n",
    "    'small_appliances': 'electronics',\n",
    "    'small_appliances_home_oven_and_coffee': 'electronics',\n",
    "    'computers_accessories': 'electronics',\n",
    "    'computers': 'electronics',\n",
    "\n",
    "    'sports_leisure': 'leisure',\n",
    "    'consoles_games': 'leisure',\n",
    "    'musical_instruments': 'leisure',\n",
    "    'toys': 'leisure',\n",
    "    'cine_photo': 'leisure',\n",
    "    'dvds_blu_ray': 'leisure',\n",
    "    'cds_dvds_musicals': 'leisure',\n",
    "    'music': 'leisure',\n",
    "    'books_general_interest': 'leisure',\n",
    "    'books_imported': 'leisure',\n",
    "    'books_technical': 'leisure',\n",
    "\n",
    "    'christmas_supplies': 'misc',\n",
    "    'stationery': 'misc',\n",
    "    'party_supplies': 'misc',\n",
    "    'auto': 'misc',\n",
    "    'watches_gifts': 'misc',\n",
    "    'agro_industry_and_commerce': 'misc',\n",
    "    'industry_commerce_and_business': 'misc',\n",
    "    'security_and_services': 'misc',\n",
    "    'signaling_and_security': 'misc',\n",
    "    'market_place': 'misc',\n",
    "    'pet_shop': 'misc',\n",
    "    'other': 'misc'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>perfumaria</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>housewares</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>telefonia</td>\n",
       "      <td>telephony</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>relogios_presentes</td>\n",
       "      <td>watches_gifts</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>alimentos_bebidas</td>\n",
       "      <td>food_drink</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bebes</td>\n",
       "      <td>baby</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>papelaria</td>\n",
       "      <td>stationery</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tablets_impressao_imagem</td>\n",
       "      <td>tablets_printing_image</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>brinquedos</td>\n",
       "      <td>toys</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>telefonia_fixa</td>\n",
       "      <td>fixed_telephony</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ferramentas_jardim</td>\n",
       "      <td>garden_tools</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fashion_bolsas_e_acessorios</td>\n",
       "      <td>fashion_bags_accessories</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>eletroportateis</td>\n",
       "      <td>small_appliances</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>consoles_games</td>\n",
       "      <td>consoles_games</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>audio</td>\n",
       "      <td>audio</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fashion_calcados</td>\n",
       "      <td>fashion_shoes</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>malas_acessorios</td>\n",
       "      <td>luggage_accessories</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>climatizacao</td>\n",
       "      <td>air_conditioning</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>construcao_ferramentas_construcao</td>\n",
       "      <td>construction_tools_construction</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>moveis_cozinha_area_de_servico_jantar_e_jardim</td>\n",
       "      <td>kitchen_dining_laundry_garden_furniture</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>construcao_ferramentas_jardim</td>\n",
       "      <td>costruction_tools_garden</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fashion_roupa_masculina</td>\n",
       "      <td>fashion_male_clothing</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pet_shop</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>moveis_escritorio</td>\n",
       "      <td>office_furniture</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>market_place</td>\n",
       "      <td>market_place</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>eletronicos</td>\n",
       "      <td>electronics</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>eletrodomesticos</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>artigos_de_festas</td>\n",
       "      <td>party_supplies</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>casa_conforto</td>\n",
       "      <td>home_confort</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>construcao_ferramentas_ferramentas</td>\n",
       "      <td>costruction_tools_tools</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>agro_industria_e_comercio</td>\n",
       "      <td>agro_industry_and_commerce</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>moveis_colchao_e_estofado</td>\n",
       "      <td>furniture_mattress_and_upholstery</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>livros_tecnicos</td>\n",
       "      <td>books_technical</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>casa_construcao</td>\n",
       "      <td>home_construction</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>instrumentos_musicais</td>\n",
       "      <td>musical_instruments</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>moveis_sala</td>\n",
       "      <td>furniture_living_room</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>construcao_ferramentas_iluminacao</td>\n",
       "      <td>construction_tools_lights</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>industria_comercio_e_negocios</td>\n",
       "      <td>industry_commerce_and_business</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>alimentos</td>\n",
       "      <td>food</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>artes</td>\n",
       "      <td>art</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>moveis_quarto</td>\n",
       "      <td>furniture_bedroom</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>livros_interesse_geral</td>\n",
       "      <td>books_general_interest</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>construcao_ferramentas_seguranca</td>\n",
       "      <td>construction_tools_safety</td>\n",
       "      <td>construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>fashion_underwear_e_moda_praia</td>\n",
       "      <td>fashion_underwear_beach</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>fashion_esporte</td>\n",
       "      <td>fashion_sport</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>sinalizacao_e_seguranca</td>\n",
       "      <td>signaling_and_security</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>pcs</td>\n",
       "      <td>computers</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>artigos_de_natal</td>\n",
       "      <td>christmas_supplies</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>fashion_roupa_feminina</td>\n",
       "      <td>fashio_female_clothing</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>eletrodomesticos_2</td>\n",
       "      <td>home_appliances_2</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>livros_importados</td>\n",
       "      <td>books_imported</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>bebidas</td>\n",
       "      <td>drinks</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>cine_foto</td>\n",
       "      <td>cine_photo</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>la_cuisine</td>\n",
       "      <td>la_cuisine</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>musica</td>\n",
       "      <td>music</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>casa_conforto_2</td>\n",
       "      <td>home_comfort_2</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>portateis_casa_forno_e_cafe</td>\n",
       "      <td>small_appliances_home_oven_and_coffee</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>cds_dvds_musicais</td>\n",
       "      <td>cds_dvds_musicals</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>dvds_blu_ray</td>\n",
       "      <td>dvds_blu_ray</td>\n",
       "      <td>leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>flores</td>\n",
       "      <td>flowers</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>artes_e_artesanato</td>\n",
       "      <td>arts_and_craftmanship</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>fraldas_higiene</td>\n",
       "      <td>diapers_and_hygiene</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>fashion_roupa_infanto_juvenil</td>\n",
       "      <td>fashion_childrens_clothes</td>\n",
       "      <td>fashion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>seguros_e_servicos</td>\n",
       "      <td>security_and_services</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>pc_gamer</td>\n",
       "      <td>computers</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>misc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             product_category_name  \\\n",
       "0                                     beleza_saude   \n",
       "1                           informatica_acessorios   \n",
       "2                                       automotivo   \n",
       "3                                  cama_mesa_banho   \n",
       "4                                 moveis_decoracao   \n",
       "5                                    esporte_lazer   \n",
       "6                                       perfumaria   \n",
       "7                            utilidades_domesticas   \n",
       "8                                        telefonia   \n",
       "9                               relogios_presentes   \n",
       "10                               alimentos_bebidas   \n",
       "11                                           bebes   \n",
       "12                                       papelaria   \n",
       "13                        tablets_impressao_imagem   \n",
       "14                                      brinquedos   \n",
       "15                                  telefonia_fixa   \n",
       "16                              ferramentas_jardim   \n",
       "17                     fashion_bolsas_e_acessorios   \n",
       "18                                 eletroportateis   \n",
       "19                                  consoles_games   \n",
       "20                                           audio   \n",
       "21                                fashion_calcados   \n",
       "22                                      cool_stuff   \n",
       "23                                malas_acessorios   \n",
       "24                                    climatizacao   \n",
       "25               construcao_ferramentas_construcao   \n",
       "26  moveis_cozinha_area_de_servico_jantar_e_jardim   \n",
       "27                   construcao_ferramentas_jardim   \n",
       "28                         fashion_roupa_masculina   \n",
       "29                                        pet_shop   \n",
       "30                               moveis_escritorio   \n",
       "31                                    market_place   \n",
       "32                                     eletronicos   \n",
       "33                                eletrodomesticos   \n",
       "34                               artigos_de_festas   \n",
       "35                                   casa_conforto   \n",
       "36              construcao_ferramentas_ferramentas   \n",
       "37                       agro_industria_e_comercio   \n",
       "38                       moveis_colchao_e_estofado   \n",
       "39                                 livros_tecnicos   \n",
       "40                                 casa_construcao   \n",
       "41                           instrumentos_musicais   \n",
       "42                                     moveis_sala   \n",
       "43               construcao_ferramentas_iluminacao   \n",
       "44                   industria_comercio_e_negocios   \n",
       "45                                       alimentos   \n",
       "46                                           artes   \n",
       "47                                   moveis_quarto   \n",
       "48                          livros_interesse_geral   \n",
       "49                construcao_ferramentas_seguranca   \n",
       "50                  fashion_underwear_e_moda_praia   \n",
       "51                                 fashion_esporte   \n",
       "52                         sinalizacao_e_seguranca   \n",
       "53                                             pcs   \n",
       "54                                artigos_de_natal   \n",
       "55                          fashion_roupa_feminina   \n",
       "56                              eletrodomesticos_2   \n",
       "57                               livros_importados   \n",
       "58                                         bebidas   \n",
       "59                                       cine_foto   \n",
       "60                                      la_cuisine   \n",
       "61                                          musica   \n",
       "62                                 casa_conforto_2   \n",
       "63                     portateis_casa_forno_e_cafe   \n",
       "64                               cds_dvds_musicais   \n",
       "65                                    dvds_blu_ray   \n",
       "66                                          flores   \n",
       "67                              artes_e_artesanato   \n",
       "68                                 fraldas_higiene   \n",
       "69                   fashion_roupa_infanto_juvenil   \n",
       "70                              seguros_e_servicos   \n",
       "71                                        pc_gamer   \n",
       "72   portateis_cozinha_e_preparadores_de_alimentos   \n",
       "73                                           other   \n",
       "\n",
       "              product_category_name_english           cat  \n",
       "0                             health_beauty       fashion  \n",
       "1                     computers_accessories   electronics  \n",
       "2                                      auto          misc  \n",
       "3                            bed_bath_table          home  \n",
       "4                           furniture_decor          home  \n",
       "5                            sports_leisure       leisure  \n",
       "6                                 perfumery       fashion  \n",
       "7                                housewares          home  \n",
       "8                                 telephony   electronics  \n",
       "9                             watches_gifts          misc  \n",
       "10                               food_drink          food  \n",
       "11                                     baby       fashion  \n",
       "12                               stationery          misc  \n",
       "13                   tablets_printing_image   electronics  \n",
       "14                                     toys       leisure  \n",
       "15                          fixed_telephony   electronics  \n",
       "16                             garden_tools  construction  \n",
       "17                 fashion_bags_accessories       fashion  \n",
       "18                         small_appliances   electronics  \n",
       "19                           consoles_games       leisure  \n",
       "20                                    audio   electronics  \n",
       "21                            fashion_shoes       fashion  \n",
       "22                               cool_stuff          home  \n",
       "23                      luggage_accessories       fashion  \n",
       "24                         air_conditioning          home  \n",
       "25          construction_tools_construction  construction  \n",
       "26  kitchen_dining_laundry_garden_furniture          home  \n",
       "27                 costruction_tools_garden  construction  \n",
       "28                    fashion_male_clothing       fashion  \n",
       "29                                 pet_shop          misc  \n",
       "30                         office_furniture          home  \n",
       "31                             market_place          misc  \n",
       "32                              electronics   electronics  \n",
       "33                          home_appliances          home  \n",
       "34                           party_supplies          misc  \n",
       "35                             home_confort          home  \n",
       "36                  costruction_tools_tools  construction  \n",
       "37               agro_industry_and_commerce          misc  \n",
       "38        furniture_mattress_and_upholstery          home  \n",
       "39                          books_technical       leisure  \n",
       "40                        home_construction  construction  \n",
       "41                      musical_instruments       leisure  \n",
       "42                    furniture_living_room          home  \n",
       "43                construction_tools_lights  construction  \n",
       "44           industry_commerce_and_business          misc  \n",
       "45                                     food          food  \n",
       "46                                      art          home  \n",
       "47                        furniture_bedroom          home  \n",
       "48                   books_general_interest       leisure  \n",
       "49                construction_tools_safety  construction  \n",
       "50                  fashion_underwear_beach       fashion  \n",
       "51                            fashion_sport       fashion  \n",
       "52                   signaling_and_security          misc  \n",
       "53                                computers   electronics  \n",
       "54                       christmas_supplies          misc  \n",
       "55                   fashio_female_clothing       fashion  \n",
       "56                        home_appliances_2          home  \n",
       "57                           books_imported       leisure  \n",
       "58                                   drinks          food  \n",
       "59                               cine_photo       leisure  \n",
       "60                               la_cuisine          food  \n",
       "61                                    music       leisure  \n",
       "62                           home_comfort_2          home  \n",
       "63    small_appliances_home_oven_and_coffee   electronics  \n",
       "64                        cds_dvds_musicals       leisure  \n",
       "65                             dvds_blu_ray       leisure  \n",
       "66                                  flowers          home  \n",
       "67                    arts_and_craftmanship          home  \n",
       "68                      diapers_and_hygiene       fashion  \n",
       "69                fashion_childrens_clothes       fashion  \n",
       "70                    security_and_services          misc  \n",
       "71                                computers   electronics  \n",
       "72                          home_appliances          home  \n",
       "73                                    other          misc  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_ser = pd.Series(product_categories_dict) \n",
    "df_PdctCat['cat'] = df_PdctCat['product_category_name_english'].map(cat_ser)\n",
    "df_PdctCat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fashion</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>1987.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fashion</td>\n",
       "      <td>c5d8079278e912d7e3b6beb48ecb56e8</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>36555a2f528d7b2a255c504191445d39</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fashion</td>\n",
       "      <td>e586ebb6022265ae1eea38f46ffe3ead</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1469.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fashion</td>\n",
       "      <td>75b4372e69a42f8ae1d908c076f547b2</td>\n",
       "      <td>22.0</td>\n",
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       "  </tbody>\n",
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       "<p>32951 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cat                        product_id  product_name_lenght  \\\n",
       "0      fashion  e3e020af31d4d89d2602272b315c3f6e                 58.0   \n",
       "1      fashion  c5d8079278e912d7e3b6beb48ecb56e8                 60.0   \n",
       "2      fashion  36555a2f528d7b2a255c504191445d39                 51.0   \n",
       "3      fashion  e586ebb6022265ae1eea38f46ffe3ead                 40.0   \n",
       "4      fashion  75b4372e69a42f8ae1d908c076f547b2                 22.0   \n",
       "...        ...                               ...                  ...   \n",
       "32946     misc  b0a0c5dd78e644373b199380612c350a                  0.0   \n",
       "32947     misc  10dbe0fbaa2c505123c17fdc34a63c56                  0.0   \n",
       "32948     misc  bd2ada37b58ae94cc838b9c0569fecd8                  0.0   \n",
       "32949     misc  fa51e914046aab32764c41356b9d4ea4                  0.0   \n",
       "32950     misc  c4ceee876c82b8328e9c293fa0e1989b                  0.0   \n",
       "\n",
       "       product_description_lenght  product_photos_qty  \n",
       "0                          1987.0                 3.0  \n",
       "1                           330.0                 1.0  \n",
       "2                          1069.0                 1.0  \n",
       "3                          1469.0                 3.0  \n",
       "4                          1313.0                 1.0  \n",
       "...                           ...                 ...  \n",
       "32946                         0.0                 0.0  \n",
       "32947                         0.0                 0.0  \n",
       "32948                         0.0                 0.0  \n",
       "32949                         0.0                 0.0  \n",
       "32950                         0.0                 0.0  \n",
       "\n",
       "[32951 rows x 5 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On fusionne pour avoir les catégories par produit.\n",
    "df_cat = pd.merge(df_PdctCat, df_PdctDtset, on='product_category_name')\n",
    "# On supprime des variables non pertinentes\n",
    "df_cat.drop(['product_category_name', 'product_category_name_english',\n",
    "             'product_weight_g', 'product_length_cm',\n",
    "             'product_height_cm', 'product_width_cm'],\n",
    "            axis='columns', inplace=True)\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 - [Order Items Data set](#37)<a class=\"anchor\" id=\"37\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.9</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:13</td>\n",
       "      <td>239.9</td>\n",
       "      <td>19.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2  000229ec398224ef6ca0657da4fc703e              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "\n",
       "   shipping_limit_date  price  freight_value  \n",
       "0  2017-09-19 09:45:35   58.9          13.29  \n",
       "1  2017-05-03 11:05:13  239.9          19.93  \n",
       "2  2018-01-18 14:48:30  199.0          17.87  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdItem.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb Order Id: 98666 \n",
      " Nb Product Id: 32951 \n",
      " Nb Seller Id: 3095\n",
      "Taille: (112650, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description\n",
    "print('Nb Order Id:', df_OrdItem['order_id'].nunique(), '\\n',\n",
    "      'Nb Product Id:', df_OrdItem['product_id'].nunique(), '\\n',\n",
    "      'Nb Seller Id:', df_OrdItem['seller_id'].nunique())\n",
    "print('Taille:', df_OrdItem.shape)\n",
    "cle = ['order_id', 'order_item_id']\n",
    "df_OrdItem[cle].drop_duplicates().shape[0] == df_OrdItem.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On calcule la somme de l'article et des frais de port par ligne\n",
    "df_OrdItem['Total_Price_Item'] = df_OrdItem['price'] +\\\n",
    "                                df_OrdItem['freight_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On calcule le nombre d'article par commande\n",
    "s = df_OrdItem.groupby('order_id')['order_item_id'].count()\n",
    "df_OrdItem['Item_Qty'] = df_OrdItem['order_id'].map(s)\n",
    "# On calcule le cout total de la commande\n",
    "s2 = df_OrdItem.groupby('order_id')['Total_Price_Item'].sum()\n",
    "df_OrdItem['Total_Price_Order'] = df_OrdItem['order_id'].map(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>Total_Price_Item</th>\n",
       "      <th>Item_Qty</th>\n",
       "      <th>Total_Price_Order</th>\n",
       "      <th>cat</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.9</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>1</td>\n",
       "      <td>72.19</td>\n",
       "      <td>home</td>\n",
       "      <td>58.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>130898c0987d1801452a8ed92a670612</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-07-05 02:44:11</td>\n",
       "      <td>55.9</td>\n",
       "      <td>17.96</td>\n",
       "      <td>73.86</td>\n",
       "      <td>1</td>\n",
       "      <td>73.86</td>\n",
       "      <td>home</td>\n",
       "      <td>58.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  130898c0987d1801452a8ed92a670612              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "\n",
       "   shipping_limit_date  price  freight_value  Total_Price_Item  Item_Qty  \\\n",
       "0  2017-09-19 09:45:35   58.9          13.29             72.19         1   \n",
       "1  2017-07-05 02:44:11   55.9          17.96             73.86         1   \n",
       "\n",
       "   Total_Price_Order   cat  product_name_lenght  product_description_lenght  \\\n",
       "0              72.19  home                 58.0                       598.0   \n",
       "1              73.86  home                 58.0                       598.0   \n",
       "\n",
       "   product_photos_qty  \n",
       "0                 4.0  \n",
       "1                 4.0  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On merge avec df_cat pour avoir le prix par catégorie par commande\n",
    "df_OrdItem2 = pd.merge(df_OrdItem, df_cat, on='product_id')\n",
    "df_OrdItem2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112650, 14)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OrdItem2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(98666, 7)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On crée une variable pour chaque catégorie de produit par commande\n",
    "df_OrdItem2['order_and_cat'] = df_OrdItem2['order_id'] + df_OrdItem2['cat']\n",
    "# On calcule la somme par catégorie par commande\n",
    "cat_amounnt = df_OrdItem2.groupby('order_and_cat')['Total_Price_Item'].sum()\n",
    "df_OrdItem2['Cat_Paid'] = df_OrdItem2['order_and_cat'].map(cat_amounnt)\n",
    "# On crée un df pour avoir la somme par catégorie de produit par commande\n",
    "df_temp_it = df_OrdItem2[['order_id', 'cat', 'order_and_cat', 'Cat_Paid']]\n",
    "df_temp_it.drop_duplicates(subset=['order_and_cat'], keep='last', inplace=True)\n",
    "\n",
    "df_OrdItem3 = df_temp_it.pivot(index='order_id', columns='cat',\n",
    "                               values='Cat_Paid')\n",
    "df_OrdItem3.fillna(0, inplace=True)\n",
    "df_OrdItem3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On garde une ligne par commande\n",
    "df_OrdItem2.drop_duplicates(subset=['order_id'], keep='last', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98666, 23)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On merge nos df.\n",
    "df_OrdItem4 = pd.merge(df_OrdItem2, df_OrdItem3,\n",
    "                       left_on='order_id', right_index=True)\n",
    "df_OrdItem4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4265020911965727 (9803, 23)\n"
     ]
    }
   ],
   "source": [
    "# Moyenne du nb d'article dans les commandes avec 2 ou plus\n",
    "tmp_ord = df_OrdItem4[df_OrdItem4['Item_Qty'] > 1]\n",
    "print(tmp_ord['Item_Qty'].mean(), tmp_ord.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Density plot montant total de commande\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.kdeplot(df_OrdItem4['Total_Price_Order'], shade=True, ax=ax)\n",
    "ax.set_title('Répartition des montants de commande, skewness : '\n",
    "             + str(round(df_OrdItem4['Total_Price_Order'].skew(), 1)),\n",
    "             fontsize=13)\n",
    "ax.set_xlabel('Montant', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il manque presque 1000 commandes dans ce data set\n",
    "Il peut y avoir plusieurs produits/items par commande\n",
    "Feature possible: nombre de produits par commande, prix produit, frais de port, prix par catégorie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.8 - [Sellers Data set](#38)<a class=\"anchor\" id=\"38\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
       "      <td>13023</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1b65fc7debc3361ea86b5f14c68d2e2</td>\n",
       "      <td>13844</td>\n",
       "      <td>mogi guacu</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ce3ad9de960102d0677a81f5d0bb7b2d</td>\n",
       "      <td>20031</td>\n",
       "      <td>rio de janeiro</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id  seller_zip_code_prefix     seller_city  \\\n",
       "0  3442f8959a84dea7ee197c632cb2df15                   13023        campinas   \n",
       "1  d1b65fc7debc3361ea86b5f14c68d2e2                   13844      mogi guacu   \n",
       "2  ce3ad9de960102d0677a81f5d0bb7b2d                   20031  rio de janeiro   \n",
       "\n",
       "  seller_state  \n",
       "0           SP  \n",
       "1           SP  \n",
       "2           RJ  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SelrsDtset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nb Seller Id: 3095 \n",
      "\n",
      "Taille: (3095, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('nb Seller Id:', df_SelrsDtset['seller_id'].nunique(), '\\n')\n",
    "print('Taille:', df_SelrsDtset.shape)\n",
    "cle = ['seller_id']\n",
    "df_SelrsDtset[cle].drop_duplicates().shape[0] == df_SelrsDtset.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il y a le même nombre de seller_id ( 3095). Cle unique seller_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - [Consolidation du data set](#4)<a class=\"anchor\" id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_accuracy</th>\n",
       "      <th>order_purchase_date</th>\n",
       "      <th>DaysSinceOrder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>SP</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>8 days 19:30:00</td>\n",
       "      <td>-11 days +10:35:35</td>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>SP</td>\n",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>16 days 15:52:55</td>\n",
       "      <td>-8 days +12:41:19</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>SP</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>26 days 01:51:06</td>\n",
       "      <td>1 days 17:58:51</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>SP</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>14 days 23:57:47</td>\n",
       "      <td>-13 days +16:04:25</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>SP</td>\n",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>11 days 11:04:18</td>\n",
       "      <td>-6 days +20:55:48</td>\n",
       "      <td>2018-07-29</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96206</th>\n",
       "      <td>17ddf5dd5d51696bb3d7c6291687be6f</td>\n",
       "      <td>1a29b476fee25c95fbafc67c5ac95cf8</td>\n",
       "      <td>SP</td>\n",
       "      <td>6760e20addcf0121e9d58f2f1ff14298</td>\n",
       "      <td>delivered</td>\n",
       "      <td>6 days 04:18:20</td>\n",
       "      <td>-12 days +20:06:37</td>\n",
       "      <td>2018-04-07</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96207</th>\n",
       "      <td>e7b71a9017aa05c9a7fd292d714858e8</td>\n",
       "      <td>d52a67c98be1cf6a5c84435bd38d095d</td>\n",
       "      <td>SP</td>\n",
       "      <td>9ec0c8947d973db4f4e8dcf1fbfa8f1b</td>\n",
       "      <td>delivered</td>\n",
       "      <td>7 days 10:34:23</td>\n",
       "      <td>-9 days +18:54:45</td>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96208</th>\n",
       "      <td>5e28dfe12db7fb50a4b2f691faecea5e</td>\n",
       "      <td>e9f50caf99f032f0bf3c55141f019d99</td>\n",
       "      <td>CE</td>\n",
       "      <td>fed4434add09a6f332ea398efd656a5c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>30 days 22:51:25</td>\n",
       "      <td>7 days 19:03:15</td>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96209</th>\n",
       "      <td>56b18e2166679b8a959d72dd06da27f9</td>\n",
       "      <td>73c2643a0a458b49f58cea58833b192e</td>\n",
       "      <td>RS</td>\n",
       "      <td>e31ec91cea1ecf97797787471f98a8c2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>12 days 22:50:06</td>\n",
       "      <td>-19 days +19:58:39</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96210</th>\n",
       "      <td>274fa6071e5e17fe303b9748641082c8</td>\n",
       "      <td>84732c5050c01db9b23e19ba39899398</td>\n",
       "      <td>SP</td>\n",
       "      <td>28db69209a75e59f20ccbb5c36a20b90</td>\n",
       "      <td>delivered</td>\n",
       "      <td>7 days 04:15:13</td>\n",
       "      <td>-13 days +18:42:36</td>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96211 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id                customer_unique_id  \\\n",
       "0      06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1      18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2      4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3      b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4      4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "...                                 ...                               ...   \n",
       "96206  17ddf5dd5d51696bb3d7c6291687be6f  1a29b476fee25c95fbafc67c5ac95cf8   \n",
       "96207  e7b71a9017aa05c9a7fd292d714858e8  d52a67c98be1cf6a5c84435bd38d095d   \n",
       "96208  5e28dfe12db7fb50a4b2f691faecea5e  e9f50caf99f032f0bf3c55141f019d99   \n",
       "96209  56b18e2166679b8a959d72dd06da27f9  73c2643a0a458b49f58cea58833b192e   \n",
       "96210  274fa6071e5e17fe303b9748641082c8  84732c5050c01db9b23e19ba39899398   \n",
       "\n",
       "      customer_state                          order_id order_status  \\\n",
       "0                 SP  00e7ee1b050b8499577073aeb2a297a1    delivered   \n",
       "1                 SP  29150127e6685892b6eab3eec79f59c7    delivered   \n",
       "2                 SP  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered   \n",
       "3                 SP  951670f92359f4fe4a63112aa7306eba    delivered   \n",
       "4                 SP  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered   \n",
       "...              ...                               ...          ...   \n",
       "96206             SP  6760e20addcf0121e9d58f2f1ff14298    delivered   \n",
       "96207             SP  9ec0c8947d973db4f4e8dcf1fbfa8f1b    delivered   \n",
       "96208             CE  fed4434add09a6f332ea398efd656a5c    delivered   \n",
       "96209             RS  e31ec91cea1ecf97797787471f98a8c2    delivered   \n",
       "96210             SP  28db69209a75e59f20ccbb5c36a20b90    delivered   \n",
       "\n",
       "         delivery_time  delivery_accuracy order_purchase_date  DaysSinceOrder  \n",
       "0      8 days 19:30:00 -11 days +10:35:35          2017-05-16             471  \n",
       "1     16 days 15:52:55  -8 days +12:41:19          2018-01-12             230  \n",
       "2     26 days 01:51:06    1 days 17:58:51          2018-05-19             103  \n",
       "3     14 days 23:57:47 -13 days +16:04:25          2018-03-13             170  \n",
       "4     11 days 11:04:18  -6 days +20:55:48          2018-07-29              32  \n",
       "...                ...                ...                 ...             ...  \n",
       "96206  6 days 04:18:20 -12 days +20:06:37          2018-04-07             145  \n",
       "96207  7 days 10:34:23  -9 days +18:54:45          2018-04-04             148  \n",
       "96208 30 days 22:51:25    7 days 19:03:15          2018-04-08             144  \n",
       "96209 12 days 22:50:06 -19 days +19:58:39          2017-11-03             300  \n",
       "96210  7 days 04:15:13 -13 days +18:42:36          2017-12-19             254  \n",
       "\n",
       "[96211 rows x 9 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fusion de customer et order\n",
    "df = pd.merge(df_Cust, df_OrdDtset, on='customer_id')\n",
    "# On supprime des variables non pertinentes\n",
    "df.drop(['order_approved_at', 'order_delivered_carrier_date',\n",
    "        'order_delivered_customer_date', 'order_estimated_delivery_date',\n",
    "         'customer_zip_code_prefix', 'customer_city',\n",
    "         'order_purchase_timestamp'],\n",
    "        axis='columns', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96211, 10)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.merge(df, df_OrdRvews, on='order_id')\n",
    "df1.drop(['review_comment_title', 'review_comment_message',\n",
    "          'review_creation_date', 'review_answer_timestamp',\n",
    "          'review_id'], axis='columns', inplace=True)\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96211, 18)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.merge(df1, df_OrdPaymt3, on='order_id')\n",
    "df2.drop(['payment_type', 'payment_installments', 'payment_value',\n",
    "         'payment_sequential'], axis='columns', inplace=True)\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96211, 36)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df2, df_OrdItem4, on='order_id')\n",
    "df3.drop(['order_item_id', 'shipping_limit_date',\n",
    "         'price', 'freight_value'], axis='columns', inplace=True)\n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = pd.merge(df3, df_SelrsDtset, on='seller_id')\n",
    "df5.drop(['seller_zip_code_prefix', 'seller_city',\n",
    "         'seller_state'], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_accuracy</th>\n",
       "      <th>order_purchase_date</th>\n",
       "      <th>DaysSinceOrder</th>\n",
       "      <th>review_score</th>\n",
       "      <th>Pymt_Sq_Nb</th>\n",
       "      <th>order_and_payment</th>\n",
       "      <th>Type_Paid</th>\n",
       "      <th>boleto</th>\n",
       "      <th>credit_card</th>\n",
       "      <th>debit_card</th>\n",
       "      <th>not_defined</th>\n",
       "      <th>voucher</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>Total_Price_Item</th>\n",
       "      <th>Item_Qty</th>\n",
       "      <th>Total_Price_Order</th>\n",
       "      <th>cat</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>order_and_cat</th>\n",
       "      <th>Cat_Paid</th>\n",
       "      <th>construction</th>\n",
       "      <th>electronics</th>\n",
       "      <th>fashion</th>\n",
       "      <th>food</th>\n",
       "      <th>home</th>\n",
       "      <th>leisure</th>\n",
       "      <th>misc</th>\n",
       "      <th>State_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>SP</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>8 days 19:30:00</td>\n",
       "      <td>-11 days +10:35:35</td>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>471</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1credit_card</td>\n",
       "      <td>146.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>146.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a9516a079e37a9c9c36b9b78b10169e8</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>146.87</td>\n",
       "      <td>1</td>\n",
       "      <td>146.87</td>\n",
       "      <td>home</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1home</td>\n",
       "      <td>146.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>146.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>SP</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>26 days 01:51:06</td>\n",
       "      <td>1 days 17:58:51</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>103</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2credit_card</td>\n",
       "      <td>157.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>157.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bd07b66896d6f1494f5b86251848ced7</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>157.73</td>\n",
       "      <td>1</td>\n",
       "      <td>157.73</td>\n",
       "      <td>home</td>\n",
       "      <td>55.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2home</td>\n",
       "      <td>157.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>157.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>SP</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>14 days 23:57:47</td>\n",
       "      <td>-13 days +16:04:25</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>170</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306ebacredit_card</td>\n",
       "      <td>173.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>173.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a5647c44af977b148e0a3a4751a09e2e</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>173.30</td>\n",
       "      <td>1</td>\n",
       "      <td>173.30</td>\n",
       "      <td>home</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306ebahome</td>\n",
       "      <td>173.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>173.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4c06b42fbf7b97ab10779cda5549cd1c</td>\n",
       "      <td>07d190f123147d9e89d4b922543d7948</td>\n",
       "      <td>MA</td>\n",
       "      <td>997fd4b37386d10b57d4ac8cdec011a6</td>\n",
       "      <td>delivered</td>\n",
       "      <td>31 days 09:43:34</td>\n",
       "      <td>-8 days +19:42:53</td>\n",
       "      <td>2018-07-08</td>\n",
       "      <td>53</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>997fd4b37386d10b57d4ac8cdec011a6credit_card</td>\n",
       "      <td>315.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>315.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8ed094bfe076c568f6bb10feada3f75d</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>315.75</td>\n",
       "      <td>1</td>\n",
       "      <td>315.75</td>\n",
       "      <td>home</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>997fd4b37386d10b57d4ac8cdec011a6home</td>\n",
       "      <td>315.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>315.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8276de07ef25225d412b8462d73f8664</td>\n",
       "      <td>332cf4e83e16004ba7dca932ce82475b</td>\n",
       "      <td>RS</td>\n",
       "      <td>b63fff1ef8b6201df68bb611ee41129e</td>\n",
       "      <td>delivered</td>\n",
       "      <td>29 days 15:37:58</td>\n",
       "      <td>-6 days +13:53:25</td>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>515</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>b63fff1ef8b6201df68bb611ee41129eboleto</td>\n",
       "      <td>146.21</td>\n",
       "      <td>146.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>aba86c093ccdbac75b09111d57e50004</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>146.21</td>\n",
       "      <td>1</td>\n",
       "      <td>146.21</td>\n",
       "      <td>home</td>\n",
       "      <td>41.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b63fff1ef8b6201df68bb611ee41129ehome</td>\n",
       "      <td>146.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>146.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96206</th>\n",
       "      <td>502e8addaeba926b9c154da9f3bbbecf</td>\n",
       "      <td>38e482d7770a43f5929378c4dec76d5e</td>\n",
       "      <td>RN</td>\n",
       "      <td>f80896ddec0878ff2475719a8784d53c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>12 days 07:05:58</td>\n",
       "      <td>-27 days +22:08:29</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>14</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>f80896ddec0878ff2475719a8784d53ccredit_card</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>c7b8e643fca22f6d8ec5f4b76b72e65f</td>\n",
       "      <td>cb810bd318f51b3dc3c753944d61a1fa</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>1</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>fashion</td>\n",
       "      <td>45.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>f80896ddec0878ff2475719a8784d53cfashion</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96207</th>\n",
       "      <td>59168d27c5da6a50e3d1f03d482e80b2</td>\n",
       "      <td>393c2ace23a135d0bcc7e70167448e16</td>\n",
       "      <td>BA</td>\n",
       "      <td>c382d84d35f4d874b1f1c372f4bde27c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>7 days 02:11:35</td>\n",
       "      <td>-18 days +11:54:15</td>\n",
       "      <td>2018-07-20</td>\n",
       "      <td>41</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>c382d84d35f4d874b1f1c372f4bde27cboleto</td>\n",
       "      <td>161.37</td>\n",
       "      <td>161.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731cf6fbf44cc64a9583fabe2c00750e</td>\n",
       "      <td>398cb257329ef7af7f1943a8974a3cbc</td>\n",
       "      <td>161.37</td>\n",
       "      <td>1</td>\n",
       "      <td>161.37</td>\n",
       "      <td>food</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c382d84d35f4d874b1f1c372f4bde27cfood</td>\n",
       "      <td>161.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96208</th>\n",
       "      <td>eda10d7eb51a675d000956fa1aabc7dd</td>\n",
       "      <td>5c22dbb9d8b5385953f2f1e6d9d115a7</td>\n",
       "      <td>MG</td>\n",
       "      <td>b494f624c631105b24fe497277085909</td>\n",
       "      <td>delivered</td>\n",
       "      <td>12 days 04:17:57</td>\n",
       "      <td>-14 days +23:16:46</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>b494f624c631105b24fe497277085909credit_card</td>\n",
       "      <td>72.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a2fce4213a6ac271129618259a334261</td>\n",
       "      <td>9140ef75cc8211c4035935e80e567204</td>\n",
       "      <td>72.18</td>\n",
       "      <td>1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>misc</td>\n",
       "      <td>51.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b494f624c631105b24fe497277085909misc</td>\n",
       "      <td>72.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96209</th>\n",
       "      <td>b90b857a277b080d8078332b3636ab58</td>\n",
       "      <td>7f5f43a9394964ac51bc5d1e0204ead4</td>\n",
       "      <td>GO</td>\n",
       "      <td>d76fe905f4588ad69ab3b36527bb267d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>9 days 21:16:46</td>\n",
       "      <td>-13 days +18:16:44</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>d76fe905f4588ad69ab3b36527bb267dcredit_card</td>\n",
       "      <td>224.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>224.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d6e7ac236e583af1e1543d8068c91676</td>\n",
       "      <td>377bc18334c78dd2f1535ef22a3ad225</td>\n",
       "      <td>224.44</td>\n",
       "      <td>1</td>\n",
       "      <td>224.44</td>\n",
       "      <td>construction</td>\n",
       "      <td>55.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>d76fe905f4588ad69ab3b36527bb267dconstruction</td>\n",
       "      <td>224.44</td>\n",
       "      <td>224.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96210</th>\n",
       "      <td>1ed0c832c2dd99570a59260e71768bdf</td>\n",
       "      <td>82d46759af0369aad49084bacf85a6c3</td>\n",
       "      <td>MG</td>\n",
       "      <td>51c6d2f460589fa7b65f2da51e860206</td>\n",
       "      <td>delivered</td>\n",
       "      <td>35 days 07:33:24</td>\n",
       "      <td>7 days 19:37:33</td>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>289</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>51c6d2f460589fa7b65f2da51e860206credit_card</td>\n",
       "      <td>199.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>c98bf47f7bea8f3aee82fa023786b8a1</td>\n",
       "      <td>51a04a8a6bdcb23deccc82b0b80742cf</td>\n",
       "      <td>199.92</td>\n",
       "      <td>1</td>\n",
       "      <td>199.92</td>\n",
       "      <td>electronics</td>\n",
       "      <td>33.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51c6d2f460589fa7b65f2da51e860206electronics</td>\n",
       "      <td>199.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96211 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id                customer_unique_id  \\\n",
       "0      06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1      4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "2      b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "3      4c06b42fbf7b97ab10779cda5549cd1c  07d190f123147d9e89d4b922543d7948   \n",
       "4      8276de07ef25225d412b8462d73f8664  332cf4e83e16004ba7dca932ce82475b   \n",
       "...                                 ...                               ...   \n",
       "96206  502e8addaeba926b9c154da9f3bbbecf  38e482d7770a43f5929378c4dec76d5e   \n",
       "96207  59168d27c5da6a50e3d1f03d482e80b2  393c2ace23a135d0bcc7e70167448e16   \n",
       "96208  eda10d7eb51a675d000956fa1aabc7dd  5c22dbb9d8b5385953f2f1e6d9d115a7   \n",
       "96209  b90b857a277b080d8078332b3636ab58  7f5f43a9394964ac51bc5d1e0204ead4   \n",
       "96210  1ed0c832c2dd99570a59260e71768bdf  82d46759af0369aad49084bacf85a6c3   \n",
       "\n",
       "      customer_state                          order_id order_status  \\\n",
       "0                 SP  00e7ee1b050b8499577073aeb2a297a1    delivered   \n",
       "1                 SP  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered   \n",
       "2                 SP  951670f92359f4fe4a63112aa7306eba    delivered   \n",
       "3                 MA  997fd4b37386d10b57d4ac8cdec011a6    delivered   \n",
       "4                 RS  b63fff1ef8b6201df68bb611ee41129e    delivered   \n",
       "...              ...                               ...          ...   \n",
       "96206             RN  f80896ddec0878ff2475719a8784d53c    delivered   \n",
       "96207             BA  c382d84d35f4d874b1f1c372f4bde27c    delivered   \n",
       "96208             MG  b494f624c631105b24fe497277085909    delivered   \n",
       "96209             GO  d76fe905f4588ad69ab3b36527bb267d    delivered   \n",
       "96210             MG  51c6d2f460589fa7b65f2da51e860206    delivered   \n",
       "\n",
       "         delivery_time  delivery_accuracy order_purchase_date  DaysSinceOrder  \\\n",
       "0      8 days 19:30:00 -11 days +10:35:35          2017-05-16             471   \n",
       "1     26 days 01:51:06    1 days 17:58:51          2018-05-19             103   \n",
       "2     14 days 23:57:47 -13 days +16:04:25          2018-03-13             170   \n",
       "3     31 days 09:43:34  -8 days +19:42:53          2018-07-08              53   \n",
       "4     29 days 15:37:58  -6 days +13:53:25          2017-04-02             515   \n",
       "...                ...                ...                 ...             ...   \n",
       "96206 12 days 07:05:58 -27 days +22:08:29          2018-08-16              14   \n",
       "96207  7 days 02:11:35 -18 days +11:54:15          2018-07-20              41   \n",
       "96208 12 days 04:17:57 -14 days +23:16:46          2018-08-01              29   \n",
       "96209  9 days 21:16:46 -13 days +18:16:44          2018-08-18              12   \n",
       "96210 35 days 07:33:24    7 days 19:37:33          2017-11-14             289   \n",
       "\n",
       "       review_score  Pymt_Sq_Nb                            order_and_payment  \\\n",
       "0               5.0           1  00e7ee1b050b8499577073aeb2a297a1credit_card   \n",
       "1               4.0           1  b2059ed67ce144a36e2aa97d2c9e9ad2credit_card   \n",
       "2               5.0           1  951670f92359f4fe4a63112aa7306ebacredit_card   \n",
       "3               5.0           1  997fd4b37386d10b57d4ac8cdec011a6credit_card   \n",
       "4               5.0           1       b63fff1ef8b6201df68bb611ee41129eboleto   \n",
       "...             ...         ...                                          ...   \n",
       "96206           4.0           1  f80896ddec0878ff2475719a8784d53ccredit_card   \n",
       "96207           2.0           1       c382d84d35f4d874b1f1c372f4bde27cboleto   \n",
       "96208           5.0           1  b494f624c631105b24fe497277085909credit_card   \n",
       "96209           4.0           1  d76fe905f4588ad69ab3b36527bb267dcredit_card   \n",
       "96210           5.0           2  51c6d2f460589fa7b65f2da51e860206credit_card   \n",
       "\n",
       "       Type_Paid  boleto  credit_card  debit_card  not_defined  voucher  \\\n",
       "0         146.87    0.00       146.87         0.0          0.0      0.0   \n",
       "1         157.73    0.00       157.73         0.0          0.0      0.0   \n",
       "2         173.30    0.00       173.30         0.0          0.0      0.0   \n",
       "3         315.75    0.00       315.75         0.0          0.0      0.0   \n",
       "4         146.21  146.21         0.00         0.0          0.0      0.0   \n",
       "...          ...     ...          ...         ...          ...      ...   \n",
       "96206    1789.02    0.00      1789.02         0.0          0.0      0.0   \n",
       "96207     161.37  161.37         0.00         0.0          0.0      0.0   \n",
       "96208      72.18    0.00        72.18         0.0          0.0      0.0   \n",
       "96209     224.44    0.00       224.44         0.0          0.0      0.0   \n",
       "96210     199.92    0.00       199.92         0.0          0.0      0.0   \n",
       "\n",
       "                             product_id                         seller_id  \\\n",
       "0      a9516a079e37a9c9c36b9b78b10169e8  7c67e1448b00f6e969d365cea6b010ab   \n",
       "1      bd07b66896d6f1494f5b86251848ced7  7c67e1448b00f6e969d365cea6b010ab   \n",
       "2      a5647c44af977b148e0a3a4751a09e2e  7c67e1448b00f6e969d365cea6b010ab   \n",
       "3      8ed094bfe076c568f6bb10feada3f75d  7c67e1448b00f6e969d365cea6b010ab   \n",
       "4      aba86c093ccdbac75b09111d57e50004  7c67e1448b00f6e969d365cea6b010ab   \n",
       "...                                 ...                               ...   \n",
       "96206  c7b8e643fca22f6d8ec5f4b76b72e65f  cb810bd318f51b3dc3c753944d61a1fa   \n",
       "96207  731cf6fbf44cc64a9583fabe2c00750e  398cb257329ef7af7f1943a8974a3cbc   \n",
       "96208  a2fce4213a6ac271129618259a334261  9140ef75cc8211c4035935e80e567204   \n",
       "96209  d6e7ac236e583af1e1543d8068c91676  377bc18334c78dd2f1535ef22a3ad225   \n",
       "96210  c98bf47f7bea8f3aee82fa023786b8a1  51a04a8a6bdcb23deccc82b0b80742cf   \n",
       "\n",
       "       Total_Price_Item  Item_Qty  Total_Price_Order           cat  \\\n",
       "0                146.87         1             146.87          home   \n",
       "1                157.73         1             157.73          home   \n",
       "2                173.30         1             173.30          home   \n",
       "3                315.75         1             315.75          home   \n",
       "4                146.21         1             146.21          home   \n",
       "...                 ...       ...                ...           ...   \n",
       "96206           1789.02         1            1789.02       fashion   \n",
       "96207            161.37         1             161.37          food   \n",
       "96208             72.18         1              72.18          misc   \n",
       "96209            224.44         1             224.44  construction   \n",
       "96210            199.92         1             199.92   electronics   \n",
       "\n",
       "       product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                     41.0                      1141.0                 1.0   \n",
       "1                     55.0                       955.0                 1.0   \n",
       "2                     48.0                      1066.0                 1.0   \n",
       "3                     43.0                      1128.0                 1.0   \n",
       "4                     41.0                       154.0                 1.0   \n",
       "...                    ...                         ...                 ...   \n",
       "96206                 45.0                       723.0                 1.0   \n",
       "96207                 49.0                      1879.0                 1.0   \n",
       "96208                 51.0                       433.0                 1.0   \n",
       "96209                 55.0                       835.0                 8.0   \n",
       "96210                 33.0                        63.0                 1.0   \n",
       "\n",
       "                                      order_and_cat  Cat_Paid  construction  \\\n",
       "0              00e7ee1b050b8499577073aeb2a297a1home    146.87          0.00   \n",
       "1              b2059ed67ce144a36e2aa97d2c9e9ad2home    157.73          0.00   \n",
       "2              951670f92359f4fe4a63112aa7306ebahome    173.30          0.00   \n",
       "3              997fd4b37386d10b57d4ac8cdec011a6home    315.75          0.00   \n",
       "4              b63fff1ef8b6201df68bb611ee41129ehome    146.21          0.00   \n",
       "...                                             ...       ...           ...   \n",
       "96206       f80896ddec0878ff2475719a8784d53cfashion   1789.02          0.00   \n",
       "96207          c382d84d35f4d874b1f1c372f4bde27cfood    161.37          0.00   \n",
       "96208          b494f624c631105b24fe497277085909misc     72.18          0.00   \n",
       "96209  d76fe905f4588ad69ab3b36527bb267dconstruction    224.44        224.44   \n",
       "96210   51c6d2f460589fa7b65f2da51e860206electronics    199.92          0.00   \n",
       "\n",
       "       electronics  fashion    food    home  leisure   misc  State_Customer  \n",
       "0             0.00     0.00    0.00  146.87      0.0   0.00              25  \n",
       "1             0.00     0.00    0.00  157.73      0.0   0.00              25  \n",
       "2             0.00     0.00    0.00  173.30      0.0   0.00              25  \n",
       "3             0.00     0.00    0.00  315.75      0.0   0.00              25  \n",
       "4             0.00     0.00    0.00  146.21      0.0   0.00              25  \n",
       "...            ...      ...     ...     ...      ...    ...             ...  \n",
       "96206         0.00  1789.02    0.00    0.00      0.0   0.00              25  \n",
       "96207         0.00     0.00  161.37    0.00      0.0   0.00              25  \n",
       "96208         0.00     0.00    0.00    0.00      0.0  72.18               5  \n",
       "96209         0.00     0.00    0.00    0.00      0.0   0.00              22  \n",
       "96210       199.92     0.00    0.00    0.00      0.0   0.00              25  \n",
       "\n",
       "[96211 rows x 37 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_input_to_le = ['customer_state']\n",
    "\n",
    "# Label encoder pour connaître l'état de chaque client\n",
    "le = preprocessing.LabelEncoder()\n",
    "df5['State_Customer'] = le.fit_transform(df.loc[:, 'customer_state'])\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "df_temp_cat2 = df5[['customer_unique_id', 'order_id', 'construction',\n",
    "                    'electronics',\n",
    "                    'fashion', 'food', 'home', 'leisure', 'misc']]\n",
    "\n",
    "df_temp_cat2.set_index(['customer_unique_id', 'order_id'], inplace=True)\n",
    "\n",
    "df_temp_cat3 = pd.DataFrame(df_temp_cat2.stack(dropna=False)).reset_index()\n",
    "df_temp_cat3.columns = ['customer_unique_id', 'order_id', 'Cat', 'Value']\n",
    "\n",
    "# On crée une variable pour chaque catégorie de produit par client\n",
    "df_temp_cat3['cat_cust'] = df_temp_cat3['customer_unique_id'] +\\\n",
    "                            df_temp_cat3['Cat']\n",
    "\n",
    "# On calcule la somme par catégorie par client\n",
    "cat_cust = df_temp_cat3.groupby('cat_cust')['Value'].sum()\n",
    "df_temp_cat3['Cat_Amt_Cust'] = df_temp_cat3['cat_cust'].map(cat_cust)\n",
    "# On crée un df pour avoir la somme par cat par client\n",
    "df_temp_cat4 = df_temp_cat3[['customer_unique_id', 'Cat', 'cat_cust',\n",
    "                             'Cat_Amt_Cust']]\n",
    "df_temp_cat4.drop_duplicates(subset=['cat_cust'], keep='last', inplace=True)\n",
    "\n",
    "df_Cat_Cust = df_temp_cat4.pivot(index='customer_unique_id',\n",
    "                                 columns='Cat', values='Cat_Amt_Cust')\n",
    "df_Cat_Cust.fillna(0, inplace=True)\n",
    "df_Cat_Cust.columns = ['construction_Total_Customer',\n",
    "                       'electronics_Total_Customer',\n",
    "                       'fashion_Total_Customer', 'food_Total_Customer',\n",
    "                       'home_Total_Customer',\n",
    "                       'leisure_Total_Customer', 'misc_Total_Customer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "df_temp2 = df5[['customer_unique_id', 'order_id', 'boleto', 'credit_card',\n",
    "                'debit_card', 'not_defined', 'voucher']]\n",
    "\n",
    "df_temp2.set_index(['customer_unique_id', 'order_id'], inplace=True)\n",
    "\n",
    "df_temp3 = pd.DataFrame(df_temp2.stack(dropna=False)).reset_index()\n",
    "df_temp3.columns = ['customer_unique_id', 'order_id', 'Payment_Type', 'Value']\n",
    "df_temp3\n",
    "\n",
    "# On crée une variable pour chaque catégorie de paiement par client\n",
    "df_temp3['paym_cust'] = df_temp3['customer_unique_id'] + \\\n",
    "                        df_temp3['Payment_Type']\n",
    "\n",
    "# On calcule la somme par type de paiement par client\n",
    "typ_pmt_cust = df_temp3.groupby('paym_cust')['Value'].sum()\n",
    "df_temp3['Pmt_Cust'] = df_temp3['paym_cust'].map(typ_pmt_cust)\n",
    "# On crée un df pour avoir la somme par type de paiement par client\n",
    "df_temp_cu_pmt = df_temp3[['customer_unique_id', 'Payment_Type',\n",
    "                           'paym_cust', 'Pmt_Cust']]\n",
    "df_temp_cu_pmt.drop_duplicates(subset=['paym_cust'], keep='last', inplace=True)\n",
    "\n",
    "df_Pymt_Cust = df_temp_cu_pmt.pivot(index='customer_unique_id',\n",
    "                                    columns='Payment_Type', values='Pmt_Cust')\n",
    "df_Pymt_Cust.fillna(0, inplace=True)\n",
    "df_Pymt_Cust.columns = ['boleto_Total_Customer', 'credit_card_Total_Customer',\n",
    "                        'debit_card_Total_Customer',\n",
    "                        'other_payment_Total_Customer',\n",
    "                        'voucher_Total_Customer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boleto_Total_Customer</th>\n",
       "      <th>credit_card_Total_Customer</th>\n",
       "      <th>debit_card_Total_Customer</th>\n",
       "      <th>voucher_Total_Customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </tbody>\n",
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       "<p>93104 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                                  boleto_Total_Customer  \\\n",
       "customer_unique_id                                        \n",
       "0000366f3b9a7992bf8c76cfdf3221e2                    0.0   \n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f                    0.0   \n",
       "0000f46a3911fa3c0805444483337064                    0.0   \n",
       "0000f6ccb0745a6a4b88665a16c9f078                    0.0   \n",
       "0004aac84e0df4da2b147fca70cf8255                    0.0   \n",
       "...                                                 ...   \n",
       "fffcf5a5ff07b0908bd4e2dbc735a684                    0.0   \n",
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       "ffffd2657e2aad2907e67c3e9daecbeb                    0.0   \n",
       "\n",
       "                                  credit_card_Total_Customer  \\\n",
       "customer_unique_id                                             \n",
       "0000366f3b9a7992bf8c76cfdf3221e2                      141.90   \n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f                       27.19   \n",
       "0000f46a3911fa3c0805444483337064                       86.22   \n",
       "0000f6ccb0745a6a4b88665a16c9f078                       43.62   \n",
       "0004aac84e0df4da2b147fca70cf8255                      196.89   \n",
       "...                                                      ...   \n",
       "fffcf5a5ff07b0908bd4e2dbc735a684                     2067.42   \n",
       "fffea47cd6d3cc0a88bd621562a9d061                       84.58   \n",
       "ffff371b4d645b6ecea244b27531430a                      112.46   \n",
       "ffff5962728ec6157033ef9805bacc48                      133.69   \n",
       "ffffd2657e2aad2907e67c3e9daecbeb                       71.56   \n",
       "\n",
       "                                  debit_card_Total_Customer  \\\n",
       "customer_unique_id                                            \n",
       "0000366f3b9a7992bf8c76cfdf3221e2                        0.0   \n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f                        0.0   \n",
       "0000f46a3911fa3c0805444483337064                        0.0   \n",
       "0000f6ccb0745a6a4b88665a16c9f078                        0.0   \n",
       "0004aac84e0df4da2b147fca70cf8255                        0.0   \n",
       "...                                                     ...   \n",
       "fffcf5a5ff07b0908bd4e2dbc735a684                        0.0   \n",
       "fffea47cd6d3cc0a88bd621562a9d061                        0.0   \n",
       "ffff371b4d645b6ecea244b27531430a                        0.0   \n",
       "ffff5962728ec6157033ef9805bacc48                        0.0   \n",
       "ffffd2657e2aad2907e67c3e9daecbeb                        0.0   \n",
       "\n",
       "                                  voucher_Total_Customer  \n",
       "customer_unique_id                                        \n",
       "0000366f3b9a7992bf8c76cfdf3221e2                     0.0  \n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f                     0.0  \n",
       "0000f46a3911fa3c0805444483337064                     0.0  \n",
       "0000f6ccb0745a6a4b88665a16c9f078                     0.0  \n",
       "0004aac84e0df4da2b147fca70cf8255                     0.0  \n",
       "...                                                  ...  \n",
       "fffcf5a5ff07b0908bd4e2dbc735a684                     0.0  \n",
       "fffea47cd6d3cc0a88bd621562a9d061                     0.0  \n",
       "ffff371b4d645b6ecea244b27531430a                     0.0  \n",
       "ffff5962728ec6157033ef9805bacc48                     0.0  \n",
       "ffffd2657e2aad2907e67c3e9daecbeb                     0.0  \n",
       "\n",
       "[93104 rows x 4 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_Pymt_Cust['other_payment_Total_Customer'].sum())\n",
    "df_Pymt_Cust.drop(['other_payment_Total_Customer'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On calcule le temps de livraison moyen par client\n",
    "df5['delivery_time'] = df5['delivery_time'].astype('timedelta64[D]')\n",
    "mean_delivery = df5.groupby('customer_unique_id')['delivery_time'].mean()\n",
    "df5['Delivery_Time_Mean_Customer'] = df5['customer_unique_id']\\\n",
    "                                     .map(mean_delivery)\n",
    "\n",
    "# On calcule la précision des dates de livraison par client\n",
    "df5['delivery_accuracy'] = df5['delivery_accuracy'].astype('timedelta64[D]')\n",
    "mean_acc_delivery = df5.groupby('customer_unique_id')['delivery_accuracy']\\\n",
    "                    .mean()\n",
    "df5['Delivery_Accuracy_Mean_Customer'] = df5['customer_unique_id']\\\n",
    "                                         .map(mean_acc_delivery)\n",
    "\n",
    "# On calcule la somme totale dépensée par le client\n",
    "tot_sum = df5.groupby('customer_unique_id')['Total_Price_Order'].sum()\n",
    "df5['Total_Amount_Customer'] = df5['customer_unique_id'].map(tot_sum)\n",
    "\n",
    "# On calcule le nombre total d'articles commandés par le client\n",
    "item_sum = df5.groupby('customer_unique_id')['Item_Qty'].sum()\n",
    "df5['Item_Quantity_Customer'] = df5['customer_unique_id'].map(item_sum)\n",
    "\n",
    "# On calcule le nombre de commande passé par le client\n",
    "order_number = df5.groupby('customer_unique_id')['order_id'].count()\n",
    "df5['Total_Order_Customer'] = df5['customer_unique_id'].map(order_number)\n",
    "\n",
    "# On calcule le score moyen par client\n",
    "mean_score = df5.groupby('customer_unique_id')['review_score'].mean()\n",
    "df5['Review_Score_Mean_Customer'] = df5['customer_unique_id'].map(mean_score)\n",
    "\n",
    "# On calcule le nombre moyen de paiement par commande par client\n",
    "paymt_nb_cu = df5.groupby('customer_unique_id')['Pymt_Sq_Nb'].mean()\n",
    "df5['Order_Paiement_Nb_Mean_Customer'] = df5['customer_unique_id']\\\n",
    "                                         .map(paymt_nb_cu)\n",
    "\n",
    "# On calcule la longueur moyenne desc pdt par client\n",
    "desc_cu = df5.groupby('customer_unique_id')['product_description_lenght']\\\n",
    "          .mean()\n",
    "df5['Product_Desc_Length_Mean_Customer'] = df5['customer_unique_id']\\\n",
    "                                           .map(desc_cu)\n",
    "\n",
    "# On calcule le nb de photo de pdt par client\n",
    "photo_cu = df5.groupby('customer_unique_id')['product_photos_qty'].mean()\n",
    "df5['Product_Photo_Nb_Mean_Customer'] = df5['customer_unique_id'].map(photo_cu)\n",
    "\n",
    "# On garde la dernière date de commande par client.\n",
    "recency = df5.groupby('customer_unique_id')['DaysSinceOrder'].min()\n",
    "df5['Last_Order_Customer'] = df5['customer_unique_id'].map(recency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.drop_duplicates(subset=['customer_unique_id'], keep='last',\n",
    "                    inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.merge(df5, df_Pymt_Cust, left_on='customer_unique_id',\n",
    "               right_index=True)\n",
    "dff = pd.merge(df6, df_Cat_Cust, left_on='customer_unique_id',\n",
    "               right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cust_Var = ['customer_unique_id', 'State_Customer', 'order_purchase_date',\n",
    "            'Delivery_Time_Mean_Customer',\n",
    "            'Delivery_Accuracy_Mean_Customer', 'Last_Order_Customer',\n",
    "            'Total_Amount_Customer', 'Item_Quantity_Customer',\n",
    "            'Total_Order_Customer',\n",
    "            'Review_Score_Mean_Customer', 'Order_Paiement_Nb_Mean_Customer',\n",
    "            'Product_Desc_Length_Mean_Customer',\n",
    "            'Product_Photo_Nb_Mean_Customer',\n",
    "            'boleto_Total_Customer', 'credit_card_Total_Customer',\n",
    "            'debit_card_Total_Customer', 'voucher_Total_Customer',\n",
    "            'construction_Total_Customer', 'electronics_Total_Customer',\n",
    "            'fashion_Total_Customer', 'food_Total_Customer',\n",
    "            'home_Total_Customer', 'leisure_Total_Customer',\n",
    "            'misc_Total_Customer']\n",
    "Customer_Base = dff[Cust_Var]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>State_Customer</th>\n",
       "      <th>order_purchase_date</th>\n",
       "      <th>Delivery_Time_Mean_Customer</th>\n",
       "      <th>Delivery_Accuracy_Mean_Customer</th>\n",
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       "      <th>voucher_Total_Customer</th>\n",
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       "      <th>home_Total_Customer</th>\n",
       "      <th>leisure_Total_Customer</th>\n",
       "      <th>misc_Total_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>25</td>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>471</td>\n",
       "      <td>146.87</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>146.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>146.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103</td>\n",
       "      <td>157.73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>157.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>157.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>170</td>\n",
       "      <td>173.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>173.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>173.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07d190f123147d9e89d4b922543d7948</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-07-08</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>53</td>\n",
       "      <td>315.75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>315.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>315.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>332cf4e83e16004ba7dca932ce82475b</td>\n",
       "      <td>25</td>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>515</td>\n",
       "      <td>146.21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>146.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96206</th>\n",
       "      <td>38e482d7770a43f5929378c4dec76d5e</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1789.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96207</th>\n",
       "      <td>393c2ace23a135d0bcc7e70167448e16</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-07-20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>41</td>\n",
       "      <td>161.37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96208</th>\n",
       "      <td>5c22dbb9d8b5385953f2f1e6d9d115a7</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>29</td>\n",
       "      <td>72.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96209</th>\n",
       "      <td>7f5f43a9394964ac51bc5d1e0204ead4</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>12</td>\n",
       "      <td>224.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>224.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96210</th>\n",
       "      <td>82d46759af0369aad49084bacf85a6c3</td>\n",
       "      <td>25</td>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>289</td>\n",
       "      <td>199.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93104 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_unique_id  State_Customer order_purchase_date  \\\n",
       "0      861eff4711a542e4b93843c6dd7febb0              25          2017-05-16   \n",
       "1      060e732b5b29e8181a18229c7b0b2b5e              25          2018-05-19   \n",
       "2      259dac757896d24d7702b9acbbff3f3c              25          2018-03-13   \n",
       "3      07d190f123147d9e89d4b922543d7948              25          2018-07-08   \n",
       "4      332cf4e83e16004ba7dca932ce82475b              25          2017-04-02   \n",
       "...                                 ...             ...                 ...   \n",
       "96206  38e482d7770a43f5929378c4dec76d5e              25          2018-08-16   \n",
       "96207  393c2ace23a135d0bcc7e70167448e16              25          2018-07-20   \n",
       "96208  5c22dbb9d8b5385953f2f1e6d9d115a7               5          2018-08-01   \n",
       "96209  7f5f43a9394964ac51bc5d1e0204ead4              22          2018-08-18   \n",
       "96210  82d46759af0369aad49084bacf85a6c3              25          2017-11-14   \n",
       "\n",
       "       Delivery_Time_Mean_Customer  Delivery_Accuracy_Mean_Customer  \\\n",
       "0                              8.0                            -11.0   \n",
       "1                             26.0                              1.0   \n",
       "2                             14.0                            -13.0   \n",
       "3                             31.0                             -8.0   \n",
       "4                             29.0                             -6.0   \n",
       "...                            ...                              ...   \n",
       "96206                         12.0                            -27.0   \n",
       "96207                          7.0                            -18.0   \n",
       "96208                         12.0                            -14.0   \n",
       "96209                          9.0                            -13.0   \n",
       "96210                         35.0                              7.0   \n",
       "\n",
       "       Last_Order_Customer  Total_Amount_Customer  Item_Quantity_Customer  \\\n",
       "0                      471                 146.87                       1   \n",
       "1                      103                 157.73                       1   \n",
       "2                      170                 173.30                       1   \n",
       "3                       53                 315.75                       1   \n",
       "4                      515                 146.21                       1   \n",
       "...                    ...                    ...                     ...   \n",
       "96206                   14                1789.02                       1   \n",
       "96207                   41                 161.37                       1   \n",
       "96208                   29                  72.18                       1   \n",
       "96209                   12                 224.44                       1   \n",
       "96210                  289                 199.92                       1   \n",
       "\n",
       "       Total_Order_Customer  Review_Score_Mean_Customer  \\\n",
       "0                         1                         5.0   \n",
       "1                         1                         4.0   \n",
       "2                         1                         5.0   \n",
       "3                         1                         5.0   \n",
       "4                         1                         5.0   \n",
       "...                     ...                         ...   \n",
       "96206                     1                         4.0   \n",
       "96207                     1                         2.0   \n",
       "96208                     1                         5.0   \n",
       "96209                     1                         4.0   \n",
       "96210                     1                         5.0   \n",
       "\n",
       "       Order_Paiement_Nb_Mean_Customer  Product_Desc_Length_Mean_Customer  \\\n",
       "0                                  1.0                             1141.0   \n",
       "1                                  1.0                              955.0   \n",
       "2                                  1.0                             1066.0   \n",
       "3                                  1.0                             1128.0   \n",
       "4                                  1.0                              154.0   \n",
       "...                                ...                                ...   \n",
       "96206                              1.0                              723.0   \n",
       "96207                              1.0                             1879.0   \n",
       "96208                              1.0                              433.0   \n",
       "96209                              1.0                              835.0   \n",
       "96210                              2.0                               63.0   \n",
       "\n",
       "       Product_Photo_Nb_Mean_Customer  boleto_Total_Customer  \\\n",
       "0                                 1.0                   0.00   \n",
       "1                                 1.0                   0.00   \n",
       "2                                 1.0                   0.00   \n",
       "3                                 1.0                   0.00   \n",
       "4                                 1.0                 146.21   \n",
       "...                               ...                    ...   \n",
       "96206                             1.0                   0.00   \n",
       "96207                             1.0                 161.37   \n",
       "96208                             1.0                   0.00   \n",
       "96209                             8.0                   0.00   \n",
       "96210                             1.0                   0.00   \n",
       "\n",
       "       credit_card_Total_Customer  debit_card_Total_Customer  \\\n",
       "0                          146.87                        0.0   \n",
       "1                          157.73                        0.0   \n",
       "2                          173.30                        0.0   \n",
       "3                          315.75                        0.0   \n",
       "4                            0.00                        0.0   \n",
       "...                           ...                        ...   \n",
       "96206                     1789.02                        0.0   \n",
       "96207                        0.00                        0.0   \n",
       "96208                       72.18                        0.0   \n",
       "96209                      224.44                        0.0   \n",
       "96210                      199.92                        0.0   \n",
       "\n",
       "       voucher_Total_Customer  construction_Total_Customer  \\\n",
       "0                         0.0                         0.00   \n",
       "1                         0.0                         0.00   \n",
       "2                         0.0                         0.00   \n",
       "3                         0.0                         0.00   \n",
       "4                         0.0                         0.00   \n",
       "...                       ...                          ...   \n",
       "96206                     0.0                         0.00   \n",
       "96207                     0.0                         0.00   \n",
       "96208                     0.0                         0.00   \n",
       "96209                     0.0                       224.44   \n",
       "96210                     0.0                         0.00   \n",
       "\n",
       "       electronics_Total_Customer  fashion_Total_Customer  \\\n",
       "0                            0.00                    0.00   \n",
       "1                            0.00                    0.00   \n",
       "2                            0.00                    0.00   \n",
       "3                            0.00                    0.00   \n",
       "4                            0.00                    0.00   \n",
       "...                           ...                     ...   \n",
       "96206                        0.00                 1789.02   \n",
       "96207                        0.00                    0.00   \n",
       "96208                        0.00                    0.00   \n",
       "96209                        0.00                    0.00   \n",
       "96210                      199.92                    0.00   \n",
       "\n",
       "       food_Total_Customer  home_Total_Customer  leisure_Total_Customer  \\\n",
       "0                     0.00               146.87                     0.0   \n",
       "1                     0.00               157.73                     0.0   \n",
       "2                     0.00               173.30                     0.0   \n",
       "3                     0.00               315.75                     0.0   \n",
       "4                     0.00               146.21                     0.0   \n",
       "...                    ...                  ...                     ...   \n",
       "96206                 0.00                 0.00                     0.0   \n",
       "96207               161.37                 0.00                     0.0   \n",
       "96208                 0.00                 0.00                     0.0   \n",
       "96209                 0.00                 0.00                     0.0   \n",
       "96210                 0.00                 0.00                     0.0   \n",
       "\n",
       "       misc_Total_Customer  \n",
       "0                     0.00  \n",
       "1                     0.00  \n",
       "2                     0.00  \n",
       "3                     0.00  \n",
       "4                     0.00  \n",
       "...                    ...  \n",
       "96206                 0.00  \n",
       "96207                 0.00  \n",
       "96208                72.18  \n",
       "96209                 0.00  \n",
       "96210                 0.00  \n",
       "\n",
       "[93104 rows x 24 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - [Analyse exploratoire](#5)<a class=\"anchor\" id=\"5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def densite(df, lines=5, cols=5):\n",
    "\n",
    "    df = df.select_dtypes(include='number').copy()\n",
    "\n",
    "    fig, ax = plt.subplots(lines, cols, figsize=(min(15, cols*3), lines*2))\n",
    "\n",
    "    for i, val in enumerate(df.columns.tolist()):\n",
    "        bp = sns.distplot(df[val], hist=False, ax=ax[i//cols, i % cols],\n",
    "                          kde_kws={'bw':0.1})\n",
    "        bp.set_title(\"skewness : \"+str(round(df[val].skew(),1)), fontsize=12)\n",
    "        bp.set_yticks([])\n",
    "        imax = i\n",
    "\n",
    "    for i in range(imax+1,lines*cols):\n",
    "        ax[i//cols, i%cols].axis('off')\n",
    "   \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "densite(Customer_Base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def correlations(df, largeur=14, hauteur=14):\n",
    "\n",
    "    # Table des corrélations\n",
    "    corr = df.corr()\n",
    "\n",
    "    # Add the mask to the heatmap\n",
    "    plt.figure(figsize=(largeur, hauteur))\n",
    "\n",
    "    sns.heatmap(corr, center=0, cmap=sns.color_palette(\"coolwarm\", 7),\n",
    "                linewidths=0.5,\n",
    "                annot=True, annot_kws={\"size\":9}, fmt=\".02f\",square=True)\n",
    "\n",
    "    plt.title('Matrice des corrélations', fontsize=18)\n",
    "    plt.xticks(fontsize=12, rotation=90)\n",
    "    plt.yticks(fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations(Customer_Base)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - [Sélection des variables et export dataframe](#6)<a class=\"anchor\" id=\"6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 - [Passage au logarithme et sélection des variables quantitatives pour le Standard Scaler](#61)<a class=\"anchor\" id=\"61\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(93104, 16) (93104, 5) (93104, 3)\n"
     ]
    },
    {
     "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>Last_Order_Customer</th>\n",
       "      <th>Review_Score_Mean_Customer</th>\n",
       "      <th>Product_Photo_Nb_Mean_Customer</th>\n",
       "      <th>Delivery_Accuracy_Mean_Customer</th>\n",
       "      <th>Delivery_Time_Mean_Customer</th>\n",
       "      <th>Total_Amount_Customer</th>\n",
       "      <th>Item_Quantity_Customer</th>\n",
       "      <th>Total_Order_Customer</th>\n",
       "      <th>Order_Paiement_Nb_Mean_Customer</th>\n",
       "      <th>Product_Desc_Length_Mean_Customer</th>\n",
       "      <th>boleto_Total_Customer</th>\n",
       "      <th>credit_card_Total_Customer</th>\n",
       "      <th>debit_card_Total_Customer</th>\n",
       "      <th>voucher_Total_Customer</th>\n",
       "      <th>construction_Total_Customer</th>\n",
       "      <th>electronics_Total_Customer</th>\n",
       "      <th>fashion_Total_Customer</th>\n",
       "      <th>food_Total_Customer</th>\n",
       "      <th>home_Total_Customer</th>\n",
       "      <th>leisure_Total_Customer</th>\n",
       "      <th>misc_Total_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>471</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.996334</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.040536</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.996334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.996334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.067205</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.862758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.067205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.067205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.160778</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.972606</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.160778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.160778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.758113</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.029088</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.758113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.758113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>515</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.991860</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>5.043425</td>\n",
       "      <td>4.99186</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.991860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Last_Order_Customer  Review_Score_Mean_Customer  \\\n",
       "0                  471                         5.0   \n",
       "1                  103                         4.0   \n",
       "2                  170                         5.0   \n",
       "3                   53                         5.0   \n",
       "4                  515                         5.0   \n",
       "\n",
       "   Product_Photo_Nb_Mean_Customer  Delivery_Accuracy_Mean_Customer  \\\n",
       "0                             1.0                            -11.0   \n",
       "1                             1.0                              1.0   \n",
       "2                             1.0                            -13.0   \n",
       "3                             1.0                             -8.0   \n",
       "4                             1.0                             -6.0   \n",
       "\n",
       "   Delivery_Time_Mean_Customer  Total_Amount_Customer  Item_Quantity_Customer  \\\n",
       "0                          8.0               4.996334                0.693147   \n",
       "1                         26.0               5.067205                0.693147   \n",
       "2                         14.0               5.160778                0.693147   \n",
       "3                         31.0               5.758113                0.693147   \n",
       "4                         29.0               4.991860                0.693147   \n",
       "\n",
       "   Total_Order_Customer  Order_Paiement_Nb_Mean_Customer  \\\n",
       "0              0.693147                         0.693147   \n",
       "1              0.693147                         0.693147   \n",
       "2              0.693147                         0.693147   \n",
       "3              0.693147                         0.693147   \n",
       "4              0.693147                         0.693147   \n",
       "\n",
       "   Product_Desc_Length_Mean_Customer  boleto_Total_Customer  \\\n",
       "0                           7.040536                0.00000   \n",
       "1                           6.862758                0.00000   \n",
       "2                           6.972606                0.00000   \n",
       "3                           7.029088                0.00000   \n",
       "4                           5.043425                4.99186   \n",
       "\n",
       "   credit_card_Total_Customer  debit_card_Total_Customer  \\\n",
       "0                    4.996334                        0.0   \n",
       "1                    5.067205                        0.0   \n",
       "2                    5.160778                        0.0   \n",
       "3                    5.758113                        0.0   \n",
       "4                    0.000000                        0.0   \n",
       "\n",
       "   voucher_Total_Customer  construction_Total_Customer  \\\n",
       "0                     0.0                          0.0   \n",
       "1                     0.0                          0.0   \n",
       "2                     0.0                          0.0   \n",
       "3                     0.0                          0.0   \n",
       "4                     0.0                          0.0   \n",
       "\n",
       "   electronics_Total_Customer  fashion_Total_Customer  food_Total_Customer  \\\n",
       "0                         0.0                     0.0                  0.0   \n",
       "1                         0.0                     0.0                  0.0   \n",
       "2                         0.0                     0.0                  0.0   \n",
       "3                         0.0                     0.0                  0.0   \n",
       "4                         0.0                     0.0                  0.0   \n",
       "\n",
       "   home_Total_Customer  leisure_Total_Customer  misc_Total_Customer  \n",
       "0             4.996334                     0.0                  0.0  \n",
       "1             5.067205                     0.0                  0.0  \n",
       "2             5.160778                     0.0                  0.0  \n",
       "3             5.758113                     0.0                  0.0  \n",
       "4             4.991860                     0.0                  0.0  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On isole les variables avec un coefficient de skewness élevé (>2)\n",
    "# pour les passer au log.\n",
    "VarToLog = ['Total_Amount_Customer', 'Item_Quantity_Customer',\n",
    "            'Total_Order_Customer', 'Order_Paiement_Nb_Mean_Customer',\n",
    "            'Product_Desc_Length_Mean_Customer',\n",
    "            'boleto_Total_Customer', 'credit_card_Total_Customer',\n",
    "            'debit_card_Total_Customer',\n",
    "            'voucher_Total_Customer',\n",
    "            'construction_Total_Customer', 'electronics_Total_Customer',\n",
    "            'fashion_Total_Customer',\n",
    "            'food_Total_Customer', 'home_Total_Customer',\n",
    "            'leisure_Total_Customer', 'misc_Total_Customer']\n",
    "\n",
    "OtherVar = ['Last_Order_Customer', 'Review_Score_Mean_Customer',\n",
    "            'Product_Photo_Nb_Mean_Customer',\n",
    "            'Delivery_Accuracy_Mean_Customer', 'Delivery_Time_Mean_Customer']\n",
    "\n",
    "Quali = ['customer_unique_id', 'State_Customer', 'order_purchase_date']\n",
    "\n",
    "RFM = ['Total_Amount_Customer', 'Total_Order_Customer', 'Last_Order_Customer']\n",
    "\n",
    "df_rfm = Customer_Base.loc[:, RFM].copy()\n",
    "df_rfm.columns = ['Montant', 'Frequence', 'Recence']\n",
    "\n",
    "df_other = Customer_Base.loc[:, OtherVar].copy()\n",
    "\n",
    "df_log = np.log1p(Customer_Base.loc[:, VarToLog]).copy()\n",
    "\n",
    "df_quali = Customer_Base.loc[:, Quali].copy()\n",
    "\n",
    "\n",
    "print(df_log.shape, df_other.shape, df_quali.shape)\n",
    "\n",
    "df_var1 = pd.concat([df_other, df_log], axis=1, join='outer')\n",
    "df_var1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LqN+qarDA5RaRlaxFTkYs5DIBp8rZrZ0oWKobPeewtIRopMRHo7bJGuKIiIi6ZrI6UFpjwsjsBACAIAi45pJBePz2S3D99AswZnAijpxugMhC337plwU7d/1UhU3fFEIURYzOScT+47VIio3CDTOGdfie6RcOxL92ncGh03W4bHR63wVL1AvqDc0QRSAlPhqZSRoAQEW9ud3kHRH1XHmtGQCQlaKDWinHgBQdzlSx5wVRsFQ1tCQvEjVIrrdAb7bD5nBBrZSHODIioo4dL2mECGBUS/LifGMHJ+GHYzUorTFhUBqv07vS73pemJsdeOeLE4iJViI7LQbHWrrpLL/5QkSrO87VjMhOQJxWhfwTdX0YLVHv8D6RSo6LQlqiBjJBQEWdOcRREfVf5XUmyAQB6YmeZGFaYjRqGvlkmChYqhusEACkxkcjJT4KAFDHWmVEJHEFxU1QK+UYnBHb7utjBicCAI6XsO6FP/pdz4sv95bCanPijvmjMTDV/yloZDIBk4YlY9dP1XC63FDI+11ehyJIXZPngi4lPhoKuQypCdEcNkLUi8pqzEhLjIZS4Tl3pCZEY19BLc8nREFS3WhBYqwaKqUcKXHRADyJ+qxkbYgjIyLq2LGSRgwbENfhtUC8TgVtlAKVDbxO90e/u6Laf6IWo7ITAkpceI3MToDN4UJpDYtAUXirbbJCIRcQH6MGACTFqtFgtIU4KqL+q7LBgsykszdRKfHRcIsiGgx8MkwUDNUNFqQmeHo2pcSfTV4QEUmV3mxHRZ25wyEjgKcGRkaSFpXsIe2XfpW80JtsKK81+7rfBGrYgHgAwElOV0NhrrbJiqS4aMhaZtaJj1GjycTkBVFvEFuSFMktXdkBT9d2AKjhzRVRUDQYbUiK87SxGI0SKqXM18uQiEiKCoobAcBXrLMjGUkaVNYzeeGPfpW8ONbyAeksu9WZhBg1kuOicLKMFeIpvNXqm5ESd/ZGKl6nht5kh9vNSsZEwWa0OOBwupEYe07youUJcS3rXhD1mNstwmC2I16nAuB5UpkSxxlHiEjajhU3IlqtwKC0zkcEZCRpYbA4YLI6+iiy8NWvkhdHixuhjVIguweVWocNiMeJsiZOV0NhrclkQ2Ks2vd7QowablGE0WIPYVRE/VN9y9CQ5HOSF3E6FZQKGXteEAWB0eqAKAJx2rPntfgYNfRmntOISLoKShoxYmA85LLOb7kzWmYGZH26rvWr5MXpCgOGZsVBJhO6vY2hWbEwWhy+i1GicOMWRZgsDsRoVL5l8TrPBV8jh44QBV19y4wHSef0dpIJApLjojjjCFEQ6FvOXXHas+e1OK0KBjPPaUQkTTVNVtQ0WrscMgKcTV5w6EjX+k3ywmpzorLO3OE0NP7KSfe8v7jKGIywiPqcpdkJl1tE7DnJi4SWwp1NRj6lIgo2b7L73OSF93fWmiHquSaT59zlTcQDQKxWBb3ZwZ6yRCRJX/xQArlMwEUjUrpcNznOMztgJXtedKnfJC9Kqo0QAQzO6P6QEQAYmKqFXCbgDJMXFKYMLd1oY7RK3zLvBR9vpIiCr97QDLVKDo269ezj8Tq176aLiLpP39LDIk7XuueF0+WG1eYMVVhERG24RRHfHazAdwcrMXVceqt6WB2RyQSkJ0az54UfFF2vEh6KKj3Jhpwe9rxQKuTIStbiTKUhGGER9TlvXYtze17EapUQBKCR06USBV29vhnJsVEQhNZDFs8tlNuT4YxEkU7fkgQ8d9hIbMvPerMdmihlu+8jIupLbreIN/55FLt+qkZOegwWTB3s93vTk7Qo4cPzLvWbnhdFlQYkxUa1umHrruz0GJypMrIrIoUlg8VTqfjctiCXyRCrVbHnBVEvaDDY2n2ykqBTsVAuURDoTXZEqxVQKeW+Zd5EhoFFO4lIIg6eqsOun6qxYGoOHr31Ir96XXhlJmlQq7fC4XT1YoThr18lL3o6ZMQrJyMW5mYn6vQs2knh5+ywkdaJPHZhJ+odjSabr67Muc4O12K7I+oJvdnmmybV69yeF0REUrDraDViNErMvzynTW/MrqQnaSCKQHUDC313pl8kL4wWO+r0zT0u1umVk+5JgrBoJ4Ujo8UOAYAuuvWosFiNik+AiYLM5XbDaLa3ubECPFM5Apzlh6inmsz2VkNGgLM9L5i8ICIpsNqcOHiqDhePTIVCHvgtdmaSFgBQ2cCinZ3pF8kLb3HNnta78BqQooNcJqCoinUvKPwYLA7oNMo2c0rHapRMXhAFmcHsgAggTtdZzwsmL4h6wmC2t2lj2mgl5DKBw0aISBIKy/VwON2YPLzr2UXak56ogSAA5bWmIEfWv/SL5EVRpQECzvaY6CmlQoYBKTqcqWTPCwo/RrO93dovMVoVDBZOK0cUTN5ZEOK1bdtcrFYJAUATC+US9YjR4kBMdOuinDJBQIxGyZ4XRCQJpTWepEN2N+9HVUo50hI0vu1Q+/pF8uJ0hQHpSRpEq4M3eUp2egyKWbSTwpDBYkeMpm3l9ViNCg6nG812FgIiChZvPYv2el6cLZTLmyui7vJOh9reeS1Oq2bPCyKShNJaExJj1dD2YPajAak6lLHnRafCPnnhdos4WdaE4QPjg7rdnIwYWGxO1DaxaAqFF4PF4Stkdi7vhR+HjhAFj75lSEh7NS88y9UcNkLUA8aWGbRi2utRqFH6XiciCqXSGhMGpOh6tI2BqTrUNjXDanMGKar+J+yTFyU1RlhtLowYFNzkxeB0T/2MMyzaSWHGYLa3e5HnTWgYeKFHFDRNJk+B3PYShoAnqcHkBVH3eRPu7fW8iGEtJyKSAIfTjap6Cwam9jB50ZL8KK81ByOsfinskxcFxU0AgBEDE4K63awULRRygckLCisOp6d7bWwHw0YAT00MIgoOvckGnUbZYWXx+BhOUUzUE0ZrZz0vVL7XiYhCpbLeDJdb7HHPiwGpnhlHSjl0pENhn7w4WtyAtIRoJMS0HW/cEwq5t2gnZxyh8OF7QtXJsBEDn1IRBU2TyY44bcfnn3idGkazHU6Xuw+jIuo/vOc1XXTbpLwuWgmb3QWHk7WciCh0qlqmN81M1vZoO0mxUYhWK1DGop0dCuvkhaXZgWNnGjFxWHKvbD8nPQbF1SzaSeHDO/a33dlGNBw2QhRserOtw3oXgGfYiAiwqCBRN52tedH+sJFz1yEiCgVv8iI1IbpH2xEEAQNTtOx50YmwTl4cPFUPl1vEhSNSe2X7g9JiYLW5UK9v7pXtEwWbt1dFe8kLpUKGaLWCw0aIgqjJZEdcp8kLtW89Igqc0eKAIADadnpeeJPyTF4QUShVN1iQGKuGWinv8bYGpOpQVmOCmw/P2xXWyYu9BTVIiFFjSGZsr2x/QEvRFWa/KFx4n+7GatufpilWo+SwEaIgcYsiDGa7L0HRnrPJCxbtJOoOk8UOXbQSMkFo85qv54WV5zUiCp2qBgvSEzVB2daAVB2a7Xx43pGwTV4YLHYcPl2PS0eltXtCC4aslnFLHHdE4aKzKeUATy0MPqEiCg6TxQGXW+w8eRHD5AVRTxgtjo7Paex5QUQhJooiqhqsSAtS8sI74wjvP9sXtsmLPUer4XKLuHxseq/tI1qtQEp8FEo5XQ2FCYPFDqVChihV+93WYjUq9rwgChJvQiKug2lSAc+TYZkgMHlB1E1Gix0x7QwZAc4W8WTygohCxWhxwGpzBq3nRVZKy8Nz9vxvV9gmL3b/VIVBqTrf0I7eMiBFx8wXhQ2j2Y5YjRJCB72RYjVK1rwgChJ9S1vqrOeFTBAQp1Ohych2R9QdRquj3WKdAKCJUkAmCL4ZSYiI+lpFnechd0aQkhdRKgUSY9W+IqDUWlgmL6obLSiqNOLSMWm9vq+BqTpUN1pgc3AaLpI+QyfdawFPF1uj1QG3m0WAiHrK1/Oik4KdgGfGkUb2vCDqFqPF0e7034AnOajTKNnzgohCpqjKAADITo8J2jYzEjWorGfyoj1hmbz44Wg1AOCSkX2TvBDFs1k1IikzmO2I7aQLe6xWBVEETM280CPqKb3J2/Oiq+SFGnomL4gC5nK7YbI62p1ByytGo4TJynMaEYVGUYUByXFRnT48DFR6khaVDRaInHGkjbBLXoiiiD3HajB8QByS4qJ6fX/eYSkcOkLhwGCxd9i9FjinMjuHjhD1mN5kh0atgFLR+dRo8To1p0ol6gaT1QkAnZ/XopUcNkJEIVNUaQz6zJeZSRrY7C40Gvng43xhl7woqzWjos6MS0f3fq8LAEiJj4ZKKeN0qSR5oijCaLF3+oTK+5qBXWyJeqzJbPPNJtKZeJ0KJqsDDqe7D6Ii6j+8ifbOzms6DWfRIqLQMJjtqDc0Y3BGcJMX6Umeop2VrHvRRtglL3YfrYJMEHDhyNQ+2Z9MEFi0k8KC1eaC0yV2OmzEO26YT6mIeq7JZOt0phGvuJaCnnozn6AQBcI7O1ZXPQp5TiOiUNh5pAoAMGJQfFC3m5HkKf5ZybIFbYRV8kIURfxwtAajByd0moUPtgEpOpTVmjnuiCTNe/HWec8LzwWggcNGiHpMb7J3We8CODsbCYeOEAXG26Oi00LU0UqYm51wudmziYj6jtXmxKe7izFmcCJy0oPb8yJOq4I2SoGyWiYvzhdWyYvCcgPqDc24dFTfDBnxGpiqg8nq4IUnSZrvCZW24ydU2mglBIHDRoh6ShRFNJnsvl4VnfEmOJo4dpUoIN7zWqc9ClsSG+aW+hhERH3hq32lMFkd+MXPhgR924IgYGCqDmUsW9BGWCUv9hyrhlIhw+ThKX263wEpnnFH/ACRlBnMnoREZz0vZILgmS6VXWyJesRic8LpciPej2Ej8b5hI2x3RIEwWuyQCQI0UYoO1/EVouZ5jYj6iKXZgc9+KMWkYclBr3fh5en5b4KbPf9bCZvkhSiKOHiqDmNyEhGt7vgk1hs44wiFA6NvbHDnN1OxGiWHjRD1kLcnnj89L3QaJeQyAU2cLpUoIEaLAzqNEjJB6HCdmGilb10ior5wqLAeVpsTcy/L7rV9DEzVwe5wo7bR2mv7CEdhk7yoabKiTt+MsUMS+3zf2iglEmPVnHGEJM2fwmae11mZnain9C2JCH9qXsgEAXE6FYeNEAXIYLb7ajV1xJuwN1p5XiOivlFQ0gSNWtFrvS6Asw/PS/nwvJWwSV4cLWoAAIzJ6fvkBQDOOEKSZzQ7oI1SQCHvvFnHalW+RAcRdY++pedFvB89L7zrNbHHE1FAjBZHl70JOWyEiPra8ZJGDB8YD5ms415hPZWVrIVcJqC42thr+whH4ZO8ONOIpNgopCZEh2T/A1N1qKy3wOF0hWT/RF0xWOxdXuQBnFaOKBi8Q0Di/Oh5AXgqh3PYCFFgPOe1znteaFuGjZjYo5CI+kCj0YbqRitGBnl61POplHIMSNXhdIWhV/cTbsImeXG60oBhA+MgdDLusTcNyYiFyy2iqJLZL5Img9neaUV2r1iNClabi4k4oh5oMtmhVskRpfKvBlN8jJrDRogCZLQ4Oi1CDQAKuQzaKAWHQxJRnygs1wMALhjQu8kLABiaGYvTlQa43Sza6RUWyQu92Y5Gow3ZaTEhi2HogDgAwKmWDyyR1PjzhAo4O+UcL/SIuk9vtvk104hXvE4Nc7OTSUMiPzmcblhtTsT40c500UoYrexRSES9r6TGBJkg+Gaj7E1Ds+Jgs7tQXmfu9X2Fi7BIXhRXeXo75KSHLnkRq1EhLVGDU2VMXpA0Gcx2xPlxkedNcLDuBVH3NZnsfs004uUt7OmtlUFEnTP6WYQaAGK0Ks6iRUR9orTaiIwkDVRKea/va2impyBoIR+e+4RJ8sIz1mdQCHteAMCwrDicKtdzvl2SHIfTDXOz06/khbcLrsHMnhdE3aU32fyaacTLW9izickLIr94ewd2NWwEAOI0KhjYm5CI+kBJjQmD0nR9sq+U+GjE6VQ4XtrUJ/sLB2GRvDhTZURaogbRav/GFveWUTkJMFkdKGLhFJIY7xMqf54Ex/iGjfAmiqg7RFFEo9GGxJgov99zNnnBuhdE/vCeo/xJXsTq2POCiHqf0eIpZTAwtW8eqAuCgJGDElBQ3AiRD88BhEnyoqTaiI4GR5oAACAASURBVOw+ynB1ZsLQZMhlAvYdrwl1KESt6M0BXORx2AhRj5ibnbA73UiICXzYCJMXRP4xBDBsJE6rgsnqgNPl7u2wiCiClVSbAKDPel4AwMhB8dCb7ahqsPTZPqVM8skLo8WOeoMNOemxoQ4FmigFxgxOxL6CWg4dIUnxjqP3Z9pGtVIOlUIGI4eNEHVLg6EZAAJKXuiilZDLBA4bIfKTd9iIP1OAewtRs/cFEfWmwgo9BKBP70tHZicAAI6eaeyzfUqZ5JMX3mKd2SEs1nmuy0anod7QjIOn6kIdCpGP3ux5mutPzQtBEBCjUbHnBVE3NbZMeZoQ63/yQhAExOtU7HlB5CeDxQ6FXEC0uuuieN5zn57JCyLqRYXlBmQma6GJ6rtSBqnx0chM1mLPseo+26eUST95Ud2SvJDAsBEAuHhUKpLjovDJzmKOPSLJ8F6w+fOECgBitUpe5BF1U0NL8iKQmheAp+4FkxdE/jGaHYjRqCAIQpfrxmk9iUT2vCCi3uIWRZyu0GNoVt+OBhAEAVPGpOFUmR6VnDJV+smLM1VGpMZHQxPV9ZjHviCXyTBvSjaKKg3IP1Eb6nCIAHiSF9ooBZQK/5o0b6KIuq/R2AyZIPjV0+lcCbFRqDew3RH5w2Cx+1XvAvAk5AH2vCCi3lPdYIG52YkhmXF9vu8pY9IhAPh895k+37fUSD95UWlEToY0hox4TRufgawULT7YforFoUgSDCa7XzONeCXGRKGRN1FE3dJosCE+RgWZrOsnwudKjo1Cg6GZvfaI/GC0OPwqQg1w2AgR9b6fihoAAMMHxvf5vhNjo3DJ6DT8c0dRxM8WKOnkhcFiR72hWRLFOs8ll8lw/fQLUKdvxveHK0MdDhH0ZntAT4HjY1Sw2Jxotjt7MSqi/qnBaAuoWKdXUlwUHE43DBYWyyXqisFs83sopFIhh0atgIEFcYmolxw4WYeMJA3SEzUh2X/u5TmwOVz4d355SPYvFZJOXpypNAAABkus5wUAjB2ciKGZsfjnzjPsfUEhpzfbfNXW/eEdq+8tPEhE/vMkLwKrdwEASbGe99TprcEOiahfcYsimkx2JAZQFDdWq4I+wp9IElHvMFkdOF7ShMnDU0IWQ2ayFmOHJGPX0eqI7sEp6eRFUaURAoBBadJLXgiCgLlTslFvsOFwYX2ow6EI5hZFNBptSAzgSbD3qXETkxdEAXGLIur1VqTEdSN50fKeen1zsMMi6leMZjtcbjGgHk6czYeIesuBE7VwiyImDQtd8gIArpycheoGi29Ci0gk6eTFmUoDMpK1iFb33XQ0gRg/NAlxOhW+O1gR6lAoghktDjhdIhJj/b+Z8k7x2MDkBVFAmow2OF0ikuOjA36vt+dFvYHJC6LOeM9NgSQvEmKi0Mi2RUS9YMeRKqQlakI+GuDy8ZmQywTsK4jcSSMkm7wQRRFFVUbkpEuv14WXXCbDtHEZOHS6nt3vKWQaWi7WAulem9BS3JOfW6LA1LX0mkiJD7znhSZKAY1a4dsGEbWvsRvTESfFqdFotMPl5lBeIgqe2iYrTpQ24fKx6X5N3dybYjQqDM2Kw5GiyO31L9nkRaPRBoPZjsEZ0irWeb4rxmdAFMHCnRQyvuRFABd5KqUc2igFkxdEAapt8tSrSOlGzwvAM3SEw0aIOuc9N8UH0PMiMTYKblGEnkU7iSiI9hXUAACmjE4LcSQeYwYnoqTaFLGzK0k2eVHUUqxTatOkni81QYORg+Lxn4MVcEdw8RQKnYaWKU8D6XkBtHSxZfKCKCC1TVYIwtkhIIFKiY/2JUCIqH0NxmbIZQJiNEq/3+NN4DdwGnAiCqJ9x2uRnR7TreGivWHs4EQAwNGWqVsjjWSTF2eqjJDLBAxK1YU6lC5dMSETdfpmHC9pCnUoFIHqDc1QKWTQRft/kQd4ur3zJoooMLVNzUiMUUMh797pMz1Rg5pGK7u2E3WisWU6YlkAXbSTWhL4rClDRMFS22hFUaUBF40IbaHOc2Wnx0AXrcQRJi+k5XSFAVkpWigV8lCH0qULh6cgWq3A94dYuJP6XoPRhoTYqIDH4aUmRKOmycoeQ0QBqNVbuz1kBADSEqPhcouoa+INFlFHGg22gIp1AvAVrW4wsm0RUXDsO1YFACGdIvV8MkHA6JwE/HSmISKv4SWZvHC63DhdYcAFWXGhDsUvKqUcl41Ow77jtbA0O0IdDkWYRkOz74lTINISNHA43WhkF1siv9U09ix5kZGoBQBUNliCFRJRv9NgbA44eRGtViBarUCDnuc0IgqO/OM1SIqNQnqiJtShtDJ2cBIMZjvKakyhDqXPSTJ5UVJtgs3hwohBCaEOxW/TxmfA4XRjz7GaUIdCEaZO3xzQNKleaQmeG7DqRt5EEfnDZHXAYLYjM1nb7W2kJ3kugKqZvCBql9PlRr3ehtSE7kxHrOawESIKCqfLjYMn6zB2SGLIZxk535iWuheHCiNv1hFJJi+OlzYCAIYPCI+eFwCQkx6DASlaDh2hPmW1OaE327uVEU5reU91I+teEPmjvNbzhCOrB8kLXbQS2igFqpi8IGpXnb4ZblFEWkLg57WUeM9wSCKinios18Nqc2Ls4KRQh9JGQowawwfE4T+HIm/CCGkmL0qakJaoQZwu8K7woSIIAq6YkImiSiMKK/ShDocihPcGqDvJi/gYNZQKGWrY84LILxV1ZgDoUc8LwNP7orKe7Y6oPd5eSd1JXqQnaVDdYGFBXCLqsQMn66CQyzA6R5ojAaZPHoDapmYcOR1ZhTsll7xwOF0oKGnE6GxpflA6M21cBrRRCny6qzjUoVCEqKrvfvJCJghITYhGdQOfUhH5o7zOjGi1POCx+OcblBqD0hpjxD0tIfKHtzdgamLgw0Yyk7RwuUXUsiAuEfWAKIrIP1GLiS2TMkjRhSNSkBCjxv/7tjCiEraSS14cL2mC3eHGhAuSQx1KwKLVCsy8aCAOnKzDiVJOm0q9r7LB4ktCdMfAFB3OVBmCHBVR/1RRZ0ZmkrbHY1+z02NgtblQwyFbRG1UN1oQrVYgJsDpv4GzNWUq683BDouIIkhxtRF1+mZcNjYj1KF0SCGX4caZw1BaY8K/dpeEOpw+I7nkxcFT9VApZRiVHR/qULrlmksGIjkuCv/3rwJYmp2hDof6uaoGC5Ljo6CQd68pD86MRZPJjgYWOCPqlCiKKK0xISulZ0NGAE+NJABMHBK1o6bBgrSE6G4lCX2z+XBYFhH1wCc7ixGlkmPKOOkmLwDPFK6XjErFlv8U4XhJY6jD6ROSSl643G7sO1GDMTmJUCrkoQ6nW6JUCvxq7ijUNVnxlw8OsHAU9aqqenOPpm8akhkLADhdwZsoos5UNVhgbnZiSGbPC0lnJmuhkMtQXGUMQmRE/UtVg8VXUDpQmigF4nQqVNax5wURdc/+47XIP1GLOZcOQqxWFepwOiUIAm6dMxIp8VH427afoDfbQx1Sr5NU8uJQYT30JjumjZd2lqsro7ITsHThWFTWW7By/R58+O9TMFkdoQ6L+hm7w4XKegsGpOi6vY1BqTFQyAWcrmTygqgzheWeNjI0q+fJC4VchoGpOhQxaUjUisFsR73Bhuy0mG5vIytZi9KWmYGIiALxw7Fq/HXLYQzOiMHsiweGOhy/RKsVWHrtOFianVi37Se43f27npakkhff/liBOJ0K44dKb0qaQE0anoKn77oMl45Kxed7SrD89V04eKou1GFRP1JcbYTLLWJoVmy3t6FUyJCdFoNjxe13NatqsOD7Q5U4crq+338ZEnWmsEIPjVqBjKTu93Q618hB8SisMMBq4/BCIq+ilkT64IzuJy8uyIpDaY2JbYuoCyKLRrdy+HQ91n98FMOy4vDQ4kmIUkmzUGd7BqbqcPOs4ThW3IhtO4pCHU6vkkzyoqTaiEOF9Zg+MQtymWTC6pGEGDXumD8aj99+CZLiovDSpkPY9n0RK8xTUPieBPewG/tFI1NRXGVE+TndbF0uNzZ8dRIr1u/G3z89huc+PIgn3tyLOg6Dogh1qkyPIZmxkPWwWKfXuCFJcLlFFHSQOCSKREWVBgiCp6htdw0bEA9RBKetJ+qEKIr48zv5eHz9Lib64DnHv/rRYWQla/HAdRPCKnHhdcWETEwdl46Pd5zBkaL6UIfTaySTJfh4xxlEq+WYedGAUIcSdANTdXjk5gtx2Zh0bPm+CK/8v8MwRMCYJOpdhRV6pMRH9Xg83mVj0iETBPznYAUAwGpz4qn/+wFf7ivFVROz8Ke7L8PduaNRr2/G0+/sZx0Xiji1TVaU15kxOicxaNu8YEAc1Co5Dp/uvxcYRIEqqjQiM1nboxuHIZmxEATgRCmTF0QdKaww4FS5HvsLarDh65OhDiekSmtMeGHjQcTHqPHgDROhiQq/xIXXzbNHICtFi3XbjvbbYvySSF6cKG3C/hO1mH3xIGiiAp8aKxyolHLcOX8Ubpw5DIdP12PF+t34/lAlu2xRt7jdIk6W6YMy/j5Oq8Ilo1Lx1b4yfPTdaaz+xz7kH6/BLdeMwJJrRiA9UYPLxqTjjzdPhtPpxl/eP4Amky0Ifwui8HDgRC0AYPLw4E3hrZDLMG5wIvYdr4XD6QradonCldPlxqlyPYZmdn8oJOAZ/z0oLQbHihuCFBlR//P9oQqolDJMGZeBAydq4XK7Qx1SSNQ0WvCXD36EWiXHH26YiDiJF+jsilopx70Lx8LhdGPTN4WhDqdXhDx54RZFbPj6JBJi1JhzyaBQh9OrBEHAzIsG4onbL0FmshZ///QYnt1wANUNnNKLAnOyrAkGsx0ThgbnZuqWOSMwJDMWn+w8A5fLjdV3T8FVk7JarTMgRYcHr58Ig8WBv3zwI4vQUsTYd6IWA1J0SE0ITr0Lr6smZcFkdWDP0ZqgbpcoHB0rboTV5sTEYSk93tbFI1NRWG5AZT1nHSE6n8vtxr6CWlw4PBU/m5QFc7PTNxQ5kjQabVj7/o9wudz43Q0TkRwfHeqQgiIjSYsrxmdgb0FNv+zpH/Lkxa4jVSiuMuK6K4dCrQrP6VEDlZmsxbKbJuOWa0aguNqIR9/4AR/vPMMxZ+S3H47VQKWUYeIFwUleRKkU+OPNk/HSf1+BZ349BRM6uHgckhmLB34xDtUNFryw8SCa7fzMUv92psqAU2V6XDYmLejbHpWdgKxkLf65uxg2B3tfUGTbf7wGUSo5xgRheNbUsZ7hkN+1DIckorNOlelhsTkxaVgyJg1PhVwmRNykAiarA899+COMFgcevH4ispK1oQ4pqKZPzoLLLfbL78CQJi8aDM34YPspDM6IxaW9cGEoZTJBwFWTsvDUnZdh4gVJ2PzdaTz4yvd4adMhbM8vY10B6pCl2YEfjlVjwtDkoCb8ZIIAXbQSMlnnBQlH5STinryxOFNpxHMfHORnlfq1f+4sRrRajqsmZnW9coAEQcCimcNQ3WDBPz4rgMMZmd12iUxWB/YW1GLisGQoFT2/NI3TqXHRyBR8vb8cJdXGIERI1H8cKqyHXCZgzOBEaKOVGJWdgL0FNREzoUBFnRl/fmc/qhssuP+/xmFID4eqSVFGkhajshPwzY/l/W62wJBVJDGY7Xjp/x2Cw+XGnfNHBa2Ce7hJiFFj6bXjcLrCgB2HK3H4dD1+bMl+piVqMG5wIsYOScKIQfFQKyOjZwp1btuOM7A0OzFvSnbIYpg8PAV3LxiNN/9VgEde340xgxMxJDMWibFqJMZGITHG8yc/sxTOfjhWjf0napE3bXCvFfAak5OIhVcMxpb/FOFkqR5jhybD4XDC5nDBZneh2eGCUi5DWqIGA1O0GD4wHpnJWggRes6k/umTnWfQbHdi7mXBO6/dOGs4jpc2Ye37PyL3CgPUck+bcYsiRBHQRSsxJMNz3mJ7okjhdovIP1mH4QPjEa32nNemjEnH+k+O4lSZHsMHxoc4wt5RXmtC/olanCo34OiZBqiVcvzu+okYmZ0Q6tB6zYzJA/Dq5sM4eKoOk4b3fDieVPR58sLmcGHvsRps/s9pmK0OLL12HDKS+ldXne4YkhmLIZmxEEUR1Y1WHD5djyOnG/DtwQp8tb8McpmA5LgoZKToEKdRIiU+GtnpMRiSEev78qH+77uDFfhibyl+NiETg9K6P5VcMFwyKg0XZMXhq/1lOHCyrt1ZE7RRirPJjLizSY04rQpRKgXUKjmilHLPnyo5FPKQj2QjAuBJXPzvJ8cwNCu21xOFC6YORk56DLbnl6OguAFOpwtRKgVUSk+7sDlc+OFoNb5pGVqoi1ZixMB4DB8Uj5GDEpCVoo3YBwAU/nYcrsSXe0txxYQMDEjRBW27sRoVHlo0Ce98cRwbvjje4XoJMWqMzk7A6JxEDBsQh8S4KLYn6rd2H61CdYMFC6cN9i2bPDwFaqUcn/9QgmED4vpNMs9kdeDAiVp8d6jCV9MjLVGDmRcNwM8vze7xbH1SN3FYEpLjovDhv09hVE5CWE7/2h5BDHC6i8ZGc5vuJ2eqDCivNcMteuYN9ma10fKzIJOjrtGMqgYrKuvNcLlFZCZr8V9XDsXA1OCdqHoiKUmH+npTqMNow+F0o6jSgMJyA+oNzTBY7ahpsMLSfLZYokatQIxWBbVSDpVCBqVCBqVCDpVS8PypkEOpECCTCRAgwDsqQBCEVl9Q3h9bfWW1LPQtO28dQRAgABg3NAm6aM9MMd5jKZMJSEhgYqq72mtrf9t6BNFqBW6aNbxXb/S70x6cLjf0Zjv0JhuaTGf/bGz502CywdJFXRe5XIBaIfd8llVyKOUyyGUCBJkAuQDPZ1gmQC4IiIpSwOlwQyYTIJcJkMk8Q19kMhnkwtn1PO+BZ52W38+9MBVxzjFu/8eWBe1/VYodvkdEdLQKVqu9nW21/56Ovo5FETBaHKioM2PG5AEYO6T1mHC2tZ5pr629vvUIBEHATbOGQxvdd7NgddT2RFFEg9GG0xV6FFUYUFhhQKPRM+uPUiFDnE6NmGgllEoZVHI5lEoZFLKzn3eZIADeNtLynyADZIJnmSB4zg1Cy3miq2vXjl7WaNSwWGzocgOdbMPfFbp6v9jyP8+fnpbubWJiywu+10QgSqNEfaMVepMdFfVmzLpoACadV/+Hba1n2mtr67b9BIVChluuGdFr5zWNTo3ySr3vukcQAL3JjtIaE063TBnpva5SKmSI06qgViqgUsmglMt87ch7bvH8jrPnHe+55pxzjFwmAIKnjQnnX0ud84sA+M4V3oVnr7Ha+cuce93W5gdAaKdltLm+63IbHt5/KU20CmaL3deOgPPaUsvK4rkvtPwotl7Lt54oijBbnaioM+PKiZkYOySpVcxsaz3TXls7cLIWW74rQkKsGg9cNx4yQfCdc/59oBz/2l2MsYOTMDBN13Jd5fksi2Lbfz/Pz20/D55Vzq7U3mfCt/zc/7e8RROlgtlqa7V+q7/Fuftp+dktinC63HC63Gi2u9BksqHBaIOxpVhlakI0Lh6ZhgtHpPjuVfwltfvDQOM5XaHH61t/QlqSFuOHJuKiEamI16lbrRNubS3g5AURERERERERUV9iH20iIiIiIiIikjQmL4iIiIiIiIhI0pi8ICIiIiIiIiJJY/KCiIiIiIiIiCSNyQsiIiIiIiIikjQmL4iIiIiIiIhI0pi8ICIiIiIiIiJJY/KCiIiIiIiIiCSNyQsiIiIiIiIikrSgJi9eeeUVzJs3D/PmzcOaNWvafX369OnIy8tDXl4e3n333WDu3i9LlizBvHnzfDEcPHiw1es7d+5Ebm4uZs+ejeeff77P49u4caMvtry8PFx44YVYvXp1q3VCeRxNJhPmz5+PsrIyAP4dr4qKCtx0002YM2cO7r33XpjN5j6LlwJ3/r+x1HT1PSMFL774IubOnYt58+bh//7v/0IdDoUJqbe9zoRDu+wM22z/JeV2JfV2w3bRf3z88ceYO3cuZs+e3e59w7Fjx/CLX/wC11xzDVasWAGn09mr8UjtnlFK94dSvxcMOTFIduzYId5www2izWYT7Xa7eMstt4hffPFFq3V+/etfi/n5+cHaZcDcbrc4bdo00eFwtPu61WoVr7zySrGkpER0OBzi7bffLn7zzTd9HOVZJ06cEGfNmiXW19e3Wh6q4/jjjz+K8+fPF8eMGSOWlpb6fbzuvvtu8ZNPPhFFURRfeeUVcc2aNX0dOvnp/H9jqfHneybU9uzZIy5atEh0OByi1WoVp0+fLhYWFoY6LJI4qbe9zoRDu+wM22z/JeV2JfV2w3bRf1RVVYnTp08XGxsbRbPZLObm5oonT55stc68efPEAwcOiKIoisuXLxfffffdXotHaveMUr4/lNq9oBQEredFSkoK/vjHP0KlUkGpVGLo0KGoqKhotc6RI0fw+uuvIzc3F6tXr4bNZgvW7v1y+vRpAMDtt9+OBQsW4J133mn1+qFDh5CdnY2BAwdCoVAgNzcXn332WZ/GeK7HH38cDz74IBITE1stD9Vx/PDDD7Fq1SqkpqYC8O94ORwO7N27F9dccw0A4Be/+EVIjyl17vx/Y6nx53sm1C655BK89dZbUCgUqK+vh8vlgkajCXVYJHFSb3udCYd22Rm22f5Lyu1K6u2G7aL/2LlzJy677DLEx8dDo9HgmmuuaXUtXl5ejubmZkycOBFA71+rS+2eUcr3h1K7F5SCoCUvhg0b5vvQnzlzBv/6179w5ZVX+l43m80YNWoUHnroIWzevBkGgwF//etfg7V7vxgMBkyZMgWvvvoq3nzzTbz//vvYsWOH7/WamhqkpKT4fk9NTUV1dXWfxui1c+dONDc34+c//3mr5aE8jk8//TQuuugi3+/+HK/GxkbodDooFAoAni+sUB1T6tr5/8ZS09X3jFQolUq89NJLmDdvHqZMmYK0tLRQh0QSJ/W215lwaZedYZvtn6TcrsKh3bBd9A9dXa+f/3pvX6tL7Z5RqveHUrwXlIKgF+w8efIkbr/9djz88MPIycnxLddqtVi/fj2GDh0KhUKB22+/Hd9++22wd9+pSZMmYc2aNYiJiUFiYiKuu+66VjG43W4IguD7XRTFVr/3pffffx+/+tWv2iyXwnH08ud4tbcsVMeU+o+Ovmek5IEHHsCuXbtQWVmJDz/8MNThEPW6cGiXnWGbpVCQerthuwh/XV2vh+r+Ryr3jFK9PwyHe8FQCGryYv/+/bjtttvw+9//Htdee22r1yoqKrBp0ybf76Io+p7G95V9+/Zh165dHcaQnp6O2tpa3++1tbUh6Wpot9uxd+9ezJgxo81rUjiOXv4cr8TERBiNRrhcrg7XIQpEZ98zUlBYWIhjx44BAKKjozF79mwcP348xFER9S6pt8vOsM1SqEi53bBd9B9dXa+f/3pdXV2vX6tL6Z5RiveH4XIvGApBS15UVlbiN7/5DdauXYt58+a1eT0qKgrPPvssSktLIYoi3n33XcyaNStYu/eL0WjEmjVrYLPZYDKZsHnz5lYxTJgwAUVFRSguLobL5cInn3yCn/3sZ30aIwAcP34cOTk57Y4tlMJx9PLneCmVSlx00UX49NNPAQBbtmwJyTGl/qGr7xkpKCsrw8qVK2G322G32/H111/jwgsvDHVYRL0mHNplZ9hmKRSk3m7YLvqPyy+/HLt27UJDQwOsViu++OKLVtfiWVlZUKvV2L9/PwBg69atvXqtLrV7RineH4bLvWAoBC1N88Ybb8Bms+GZZ57xLVu0aBG2b9+OBx54AOPGjcPq1atx7733wuFwYPLkye12helN06dPx8GDB7Fw4UK43W7ceOONmDRpEvLy8rBu3TqkpaXhmWeewf333w+bzYYrr7wSc+bM6dMYAaC0tBTp6emtlt11112SOY5earW6w+O1YsUKzJgxA1dffTVWrVqFP/7xj3jttdeQkZGB5557LiTxUvjr6Htm8eLFIYyqtSuvvBKHDh3CwoULIZfLMXv2bElemBIFSzi0y86wzVIoSL3dsF30H2lpaXjwwQdxyy23wOFw4LrrrsP48eNb3VusXbsWK1euhMlkwpgxY3DLLbf0WjxSu2eU4v1huNwLhoIgiqIY6iCIiIiIiIiIiDoS9IKdRERERERERETBxOQFEREREREREUkakxdEREREREREJGlMXhARERERERGRpDF5QURERERERESSxuQFUZiYMWMGDh8+jFdeeQVfffVVn+zT6XRi3bp1yM3NRW5uLubNm4cnnngCjY2NvbbPjRs34t133wUAbNiwAevWrWuzvLvy8/Nxxx13IC8vD7m5ubj77rtx4sSJbm+vtLQU999/f49iIiIiovBWVlaGUaNGIS8vz/ffggULsGnTph5td/78+dizZ0+33ms0Gv2a8nTPnj0YP368L+7c3Fzccsst2LlzZ7f221179uzB/Pnz+2Rf33zzDV588UUAwEcffYRf//rXAW9jxIgRmDFjBs6fuPPll1/GiBEjcPjw4aDEGgiDwYCnnnoKubm5yMvLw8KFC7Fx48YebXPlypU4cuRIkCLsuR4lL5566infB33s2LG45pprfL83Nze3+x69Xo/bbruty21v3LgRS5cu9TsWb2OTiuXLl6OgoMCvdTdt2oTrr78eeXl5mDt3Lh577DEYjcZu7/vrr7/GK6+80u33k7Tt2bMHTqezT/b10EMP4aeffsI777yDjz/+GFu2bEFmZiYWLVoEk8nUK/vcv3+/7/tj8eLFuPvuu9ss7469e/fid7/7HR588EFs3boVH3/8MebPn48lS5agoaGhW9usqKhAUVFRt2Oi8CGV892nn36K6667DnPmzEFubi7uu+8+nDx50u+/x5w5c7Bv3z6/1/fHyZMn8Zvf/AYLFixAbm4ulixZgvz8/G5vz9/jRt23YcMGLFiwAHPnzsW8efPw0EMPoaKiwq/3Tpo0CWVlZUGLB0YK+gAAIABJREFUZcmSJZgxY4bvQnvevHlYtmwZrFZrp+/7+uuv8dRTTwUtju44dOgQHnvssS7Xe/nllzFlyhTU1ta2Wn7ujemIESMCPhf98Y9/xIgRI7B79+5Wy8vKyjBy5EisXr06oO0Fy+bNm3HDDTf4rmsfffRRGAyGbm/v3BtNKYuKisLWrVt9/61fvx7/8z//4/f9QLDp9Xq/b6AHDRrki/vjjz/GI488goceeggHDx7s5ShD4/Dhw9Dr9T3ejiiKrc6poijiX//6F+Li4nq87UDZbDbcfPPNSEtLw+bNm7F161a8+uqrWLduXY8SGDt37myToAklRU/evHLlSt/PM2bMwNq1azFu3LhO39PU1BT07M3+/fsBeD4wO3fuxOWXXx7U7XfHjh07/Mp2vvLKK9izZw9ee+01JCUlwW6346mnnsLSpUvx9ttvd2vfhw4dgsVi6dZ7Sdq+/fZbHDlyBGvWrIFcLseVV16JtWvXYu/evXC5XBg9ejRWrlwJnU6HGTNmYP78+di9ezf0ej3uvPNO5Ofn46effoJCocBrr72GtLS0Dvd16NAh7N27F19++SWio6MBAEqlEnfddRfy8/OxYcMG3HXXXZgxYwZefPFFX9s/9/e//e1v+Prrr9Hc3Ayr1Yply5Zh1qxZePnll1FeXo7a2lqUl5cjLS0Nzz77LA4ePIjt27djx44diIqKQkNDAxobGzFlypRWy9966y089thjmDp1KgBgxYoVGD58OG699dYO/z4vvfQSli5dirFjx/qWLViwAGq1Gi6XC3v27MGTTz6JTz75BABa/V5YWIgVK1bAbrdDFEVcd911WLRoEVauXInq6mrccccdeOONN/DVV1/hlVdegdvthlarxfLlyzF+/Hi8/PLLKCkpQXV1NWprazFmzBhceuml2LJlC8rKyvDQQw/5nja89tpr+OKLL+B2u5GVlYVVq1YhLS0NS5YsQVxcHE6fPo3FixdjyZIlPfswUUCkcL578803sW3bNjz77LMYOnQoAGD79u247bbb8Pe//x0jRowI2r78VVhYiF/96ldYs2aN79z7/fff4+6778YHH3zgizMQvXGdQGd5b6Zef/11ZGRkwO12Y9u2bbjhhhuwceNGpKen93lMDz/8MObMmQPAcy333//933jppZewbNmyDt9z9dVX4+qrr+6rENt16tQpVFdX+7WuyWTCsmXL8MYbb0AQhKDFkJmZia1bt+Kyyy7zLduyZQuSkpKCto9A/O1vf8N3332HV199FcnJyXA4HPjTn/6Ee+65B++99163thmsG82+lpaWhuzsbOzYsQOrV6+G1WqFTqfD22+/jVdffRX//Oc/IZfLMXjwYDz66KNISUnBqVOn8Mgjj8BqtWLIkCG+6/mysjLk5ubiwIED7f7++uuvY/PmzVAoFMjOzsYzzzyD5cuXo7m5GXl5efjoo48gl8v9jn3kyJFYsmQJ3nzzTTz//PMwGo14+umnceLECTgcDkyZMgUPP/wwFAoFXnrpJXz55ZdQKpVISEjAn//8Z6SmpuLgwYN46qmnYLVaoVQq8fDDD2PKlCndOpad7X/cuHG4++67sWPHDtTU1ODOO+/EjTfeCJfLhTVr1mD79u2IiYnB+PHjUVhYiD/84Q94//334XK5EBMTg+zsbNTW1uLuu+9GZWUl5HI5/vKXv/h1/lqwYAG2bduGiy++GIDnnvSCCy5o9VAjPz8fa9euhdVqhUwmw3333Yfp06fDYrHg8ccfR3FxMZqamqDVarF27VoMGTIES5YswcSJE5Gfn4/KykpMmTIFTz75JGSyjvsdfPrpp9BoNLjrrrt8y7KysvDCCy/A4XAAQIfX7KNGjcKTTz6J/Px8KJVKDBgwAH/+85+xbt061NTU4A9/+APWrFmDtLQ0PP744ygvL4coili4cCHuvPNOlJWV4dZbb8XUqVNx5MgRuFwuPPDAA/jggw9w+vRpjB07Fs899xxkMlmHx+Ojjz7Cpk2bWrWT9vQoedGZH374Ac8++yxsNhuUSiUefPBBTJs2DcuXL4fZbEZeXh62bt2KDz/8EBs3boTD4YBer8c999yDG264IaB9bdiwAVdddRW0Wi3+8Y9/+C6giouLcdddd+GSSy7B0aNH4Xa7cf/99+P9999HUVERJkyYgLVr10IQBHz++ed47bXX4Ha7odPpsHz5cowbNw7PP/88LBYLVqxYAQCtfl+8eDEuvvhi7N+/H5X/n737jo+iWvsA/pttqQQSEkDpLYQOKmog9CokgdCkio2LICJYQNqloxdRUPRVrFyKAtJR6qVEkS4dEQSEIJLQ07NtzvvHZobdNALskGzy+34+90p2N7tnJ3v2nHnmOc+5cgURERGYMmUKPvjgA9y8eRMjR47Mc4KbkpKCr776CuvXr1cHGZPJhHfeeQfbtm2D1WrFJ598kuvrb9y4EfPnz4der4der8eYMWMgSRJWrFgBu90Of39/vP7665g3bx42btwIvV6PatWqYeLEiQgODkbfvn3RqFEjHD58GLdu3cKzzz6L+Ph4HDx4EGazGXPnzkXNmjWRmJiIGTNm4OzZs7BarWjWrBnefvttCCHw+OOPo2XLljh9+jTmzp2L2rVr3+9HhvKhZcuW2LdvH/r374/27dvjk08+gV6vx6pVqyBJEj788EPMnj0bkydPBuCIwi5fvhwbNmzAm2++idWrVyMsLAyvvvoqVq9ejVdeeSXX1zp06BDq1aunBi6cNWvWDL/++muebb18+TJ2796NRYsWwdvbGz/99BM+/vhjtG/fHgBw8OBBrFmzBv7+/njllVewdOlSjBgxAtu2bUPNmjXRv39/zJs3DwDQvn17l9utViuWL1+OZs2aISUlBdu3b89zkgsAJ06cwKRJk7Ld3rFjRwDA+fPnc/3dr7/+Gm3atMG//vUvXLt2DTNnzkTfvn0xffp0TJs2DV9//TXOnTuHSZMmYenSpahYsSL27NmDYcOGYdOmTQAcA9ratWthNBrRokULBAcHY8mSJfjf//6H999/H5GRkVizZg3OnDmDH374AQaDAcuWLcOECRPw5ZdfAgACAgKwYcOGPN8nPXwPY7zLyMjARx99hNWrV6NKlSrq7W3atMH58+cxd+5cfPbZZ+jbty+Cg4Nx7tw59O/fH02aNMH48eNhNptRvXp1lwnVwYMH8cEHHyAjIwM6nQ4jRoxAy5Yt8cMPP2DNmjVITU1FqVKlsGDBglzb9fnnn6N3794uFw0iIiLwwQcfwMvLCxcvXkSPHj3UK1POPyckJGDMmDHqSUmbNm3w2muvZTtuuR3fH374Adu3b4fNZsOVK1fw6KOPIiYmBt999x0uXLiAl156Sc3gWLZsGZYtWwZZlhEUFISJEyeiatWqeOutt5CSkoJLly6hbdu2eOONN/L5V/dM8fHxWLp0KXbu3KleGdTpdOjWrRtOnDiB+fPnIzY2Fg0aNMDp06fxxhtvIDAwENOmTYMkSahfvz5kWVafb/v27fjss89gtVrh7e2NMWPGoHHjxpg3bx6OHDmCq1evolatWpg9e3a+2yhJEp566in8/PPPABzZqcuWLVP7zeDBg9GvXz+sWrUKmzdvxvz58+96YvPCCy9g9+7dSEtLw/Dhw7Fp0yacOXMGZcqUweeffw5fX1+cO3cOM2bMwO3bt2G32zFw4ED07NkT+/btw5w5c1CxYkX8+eefsNlsmDJlCh599FF8/PHHSE5OxtixY/Huu+/m+b6io6Nx9OhRfPPNN3jppZdyfMzcuXNx/PhxyLKMkSNHonXr1nc9Xp07d8aKFSuQkZEBb29vAMDGjRvxzDPPqH+rvI5PXsd369at0Ol0uHjxIry9vfGf//wnzxO6tLQ09SQ6ODgYANST1q1bt8JisWD+/Pm4deuWmrEyb9489ectW7bgs88+gyRJ0Ov1GD16NEwmk8uJ5qhRo3I98R84cCDq1q2LI0eO4ObNm+jduzeuX7+O/fv3Iz09HXPnzkWtWrXyPB716tVD27Zt8ccff+QrSJ2Xw4cPIy4uDhkZGTh79iy2b98Of39/rFy5Er/88gtWrFgBX19fzJs3D++88w6+/vprvPXWW+jfvz969eqF3377Df3797/r62zbtg2rVq3C8uXLUbJkSbz77rtYvHgx3n33XURFRWHt2rX31f6wsDCsX78eADBz5kzUrVsX7733Hux2O9555x18++23iIyMxH//+1/s2bMHJpMJ33zzDY4dO4aWLVvi1VdfxfTp09GqVSucOHECY8eOxdq1a/M8Ac9Nbq8/ePBgWCwWBAYGYunSpThx4gT69u2LHj16YPXq1Th58iR+/PFHSJKEoUOHAgAaNmyIPn364NatWxg1ahRWrVqFS5cuYc6cOahcuTKmT5+Or7/+GjNnzrxru5QM3okTJ8JkMmH16tWIiYlRA/CJiYkYO3Ysvv76a1SoUAEJCQno3bs3atWqhWPHjiEgIADLli0DAPz73//GkiVLMHHiRABAXFwcFi1ahLS0NDzzzDPYv3+/S5AyqxMnTuCxxx7LdnvdunXv+j6OHDmC/fv3Y8OGDZAkCe+//z5Onz6NUaNGYf369WpfGDBgANq2bYsXXngBycnJ6N+/Px555BE0bNgQf//9N1q2bImpU6di0qRJmDFjBtatWwej0Yi2bdviyJEjqF69eq7HA4BLP8mNJsEL5cR9/vz5qF+/Pk6fPo3nnnsOq1evxrvvvosePXpg7dq1SElJwcqVK/Hll1+iVKlSOHjwIIYOHXpPwYubN29i8+bN6knQRx99hL/++gtVq1YF4JgojR8/HtOnT8eECRPw3nvvYc2aNdDr9WoNAR8fH0ybNg1Lly5FhQoVsGvXLgwdOhSbN2++6+tfvnwZixYtQmpqKjp16oTo6Gi89dZbWLdu3V1P5s+dOwd/f39UrFjR5XZfX998LYGZNWsWPv74Y9SvXx+xsbE4cOAAXnnlFfTs2RNpaWl4/fXXsXz5cuzZswcrV66Ej48P5syZg/Hjx2P+/PkAgCtXrmDp0qX47bff0K9fP3zxxRcYN24cpk2bhiVLlmDy5MmYMWMGGjVqhFmzZsFut2P06NFYuHAhBg4ciIyMDHTo0AEff/zxXdtL7rdz504kJyer6xKtVqvL1ZYOHToAACpWrIjg4GCEhYUBcKQHPuhVjLulkJUvXx6zZs3C+vXrcfHiRRw9ehSpqanq/U8++aT65VSnTp17ak/37t3x6aef4ubNm9i0aRNatWqFgICAPH9Hp9O5TLrvRfv27TFmzBgcO3YM4eHhmDBhQrbBd+/evXj66afV/hweHo6goCB1AGvatClKlCgBAChTpgyaN28OwPG3uH37NgBgx44dOH78OHr06AEAkGXZJXX6iSeeuK/2k3Ye1nh3+vRp+Pn5uQQuFE2bNlUDXAAQGBioBrmioqLw4osvIiYmBgcOHMDGjRsBALdu3cL48ePx7bff4tFHH0V8fDx69+6N5cuXA3CMT//73//ynEAAjslS165ds93esmVLAI4xODfLli1DtWrV8O9//xupqakYN24cUlJSXI5bXscXcAQF169fj5CQEHTu3Blbt27FwoULcfLkSQwYMACDBg3C3r178eOPP+K7776Dt7c3YmNjMWLECHVSbrVa8dNPP+X5PouKo0ePolq1ajmmNDdt2hRz584FANSsWRNz586FxWJB69atMXv2bISHh+PHH39UPyMXLlzAnDlzsHDhQgQGBuLPP//ECy+8gC1btgBwzI9+/PFHGAz3NtVMTEzExo0b0aZNG6SmpuKHH37AF198gcDAQBw5cgQvvPAC+vXr5/I7dzuxCQ4OxooVK/DFF19gwoQJ2LhxI0JCQtCzZ09s27YNzzzzDEaMGIFZs2ahbt26SE5OxrPPPosaNWoAcGQiTpo0CbVr18Y333yDOXPmYPHixRgxYgQ2b95818AFAHh5eeGDDz5Av3798PTTT+d4QlGhQgVMnToVZ86cwcCBA7Fx40YEBQXl+bxBQUFo3Lgxtm/fjs6dO+PgwYOoXr06SpYsqdanyu349OvXL8/je+DAAfz4448oV64cpk2bhi+++AL/+c9/cm3L+fPn4e3tne17ysfHB9HR0Xc9RrNmzcLs2bPRqFEj7Nq1C/v27cPw4cNdTjTzOvEHHJ+7pUuX4ujRo+jduzc+++wzvPPOO5g5cyYWL16MadOm5fl5sVqtaN269X0tU1GyHADAbrcjMDAQ77//Pm7cuIFatWqp36c///wzunfvDl9fXwDAc889h88//xzXr1/H6dOn0a1bNwDA448/jpo1a971dffs2YNOnTqp/Xrs2LEA8MDLuyRJUgNiO3fuxPHjx9UaHkogvGzZsggLC0NMTAxatGiBFi1aIDw8HCdPnoROp0OrVq0AAPXq1VO/c+9Hbq+vULKw6tatC4vFgrS0NMTGxqJr167w8vICADz77LO5Xs1v0KABKleuDACoXbs2tm7dmq92lS5dGg0aNMCOHTvQsmVLHDx4EFOmTFHvP3LkCK5du4ZXX31VvU2SJJw+fRqdOnVCxYoVsWjRIly8eBH79+9H48aN1ce1bt0aOp0O/v7+qFy58l3nyZIk3ffyjtDQUOj1evTq1QsRERHo2LEjGjRo4PKYtLQ0HDp0CN988w0AoESJEujevTt+/vlnNGzYEEajEW3atAHgmNs2btxY/cyXKVMGiYmJeR4PAC79JDeaBC8OHz6MatWqqdHKWrVqoWHDhtn+KP7+/vjss8+wY8cOXLhwAadOnbrn5Q4rVqxAWFiYGgl+6qmnsHDhQvUKq5eXlzqJqlSpEqxWK/z8/AAAISEhuH37No4cOYJmzZqhQoUKABxXjUqWLIlTp07d9fWVD1aJEiVQsWJF9SQkPx7kQwY4ou1Dhw5Fq1at0LRpU7z44ovZHvPzzz+jR48e6tXzQYMGoUWLFmrNBOXktlKlSpAkCREREQAcJ7vKOrfY2Fj8/vvvamQwIyMDJpNJfQ2eUBUcWZYxbtw49TOempoKs9ms3u/8dzIajff03I899hi+/PJLpKenw8fHBxaLBampqQgMDMTevXvRsGFD9bHOn2OLxQIAOHnyJIYNG4bnn38ezZo1Q5MmTVy+0JUBEbj3vhAQEIBOnTph3bp1WL9+fY4ZFVk1atQIR48eRWhoqMvtU6ZMQfv27WEwGFzaoKTYAY5+vnnzZuzevRt79uzBp59+ilWrVrk8jyzL2VKBhRBqX3P+WwDIcUIvy7Ka7gg4jqXzYKVMdKjweJjjnfNn0pnS5xSPP/44AOD69es4f/68etLQpEkTVKtWDYAjs+ratWvqlSjAEeBTCtiGhYXddQKh/M79BgVbtGiBIUOG4O+//0bTpk0xevRo+Pv748aNG+pj8jq+gGPCqSx/K1++PJo1awZJklCpUiWkp6fDYrFg586d+Ouvv1wCRbdu3VJrSynHq7jIrWaSxWJRv8OUcf3MmTMwGAxqqndkZKR6xVxJ0XauTyJJEuLi4gA4vnPzG7iYNWsWPvvsM/U7uHXr1njuuedgMBjw+eefIzY2FhcuXMAff/yRY7+524mNkmFXqVIlhIaGqp+ZChUqIDExERcuXEBcXBzGjRun/k5GRgZ+//13VK9eHY8++qh6MapOnTpq8Oxe1apVCyNHjsSbb76ZbQwBHHWeAMeJRPXq1XH48OF8LY1RspQ6d+6MNWvWuFz5BXI/Pn5+fnke37p166rLiOrUqXPXE7oH+T4AgC5dumD48OFo2bIlmjVr5pL+rsjtxF/5HlSyO5ULCc4XCpTvjbt9Xu53XqvUvMhq1apVLuN31vmCLMsu/dJ5LqL0oazzJOfxQK/XuzxfUlLSA9UYURw/flydM8myjI8++kg930pKSoIkSdDpdFi8eDGOHz+OPXv2YObMmWjevDmio6OzzYnOnDmDatWq3XNAM6/XVygBCuU2IUS218kr48P5sfc6J+3WrRvWrVsHi8WCNm3auDyX3W5H9erVXWpOJCQkICgoCN999x2WL1+O/v37IyoqCqVKlXIJON3rPLlRo0Y5Frbftm0bDh48qGYo5zRnDwgIwNq1a3Ho0CHs3bsXI0eOxEsvveSS+SPLcrY2OH92jUajy98kp/OOvI7H+vXr8zXP1WS3kZwm8Vk7JuCIjsbExCA+Ph5PPPEEXn/99Xv6sMiyjGXLliEuLg5t2rRBmzZtcOrUKaxZs0ad8Of3hCGn9lqt1jy/LIDsH6x7ERoairS0NFy6dMnl9vT0dLz88su4du1anq//9ttvY8mSJahTpw5WrlyZY40Nu92e5xek8/HR6XQ5roez2Wz45JNP1EI+y5cvdxngeUL1cOn1evVvGBERgSVLlsBisUCWZUycOBEffvihW16nQYMGeOqpp/DOO+8gMTERly5dQv/+/fHaa6/h9OnT6heac3bBvn371IJkBw4cQL169fDCCy/gySefxLZt22C32+/p/eV1e//+/bFw4UIIIbJFh3MydOhQfPLJJy4TOiXtODQ0FEFBQfjnn39w48YNCCFcrsS++eab2LBhA7p06YJJkybB398fcXFx0Ov1ap8MDw/Hrl271P68Z88eXLlyxSXIczcRERFYsWKFWgz1o48+wujRo/P9+/TwPazxLjQ0FFarNcfdcfbt2+cSKFEC9Arn11G+42VZRmhoqEtxuWXLlqknqfn9Xm/YsGGOBd0+/vhj/PTTT3mOYY0aNcK2bdvQq1cvXLp0CT179sx20eBuxzc/Y7zdblczOdauXauuq1UyoYrTGNaoUSNcvHgxW+FIwPVz5HxMsn5OlWMsyzLCw8NdPkPLly9XrxTfy3EdPXo01q5di3Xr1mHdunUYNWoUDAYD4uPj0a1bN1y+fBmPP/44Ro4cmePvKyc2Sjt++OEHl0KazpPo3CbUJUqUyPZelCy4Bwm2ZzVw4EBUrlwZM2bMyHaf88mVLMv5Pslr27Ytjh49iitXruDAgQPqCbvzc+V0fO52fO/1fdeoUQM2mw0XLlxwud1sNmPw4MFISEjI8zth1KhR+O6771CvXj2sWrUqxyUTdzvxz/qdkNPf+26fF62/E5o3b46VK1eqgaJFixahSZMmCA4ORt26ddUTu5MnT6rf+QEBAbBarTh79iwAuMxRmjZtiq1bt6pzh3nz5mHBggUwGAyw2+339Xk9duwYvv/+e7WWWEREBBYsWAAhBCwWC4YOHYrFixfjjz/+QGRkJKpXr44hQ4bg+eefx/Hjx1GtWjVIkqQuMT558iQGDRp038Gt3F4/Ly1btlSDCjabzSXomNtc8360bdsWhw8fxpIlSxATE+Nyn/Kde+DAAQDAqVOn0LFjRyQkJGDXrl2IiYlBr169ULVqVWzfvj1f8+TcdOjQASkpKfjyyy/V57l06RLee+89NeiT25x9x44deP7559G4cWO89tpr6lJC4M6x8vf3R8OGDdUASXJyMtasWXNPtSbzOh75pUnwonHjxjhz5oxa4fb06dM4dOgQnnrqKej1erUjHT9+HCEhIXjllVcQERGBHTt23NOH+pdffkFSUhK2bduG7du3Y/v27fj5558RGBiopjXmR3h4OGJjY9Vo165du3D9+nXUr18fQUFBOHnyJIQQSElJUddg3o3BYMj1KpnC29sbL730EsaNG6deaTKbzZgxYwYsFgtCQkJyfX0lrc1ms6Ffv36YOHEiTp06BZvNBoPBoHbI5s2bq8VPAMcX5FNPPXVPUU/nLwyz2YwhQ4bg+++/z/fvk3u1adMGH374IVavXo1hw4ahfPnyiImJQefOnSGEwDvvvOO213r//fdRr149DBgwACNHjoTVaoVer4efnx+2bdsGAHjrrbewcOFC9cqPkgobGRmJW7du4ZlnnkHnzp3h6+uLxMTEu+5S0qJFCyxdulRd2pTb7WFhYShZsiT69OmTr/fyxBNPYPr06ZgxY4ZaAX3Lli1YuHAhgoODUaNGDfTp0wc9evRA79691UwsABg2bBjWr1+P6Oho9O7dG+3atUOTJk1Qo0YNeHl5oWfPnqhevTomTZqE4cOHIzIyEh988AE+//xz9QQpP3r16oVWrVqhd+/e6NKlC06fPo333nsv379PD9/DGu98fHzw5ptv4u2333apz7Jt2zYsWLAAr7/+erbfCQ4ORmhoKFauXAnAMRk9d+6c2u5z586pBa9PnjyJjh07umQ95MfLL7+M77//Hnv27FFvi42NxZIlSxAWFoaAgABkZGSobVYK4gKOwpFffvkl2rdvjwkTJqBq1ar4888/XY5bXsc3v5o3b47169fj+vXrAIAlS5bkmKlYHCjFf9944w2XyeLKlSuxZcuWbFe6a9WqBSEEYmNjATg+b8rFofDwcPz666/qZyo2NhbR0dEPtCtUVidOnEBQUBCGDRum9hsA2Sb493Ni46xq1aouV82vXLmCyMjIuxaOvd8ToHfffRexsbHZllUpJ1cnT55EXFxcvoPfJpMJ7du3x+jRo7Nd+QVyPz75Pb75ZTKZMHjwYIwfP17tbxaLBTNnzkR6ejrKli2LwMBAl3mt8po2mw1t2rRBeno6+vbti0mTJuH06dOwWCwuxzm3E/+sQYu8POjn5UH17NkT4eHh6NWrF5555hn8/vvval2YDz/8EBs2bEBUVBT+7//+T82WK1GiBN5++20MHjwYPXr0UDMNAMdJevfu3dG3b19ERUXh+vXrGDlyJEJCQtCgQQN06dLlrlvcx8XFqTtoxcTE4P3338fs2bPV5cbjx49HWloaoqKiEBUVhdDQULz88ssICwvDM888gx49eqB79+5YuXIlxo4dC5PJhHnz5uGTTz5B165dMWnSJMybN++uf6dz586hcePGLv9LTk7O9fXz0r17dzRo0ADdunVDnz59YDQa1Uz0p59+Grt27cK0adPy/mPlg5eXF9q0aQOLxZItuzcoKAjoUFYVAAAgAElEQVQff/wxZs2ahejoaIwePRqzZs1ChQoV8OKLL2LZsmWIiopC//79UbduXTVz7X6YTCZ8++23OHv2rHqcXnvtNQwdOhQ9e/YEkPucvUWLFqhRowYiIyPRvXt3HD58WF3a0b59e7z99tvYtWsXZs+ejT179iAqKgo9e/ZEhw4d0L1793y3Ma/jkW/CTVq3bi2OHTum/rx7927RvXt3ERkZKaKjo8XWrVuFEEJYLBbRt29f0blzZ5GUlCQGDx4sOnToIDp27Cj+/e9/iyeffFJcvHhRLF++XAwdOjTP1xwyZIiYPXt2ttu///570bJlS3HhwgXx+OOPq7fPnz9fjBs3Tv05OjpaxMbGCiGE+PHHH0V0dLTo0qWL6Nmzpzh48KAQQojbt2+LwYMHi3bt2okBAwaIyZMni+nTpwshhOjTp4/6vrL+PHPmTNG2bVuxe/fuPN+DLMvi22+/FVFRUSI6Olp07NhRTJo0SSQnJ9/19Tdt2iQiIyNFt27dRExMjPjpp5+EEEIcOHBAtGrVSkyfPl3Y7Xbx4Ycfis6dO4uOHTuKV155RcTHx2dr79WrV0Xt2rXVdn377bdi5MiRQgghbty4IUaNGiW6dOkiOnToIKZOnSqsVquwWq0iNDRUJCYm5vkeqehJTEwUv/76a4G24eLFi6JFixYiLS2tQNtBxU9BjHeKzZs3i2effVZ07txZdOjQQQwdOlScOnVKvT/ruHT+/HnRp08fERkZKYYPHy46d+4sDhw4oLa7R48eIioqSkRGRqq/dy/tEUKII0eOiOeff15ERkaKzp07i4EDB4rDhw+r93/11VeiVatWonv37mL+/PnquBwfHy+ef/550aVLF9GtWzcxbtw4YTabsx233I5v1na++OKLYs2aNUIIx3dUaGioyMjIEEII8d///ld06dJFREZGin79+omzZ88KIYR48803xbfffpvv91pULF++XMTExIguXbqI9u3bizfeeEPExcUJIbJ/vo8ePSpiYmJEdHS0GDNmjAgPDxeXLl0SQgixYcMGER0dLaKiokRMTIz62fr444/FlClT8tWWAQMGiI0bN+Z4X1pamhgyZIjabyZOnCiefvppce7cObFy5Urxr3/9SwjhmKe88cYb6jxl8uTJwmKxCCGECA0NFTdu3BBCCLFx40YxYMAA9flfe+01sWjRIiGEEKdOnRIDBgwQkZGRolOnTuK7774TQgixd+9e0aVLF/V3nH++cOGCaN++vXj11VfzfI85HY9ffvlF1KpVS+zdu1dt58yZM0XXrl1Ft27dxJ49e+567MaMGSO++uorIYQQBw8eFKGhoeL06dPZXjO345Pf4yuEyPZzXhYsWCCio6PVee3EiRPVuWJSUpIYMmSIaNeunejfv7+YOnWq2s6tW7e6zGvXrVsnhBDit99+E61btxZTp04VdrtdzJ07V3Tu3Fl06tRJDBkyRFy5ckUI4fpZunHjhggNDVXbtGjRIvHaa6/leTyUv4PyeSHP9ssvv6hjghBCTJs2TcyaNasAW0TuIAlRiDZuJaKHauTIkfjrr79yvG/OnDlqxL+w+eijj7B8+XJMmTIF7dq1AwCsW7dOLdiVVVRU1F0j9ERERERFUb9+/VyKpjtbsmRJvmoceVo7EhIS8M477+D69euQZRlhYWGYPHlyvjNiZ86ciX379uV439ixY/Pc+UMLKSkpue4+4+fnd99bEXuaQh+8mD59urouJquJEycW+mKRnt5+IiJ6OArjeLF79+5cdxdo2rTpXbcoJioOgeXz589j1KhROd5XtWpVdReXe7V3795cdzJ56qmnXOqPPSyF7YSOiIqXQh+8ICIiIiIiIqLiTZOCnURERERERERE7sLgBREREREREREVagxeEBEREREREVGhxuAFERERERERERVqDF4QERERERERUaHG4AURERERERERFWoMXhARERERERFRocbgBREREREREREVagxeEBEREREREVGhxuAFERERERERERVqDF4QERERERERUaHm8cGLffv2ITIysqCb8VBYLBa88MIL2LRpU66POXDgAHr37o3o6Gj0798fly5deogtpKKoOPUxZytWrEDnzp3RoUMHTJo0CVarNdtjhBCYM2cOOnTogK5du2Ly5Mkwm80F0FoqSopLn1u6dCm6dOmCqKgoDB06FDdv3szxcd988w26dOmC6OhoPP/884iLi3vILaWiqrj0tddeew3t27dH165d0bVrV8ycOTPXxwohMGbMGHz99dcPsYVU1BWXvqZYsGBBnu93586diIqKQseOHTFixAikpKQ8xNZ5No8PXhQXhw8fxrPPPotDhw7l+pj4+HgMHz4ckyZNwrp169ChQwdMnjz54TWSqIg4c+YM5s2bh8WLF2PTpk1ITk7GggULsj1u1apV2LlzJ1asWIG1a9ciJCQEc+fOffgNJvIwly5dwpw5c7B48WKsX78e5cuXx7x587I9bvfu3VixYgWWLVuGdevWoX379hg7dmwBtJjIcx0+fBiLFy/G2rVrsXbtWowbNy7Hx507dw6DBg3C5s2bH3ILiYqO3377DV999VWu99+8eRNjx47FvHnzsHnzZlSsWBGzZ89+iC30bB4TvEhNTcWIESPQtWtXxMTEYMKECZBl2eUxBw8eRKtWrdQT/O3bt6NXr17o1q0b+vTpg8OHD+PWrVto3Lgx0tLSAAD//ve/MWDAAPU5OnTogHPnzqFNmzaYN28e+vXrh9atW7uckOT0vIDjS79Pnz7o3r07YmJisGTJkjxvz2r8+PH4/vvvc7xv0aJFePPNN9GgQYNcj9GmTZvQvHlz1K1bFwDQp0+fXAcooqyKex9ztm3bNrRp0wZBQUHQ6XR49tlnsW7dumyPO3nyJNq1a4eAgAD1vXHSR/lVnPucLMuw2WxITU2FLMvIyMiAl5dXtscFBwdj8uTJ8Pf3BwDUr18f//zzT76PMRFQvPvapUuXkJqaiokTJyIqKgpjx47F7du3c3yOJUuWoFevXujUqVN+Dy2Ri+Lc1wDg+vXrmDZtGkaPHp3rMdq1axfq16+PKlWqAAD69u2L9evXQwiR16ElhfAQq1evFi+++KIQQgibzSbGjx8vLly4IPbu3Su6dOki9uzZI9q1aydOnTolhBDir7/+EpGRkeLmzZtCCCHOnDkjmjVrJlJTU8XAgQPF9u3bhRBCdOjQQTRt2lSkpKSIP//8UzzzzDNCCCFat24t3nvvPSGEEPHx8aJ+/foiLi4uz+cdO3asmD9/vhBCiKtXr4qRI0cKu92e6+33Y8CAAWLjxo053jdp0iQxceJEMXLkSNG1a1fxyiuviLi4uPt6HSp+2MfumDhxovp8Qghx4cIF0aRJkxyPWbdu3cSNGzeE3W4Xs2bNEnXr1r3v16Xipbj3uU8//VTUrVtXhIeHiw4dOqivnxuz2SwGDhyovgei/CrOfe3IkSNi2LBh4p9//hE2m01MnTpVDB06NM/fGTNmjPjqq6/y/RpEiuLc12w2m3juuefErl271Pebk/nz54uJEyeqP1utVhEaGiqSk5Pz/VrFmaGggyf59fjjj2POnDkYOHAgmjZtikGDBqFy5cqIj49HfHw8XnnlFfTt2xdhYWEAgF9//RVXr17F888/rz6HJEmIi4tD+/bt8fPPP6NSpUooW7YsQkNDceDAAZw+fRodOnRQH9+2bVsAQNmyZVG6dGkkJibi6NGjeT7vmDFjcOzYMYSHh2PChAnQ6XS53u5uNpsNO3bswJIlS1ClShUsXLgQw4cPx9q1a93+WlT0sI/dIbJEv4UQOT5ft27dkJCQgEGDBsHX1xe9e/eG0Wi879el4qU497ldu3Zhy5YtiI2NRWBgIN5//32MHTsWn3/+eY6Pv3nzJkaMGAF/f3+MGjXqHo4yUfHuaw0bNsSnn36q/jx8+HBERETAYrHAZDLd5xElyllx7msffPABmjRpgmbNmmHfvn25Pk6WZUiSlO12Lc4NiyKPCV5UrFgRW7duxb59+7B371688MILmDp1Kvz8/KDX6/HFF19g2LBh6NSpExo2bAhZlhEeHu6SPnTlyhWUKVMGpUqVQv/+/VGlShU0a9YMAQEB2LVrF44fP44pU6aoj3dOYZUkCUKIPJ83LCwMmzdvxu7du7Fnzx58+umnWLVqFVq3bp3j7eXKlXPrMSpTpgwee+wxNQ2pZ8+emDFjBjIyMuDt7e3W16Kipzj3sfHjx+PEiRMAHMutHnnkEVy9elW9/+rVqzk+1+3btxEZGYkhQ4YAAA4dOoTKlSvn84hTcVec+9z27dvRpk0blC5dGgDQv39/REVF5fjYP/74A8OGDUO7du0wZswY6PX6ezrORMW5rx08eBCJiYnqCZ4QApIksR+RJopzX1u3bh2CgoKwdetWpKWlISEhAV27ds12EfmRRx7B0aNH1Z8TEhJQsmRJ+Pr63vPxLo48JsTz3XffYezYsYiIiMDbb7+NiIgI/P777wCAkJAQPPbYYxgzZgxGjx6N9PR0hIeH49dff8W5c+cAALGxsYiOjkZGRgbKlSuHwMBALF26FM2aNUNERAS2bNmC27dvq5HA3OT1vG+++SY2bNiALl26YNKkSfD390dcXFyut7tb+/btcejQIXWHkS1btqBmzZoMXFC+FOc+NmPGDLWQWd++fdGmTRts374dN27cgBACy5YtQ7t27bL93okTJzB8+HBYrVbYbDZ88cUXuZ6AEWVVnPtcnTp1sHPnTqSmpgJwjFcNGzbM9rj4+HgMGjQIw4YNw7hx43jCRfelOPe11NRUTJ8+Xa1z8fXXX6Njx47sS6SJ4tzXdu3ahXXr1mHt2rWYPn06KlWqlGP2e0REBI4ePYoLFy4AcOy8pQQX6e48JvOiW7du2L9/Pzp37gwfHx888sgjGDhwIP744w/1MTExMdi8eTPee+89TJkyBVOnTsUbb7wBIQQMBgM+++wz+Pn5AXCc6H/zzTeoU6cOdDodvL29czw5yapGjRq5Pu+wYcMwfvx4LFu2DHq9Hu3atUOTJk1QunTpHG/Pavz48ahXrx769u2b7+Oybds2LF26FF9++SVq166NSZMmYfjw4bDZbAgICMBHH32U7+ei4o197I6wsDC8+uqrGDRoEKxWKxo2bIjBgwcDcO1zEREROHDgAKKjoyHLMtq1a+eSokiUl+Lc53r06IHLly+je/fuMJlMKF++PN577z0Arn3s//7v/5Ceno5FixZh0aJFAACTyYQffvjhvo87FT/Fua+1bNkSAwcORN++fSHLMmrVqoVp06YBAI4fP44JEyZweTG5TXHua3lx7mulS5fGu+++ixEjRsBqtaJSpUr4z3/+k+/nKu4kkXVxNxERERERERFRIeIxy0aIiIiIiIiIqHhi8IKIiIiIiIiICjUGL4iIiIiIiIioUGPwgoiIiIiIiIgKNQYviIiIiIiIiKhQY/CCiIiIiIiIiAo1w73+wq1bqZDlu++uWrq0P27cSLmvRhU1xfVY6HQSAgP9CroZHot97e743h3vnX3twXhqXytM7SlMbQG0aw/72oPJb18DCt9nyhnbdv/y2z72tQfDcc09ikN7PK2v3XPwQpZFvgee/D6uOOCxoHvFvpY/fO/0oDy5rxWm9hSmtgCFrz10b31NeXxhxbbdv8LevqKA45r7sD2FiybLRjbuvYjDp69q8dRE5GTj3os49Af7GpHWNu2Lw8FTCQXdDKJiYdO+OOz/Pb6gm0FUpG3ZH4f9J9nPyLNoErzYsPciYg//rcVTE5GTH3aew6Qv9xR0M4iKvOU7zmLKV3sLuhlERZ4QAst3nMW0r/cVdFOIirSl289i2jfsZ+RZNAlemK0yMsx2LZ6aiDIV97QxIiIqehJTLQXdBCIiKqTcHrywyzJsdhnpFpu7n5qInKSZ2ceIHgYhGCgkeljib6QVdBOIiKiQcnvwwmyRAQAZPLEi0lRqhrWgm0BULNiZ5UT00MTfcgQvSvqbCrglREWX7BSUN1uZLU+ew/3Bi8wOwGUjRNpKSWfwguhhsDhN7Kw2uQBbQlT0KZkX/j7GAm4JUdFltd4Zy24lmwuwJUT3RrvgBZeNEGkqNZ19jOhhMDtN8pLTuB6fSEsJNx3BC+d+R0TuZbbdCcrfTMoowJYQ3RsNlo0weEH0MCjLRgx6qYBbQlS0OWdesJggkbYSbqUD4PJjIi05j2s3k5h5QZ5Ds8yLdC4bIdJUauayEZNRX8AtISranNcDJzF4QaSp9MygRYaF80girVicMptuJjPzgjyH24MXGU6ZF6zQTqSd1AzHBI/BCyJtOU/ykrhshEhTlsy6Mja7Y/c6InI/i42ZF+SZ3B68UNKQhLgzABGR+ymZF3ZO7og05bw2mJkXRNqyWO0w6HXqv4nI/ZyD8ulcokUeRLPMC+BO/Qsicj+l5gWDhETasrgsG+EuP0RakWUBuyxQwtex0wiLdhJpg7tokafSrOYFAGQwYk6kGWXZiMVq5xItIg05j2vcophIO0oqe4nMbVJZ/J1IG0pg0GTUw8oMXvIgmgYvmHlBpB1l2YgQgF1m8IJIK0p6rY+XnmvwiTSkZBIqmRcWZl4QaULJvCjha2TmBXkUzbZKzfpvInKvlIw7V6Q4wSPSjjLJ8/c1cZJHpCFr5ljm72sCwMwLIq0otZxKcFwjD6PxshEOOkRacS6wxJQ/Iu0o41oJHxP7GpGGsi4bYc0LIm1Y1EAhMy/Is3DZCJGHstlkeJsc26RaWV+GSDMWqwwJgK+PATZO8og0o5xE+asFOzm2EWnhzrIRBuXJs2iybEQnSQBcdx4hIvey2WX4eBkAcMcRIi2ZrXaYTHqYDCxsRqSlOzUvHMtGeBGMSBsWmx2SBPh4GWCzsZ+R59Ak86KEHyPmRFqz2mX4ejuCF0z5I9KOxSbDy6CD0aBjXyPSkHo12IfzSCItWawyTEZ95m4jLPpOnsPtwYsMix0lGTEn0pRdliEE1MwLnlARacditcNk1MNo0HG3ESINZd1thMELIm1YrHZ4GXQwMShPHsbtwQuL1Y4SvkZIEpeNEGnFZnNEyX3V4AX7GpFWzFY7vJQrVJzkEWlG6V9+3pxHEmnJnJl5wYxC8jTuz7yw2uFtMsDbZGDEnEgjyrp71rwg0p4jvZbLRoi0piwbMRl18Dbp1Z+JyL0sNru6bMRmlyEEl46QZ9CkYKfJqIe3Sc+IOZFGlNR1Xy4bIdKcxWqHycBlI0RaU8Yyo0EPL5OB80gijVisMrwyg/IAOLaRx9Bk2YiXUQdjZiSPiNzPliXzgsELIu2YrXZ4KbuNsK8RaUbJImTmBZG2lKC8yagHwHkkeQ63By+sdhkGgw5GPa9QEWnFllkZWt1thH2NSDMWmwyTQQej0bFshOm1RNpQ6jeZDDrWmCHSkLJsRMm8YF8jT+H+4IVNwKjn2mAiLdlsWWpe8OoUkWacdxsRAOwygxdEWjBbZUgADPrM4AUD80SaUGo5mZTgBfsaeQi3Bi+EELDZZRgygxc27htMpIk7BTv1Lj8TkfspmRcmA9NribRktdlhNOogSRJMBh0D80QaMVvtjoxCjmvkYdwavFCuRhkMOhY2I9JQtoKdVvY1Iq1YbY7lkLxCRaQtR6DQcTJlMjDzgkgrVpsMo0EPk5HLRsizuDV4oVaJVpaNcNAh0oSybMRk0EOvk9jXiDRks8swGnQwZJ5U2TjJI9KE1Sqra/CVGjNE5H7KuKZmXnAeSR7CrcEL5Wqw0hk46BBpw+aU5WQy6mFh5gWRJoQQjitUet2dK1Sc5BFpwmKzqxlOLNhJpJ2s4xqD8uQp3By8yDyh0ktcNkKkIZtTlpPJyCwnIq0o45qRNS+INKeksgOOHUfY14jcTwnKGziukQdy77KRzBMogz5zq1R2BCJN3OlrkuPqFIuaEWnC5jyuZV4RZmCeSBsWm6xeCWbmBZE27LKAADKXQ7LmBXkW92Ze2O4sGzEw84JIM+oJlcERKGTmBZE2rDbn5ZCc5BFpyWrlshEirTnXKGQhavI0mtS8MKgFO7lVKpEW1FR2bktMpCm1lpPeUV8GYPCCSCvmLMtGLOxrRG5ndapRyHGNPI12y0YMXDZCpBVlkNHruS0xkZaUvmYwcNkIkdasTstGjAY9bHYZQjA4T+ROtpwyCjmukYfQZtmIXlIHHSJyvztXgyXu7EOkoaxbgDvfRkTuZXFZNsJgIZEWXJaNMPOCPIw2u40YlGUj7AhEWsi+RIt9jUgLTK8lenhcdhthfyPShPO4pmYUsp+Rh9B02YgQgF1mZyByt6yBQg46RNrIadkIg4VE2rDY5DuZF5n/Zd0LIvdSxzW9Tg0WMkhInkKjZSOOHRAct3GtIpG72ewy9DoJOkli5gWRhqz27MtGGCwk0obVZofRqeaF4zb2NyJ3ct5FS6+ToNdJnEeSx9BmtxFeoSLSlNUmw6BXrk7peTJFpBHnwmYmnkwRaUaWBWx2ofYzpeYF+xuRezkvGwEc523sZ+QpNFo2IsGYuVaRhZaI3M9ml2HQSwAcgw77GZE2XAubMShPpBWLzQ4ATgU7GSwk0oJzUB5wjG/sZ+QpNCnY6bpshJ2ByN0cwQsltVYHq53Ls4i0YHO6QqX0OU7yiNzPkuWEiplORNpwrnkBgMuPyaNoUvOCy0aItGW1iTvLRozcKpVIK86TPEmSYNBzkkekBavV0a+UjAujumzEXmBtIiqKsi4bMXLZCHkQbWpeOBc24xVhIrezyzIMTul+XDZCpI2cJnksRE3kftmWjfAiGJEmnJdDAo6+ZrEySEieQZOaFy5V2TnoELmd1SbDmFnzQtkqVQieUBG5my1req2eVdmJtHBnBwSlYCeXjRBpwZYlKM8MXvIk7g1e2GToJAk6nXRn2Qg7A5Hb2ezCZa2iAGCXGbwgcrec02t5hYrI3ZSaF0phXCV4YeE8ksitsta8YOYFeRK3LxsxGJSrwdxthEgrjr6mnEzx6hSRVu5M8u6MbexrRO5ntbouG+FFMCJtWLMWxzXqYWY/Iw/h5oKdQl0/xWUjRNqx2mX2NaKHwJq5s48kOYIXJqMOFiv7GpG7WbIsG/HishEiTWQPyrNgJ3kOgzufzJpl+0bAsSsCEbmXzSbD25RZkb2YFMe9fjsd+04lwGqTERTgjYpl/BESUqKgm0VFnNUmq30MyLxCxfRaIrdTl40w84JIUza7Y1xTg/IGPZeNkMdwa/DC5hS8UP7Lq8FE7mezZ89yKsrr8PefSsA3G05lu+Id0fAfPNchVP2+IXI3m124BC+8DDpkcJJH5HbKyZMxS82Lojy2ERUER9F3p3HNqGNtGfIY7g9eGJjKTqQ150ChSal5UUQzL/aejMeXP/6O6uVL4l9RdVDK3ws3k83YeyIea3b9hSB/E7o1r1bQzaQiymqzqzv7AI4TqqQ0awG2iKhosqqZF44xTa+TIEncKpXI3axO52uAY6kWMy/IU7h32UiW7RsBDjpEWnDJclIChUUoan71djrOXU7E39dSsGlfHEIrlMLrvRrA2+T4yipTygfREVVxO82Kn/ZcRET9RxBcyqeAW01FkdUmw5B5MgU4ghec5BG5X9bdRiRJgokFconcLmvmBWs5kSdxc+aF8/aNmbuNcNAhcjurXYbR4BooLCpZTjsPX8bCzacBABKAp+qUxaBOYfAy6bM99rnOdRB7+G/sOHIZvVrVeMgtpeLAsUTLKfPCwPRaIi0oy0NMLleE2d+I3C2nWk6yEC4XxogKq4ewbKRoprITFSSrVVYDhEWpqFliqgU/7DyLsEql0K9dKPx9jSjl75Xr40MCffBYzRD8cvQKukVUVY8JkbtkneR5MfOCSBMWqwwJcDl54i4IRO6nFOxUKAFDi5XBCyr83PoJzWn7Ri4bIXI/s9WubiOnrA8uCpkXm/fHwWKV8VynMFQo459n4ELRqnF5pKRbceTsjYfQQipuHDUvsu424vl9jaiwUQKFyg4IAGDUM3hB5G5Wm2uQgsVxyZO4NXhhc7pCpddJkMBlI0TuZrPLsMsCXsaiFSiUZYG9J+NRv1pplAvyzffv1a4ciFL+Juw5Ea9h66i4stmFS2Ezk1EHm12GLDOrkMidzDa7y9VgAPAy6WG28ISKyJ2yLRvJ/LeZ52zkAdwbvHBaKyVJEgwGXZG4GkxUmCgp60rmRVFZNnLq4i3cTrEgvF65e/o9nU7C03XK4fj5G0hKs2jUOiqushU2y8x0MnPpCJFbWa2yegVY4W3SI8NiK6AWERVN1qzLRjL7HZdEkidw87IRAYNTYTODXufxV4OJChslZd2UWcDSaCwaBTt3n4iHj5cBjWqUvuffDa9XDnZZ4MCpqxq0jIqzrGuDlYwnFhEkci9LDpkX3iYDMph5QeRWtmxB+aJxEYyKB/cvG3EutKSXWLCTyM3MWTMv9ErNC8/ta2aLHYfOXEOTsJD7KrpZsYw/KoT4Y89JLh0h93JslcorVERas9pkl51GAMeyEQYviNzLas8yrqkFO9nXqPBz/7IRly2u9LCyIxC5lbL+tygtG/ntzFWYrXaE1723JSPOmtYrh/P/JOHqrTQ3toyKO6s958JmnOQRuZfFxmUjRA+DYzmk0xbgyrjmwfNIKj7cGrzImvLn7cWIOZG7Zcu88PDghSwLbNwbh3JBvqhZsdR9P88TtUIAAIfOXHdX04iQYbHD23TnhEq9QuWh/Y2osLJa7dkyL7yZeUHkdo5xzaD+zKA8eRK3BS+EEMgw2+Hj1BkYMSdyv6zBC5OH17zY93sCLl9PRdeIqtA5bZF3r4JL+aBiGX8c/vOaG1tHxZljXLO5jGuc5BFpw2KTsy0b9DYZYLbYIYTnLoskKmwyLDZ4e+UQlOc24OQB3Ba8MFvtEAB8vJyDFwakM2JO5FbKshElaKGktHti8CIp1YLvt/2JKuVKoEntMg/8fIlH8NkAACAASURBVI1rBuPs34lISuWuI/TgchrXlH5n5iSPyK0sOdS88DHpIcCTKiJ3sdpk2Owi56C8jedsVPi5LXiRbnZ84J3Ta5nuR+R+auZFZl+TJAkGveRxO/sIIbBo82lkWGx4qUvtB8q6UDwWGgIB4OhZLh2hB6eOa05XqLwMzLwg0oLFald3z1Ioc0pm8RK5R3pmX3IOyhuZeUEexG3BC2VgcZ7k+ZgMHHCI3MySZdkIkLktsYetwd9/6ip+O3MNMc2roXyIv1ues2IZf5QO8MbhPxm8oAenjF+uV6iUmhcMXhC5k8VqdxnXAKjr8nkhjMg90s2Z52tOF5u9OK6RB3Fj8MLxgc9a80K5ckVE7qGkqztP8owGnUdtlZqWYcX32/5E1UdKoOOTldz2vJIkoXHNYJz46yYDp/TAlPHLx3ltsFrzwrOChUSFmRACqRk2+PsYXW6/k3nBuSSRO2Rkjmu+TpkXBr0OEjiukWdwX/Aih0ieY7cRGwstEblR1oKdgJJ54TmTu9W//IXkVAsGdqwFne7Bl4s4eyKsDGx2GQf/YOFOejBKeq1LVXYuGyFyuwyLHXZZwM87t+AFg9FE7qBmXjgFLyRJgtGoY+YFeQT31bxQMi+cOoOPyQAhGMkjciez1Q6DXudy0u/rZfCYLKeL8cnYfuhvtH6sPKqUC3D789esUBJlg3zx87F/3P7cVLwoQfkcC3Z62DItosIsNcMKAPDzNrjc7sVlI0RudafmhesSLZNBzy3AySO4sWBnDpkXjJgTuZ3ZanfpZ4BjwpeWOfkr7NbvvgA/byO6t6imyfNLkoQWDR/B2b8Tcf6fJE1eg4oHddmIyTXLSa+TmHlB5Eap6Y55oh+XjRBpKsOcfZk/4Kh7wXGNPIHba154Z9kq1fk+InpwFotdLa6k8PMxIiW98AcJbyWbceTP64ho8Ah8s6QHu1OrRuVR0s+ExVtOwy7zSgLdn5zSawFH3QszJ3lEbpNb5gUvghG5l7ocMsdxjfMlKvzcvtuIS8HOzJSkdA46RG5jttrVooEKX2+DOvkrzH459g9kIdCy0aOavo6PlwF929XEhfhk/HfTadbdoftyp+ZFlvRao47LIYncKDUjt8wLXgQjciclKO+bZdmIv48RKWmWgmgS0T0x3P0h+ZNutkOvk9S9ggGnQcdD1uITeQKzVc62nZyft7HQBy9kWeDno/+gTpVAlA301fz1nqxdFpevpWYuUzGgd+sakCT3Fgeloi3DbIfJoINB7xrn9zLqeSWYyI1S05XMCy4bIdKScr6WdVwL8DXhys20AmoVUf65NfPCxyu3dD8OOkTuYrbaswcvfIywWGVYC3GxpePnb+BmkhmtGpV/aK/ZrXlVtH2sAjbvv4TdJ+If2utS0ZBusWVLrQWAAD8TklJ5hYrIXXJbNqLTSTAZdQwWErlJeub5WtaLORzXyFO4sWBn9iKCSjCDy0aI3MdstcMrS1/zz5zwFeainTsPX0ZJPxMa1Qx+aK8pSRL6tquJ0AolsWTrGVxPTH9or02eL91scynWqSjlZ0IiJ3lEbpOaboPJoMu2JBJwZPGaeRGMyC0yzLZs52sAUMLXiJR0K+uEUaHn1swLbxMzL4i0Zsmx5oUj1TYlo3AGCm8mZeDY+Rto3vCRbKmKWtPpJLwcVQdCAIs2n2H9C8q3DIs9x8yLkv5euJ1iLoAWERVNKRnWbPUuFN4mPdI5jyRyi3SzPVumPACU9DMBAJLTCu9FMCLAzbuNZN0zmFWiidzPsWwk624jjoFIWTdc2Ow8chkQQIuG2hbqzE1wSR90b1kNx8/fwL7fEwqkDeR5cs288Dch3WznjiNEbpKabs22ZEQR6O+FW0kZD7lFREVTujn7Mn8AKOHrCF5w6QgVdm5cNpI988LLqIcER5SPiB6cEAKp6Tb4erleoVKKnBXGop3pZhu2/3YZjUNDEFzSp8Da0faxCqj2aAC++9+fTPmnfMntClUpfy8AQCKzL4jcIjXDlq1YpyKklA+uJTJ4QeQO6Zacg/IBmZkXSdxxhAo5TTMvJEmCtxershO5S2KqBWarHWUCXYMASrptanrh62v/++1vpJlt6BJeuUDbodNJeOGZMFisdny66jjXUNNd5bQcEgBK+jsmebdTOMkjcofUPJaNhJTyxq1kMyzMdCJ6YBm5BOWV4EVyauG7CEbkzH2ZF7lM8rxNBqQV0nX4RJ4mIXMbq7JBrsGLwlqw82ZSBn7acwGNawaj6iMBBd0clA/xx8uRdXDuciJmLPoNJ87fgCyzBgZlZ5dl3E6xIMAv+wlVKT9H5gXrXhA9OCEEklIt8PfJedlISCnHeHed2RdED8Rml3Ez2axmDzoL8HWMdcxMpcLOLcGLdLMNSSkWBJXI3hnKBfni8rVUd7wMUbGXcMuxW0bZQF+X2729DJBQuAp22uwyvvrxd0AAfdvWLOjmqJ4IK4PXezVEcpoFHy4/ipHzduGbDadwMT65oJtGhciVG2mw2WVUKlMi232lSijBC07yiB7UrWQzktOsKB/in+P9SvDi2m3uFkX0IOJvOsa1imWz9zUfLwMMegnJXDZChVzOYe57dCE+GQJAtUezX1mt8kgJbNl/CVabDKPh4e4yQFTUJNxMg0EvoXSAt8vtOkmCr7eh0NS8kIXAfzf9gT/ibuPlyNoILlVwtS5y0qB6acwa2hRHzl7H4TPXcOCPq9h17ArqVQtC56cqo1alUtn2QKfi5VJCCgCgUg6TPD9vxySPNS+IHtz5f5IA5DyHBBi8IHIXdVwrk31ckyQJJXxNLNhJhZ5bghfn/0kEAFTJIS28SrkA2GWBv6+lFIq08XuVlmHDreQM3Ew241ayGTeT7vw7Jd0Kg16CUa+DIfN/er0EWRaQZZH5RWBEuRB/eOklVAjxR8Uy/jmuNSPKj4Rb6Qgp5QOdLvuJdemS3ohLKPjsAatNxoKNf2DPyXh0jaiKpvUeKegm5cho0KFJWBk0CSuDtAwrdhy+jK0HLmHW94dRPtgPLRo+igY1SqNMKR8GMoqhiwnJMBp0KFfaN9t9kiShlL8Xrt7iyRTRgzp/JQkGvZRjlhMAlPA1wsuox7XbXDZC9CDiribDoM95XAOA8sF+OPP3bQghOO+hQsstZ9F/XUlG2UAf+OdQbKlKOcdgdDE+uVAHL6w2GX9fS8HFhGTExSfjYkIKrtxIRUaWon4SgAB/E4JKeKOknwl2uwyrTUaq1QabXYZdFtBJEnQ6QJaBv+KT8OuJeJd19SGlvFGpTAlULOOPR4P9UC7IFyV8jfD1NjI7hfKUcCst25IRRZOwMlgZex5Xb6ejzEPOdBBCIP5mGn6/cAvbD/2NKzfSENO8KiKbVnmo7bhfvt5GdAmvgvZPVMTe3xMQe+Qyvt/2J77f9idK+ptQIdgPZYN8UTbIF+Uy/xsc4J1jEImKhktXU1AhxA96Xc7fyQ2ql8bPR/9BUqpFLXRGRPfu/D9JqFimRK7zH0mSUD7ED8fO30Ave3UY9JwnEd2PuIS8x7Umtcvg2w1/4PyVJFR/tORDbp32ZFngWmI6biRmwGjQoUwpHwT4mXIM1FhtdqRb7Ai0ywXQUsrLPQcvcpqsZ1hsaBQaku0+nU5CmUAfVHkkAEfO3UCpAC+Y9LpsH5Js5fLufgPEXWrs5XS3XXYEGiw2GRlmG5LTrUhJs+JGUgYSbqbBnhlg8DYZUD7YF41qBqOUvxdK+plQqoQJJf1MCPAz5drpcxMY6Ie/Lt1E/M00/HMtFZdvpOHK9RTsORmfrZ1Gow6+Xkb4eOnhYzLAx8sAHy89vE0G6HQS9JIEnU6CJDmOr05yLBmQMu+TdJIjeJIZQHHcB/W2PI+ZU2NEjkfQ+f47JAC1KwfBO8vWSzyxezA5Hb/q5UsirFJgjn2tecNHsevYFfy05yJqlL8z6EiS428rCwEhC8hwZAbJAoBA5r8dPwsh7vwsCwgIyLLj922ygN0uO4J0dhkCjtvTLTbcTLpTCb5ckC+e6xSG2pUDtTw8Lu/dXby9DGjVuDxaNS6Pq7fTce5yEi7GJ+Ha7XScvZyIE3/dVB+r10nw9zXBz9uAAD8TAnxNCPAzwmTQQ6+ToNfroNNJMOgk6PUS9DodnFt6pztKLv9x/qcsC9hlAbuQIdsdvbJOlSA1UKy8d/a1B5PT8TMa9ahTNSjHvgYAnZ6ujBN/3cRPey+ifrXSkABImd/NAACR+T2pflmKLN+xjv6m/ixcv3eV31VvE3cel/ls8Ps7ESmp5izPkfX1hfOvOz1WOL2G43ECOYwDys8SIGV+MnUSAMnxXh3/lODndxtpqeYst2d+ljN/Vz02ynNJdz7rzvdLknTnduUJ8nkMlXb7J6QgJSXD5W/g8n6Q5Zi73gUJjr6WdQcM9rUHk9PxKxvki2qPBuR4n3Jbz1Y1sGDjKazffQE1K5RSP19w+twIp/8TTt1G+Yw4/71d+ldOnwmnm9V+5dRXfP1uIzWz7wmR9Tkdt9kzL2jZZAHZ7vivXXaMn7IsYJNx5zE2O9LMdpitdnV+J0GZyznmc5LkmPNJcIzHNruA0aCDyaiDl0EPL6MORqMeJf29YbHYoNM5xinHczjGI71OgiRJ0Cvzw3z0ydz74/0pcSMNycnK95ZASroNYZVKqbWEFOxrDyan4+frbUTdPMa1J2uXw6b9l7Dj0GWkptscnx/lQ+c6FLkMcFm/lx23uY46Wce4LE+h3ud/JQkpKWb1d5zvy/o9DTguFNvkO3NTq13AbhfqzxabjDSzDUmpFly9nQ6bzTUY4W3SI7CEN/Q6CbIQSM+wIdVsc9ndyNfbCH8fA0xO/cyQ2acMeslprqfM+3TQ6zP7n06n9jUp87/K+Zgyfjruw537nPqd0g91kgQBwO9aKlKSM9TjmdN3mRBOY7x67B3qVg1CCQ8f1ySR9dNFRERERERERFSIMPeOiIiIiIiIiAo1Bi+IiIiIiIiIqFBj8IKIiIiIiIiICjUGL4iIiIiIiIioUGPwgoiIiIiIiIgKNQYviIiIiIiIiKhQY/CCiIiIiIiIiAo1Bi/o/9m77/AoqvUP4N+tqQQSCJGqotKRJiKEUKRID80LiEizXVEUUCAKYqIgKIICiohe9QIKgldR7xWkXBCSgChchB9VICRAQoD0tm3O74/NDrvJpsEM2U2+n+fhIbs7O3symXfPmfeUISIiIiIiIvJoTF4QERERERERkUfTK73DH3/8EatWrYLVasWECRMwbtw4pT/Cq4wfPx5paWnQ6+2HOiYmBm3btq3kUlFVUJ1jrTrGVU5ODsaMGYOPP/4YDRs2RFxcHN5++22YTCYMGDAA06dPr+wiVhllxdaJEyfw2muvITc3Fw888ACio6Plc1FpK1euxM8//wwA6NGjB2bNmlXs9W+//RZBQUEAgL/97W+qfheUFXu387zctGkT1q1bJz++ePEiIiMj8frrr8vP3e7jQxXjSbHmjqfFnzNPisWiGJuex9NizdNiy5PiifFTBqGglJQU0atXL5Geni5yc3PFkCFDxJkzZ5T8CK8iSZLo1q2bsFgslV0UqmKqc6xVx7j63//+JwYPHixatWolkpKSRH5+vujRo4dITEwUFotFTJ48Wezevbuyi1kllCe2Bg0aJA4fPiyEECIqKkqsX79elbLExsaK0aNHC5PJJMxms3jiiSfEL7/84rLNM888Iw4dOqTK5xdVVuxV5nl5+vRp0bdvX3H9+nWX52/n8aGK8aRYc8fT4s+ZJ8diUYzNyudpseZpseXJ8cT4KU7RaSNxcXF46KGHUKtWLfj7++ORRx7B1q1blfwIr3Lu3DkAwOTJkzF06FCXLBrRrajOsVYd4+qbb77B/PnzUbduXQDAn3/+iTvvvBONGjWCXq/HkCFDqs3fX21lxdalS5dQUFCAdu3aAQBGjBih2rEPDQ3FnDlzYDQaYTAYcM899+Dy5csu2xw7dgyrV6/GkCFDEBMTA5PJpEpZgLJjrzLPyzfeeAPTp09HSEiIy/O38/hQxXhSrLnjafHnzJNjsSjGZuXztFjztNjy5Hhi/BSnaPIiNTUVoaGh8uO6deviypUrSn6EV8nKykKXLl3w4Ycf4osvvsCGDRsQGxtb2cWiKqA6x1p1jKsFCxbggQcekB9X57+/2so6tkVfDw0NVe3Y33fffXJjMiEhAT///DN69Oghv56bm4sWLVrglVdewXfffYesrCx89NFHqpQFKDv2Kuu8jIuLQ0FBAQYMGODy/O0+PlQxnhRr7nha/Dnz1FgsirHpGTwt1jwttjw1nhg/7imavJAkCRqNRn4shHB5XN20b98e77zzDmrUqIGQkBCMGjUKe/bsqexiURVQnWONcVW9//5qK+vYVsaxP3PmDCZPnoxZs2bhrrvukp8PCAjAmjVrcM8990Cv12Py5MmqxkJZsVdZ5+WGDRswadKkYs/f7uNDFeOJseaOp8SfM0+NxaIYm57BU2PNU2LLU+OJ8eOeosmLO+64A1evXpUfX716VR7mXB39/vvviI+Plx8LIW7rQlNUdVXnWGNcVe+/v9rKOrZFX7927Zqqx/6PP/7AxIkTMXPmTAwfPtzltcuXL2Pz5s3yY7VjoazYq4zz0mw24+DBg3j44YeLvXa7jw9VjKfFmjueFH/OPDEWi2Jseg5PjDVPii1PjCfGT8kUTV507doV8fHxSEtLQ35+Pn755Rd0795dyY/wKtnZ2XjnnXdgMpmQk5OD7777Dn379q3sYlEVUJ1jjXEFtG3bFufPn8eFCxdgs9nw008/VZu/v9rKiq0GDRrAx8cHf/zxBwBgy5Ytqh375ORkTJ06FUuWLMGgQYOKve7r64t3330XSUlJEEJg/fr1qsZCWbFXGeflqVOncNddd8Hf37/Ya7f7+FDFeFKsueNp8efME2OxKMam5/C0WPO02PLEeGL8lEzRNE1YWBimT5+OJ554AhaLBaNGjcL999+v5Ed4lV69euHIkSMYNmwYJEnCY489hvbt21d2sagKqM6xxrgCfHx8sGjRIrzwwgswmUzo0aMH+vfvX9nFqhJKiq2nnnoK06ZNQ5s2bbBkyRLMnTsXOTk5aNWqFZ544glVyvLZZ5/BZDJh0aJF8nNjxozBrl275LLExMTg73//OywWCzp06OB2iKlSSoq9yMhIfPLJJwgLC7vt52VSUhLuuOMOl+ec/1a38/hQxXhSrLnjafHnzBNjsSjGpufwtFjztNjyxHhi/JRMI4QQlV0IIiIiIiIiIqKSKDpthIiIiIiIiIhIaUxeEBEREREREZFHY/KCiIiIiIiIiDwakxdERERERERE5NGYvCAiIiIiIiIij8bkBRERERERERF5NMWTFwcOHMDgwYMr9J5mzZohLS2t1G3+/PNPvP766zddru+//x6RkZGIjIzEgw8+iIiICPnx77//Xup7o6KicPLkyVK3uXDhAh544IFyleX333/HlClTMGzYMAwePBjPPPMM/vrrr3L/LkUlJibixRdfvOn3U9UTExODFStWALDfF9pxfk2ePLnMWFPDM888g3/961+lbjNt2jQ5Jps1a4YhQ4YgMjIS48ePL/V95T3/ly1bhgULFpS5nc1mw2effYYRI0YgMjISAwcOxJIlS2A2m8t8b0k2bNiADRs23PT7SV1bt24t8zx7+OGHcfTo0WLPHz16FNOmTQNw6/XUzUpLS0OzZs1K3eavv/6S46tnz57o2LGj/PiLL74o9b3lPX+7d++OEydOlLldcnIyZs2ahSFDhmDo0KF49NFH8d///rfM95VECIGJEyciKyvrpvdBVVNJcauEuLg4OYbCw8Px0EMPyY//85//lPre5cuXl3nOW61WNGvWrFzn9ZkzZzB16lQMHToUQ4YMwfjx43Ho0KEK/T7OMjMzMXHixJt+P1UNK1euxI4dOxTb36ZNm7B+/XoAwNdff41PPvlEsX078Hqv6tNXdgHK66+//sKVK1du+v3Dhg3DsGHDAABz5szBfffdhylTppTrvbGxsXjiiSdu+rOd7d+/H1FRUfjwww/RsmVLAMB3332HcePGYevWrQgODq7wPi9evIiEhARFykdVz5o1a+SfY2NjK7EkpVu+fLn8c7NmzfDll18iJCSkzPcpff7PnTsXBQUF+Oc//4nAwEDk5uZi5syZmD9/Pt5+++2b2ufvv/+O1q1bK1ZG8hxt2rSRz91brafUdO+992LLli0AgH/961/Ytm0bVq9eXa73Knn+Xrt2DWPHjsXMmTOxePFiaDQaHD9+HJMnT8ayZcvQpUuXCu/TZrMhPj5ekfIRlVfXrl3lmFqxYgXS09PLnbyMj4+X24C36uzZs5g0aRLeeecddO3aFQCwb98+PP3009i4cSPuueeeCu8zIyMDx44dU6R85L0OHDiAe++9V7H9/fHHH7jvvvsAAGPHjlVsv854vVf1qZK8yMvLw7Rp03DhwgUEBQUhJiYGderUQXR0NE6ePAmNRoOIiAjMmDEDer1rETZt2oSvv/4akiShVq1amDdvHvz9/bF8+XJkZ2cjKioKb7/9NjZu3Ii1a9dCq9WiTp06mDdvHu6+++6bLvPly5cRHR2Ny5cvAwBGjBiBSZMmYcmSJUhLS8NLL70k974uXboUJpMJV69eRffu3fHmm2+W+3OWL1+O559/3qXSGj58OPz8/CBJEuLi4rB48WK5QnR+fObMGcybNw9msxlCCIwePRojRozA/PnzceXKFTz11FNYs2YNtm3bhlWrVkGSJAQGBiIqKgpt2rTBsmXLkJKSgsuXL+Pq1au4//770bFjR2zZsgWXLl3C7NmzMXDgQAgh8NFHH2HHjh2QJAmNGjXC/PnzERoairFjx6JOnTo4e/Ysxo0bh3Hjxt30MSdXmzdvxueffw6tVovg4GCMGDEC//jHP+Dv74/c3Fx8++232LdvH1atWgWLxQJfX1/Mnj0b7du3R05ODl577TWcPHkSdevWhU6nQ8eOHQHYe54++OADfPXVVwCACRMm4JNPPkG9evXKXZbFixcjLCwMCxcuxJEjR5CbmwshBN566y107NgRc+bMQUZGBpKSktCzZ0888cQTmDNnDlJTU1G/fn1cv379lo+Pu/O6WbNmxc7/Dz/8EP/9739hMpmQn5+PqKgo9O7du1yfceHCBWzduhX79u1DQEAAACAgIAAxMTE4cuQIAODll19G69at5V4p58fr1q3DN998A4PBAF9fX8TExODMmTP49ddfceDAAfj4+GDkyJF4++238dtvv0Gr1aJdu3aYM2cOAgIC0L17dwwfPhzx8fHIysrCU089hd9//x3Hjh2Dj48PVq1ahdDQUCQnJyMmJgZXrlyBxWLBkCFD8PTTT+PChQuYNGkSGjdujOTkZHz11VeoXbv2LR/7quiDDz7Ajz/+iFq1auHOO+8EAJjNZixZsgQHDx6EzWZDy5YtMXfuXAQGBgIAvvrqK5w8eRJmsxmTJk3CqFGjcODAAbz55ptYs2ZNsXqqJLm5uXjrrbdw6NAh6HQ69OnTB9OnT0dCQgJiYmKQm5uLq1evonnz5nj//ffh4+OD1q1bo3fv3jh58iSWLFmC5ORkLFu2DH5+fookFsxms9vzcu/evS7nb+/evfH6668jPT0dV69eRYMGDfDBBx+UK9EIAOvWrUPnzp0xZMgQ+bmWLVti+fLlCA4OhtVqRatWrXDw4EEEBQW5PNZqtYiKikJiYiK0Wi3atGmD6OhoREVFAQDGjRuHTz/9FBkZGXjrrbeQkZEBjUaDJ598EkOHDkVcXBxWrFiB0NBQXLhwAf7+/njyySexdu1aJCQkYMCAAZg9ezYAYMeOHfj4449htVrh5+eHOXPmoG3btli2bBmOHTuG1NRUtGzZEosXL77lY19dzZw5E61atcLkyZMB2OPrt99+Q5cuXdy274peiDg/Pn/+PF5//XWkpaVBq9Xi73//OwYOHAgA2LhxI+bPn4+0tDRERkZi+vTpAIBdu3a5rU9XrFiB//3vf0hNTUWzZs2wZMmSm/r9srKyEB0djVOnTgEAevXqhRdffFH+Hlm4cCE0Gg3uuusuvPnmm8jPz8eVK1fQqlUrLFu2DFpt+QZHf/zxx/jb3/4mJy4AoFu3bnjvvffg4+ODCxcuYOTIkXKvs/PjK1euYPbs2cjMzARgby+88MILiIqKQm5uLiIjI7Flyxb89ttvePfdd2EymWAwGDB9+nR069YNmzZtwq5du2C1WpGcnIz69etj+PDh+Oqrr5CQkIApU6bIdeXGjRuxceNGSJKEkJAQ+e/68ssvIycnB0lJSejduzdmzJhxU8e7OnLXVvv1119LjJ/AwECcOnUKKSkpaNasGRYvXoyAgAAsX74c27dvh8FgQHBwMN5++21s374dx44dwzvvvAOdToedO3e6tPOuX79eoXg0GAzYtWsXYmNj4evri7S0NDnhd+bMGcTExMjf2ZMnT8awYcNw4MABLFu2DI0aNcKZM2dgtVoRHR0tt29vBq/3qsD1nlDY/v37RfPmzcUff/whhBBiw4YNYtSoUWLWrFnizTffFJIkCZPJJCZPnixWr14thBCiadOm4vr16+LAgQPiscceE3l5eUIIIfbu3Sv69+8vhBDi22+/FU8//bQQQoi4uDjRp08fcf36dfm1AQMGCEmSylXG2bNni08//dTludGjR4svv/xSCCFEZmamGDx4sPj555+FEEJERESI48ePCyGEmDZtmjh48KAQQojs7GzRqVMnceLECZGQkCA6duxY5me3adNGnDt3rsTXY2NjxdChQ90+njVrllzulJQUMX36dGGz2Vy2OX36tAgPDxdJSUlCCPsxDA8PFzk5OWLp0qWiT58+Ijs7W+Tl5YkOHTqId955RwghxNatW8WAAQOEEEJs2rRJzJgxQ1gsFiGEEOvWrRPPPPOMEEKIMWPGiHnz5pX5e1LFVfUTEwAAIABJREFUnDhxQnTu3FlcvnxZCCHE559/Lh555BHRvHlzcfHiRSGEEOfPnxeDBw8WaWlpQogbf+vc3FyxYMECMWvWLCFJkrh+/bro3r27WL58uRBCiF69eok///xTCHEj1ipalnnz5olDhw6JF154QdhsNiGEEKtXr5bPi9mzZ4sJEybI+3juuefEsmXLhBBCJCQkiHbt2olvv/223MejaDlLO6+dz//ExEQxYcIEUVBQIIQQ4vvvvxeRkZFCCCGWLl0q3nrrrVI/99///rcYPXp0qdvMnDlTfP7558Uem81m0apVK5fvpW+++abYe5YuXSpefPFFYbFYhNVqFbNmzRLR0dFCCPt3zeLFi4UQQmzZskW0aNFCnDp1SgghxDPPPCM++eQTIYQQjz32mNi9e7cQQoj8/Hwxbtw4sW3bNpGQkCCaNm0qDh06VOrvUN1t375dDBw4UGRnZwuLxSKefvpp8fjjj4sVK1aIRYsWyXXJe++9J+bPny+EsMeR4+eUlBTRpUsXcfr0abF//34xaNAgIYRrPVWahQsXiunTpwur1SpMJpMYN26c2L9/v1i0aJH4/vvvhRBCmM1mMXjwYLF161YhhD0mvvvuOyGEEFevXhUdO3YUZ86cEUII8fHHH4umTZuW+/d3V87Szkvn8/ezzz6T6yGbzSYmTZokvvjiCyGEa11ZkilTpogNGzaU+LrFYhFNmzYVmZmZxR5v3rxZLrfFYhFRUVEiMTHRZRuz2Sx69eolduzYIYQQIjk5WYSHh4sjR46I2NhY0bJlS3HixAkhhBATJ04UY8eOFWazWVy7dk20aNFCXLt2Tfz1119iyJAhIiMjQwhh/04MDw8XBQUFYunSpWLgwIHCarWW+3iTe/Hx8WLw4MHy41GjRom9e/eW2L4r2nZzfjxs2DCxbt06IYQQly9fFr179xbZ2dmiV69eIiYmRgghRGpqqmjdurW4fPlyqfXp8uXLxSOPPCK3gcpj+fLlcrw4zJgxQ7z99ttCCCEKCgrEhAkT5PKOGTNGbN++XQhh/z746aefhBD2uB8wYIDYvn17sVgoSf/+/cXevXtLfL1o+9T58QcffCCXOycnR0ybNk1kZ2e7bHP9+nXRpUsXuR1x8uRJ8eCDD4pLly6Jb775RnTq1EmkpKQIm80mHnnkETF9+nQhSZI4evSoaNu2rZAkScTFxYnHH39c5OfnCyGE2L17t/y3nzlzppg8eXJZh5iKcNdW69evX6nxM3r0aGEymYTZbBbDhg0TmzdvFpcvXxYdOnQQJpNJCGH/jnecm48//rh8LVS0nXcz8ei8jSNmLBaL6N27t9i2bZsQwl6/RkREiEOHDon9+/eLFi1ayPXKZ599JsaNG1fuY8Trvap5vafKyItmzZqhQ4cOAOxZpjfeeAMJCQnYtGkTNBoNjEYjxowZgy+//BJPP/20/L7du3fjwoULGDNmjPxcVlYWMjIyXPa/d+9eDBw4UO7pGTFiBBYsWICLFy+iUaNGFS5vTk4Ojh49irVr1wIAgoKCEBkZiV9//RX9+/d32fbdd9/Fnj17sGrVKpw7dw4mkwm5ubnw8/Mr12dptVpIklThMgJA37598eqrr+J///sfunTpgrlz5xbLzMfHxyM8PBwNGzYEYM++16xZU56H3LVrV7kXMTQ0FBEREQCAxo0by5n33bt34/jx4xg5ciQAQJIkl/n+t5LxJPfi4+PRrVs3eTTExIkT0aJFC0RFRaFBgwYA7MPZUlNTXeahajQaJCYmIj4+Hq+++io0Gg1CQkLQt29fRcviULNmTWzYsAFJSUk4cOCAPDoBcD0v4uLi5B7MO++8E507d77p8jjKVNp57dCoUSMsXLgQP/zwAy5cuIDDhw8jLy+v3J+j0WhuOj4NBgP69u2LRx99FD179kS3bt3Qo0ePYtv9+uuvmD17tjzqbNy4cS49TY888ggAe0yGhYWhadOm8u+WmZmJnJwcHDp0CEuXLsXSpUsB2Ee7nThxAs2aNYPBYEDbtm1v6neoLuLj49G3b1/5u3DkyJFYu3Ytdu/ejezsbMTFxQEALBaLy8gVR90UFhaG8PBwxMfHl7nWhDtxcXGIioqCTqeDTqfDunXrAACdOnVCbGws1qxZg4SEBKSmprqcv455tn/88QeaNm0qD+cdPXq0fC7crLLOS4fJkyfj4MGD+Pzzz5GQkICzZ8+iU6dO5f6cW6kDO3XqhA8++ABPPPEEunbtiilTpqBRo0awWq3yNmfPnoUQQh5tdccdd6Bv377Yu3cv2rdvj8aNG6N58+YA7DFVp04dGAwG1K5dG/7+/sjIyEBsbCyuXLniMnzY8V0LAO3atYNOp7up34Fu6Ny5M0wmE44ePQo/Pz+kpaUhNja2xPZdSTIyMnDy5Ek8+uijAIB69eq5zNN3rMMWGhqKOnXq4Pr16zhy5EiJ9Slg/xsXHRlcUfv27cPmzZsBAD4+Phg9ejQ2bNhQbAj7rFmzEBsbi08++QQJCQm4fv16heqtW4mp7t2745lnnsHFixfRtWtXzJo1C4GBgS6jJQ8fPowmTZqgTZs2AOxt/LZt2+K3334DANx///0ICwsDADRo0ADh4eHQaDRo3Lgx8vPzYTabsXv3bpw/fx6jR4+W95ueno7s7GwAbFfeDHdttdTUVBgMhhLjJyIiAkajEQDQtGlTZGZmIiwsDM2bN8fw4cPRvXt3dO/evcTpe+X5O5UVj0UlJCTAZDKhX79+AOz1a79+/bB371507twZ9evXR4sWLQDYR+l999135Tk8bvF6r2pc76mSvCh6gDUajfzPQZIklwaH47nIyEi88sor8uPU1FTUrFmz2HZFCSGK7a+83O3PXfmEEBgzZgxat26NiIgIDBo0CIcPH4YQotyf1bZtWxw5cqTYHMT58+ejf//+0Gg0LvuzWCzyz3369EGHDh0QFxeHuLg4rFy5Et9//32xcjsfZ8dzjv04vrQc3FXONpsNzz77LP72t78BAEwmk8uCUc4XrKQMnU7n8ncrKCjAuXPn4O/vLz8nSRK6dOmC999/X34uOTkZdevWBQCX8+ZWGtbuynLp0iUkJSVhwYIFmDRpEnr37o0mTZrghx9+kLdzLmvR8/hWG4FlndcOR48exfPPP49JkyahW7du6NixIxYuXFjuz2nbti3OnDmDvLw8l9/HMcxwxYoVxcrhXIZly5bh1KlTiI+Px8cff4yffvoJ7733nsv2NpvNZR9Fv7ucY7Sk+ATsU+wc26alpcHX1xdXr16Fr69vuYcbV2fu4kWSJLz66qty0ik3Nxcmk0nezvm4SpJ00+e1Xq93OQeSk5Ph6+uL6Oho2Gw2DBgwAD179kRycrJLOZ3PSSXjCyj7vHRYtGgRTp48ieHDh8sXnzdTBxad77x+/XpYrVY8/vjj8ucDrvHVuHFjbN++HQcOHMD+/fsxYcIELFiwAOHh4fI2JX1XOH6X8tSBkiTJw+4dkpOT5Qs0578D3TyNRoNRo0Zhy5YtMBgMGDVqlNyoduY4F0tqHzn+hs5/93PnzqF+/fourzu2EUKUWp9u375dkb9xeWPqpZdegkajQf/+/fHwww/j4sWLNxVT3bt3d3l++fLluOeee9CmTZsS25Xt2rXDzp07ER8fj/3792PUqFHydFUHJWLKZrNh5MiR8pQdm82Gq1evokaNGgAYUzfDXVstKSkJTZo0cdnO+bzz9fWVn3fEglarxbp163D06FHEx8dj4cKFiIiIwKxZs4p9ZmntvPLGY1FF46Q8Zb5ZvN5z5a3Xe6q0cE+dOiVnfjZu3IiOHTsiIiIC69atgxACZrMZ33zzjcv8PMCeNfr3v/+N1NRUAPaVaCdMmADAHqSOkysiIgL/+c9/5LsmfPvtty7zlisqKCgILVu2xNdffw3APtrjhx9+kMun1+thsViQnp6OkydP4pVXXkHfvn1x6dIlXLx4sUKZteeeew7Lly/H8ePH5ec2bdqEnTt3olmzZggODsalS5eQlpYGIQT+/e9/y9u9+OKL2L59OwYPHow33ngDfn5+SEpKcjk2Xbp0wZ49e+Qs6759+3Dt2jU5Y14e3bp1wzfffIOcnBwA9gsyx5xiUkfnzp0RHx8vn/sbNmzAu+++67JNly5dEBsbi7NnzwIA9uzZg6FDh6KgoAARERHYvHkzJElCZmYmdu7c6fZznM+VipYlNjYWvXr1wmOPPYbWrVtjx44d8oV0UREREdi4cSMA+4X/gQMHyn8w3CjtvHb+nX777Te0bdsWEydOxAMPPFBqGd2pX78++vfvj9dee00+/7OzsxEdHY2QkBAYjUaEhITIq9enpKTgjz/+AGBfiLBnz56oXbs2Jk6ciGnTpsnb6fV6l++vr7/+GlarFZIkYf369cW+C0tTs2ZNtGrVSr5DRGZmJkaPHo3du3eXex/VXffu3bF161ZkZWVBkiR5zmm3bt2wfv16mM1mSJKEefPmuYxocPT4XL58GfHx8cV6p8oTX4D9fP7uu+/kXo5p06bh4MGD2LdvH6ZOnSrP1T9y5Ijb87dTp07466+/5FXRy7qTT3mUdl46n7/79u3DxIkTERkZieDgYMTHx1eoDhw7diz27dvnUrf9+eefWLlyJZo2bQqdToeaNWvKiwX+9NNP8nZr167FvHnz5Ib1Qw89hOPHj8uNeKvVinvvvReSJMnfgSkpKdixY0eFYuyhhx7C3r17cf78eQDAzp07MWzYMJdEFilj+PDh2LVrF7Zt24YRI0aU2r4LDg6Wz4srV67IPf+BgYFo1aqV3LhPTk7G2LFj5V59d0qrT5XSrVs3eVSVyWRyafc62pWAPaZeeOEFDBw4EDabDUePHq1QTD355JP4+uuvXRat3bNnD9avX4/mzZsjKChI7hABXGNq8eLFWLNmDfr27Yu5c+fi7rvvxpkzZ6DT6WCz2SCEQPv27XH69Gm5Pjt16hQOHTpUoRGVERER+PHHH3Ht2jUA9mSlY60Tujnu2mp79uyp8PXRyZMnMXjwYNxzzz145plnMHHiRPlvXVqddjPx6G5/TZo0gV6vxy+//CLva9u2bRX6zi4vXu+58tbrPVVGXjRp0gQrV65EUlISateujUWLFsHf3x9vvfUWhgwZAovFgoiICDz77LMu7+vWrRueeuopTJ48GRqNBoGBgVi5ciU0Gg3atWuHDz/8EM8//zxWrlyJiRMnYsKECfLCP6tXr76l3salS5ciJiYGmzZtgsViwdChQ+XVavv27YsZM2bgzTffxJQpUxAZGQk/Pz/Uq1cP7du3x4ULF+QembJ07twZ0dHRiImJkYfT3XnnnfKdFUJCQjBy5EiMHDkSderUQY8ePeTFnp5//nnMmzcP69evh06nw4ABA9CxY0d5QZzRo0dj48aNmDt3LqZOnQqbzQY/Pz98/PHH8tCh8hg7dixSU1Pl4X3169evUO81VVyzZs3wyiuv4MknnwRgH+IVHR3tcjeAe++9FzExMZgxYwaEENDr9Vi1ahUCAgLwwgsvYP78+RgwYABCQkLkqQZF9e/fH+PHj8eKFStK3MZdWRYuXIicnBzMnDkTQ4YMgdVqRXh4OH755Re3X+bz589HVFQUBgwYgDvuuEMepn0rx6ek8/q+++6Tz/8VK1Zgx44dGDhwICRJQs+ePZGenl6hIbgxMTFYuXIlxowZA61WC4vFgn79+uH5558HADzxxBN4+eWX0b9/fzRs2FBuwNWpUwdPPfUUxo8fDz8/P+j1ekRHRwOwXyw7klHPP/88Fi1ahMjISFitVrRr167ClcWyZcsQExODH3/8EWazGcOGDcPAgQNx4cKFCu2nunJ8r44cORJBQUFo3rw50tPT8dxzz2Hx4sUYPnw4bDYbWrRogTlz5sjvM5lMGD58OCwWi9zQdzQcARSrp0ry/PPPY8GCBYiMjITNZsPAgQPRr18/XL16FVOnToW/vz8CAwPRqVMneRi7s5CQECxZsgQvv/wyDAZDhaZtlFamks5L5/N36tSpWLBgAd577z0YDAY88MADFTrvQkJC8M9//hNLlizBqlWroNFo4Ofnh0WLFsnJoLlz52LevHmoVasWwsPD5SHQw4cPx8GDBzFo0CD4+vqiQYMGGDduHDQaDfr164exY8fio48+wkcffYQFCxbg/fffhyRJePHFF9GpUyd5OlBZmjdvjjfeeAMvvfSS/F370UcflXu4MJVfaGgoWrZsCavVirCwMISFhZXYvhs/fjxefvllPPLII2jYsCEeeugheT/vvfceoqOjsXbtWmg0GixYsAChoaElfm5p9alSXn/9dbz55psYPHgwLBYLunfvjqeeegqAfWHMd999F2azGTNmzMCzzz4Lf39/1KhRAw8++GCFYqpJkyZYtWoV3n//fSxcuBCSJKF27dpYvXq13Os7Y8YMTJkyBSEhIfLURMA+1WDOnDkYPHgwDAYDWrZsKfcKt2jRAoMHD8aGDRvw/vvv44033oDZbIZWq8U777yDRo0aYf/+/eUqY48ePTBx4kRMnDgRGo0GQUFB8u3c6ea4a6tt374dO3bsqND1UfPmzTFgwACMHDkS/v7+8PX1xdy5cwHYz9OlS5cWG+UK4KbisXv37li0aJHLfgwGAz766CO89dZbWLFiBWw2G6ZOnYqHHnrolju+3OH13g3eer2nEbcy/oaIiIiIiIiISGWqjLyoLJ9++il+/PFHt69NmTIFQ4cOVb0M33//PT7//HO3rw0fPtxlcSiiyvLSSy/JQ6KLWrZsWbE5k0p57LHHkJub6/a19evXVyhjfLMct3F2Z8WKFWjcuLHqZaCq7dy5c/Lc7qLuvvtul3n2Stq/f3+Jt2jt3LkzXn31VVU+19lff/2FmTNnun3t3nvvLbYGDJEn++GHH/DZZ5+5fW3IkCFyr7eaHLdQdKdr167y4thE1QWv96o3jrwgIiIiIiIiIo/GJemJiIiIiIiIyKMxeUFEREREREREHo3JCyIiIiIiIiLyaExeEBEREREREZFHY/KCiIiIiIiIiDwakxdERERERERE5NGYvCAiIiIiIiIij8bkBRERERERERF5NCYviIiIiIiIiMijMXlBRERERERERB6NyQsiIiIiIiIi8mhen7w4cOAABg8eXNnFUN0//vEPDBo0CEOHDsXEiRORmJhY4rZCCMyePRufffbZbSwhVXXVJdYAwGw2Y9KkSdi6davb1z/55BNERkbK/yIiItChQ4fbXEqqSqp7fOXn52PmzJkYMGAAHnnkEezYsaPUfZw8eRLdunVTu6hUxVX3uCtq8+bNGDhwIPr164f58+fDYrHcxhJSVVZdYo3Xa+rz+uRFdRAXF4fNmzdj48aN+OGHH9C3b19ERUW53fbs2bOYMGECtm3bdptLSVQ1HD58GKNHj8ahQ4dK3Obpp5/Gli1bsGXLFqxduxb+/v5YtmzZbSwlkXcqKb5WrFgBf39//Pzzz/j8888RHR2NlJSUYu+3Wq344osvMGXKFOTm5t6uYhN5tfLUa6dPn8aKFSuwbt06bN26FdnZ2fjiiy9uXyGJvByv124Pr0le5ObmYtq0aYiMjMTw4cMxd+5cSJLkss3vv/+Onj17yl/Ou3btwqOPPophw4ZhzJgxOHz4MNLT09G+fXvk5eUBAF5//XU8/vjj8j769euHs2fP4uGHH8aKFSvw2GOPoVevXnj//fflbdztF7CfiGPGjMGIESMwfPhwrF+/vtTni3rttdfw9ddfF3u+Tp06eOONNxAYGAgAaNOmDS5fvux2H+vXr8ejjz6K/v37l+u4EhVVnWMNANauXYuZM2fi/vvvL9fxWrx4MSIiItCjR49ybU/VG+PLfXzt2LEDjz76KACgfv36CA8Px88//1zs/cePH8epU6ewcuXKkg8yURGMu7LrtZ07d+Lhhx9GSEgItFotRo8ejR9++KGsQ0vkojrHGq/XbhPhJb777jsxefJkIYQQVqtVvPbaayIhIUHs379fDBo0SMTHx4s+ffqIEydOCCGEOH/+vBg8eLBIS0sTQghx+vRpER4eLnJzc8X48ePFrl27hBBC9OvXT3Tt2lXk5OSIM2fOiAEDBgghhOjVq5dYtGiREEKIlJQU0aZNG5GYmFjqfqOiosTq1auFEEKkpqaKl156SdhsthKfvxkmk0mMHz9eLltJZs+eLT799NOb+gyq3hhrdo8//rj4+eefS93mzJkz4sEHHxRZWVk39RlU/TC+7IrGV+vWrUVqaqr8eOnSpWLhwoUlvj8pKUm0a9fupj6bqh/GnV1p9dq8efPkzxFCiISEBNGpU6eb+hyqvhhrdrxeU4++spMn5dWxY0csW7YM48ePR9euXTFhwgTceeedSElJQUpKCp599lmMHTsWzZs3BwDExsYiNTUVEydOlPeh0WiQmJiIvn374tdff0Xjxo0RFhaGpk2b4uDBgzh16hT69esnb9+7d28AQFhYGGrXro3MzEwcOXKk1P3Onj0bf/75J7p06YK5c+dCq9WW+HxFpaWlYdq0aQgMDMT06dNv7kASlYGxVn5ffvklHn/8cdSoUUO1z6CqhfHlnhACGo3G5Tk1Y5eqF8Zd2YQQxR4zBqmiGGu8XlOb1yQvGjVqhO3bt+PAgQPYv38/Jk2ahJiYGAQEBECn0+GTTz7Bc889h/79+6Nt27aQJAldunRxGT6UnJyMunXrolatWhg3bhzuuusuhIeHIygoCPv27cPRo0cRHR0tb+/j4yP/rNFoIIQodb/NmzfHtm3bEBcXh/j4eHz44Yf417/+hV69erl9/o477ij373/y5Ek899xz6NOnD2bPng2dTneLR5TIveoea+Vls9nwyy+/4Ntvv1V831R1Mb7cq1evHlJTU1GnTh0AQGpqqty4JbpVjLuyOWLQITU1VZW6k6q26h5rvF5Tn9ekVL/66itERUWhW7dueOWVV9CtWzccP34cABAaGooOHTpg9uzZmDVrFvLz89GlSxfExsbi7NmzAIA9e/Zg6NChKCgowB133IHg4GBs2LAB4eHh6NatG3755RdkZGSU2Vgqbb8zZ87Ef/7zHwwaNAjz589HYGAgEhMTS3y+vFJSUjBhwgQ899xzePXVVxkIpKrqHGsVcfr0aQQFBaFhw4aq7J+qJsaXe71798bGjRsB2Ou8vXv3olevXorsm4hxV7aHH34Yu3btwvXr1yGEwMaNG9GnTx/FP4eqtuoca7xeuz28ZuTFsGHD8Ntvv2HgwIHw8/NDvXr1MH78eJw8eVLeZvjw4di2bRsWLVqE6OhoxMTEYMaMGRBCQK/XY9WqVQgICAAA9O3bF//4xz/QsmVLaLVa+Pr6lutL+t577y1xv8899xxee+01bNy4ETqdDn369EGnTp1Qu3Ztt88X9dprr6F169YYO3asy/MfffQR8vPzsXbtWqxduxYAYDQasWnTJhw9ehRz587Fli1bbuXwEsmqc6yVZufOndiwYQPWrFkDAEhISECDBg3K/X4igPFVkhdeeAFvvPEGBg0aBJvNhldeeQWNGze+6f0ROWPcuedcrzVv3hxTp07FhAkTYLFY0LZtWzz11FPl3hcRUL1jjddrt4dGFJ3kRkRERERERETkQbxm2ggRERERERERVU9MXhARERERERGRR2PygoiIiIiIiIg8GpMXREREREREROTRmLwgIiIiIiIiIo/G5AUREREREREReTR9Rd+Qnp4LSSr77qq1awfi+vWcmyqUt+Pvbv/dtVoNgoMDKrlE3stbY43lKZ0a5WGs3RpvjTXAM8sEeGa5lCgTY+3WeHOsVabqeDwYa7fGW2PNk8rjSWUB1CuPt8VahZMXkiTKFQyObasr/u50q7w51lie0nlaeao7b441wDPLBHhmuTyxTNWJt8daZeLxoIrw5ljzpPJ4UlkAzytPZVBl2kjcsWScSUpXY9dE5CT+WApOJzLWiNQWezQZx89fr+xiEFV5+4+n4OSFtMouBlGVd+D4FZw4z1gj76JK8uLTn05gxvu/qrFrInKy5qfjmPkBY41IbZ/9+wRmr9xX2cUgqvI2/fcsftx7rrKLQVTlrf7h/zBr5d7KLgZRhSievOBwFiIiqqrSsgoquwhEVVqB2YYCk62yi0FERB5I8eRFnsmq9C6JyA0hmCgkut3+OHW1sotAVGUJIWC22JDPtiQREbmhePIit8Ci9C6JyA2r7UbyQmIig0g1ziMKE1KyK7EkRFWb1SZgkwTyzUxeEKmJ7UbyVoonL3Lymbwguh3M1hvDarPzGHdEarFYJaefOZydSC0miz2+8guYvCBSk8Vyo16z2qRStiTyLMqPvMhnhUN0O5idKp70bM7DJ1KLc6LQbGUjj0gt5sLkRQFHXhCpypEoBNgBRt5FtWkjPkad0rsmIidmp4onPctUiSUhqtqcE4UWJi+IVFNgLhx5wTUviFTl3IbMyjVXYkmIKkaFkRf25IVRz+QFkZqcs+Zp2UxeEKnFeeQFkxdE6nHUawUmKxelJlKRcxsyK4/JC/IeKoy8sGfLjQbFd01ETpyHr6czeUGkGo68ILo9TIUjLyTBKVpEanKOL468IG+i2sgLm40ZcyI1OWfNM3OZvCBSiyNh4e+rdxmFQUTKcq7XHFNIiEh5Zq55QV5KtTUv2MAjUpdzxcPeYCL1OOqzQH8jY41IRc7JCxMX7SRSjYlrXpCXUm3aiPOFFREpzzGUXafV8IKKSEWOWKvhb2CsEanIZObIC6LbwXk6JNe8IG+i2rQRq01Akjh1hEgtjgRhDX8jLLxHN5FqHCMvavgbOQ+fSEWcNkJ0ezhizaDXssOZvIriyYucghvD/NhDRaQex0VUoL8BVsYakWocdVmgH0deEKmJyQui28OlA4z1GnkRxZMX+QU3Fn1hbzCRejjyguj2cI41q02CxFs4EqnCZc0L9gYTqcbkNB3SyjYkeRHFkxcWmwQfgw4A170X4J+LAAAgAElEQVQgUpOjYRfA3mAiVTmPcgI4qpBILSbzjdgqMHHBTiK1OK7RuBA1eRvlkxdWAX9fvf1nZvKIVGO2SjDqtfAx6FjxEKnIeeQFwOQFkVpMFiv0OnvTtIAdYESqMVlt0Os08DHqeL1GXkXR5IUQAjabBD+fwuSFhcFApBazxQajQQeDQcuLKSIVma0SdFrNjbqN8UakCpNFQs0A+wgnrnlBpB6zRYJRr4NRr4XFyqmQ5D0UTV7YJAEBwN+HIy+I1Ga2SDAatDDqmTUnUpPFKsGg18JYOCXSYuVFFZEaTGYb/H0N0Os0LrdNJSJlmSw2+Bh1bEOS11E0eeFY8MXRO8U1L4jUY7ba5Kw57zZCpB6zxQajXgujwV5l8napROpwXFD5+ehRYOaaF0RqcdRrerYhycsonLywDzvimhdE6jOZbTAatDAYmDUnUpPZKsFo0DmNvGC8EanBZLHBx6CDr4+e00aIVGS22G+wYGQbkryMoskLR4OOa14QqU++oNLb17wQvH0jkSrMhdNGfPRMXhCpyWS2wdepXiMidZgc66Yx1sjLqDRtpLCBx0wekWrMFht89FoY9FoIYV9zhoiU57w4LmCfskVEyrtxQaWT25REpDyz1Va4bpqWsUZeRZXkhb+85gWDgUgtJoskN/IA9gYTqcVSeFtiThshUpdjcVy9XssOMCIVmcz2aSMGvY6jd8mrqLPmBe82QqQ6e9ZcJy8iyHgjUod9cVx7DxXA5AWRWqw2CQadlgtRE6nM0YY0FNZrjms4Ik+n6t1GLLzbCJFqHCtFO0ZesKFHpA6zxXXBTo4qJFKHxSpBr9fAoNfyYopIRRZrYaLQ4EhesF4j76DKgp282wiR+uxD2Z1GXjB5QaQKecFOA9dzIlKTxSZBr7Mn5RlnROqxWCUYDFpOPSavo8rICx+DDhoNe6eI1GSx2XuojHpeUBGpyWK1wajXweBIXnBUIZHibJIEIQCDTls48oJ1GpFaLIVTtAycDkleRpXkhb5wYTNeTBGpx7GwmYEjL4hUZbbYe6h85LuNMNaIlGa12qeJGPRaGHRc84JITdbCNqRRz2kj5F0UnjZSWPEULrZk4cgLIlW49FDpmLwgUpOjh0qv00IDxhqRGhwdXnqd/W4jvJgiUockCdgkIU/RAlivkfdQNHlhk25UPAa9DmYrh9YSqcFRyRj0NxYR5EgnInU4eqg0GvtCgmzkESnPefSuQa+FhQt2EqnC0V50Gb3LNiR5CVUW7LRPG2HWnEgtcqzpNJyvSKQimyTBJgk5zpi8IFKHtUi9xmkjROpwXJ85RsoDbEOS91BlzQvHAjDMmhOp48bIC6088oINPSLlOc/Dd/zPUYVEyrO4tCF17AAjUolzZ7OBi76Tl1E4eWFv5Ol1Ghh0Ol5MEanE6jTkT86as+IhUpwjrhx39THqdeyhIlLBjTakVh7hJAQ7wYiUJneAOd1thNds5C3UmTYij7xgIBCpwXnNCy62RKQe51FOAGAwcNoIkRqKrnkhANgkJi+IlObSAWZgG5K8izq3StXZF4BhFo9IHTdWZdfAyFulEqnGUjhFxHFXH4NOy1ulEqnApTdYx9s3EqmlaGczwNG75D1USl5o7PfoZiAQqcK5N5gLdhKpp+jIC6NeKyc0iEg5zkl5eSg7104jUpy7NiQ7nMlbKL7mhV6nKbydnI5ZPCKVWF3mK3KxJSK1WJyGsgO82wiRWqzOiwhyKDuRam7cYEHDkRfkdRQfeaHXsYFHpDbnCyq9TgMN2MgjUoPZUmTNC72O00aIVOByxzpOGyFSjfO6aUaum0ZeRtkFO52TFwZOGyFSi/PcYPtIJ64xQ6QG59s3AoCRC3YSqcJiczOUne1IIsVZXBbsZKyRd1F25IVVutE7pdNyriKRSordAYF39yFSRbFY03HNCyI1WK2ut0oF2BtMpIYbC3Zq5E5nxhp5CxWmjWgAcNoIkZqcs+aAffoI441IedaiyQuDjrFGpAKXO9ZxwU4i1Tgn5TUaewKD9Rp5C4WnjQg5g2c0cMFOIrU4L9jp+J+9wUTKczfygmteECmP00aIbg/nRCEAGPQaXrOR11A0eWErsmAn5+ATqaPoBZWPgYsIEqmBa14Q3R5Wl1ul8i5aRGpxl5TnNRt5C/UW7NRpYZMEJMEhf0RKKzptxKDXyndFICLlOBp5xsJbNxr09rrNJjHeiJQk3yrVedoIL6iIFOeYjsV108gbKb9gZ+GaF/rCgLAxGIgUZ7G6DvkzGnQwWzhthEhplqJTtLiQIJEqLDYBDQCdVsNpI0QqckwzdrQh9Xqu5UTeQ+EFO4WctHD0UjEYiJRnKVwcV6OxJwuNes7DJ1KD3MjTO2KNdRuRGqxWCXqnBQQBxhmRGiw2CRqNPVEIAD4cvUteRL1pI47eKa4UTaQ4i9NtiYHCkRdcsJNIcRabBJ1WA52WIy+I1OS+Dck4I1Ka1Spg0GlvdICxDUleRPFbpTrf/QDgfEUiNdinaDknL7ScNkKkArNFkkcUAjcuqjjSiUhZ9jak/WKKt0olUk/xDjAtTGxDkpdQfM0LXZGKh1lzIuVZbEUqHk4bIVKFxVYkUciRF0SqsDpdUDnuNsIOMCLlWWyuSXmjXsdpI+Q1FF/zQh55YWDFQ6QWi/XG8FqAFQ+RWor2UN0YecFeKiIlOU8bMXLBTiLVWDh6l7yY4tNG9HrOVyRSm9s1L1jxECnOWix5wcQ8kRqcF31nG5JIPcVG7xp0HL1LXkPR5IXZecifjllzIrW4mzZikwRsEuONSEnFEoVc84JIFVankRc6nRYaDduQRGqwFhm968MOMPIiyiYvLDb4FE4X4YrsROopvmCnPe44dYRIWUXXvJCnjTDWiBRVdCi7QaeF1coFO4mUVnzkhRYm1mnkJRRLXlhtEmySkJMXjospZs2JlFdssSUDe4OJ1FDSmhcWG3upiJRkH3mhkR/rdVpOGyFSQdEOMB+9DlabBElispA8n2LJC8ctdoqPvGAgECmtWA+VI9447I9IURarJE8VAZxjjRdVREqyFknK6/VadoARqaB4B5j92o23SyVvoFzywlyYvDDaA0DPNS+IVFO0N9iRNDRx5AWRouyxppMfGwt/Zo8wkbKKTxvRcGFcIhW4u9sIwNG75B0UH3nhCACueUGkHmuxBTsda14wa06kpKI9VFzzgkgdFptwWUTQoNcxIU+kgqKjnHwMbEOS91Bt2gjXvCBSj6XIStEGA5OFRGqwWG3up2hZ2cgjUlLROyD4GnXyqF4iUo595MWN9WWMTF6QF1F+2kjRNS+YvCBSnMkiyZUNYF9sCWDFQ6Q0s8V1lJNep4VWo2HdRqQwi9Umj94F7MmLArO1EktEVDWZLDb4GPXyY94CnLyJgiMv7Ce8nLzgmhdEqhBCoMBkhZ+PU8VT2ODjra6IlJVvtsLPR+fynEGv5bQRIoXlm20u9ZqvUY8CjrwgUly+yQY/o9NaTo510xhv5AUUS16YS7zbCBt4REoqMNsgAPg7NfI4lJ1IeVabBLNFcrmgAuzxxpEXRMqx2iRYrBJ8nS6oOPKCSHkWqwSrTYKvmw4wM9uQ5AWUX7CzsOLRFQ6t5cgLImU5eqJ8nXqD5cWWmCwkUowj1oomL4wGLW+VSqQgOdaMziMvdBx5QaQwR0LQeeTFjanHrNfI86m2YCcA6PUaWK1CqY8gIgD5JkfFU3zkBe/RTaQcd7EG2Ou5AsYakWIKCmPNOSnPaSNEyst3k5R3dDyzDUneQIXkhfM9urWcNkKksHxH1tyn+HxFxhuRcuTkRZGRF/4+euQVWCqjSERVUkkjLyxWCTaJ9RqRUuREoVOs+XDBTvIiit9txOgy8oLzgomUVmAqnDbiZuQF7zZCpBxH8sK/yIKdAX4G5BZwLj6RUhxJedeRF1xEkEhpN5LyxTvA2IYkb6Dggp0SjHr7OhcOvgYutkSkNHe9wVqNhndAIFJYniPWfIuMvPDlyAsiJeW7Sco7FhTk1BEi5bibNsIOMPImik4bcR51AbB3ikgN7rLmgP0+3Vwpmkg5JU0bCfAxII91G5Fi3C0i6Bh5kc/kBZFibkwbcRopr9NCp9XAxA4w8gKKJi98iiYvfA3snSJSmLusOWAf9sfFloiU4+gNLrpgp33khRWS4ILUREpwd2cfR5uSI3iJlFNaG5IjL8gbKJu8MBYdeaFHbj4rHSIlucuaAzcuqIhIGXkljbzw1UPgxsgMIro17uo1x8+cNkKknIIS76LF0bvkHRQeeeG6uwAfA3I58oJIUflmK4wGLXRa13gL8jciK9dcSaUiqnoKTFbodVp5PrBDgJ8BADgtkkghjt5glzUvCn92LFJNRLcu32yFRgMYi1yz2UfvctoIeT7lFuw0u5k24sehtURKyzfZimXMAaBmgBFZeUxeECkl32QtdqcRwD7KCQCnRRIpJN9khY9BB63WadF3H04bIVKaow2pcbrBAgAE+hmQwzYkeQHFkhcFJax5waG1RMoqMFvlVdid1fA3IiuXF1NESskzWYtNGQHsdRvAkRdESikw24pNhXSMvOBaTkTKKTBZiy34Dtg7wDI4epe8gILTRiS3a14AQG4+L6iIlJJXQm9wUIABJosNJs4PJlJEvsnmNlHoGHnBuo1IGe6S8lzzgkh5+Wb39VqtGj7IyDZVQomIKka5aSNubpXqz94pIsUVmGwu84IdggKMAIBMDvsjUoR92kjJIy+4QC6RMvJNxUdeGPVaaDScNkKkpHyT1e3U41qBPsgtsMLCRTvJwymSvBBCIK/AWqziCXQkL9g7RaSYfLP7oew1C5MX2Rz2R6SI/BKmjcgjL7jmBZEiCsxW+BVpQ2o0Gvga9Vywk0hB9lFOxUfv1gq0tyEzctiGJM+mSPIiK9cMk8WG0Fp+Ls87po3ksIFHpJgCU/FGHnBj5AXvOEKkjLwS5gYb9VrodVqOvCBSSL7J5jZR6GvUcdoIkYJKWvS9VqAPACCTyQvycIokL66k5wMAwoL9XZ7n0FoiZVltEjJzzahRmKhwFuTPaSNESjFbbMjIMSGkhm+x1zQaDQJ89ZwSSaSQAnPx0bsAUMPfgIyc6jMPXwiBnHwL79JHqhBCIDPXjMDC2307cyQvqlO8kXcqnnq7CVfS8gAAd4S4jrzgomZEykq5ngerTaBx3cBir9Xw57QRIqVcupYLIYDGYcVjDbA39K5m5N/mUhFVPRarDenZJgS7SRQ2Cg3EsfNplVAq9eUVWHDpWi4uXc0t/D8HF67kIN9khV6nRYPQANSvHQCDXotaQb4Iq+mL+xrWRJ0io5yJyut6ZgHyTVY0dNOGdEwbSWfygjycMsmL9HzotBrUrula8eh1WvgYdeydIlJIYmo2AKBRWI1irxn0Wvj76Kv87VKFEDBbpcLF3DRlv4HoJiSl5gAAGrlp5AFAkwZBiDuWApskQadVbO1romon8UoObJLA3fWCir3WqG4gYo+lIDPXLK/r5K0kIfDXxUzEHUvG0XNpSHe6s4OPQYf6dQLQuWUYwoL9kJFjQlJqDk4npcMqCZjMNnn6TJ2avmjeOBgt7wpG+6ah8DEUH7FC5M6FK/Z6zV1SPtDPAJ1Ww5EX5PEUG3lRp5af2wZcTX8jUtM9u3fKbLHh3OUsXMssgCQEDHot6tcOQL3a/sXuoEJUmRKv5MCg1xYb5eQQGuyHhCtZt7lU6svMNePo2ev431/XcOJCGvJNNvgYdagX4o+6wX7QaDQQQkAS9uSGEIBWAwTX8MXdDWshJMCAO8NqFLudM1FJkq7kwMeoK7GX894GNfHfQ5dw6WouGrtJJhJR+ZxPttdZd9crHkeO5GFSajZq3l37tpZLCVabhAtXsnHkr+vY/38puJZZAB+DDm3vrY07w2qgfp0ANKgTgJCavtCWkowPqR2IIydScCoxHScTM3D4zFXsO5qMAF89Iu6vj54dGqAuR2RQGRKvZEOjARqGFk9eaDQa1Ao0Ij2reiQvzBab3Lmu1QCSsE9fM1lsCPQzeH2ytCpTaORFHsKC3X9ptr23Dv57+CJyCyzyGhiewGSx4eSFdBw4cQWHz1yDyc2CUBoAdWr5IizYH7UCfVAz0Ah/Hz1MFhtMFhtskkDNACNqBvigVqARfj565JusQGIGkpIzkVdgRYCvAQ1DA9CwbiBqBhjZU0y3JPFKNhqGBpTY0/tAs1B8u+ccUjPyva4hY7FKuJaZj6sZ+UhJy8eVtDykpOXhSnoe0gor01qBRjzYIgx1avoiI8eM5Ou5SEjOBjT2ilfr9L/VJvDnuevY/nsSAECjAerVDkDdWn4IreWHOrV8EVrTD6G1fBES5AshBCw2AatVgiQEQoJ82KNejSWmZqNR3cASLyjua1ATAPDXpUwmL4huwfnkLNQMNCK4hk+x1xyjDJNSc9Daw5MXQgikZZlwPjkL55KzcP5yFs6nZMFskaDRAC3vCsHwiCbo0DS0wol0nVaDRnUD0ahuIPo80AiSEDiTlIGdhy7hl4NJ2PpbIurW8kOTBkG4p35N3F0vCKG1fBHoZ2C7k2RJqTmoVzugxNE6TRvVwuEz10q805anM1lsuJZZgOuZ+biaUYDrmQVIzzEh32RFgdmGApMVuQVW5BZYyrUQcICfAXWCfFE32A8N6gSgfuG/4Bo+Xnl8qgpFjrxWo8F9DWu6fa1L6zBs/z0JWw8kokPTUAD2iwgN3H+ZCtgXKXK3VpHjOcc2Rf5zeuy6D7PVhqxcM65mFODi1RxcTM1Bano+BIAAXz06twhDh6Z1cEftAOi1GuSbbbhcOP8wJS0Pqen5uHQtF1m5Ztgk+059DLrC+4+XfPJrNK6/R6CfAfVq+8PXqIdRr4XBoIVRryv+c+E/rUYDjUYDjQaFPwOQf7YfQecLNvtrkN+j0WjsK7I6baNx+t/RKHfsWwj7sROi8OfCHmxJCIjCX8S5Z9uxLQA0b1wLBj17tdWWmWtGy7tCSny9c8swfLvnHH7Ydx492zeATqtx+fs6OP/tAPex5RxX7uJSOD0QTttfzihARkae463yCxabhNx8i73iyLcgt8D+c2aOCVcz8pGWZYLT7uHvo0dYiD+aNQpGg9AAtL47BI3qBlaoISaEgM7HgEP/l4KElCwkXsnB1cx8HL+QBrNFKvW9ep0G9WrbK6pAXwN8fXTwMejgY9TZY1SnhU6ngV5bfPqK80OXVzRAzSs5yMzKL3xY0oauDx3702g0jLXbxGyV0KZJyRdLtWv6olagEf89dAlhwf7w99VDp9UUq7tKqtOEqHhdZv/ZdUcpmSZkZOQ5vVfIuyhpf87/3fg+d923BvY6xXEe2s/BwjrE/mOx7TQ3NkRKpgmZmXk39ldCjGhu7Mj5P9f9wzWm3L6/yPPuZJlsSE/PK3mDQgICuQVWNKkXxAbqbZCYmoMm9YLcfrcH+hlQO8gHccdScGdYDfj56EsdoSDgGh9lrXtZUrw6PeHCJgkUmO0dWAVmK3LyLEjNyHdpJwL2+qNxWA1E3F8fzRrVQtNGteQ7gilBq9GgWeNgNGscjLSsAvx2IhVnL2XixIV07P+/K/J2Br0WwYE+CPQ3INDPgABfA3yM9ram0aCDj0Frr9cK6zatRgNtYbtBq3W0MZ06BgpfUyJeHc9ZbBLuDKsBvY6dBWq7nlWAu+8oOdne54FGiP+/K/jXr+fwUKuwYtcJQNltyWLtSFHStuVvQzr2KRXGn+NfTr4Z1zIKcDXD3vGVlec6bVqv0yKkhg98fXTwNeoRXMMHDUIDEeCnR5C/0b5waWH7WAPAz0cPg16LnHwLMnPNMFklJKZk4UJKNn4/merydWA0aOHno4evUf//7N13XNXV/wfw110MQVQU19eRlrhSVPxlKLgSVATBkaKGMzPNnYscBLgjMbVQs2mauU3LnRsXOcuZCwciMWXd9Tm/P4hPoCzhItd8PR+Pks88537u59xz7vuecz6wslDBSqOClYUK1pbqzPWWKlj/s02tUsplR6XMLEdZ/2aVucz2es7vakC278wKID5Nj8TEdPm9yP5ZKGXV5U98d8tal9XidaxR7oVvQyrEky2hAiQkpEKSch4iSUL+Up2lYkVbxMWlQAiB8G1/4nZ06XdlVyCzwVnN3gZVK5VBzcplUa+GXaF/XZWEgN4gyYEFILOB+zhVi+Q0PbT6zEd9Va9iB6POAEuNEulaIx7GpyI6Lg3RcWn4OykdOr0Eg1GCVm+EwZh5Tr3RCKPxxZ1d2rtNHbg1rSa/7wCgVCpQoYJNKefsxZVbWUtIzoC1lSbHrOzZrzkAbDp0AycvxcCcZVUS1lZq2FprYG9nhYp2lqhoZ4WK5axQqZw1bKzUJvnF6MnrA2R+kKdmGBCfnIG45Awkp+qhVAAqlRJqVWaafydmIDo+DY8S0qDV/dvbqrR1c3kF7ZpVz7GOZa14citrBqMEpQJQZqsfnryXrkYlYs3eq3yU43+UV+tX0NaJZc2UcitrZ6/HonL5Mvifw7/XNXtZu3wnAWv2XoNOb57lLOvXWYfyVqhRuSxqVrZF9UplTNpzL7d6LDdCCCSl6HAvNhWJKVokpeiQlKZDWroOqRlGpGn10BmkzHan3vhkbKbUdG1VCx1a1MixjmWteHIrazEJ6bC1UsMm29NGnry3vt91FX/cintu+SwOpUKBcrYWqGhnBXs7S9iXtUIFO0vY21mhQllL2Fpr8g12FiT7tdEZJDxKyPxBOzlVh8fpemh1Bmj1md/ntDqj3FbM0GcGV4zG/H8kKw1d3qiFjs4vdll75uAFEREREREREdHzxD5aRERERERERGTWGLwgIiIiIiIiIrPG4AURERERERERmTUGL4iIiIiIiIjIrDF4QURERERERERmjcELIiIiIiIiIjJrDF4QERERERERkVlj8IKIiIiIiIiIzBqDF0RERERERERk1tSmPuH27dsRHh4Og8GAQYMGYcCAAaZOwmz5+/sjPj4eanXmZQ0ODoaTk1Mp56pkpaSkwM/PD8uXL0eNGjUQERGBefPmQavVomvXrpgwYUJpZ/E/o6CydfnyZUyfPh2pqalo2bIlgoKC5HuxJCxbtgw7d+4EALRr1w5Tpkx5avumTZtgZ2cHAOjTp0+Jfh4UVP6e5725YcMG/PDDD/LyvXv34OPjg1mzZsnrnvf1ocIzt7IGmF95A8yrzGVh2XuxvcxtyNy8jO1KKhnmVK+ZW31mLnUZ669CEib08OFD0aFDB5GQkCBSU1OFt7e3uH79uimTMFuSJAlXV1eh1+tLOyvPzblz54SXl5do3LixuHv3rkhPTxft2rUTUVFRQq/Xi6FDh4qDBw+Wdjb/EwpTtrp16ybOnj0rhBAiICBArFmzpsTyc+zYMdG3b1+h1WqFTqcTAwcOFHv27Mmxz4gRI8SZM2dKLA/ZFVT+SvPevHbtmnB3dxdxcXE51j/P60OFZ25lTQjzK29CmHeZy8Ky92J5mduQuXkZ25VUMsypXjO3+sxc6zLWX3kz6bCRiIgIvPnmmyhfvjzKlCmDzp07Y9euXaZMwmzdvHkTADB06FB07949R+Tsv2r9+vUIDAxE5cqVAQAXLlxA7dq1UbNmTajVanh7e780739JK6hs3b9/HxkZGWjWrBkAoGfPniV67R0cHDBt2jRYWFhAo9Hg1VdfxYMHD3Ls88cff2DFihXw9vZGcHAwtFptieWnoPJXmvfmxx9/jAkTJsDe3j7H+ud5fajwzK2sAeZX3gDzLnNZWPZeLC9zGzI3L2O7kkqGOdVr5lafmWtdxvorbyYNXjx69AgODg7ycuXKlRETE2PKJMxWcnIyXFxc8Pnnn+Pbb7/FunXrcOzYsdLOVomaM2cOWrZsKS+/zO9/SSvo2j653cHBoUSvfb169eRK7vbt29i5cyfatWsnb09NTUXDhg0xefJkbNmyBcnJyfjiiy9KLD8Flb/SujcjIiKQkZGBrl275lj/vK8PFZ65lTXA/MobYL5lLgvL3ountO8Zc/MytiupZJhTvWZu9Zk51mWsv/Jn0uCFJElQKBTyshAix/J/WfPmzbFw4UKULVsW9vb26N27Nw4dOlTa2XquXub3v6QVdG1L69pfv34dQ4cOxZQpU/DKK6/I621sbPDll1/i1VdfhVqtxtChQ0u0PBRU/krr+qxbtw5Dhgx5av3zvj5UeOZa1gDzKW+A+Za5LCx7L57SvmfMDduVZCrmWK+ZS31mjnUZ66/8mTR4UbVqVcTGxsrLsbGx8pCC/7rIyEgcP35cXhZClPgEbubmZX7/S1pB1/bJ7X///XeJX/vff/8dgwcPxocffogePXrk2PbgwQNs3LhRXi7p8lBQ+SuNe1On0+H06dPo2LHjU9ue9/WhwjPHsgaYV3kDzLPMZWHZezGxDZET25VkKuZWr5lTfWZudRnrr4KZNHjRunVrHD9+HPHx8UhPT8eePXvQtm1bUyZhth4/foyFCxdCq9UiJSUFW7Zsgbu7e2ln67lycnLCrVu3cOfOHRiNRuzYseOlef9LWkFl63//+x8sLS3x+++/AwC2bdtWotc+OjoaH3zwAUJDQ9GtW7entltZWeGTTz7B3bt3IYTAmjVrSrQ8FFT+SuPevHr1Kl555RWUKVPmqW3P+/pQ4ZlbWQPMr7wB5lnmsrDsvZhe5jZkbtiuJFMxp3rN3Oozc6vLWH8VzKThmipVqmDChAkYOHAg9Ho9evfujaZNm5oyCbPVoUMHnD9/Hr6+vpAkCf3790fz5s1LO1vPlaWlJebPn48xY8ZAq9WiXbt26NKlS2ln6z8hr7I1fPhwjB07Fk2aNEFoaChmzJiBlJQUNG7cGAMHDiyx/Hz11VfQarWYP3++vM7Pzw+//fabnJ/g4GCMHDkSer0eLVq0yLULnKnkVf58fHywcuVKVKlS5bnfm3fv3kXVqlVzrMv+fj3P60OFZ25lDTC/8gaYZ9RDVRUAACAASURBVJnLwrL3YnqZ25C5YbuSTMWc6jVzq8/MrS5j/VUwhRBClHYmiIiIiIiIiIjyYtJhI0REREREREREpsbgBRERERERERGZNQYviIiIiIiIiMisMXhBRERERERERGaNwQsiIiIiIiIiMmtFDl4sXboUwcHBRU54xowZ+OOPP4p8/JN+/PFHrFy50mTnA4DZs2fDx8cHPj4+eP3119G5c2d5OSMjI8/jkpKSMHjw4ALPv2HDBowaNapQedm4cSP69OkDHx8feHp6YtasWXj8+HFhX8pT9u/fj2XLlhX5eCo9gYGB6NixI8LCwp7puJMnT8LLyyvXbZ999hm2bt1qiuzlsHLlSrnMNG/eHB07dpSXo6Ki8jxOCIHBgwcjOTk53/NHRETAx8enUHnZt28f3nnnHfj4+KBbt26YOHEiYmJinun1ZHfu3Dl8/PHHRT6eXmxFLYe5GTFiBDZv3pzvPmPHjpXLTv369eHt7Q0fHx/4+/vne1xUVBTGjRtXYB7CwsIwZ86cAvczGo346quv0LNnT7k+Cg0NhU6nK/DYvKxbtw7r1q0r8vH035NfffW8JScny2XP3d0dTZs2lZcXLFiQ77GFbWv169cP+/btK3C/xMREBAcHy+Xf19e3wM+OggQEBODKlSvFOge9WApTvoYPH46//vrrOeXoaWw/Ul7UpZVwREQE+vbta7Lz9evXz2TnyjJjxgz5744dOyI0NBRNmjQp8LjExESTBmaWLVuGkydPIjw8HBUrVoROp8Ps2bMxatQorF69ukjnvHDhAtLS0kyWR3p+fvrpJxw8ePCp50AXR2G+3BTFe++9h/feew8A4O/vjwEDBhTq+dhGoxHHjx83WT62bt2KVatWITw8HDVr1oQkSVi+fDkGDhyIHTt2QKPRPPM5r1+/jkePHpksj/RiKYlymJ8lS5bIf9evXx/fffcd7O3tCzzu3r17uH37tsnyMWPGDGRkZOD777+Hra0tUlNT8eGHHyIwMBDz5s0r0jkjIyPx+uuvmyyPRKZkZ2eHbdu2Acj80hcSEiIvF8SUba309HT4+/ujR48emD59OlQqFe7evYvBgwdDoVCgR48eRTrvsWPHMHDgQJPkkf47vvzyy1JNn+1Hykuhghe//fYbwsPDodfrYWVlhalTp+bYHhMTg+DgYERHR0Ov16Nbt254//33AQAHDhzA4sWLIUkSypQpg6CgIOzcuROPHj3CpEmTsHDhQoSGhqJcuXK4efMm+vXrB3d3d3z88ce4f/8+hBDw9fXFu+++i3v37mHw4MFo164dzp8/j+TkZEyePBnu7u5YunQpEhISMGvWLNy6dQuzZs1CfHw8lEolRo4cCU9PT6xduxbr1q2DRqOBpaUlgoOD8dprrxX54p06dQqffPIJtFotNBoNJkyYAFdXVwQEBCA1NRU+Pj7Ytm0b1q9fjw0bNkCv1yMpKQnvv/9+oQM3KSkpWLVqFbZv346KFSsCACwsLDBt2jTs378fer0ey5YtQ1paGqZPnw4g8xe0rOWdO3dixYoVUKlUUKlUmDp1KhQKBTZu3Aij0QhbW1uMGzcOS5cuxc6dO6FSqVC3bl3MnDkTlSpVQr9+/dCsWTOcPXsWCQkJ6Nu3Lx4+fIjIyEhotVosXrwY9erVQ1JSEubMmYO//voLer0ebdq0weTJkyGEgLOzM9q1a4erV69i8eLFaNiwYZGv+cuuf//+EEJg+PDh6NKlC44cOQKdTof4+Hj4+vpi/PjxSE1NRUBAAO7cuQOlUonGjRvLvaTS0tIwYcIE3Lx5E1qtFrNnz0bLli0xbdo01KtXD8OGDUNkZCQWLlyI9PR0aDQajB8/Hm3btsXmzZuxd+9eKJVK3LlzB1ZWVliwYAFeffXVIr+eq1evYvbs2UhMTIRCocC7776L7t27IyAgAAAwYMAArFq1ChcvXsSqVavk19qrVy+MGTOmUGkIIbB48WLMnz8fNWvWBAAolUq8//77qFGjBrRaLbZu3YoDBw7giy++AJDZKypr+dSpU1iwYAGEEACAUaNGoUGDBvj888/x+PFjTJ8+HXPmzMHatWuxdu1aKJVKODg4YNasWahduzYmTZqEsmXL4urVq4iLi0OnTp1gY2ODQ4cOIS4uDnPnzsUbb7wBnU6HhQsX4vfff4fRaETjxo0xffp02Nraom3btnB2dsaVK1cwefJkdOzYscjXnIovezmcOXMmli5dKt/DQ4cOha+vL4DMAMfq1auhVCpRqVIlzJw5E3Xq1EFMTAymTZuGR48eoXr16oiLiyt2nnbv3o3w8HBIkgRbW1sEBASgfv36CAwMRExMDIYPH44vv/wSn3/+OQ4cOACtVov09HQEBATgrbfeKlQad+7cwa5du3D06FHY2NgAAGxsbBAcHIzz588DACZNmoTXX39d7n2YffmHH37A+vXrodFoYGVlheDgYFy/fh2HDx/GyZMnYWlpiV69emHevHk4deoUlEolmjVrhmnTpsHGxgZt27ZFjx49cPz4cSQnJ2P48OGIjIzEH3/8AUtLS4SHh8PBwQHR0dEIDg5GTEwM9Ho9vL298d577+HOnTsYMmQIatWqhejoaKxdu1auV8n85FZf1a9fH0FBQbhy5QoUCgXc3NwwceJEqNVqNGnSBEOGDEFERATS0tIwevRo7Nq1C9euXUPlypWxfPlylClTBjdu3MCcOXOQmJgIo9EIf39/9O7du1h5za0NFRUVlaOt9e677yIwMBBRUVFITEyEra0twsLCULt27UKlsWPHDpQrVw5Dhw6V19WsWROfffYZjEYjAKBt27ZYsWKF3M7KWn7ttdcQHByMc+fOQaPRoFatWpg3bx4+//xzxMfHY/z48QgNDUXFihURFBSEBw8eAAB69uyJIUOG4M6dOxg+fDjeeOMNXLp0CZIkYcyYMVi3bh1u3boFJycnhIaGQqFQIDIyEp9++ikyMjKgVCoxduxYtGvXDhs2bMDWrVuRmpqK8uXL49tvvy3WNafi0+l0CA0NxenTp2E0GtGoUSPMmDEDtra26NixIz777DPUrVs31zbl6dOnERISgh07dgD4N7C3Y8cOLF26FOfOncOjR49Qv359hIaGIjw8HHv27IEkSfjf//6HwMBAVKlSpch5Z/vxJW4/igLcunVLeHl5ifj4eCGEENeuXRNt2rQR8+fPF0FBQUIIIfz9/cX+/fuFEEJkZGQIf39/8csvv4jY2Fjh7Ows/vzzTyGEELt37xbDhg0TQgjRoUMHceHCBSGEEO+8844ICAiQ0xwwYID4+uuvhRBCJCcnC29vb7Fjxw5x9+5d4ejoKH777TchhBC7du0S7du3F0IIsWTJEjk/vr6+4ocffhBCCPHgwQPx1ltvieTkZNG4cWMRExMjhBBiy5YtYt26dQW9fFn2/AohRFxcnHBxcZHXXblyRbzxxhvi/v374vbt28LZ2VkIIcTjx49Fnz59REJCghBCiNOnT4uWLVsKIYRYv369GDlyZL7pnjt3TrRp0ybffRYtWiRmz56d63L79u3lPB48eFCEh4c/tc9PP/0k+vXrJ9LS0uRt7733nhBCCD8/PzFu3DghhBCRkZHC0dFRHDx4UAghRHBwsAgMDBRCCDF58mSxZs0aIYQQBoNBTJw4UXz99ddCr9cLR0dHsX379nxfAxWeo6OjiIuLE++88464deuWEEKIhw8fioYNG4q4uDixZcsWMXToUCFE5nsxffp0cfv2bXHixAnRsGFDce7cOSGEEN98840YOHCgEEKIqVOnilWrVon4+Hjh4uIi73Pt2jXxxhtviKioKLFp0ybh7OwsoqOjhRCZ7/+UKVMKne933nlH7Ny5U17W6XSiQ4cOYt++fUIIIaKjo0WbNm3E+fPn5fsmKSlJGI1G0b9/fxEVFSWEyCzTDRo0EImJieLYsWOie/fu+aYbGxsrHB0dhVarzXOfJ8ti9uUBAwbI+f7zzz9FSEjIU/scOXJEeHh4iLi4OHmbl5eXEEKIDz/8UPTt21fo9Xrx8OFD4ejoKJeVr776Srz77rtCCCEWL14sPvnkEyFJkhBCiAULFshpubm5ieXLl+d/gem5yiqHb731lti9e7cQIrMcurm5iTNnzoiIiAjRqVMn+Z7YtGmT6Nq1q5AkSYwaNUqEhYUJIYS4ffu2aNasmdi0adMzp50lq16+e/euECLzfmzTpo1ISUnJUUaioqLEoEGDREZGhhBCiK1btwofHx8hxNP1SG5++eUX0bdv33z3+fDDD8U333zz1LJOpxONGzfOcT3Wr1//1DGLFi0S48aNE3q9XhgMBjFlyhS5bndzcxMLFiwQQgixbds20bBhQ3H16lUhhBAjRowQK1euFEII0b9/f7meSk9PFwMGDBC7d+8Wt2/fFo6OjuLMmTP5vgYqfXnVV1OmTBEhISFCkiSh1WrF0KFDxYoVK4QQmeXiu+++E0IIsWLFCtG8eXPx8OFDYTQaRY8ePcTPP/8s9Hq98PT0FH/88YcQIrON2bVrV3H27NlC56tbt2451uXXhspern755RcxZ84c+bjp06fLy35+fmLv3r35pj1r1izx6aef5ruPm5ubuHTp0lPLx48fz5Hv+fPny9c2+zF9+/aVr2FSUpLw8vISO3fulMtOVrmaPn266NSpk0hJSRHp6enCxcVFnD9/XsTHxwsPDw9x//59IURmve7m5iaio6PF+vXrRatWrcTjx4/zfQ1U8rLu46VLl4r58+fL7Y5PP/1Ubtdnfe/Jr02Z/Z7KvrxkyRLRuXNnodfrhRCZ37nGjx8vL69bt05u+xQG249sP2ZXYM+LY8eO4dGjRznmcFAoFIiKikKVKlWQlpaG06dPIykpCZ999hmAzGj5lStXoFarUa9ePTRq1AgA4OHhAQ8Pj1zTadmypXzsmTNn8PXXXwMAypYti549e+Lw4cNwcnKCRqNBu3btAACNGjVCYmJijvMkJibiypUrePvttwEA1apVk8cRdunSBX5+fmjfvj1cXV3l8xTF2bNnUbduXXkYSf369eHk5IRTp06hefPm8n62trYIDw/HgQMHcPv2bVy+fPmZuhAqFAo5YlcUnp6eGDlyJNq3b4/WrVvniNhnOXz4MHr16gVra2sAwKBBg9C2bVsYDAYAkN+zWrVqQaFQwNXVFUBmxD/r17ZDhw7h0qVL+OmnnwAAGRkZsLCwkNPIen/JdJYvX46DBw9ix44duHHjBoQQSE9Ph7OzM8LCwuDv74/WrVtj0KBBqF27Nh4+fIiaNWvCyckJANCgQQNs2rQpxzkvXLiAWrVqyfvUq1cPLVq0wKlTp6BQKNC4cWO5m3yjRo2wd+/eIuc/K89Zv/pWrVoV7u7uOHLkiPyZAWRGuVesWIGDBw9i27Zt+OuvvyCEyHfemeyUysypfYpajrp27YrAwEDs27cPrVu3xvjx45/a58iRI+jWrZvcjf/tt9/G3LlzER0dDSBz2JlarUaVKlVgYWEBNzc3AJllateuXQCAgwcPIi0tDUeOHAEA6PV6VK5cWU7D2dm5SPmnknPjxg1otVr5M7JKlSrw8PDAkSNHkJGRAU9PT/me6NmzJ+bMmYN79+4hIiJC7sFYu3ZttGrVqlj5OH78ONq0aYMaNWoAAFxdXVGuXDlcvnw5x341a9bE3Llz8fPPP+POnTs4e/bsM9dHkiQVKY8ajQbu7u54++23862DDx8+jKlTp0KtzmyeDBgwABMnTpS3d+7cGUBm2alSpQocHR3l15aUlISUlBScOXMGixYtwqJFiwBktisuX76M+vXrQ6PRyJ9vZN5yq6+uXbuGH3/8EQqFAhYWFvDz88N3330ndy/Pfn84OjrKv+zWqFEDSUlJuH37NqKiovDRRx/J6WRkZODSpUto1qxZkfJZUBsqi6enJ2rVqoXvv/8eUVFROH36NP7v//6v0Okolcoil78GDRrAaDTi7bffhqurK7p27YqmTZvm2CclJQUXL16UhyPb2dnBx8cHhw8fRsOGDWFpaSmX2Vq1akGv18s9sBwcHJCYmIjY2FjExsZi5MiROfJ97do1OR+2trZFeg1kegcPHsTjx48REREBILPd8WRvtPzalPlp1qyZ/Dl+4MABXLx4Eb169QIASJKE9PT0Iueb7ceXu/1YYPBCkiS4uLhg8eLF8rro6Gj89NNPSE5OhiRJEEJg3bp18gd3fHw8LC0tceLECSgUCvk4IQSuXr2KBg0aPJVOmTJl5PSevEkkSZIrAY1GI99M2c8tv6B/Ckr2bTdv3kT16tURGhqKa9euISIiAitXrsS2bdvkgMuzkiTpqfSz5zPL/fv30b9/f/j5+aFly5bw8PDA0aNHC52Oo6Mj0tLScPfuXbnLEpA59nHMmDGYN2/eUwEOvV4v/z158mT06dMHx44dw6ZNm/D9998/NTGa0WjM8VqefB3ZgxBKpRIqleqpfBoMBixbtgyvvPIKgMxJS7PeJ+Df95dMIz09HX5+fujUqRNatmyJXr16Yd++fRBCoGbNmti7dy9OnjyJEydOYMiQIQgODoaNjU2OsXm5BcaevBeAzHJrMBjkrt75Hf8sCluGUlJS0KNHD3Tu3BnOzs7o1asX9u7dW+i07e3tUatWLZw7d+6pL4ljx47F6NGjAeSsnLJPPjhgwAB06tQJR48exeHDh7Fs2TK5wsjy5HUTQsjXDchZhgDkOkbSaDRi1qxZaNOmjfy6s5flrEYimQ+FQpFnecntS0bWtifLTla9VVR5laXs9w8AXLx4EaNHj8aQIUPg6uoKZ2dnzJ07t9DpODk54fr160hLS8vxmf7gwQMEBQVh6dKlT+Ujex7CwsJw9epVHD9+HMuXL8eOHTvw6aef5tg/t7KUV32U23XL6j6/YcMGed/4+HhYWVkhNjYWVlZWOeomMl+51VdP3utP1hnZj8nrc7Zs2bI55qz4+++/UbZs2SLns6A2VJbVq1djy5Yt6N+/P7y9vWFra4vY2NhCp+Pk5PTUDw4AsGfPHly4cAGTJk3Ksz1Yvnx5bN++HWfOnMGJEycwfvx4jBgxIscQ5tw+s7K/lifrsdzKnyRJcHR0zNHOjImJgb29PbZu3cq2oJmRJAkfffSRHJRKTU2FVqvNsU9ebUpbW9s8v3sAOdv9kiTh3XffRf/+/QFktrGSkpKKlW+2H3Oe42VqPxZYg7u4uODYsWO4ceMGgMxf2Lt37y7f3La2tmjWrBm++eYbAJmzMvfr1w/79++Hk5MTbty4gevXrwPInHV58uTJAACVSpXrh7utrS2cnJywZs0aAMDjx4+xdetWtG7dulAvyNbWFo0bN5afnBAdHY1+/fohMTER7dq1Q/ny5TF48GCMHz8eFy9eLNQ5c9O8eXNcu3ZNPsfVq1dx5swZtGrVCiqVCkajEUIIXLx4EQ4ODnj//ffh6uqKAwcOPFPk3MrKCsOGDcNHH30kj4vWarWYM2cOdDodHBwcYG9vjz///BNCCKSkpODw4cMAMj9IOnToAIPBgP79+2PmzJm4fPkyDAYD1Gq1fP3d3NywceNGOQq6evVqtGrV6pka1K6urvj2228hhIBWq8WIESPw448/Fvp4ejbx8fFISUnB+PHj0bFjR5w8eRI6nQ6SJGHt2rUICAiAq6srJk+eDFdXV1y6dKlQ523WrBlu3ryJCxcuAMicVOj06dN44403TP4aXnvtNUiShP379wMAHj58KEenVSoVFAoFDAYDbt26hfT0dIwbNw4dOnTA8ePHYTAY5C8phfHBBx9g9uzZuHv3LoDMD/ply5bh+vXreOWVV2Bvb49r165Bp9NBp9Nhz5498rG9e/fGtWvX0KtXL4SEhCAhIQHx8fE5PsPc3NywY8cOJCQkAMj84uTg4CD/El4Yrq6uWL16NfR6PYxGIz766KMcQWMyP3Xr1oVarZbvl5iYGOzevRutW7eGm5sbfv31V8THxwMANm3ahPLly6N27dpwc3OTe6k9ePAAJ0+eLFY+XFxccOjQIdy7dw8AcPToUfz9999o0qRJjvv01KlTcHJywuDBg9GyZUvs27fvmcpR9erV0aVLF0yfPh0pKSkAMuvooKAg2Nvbw8LCAvb29nK9+PDhQ/z+++8AMr8gtm/fHhUrVsTgwYMxduxYeb8n66Mff/xRDgCtWbOm0PU/AJQrVw6NGzeWx9MnJSWhb9++OHjwYKHPQebL1dUVP/zwA4QQ0Ol0WL9+/TPdH3Xq1IGVlZUcvIiOjoaXl1exJlnPrw2V/d4+evQoevXqhd69e6N27do4ePDgM7UHPT09ERcXh2+++UYut3fu3MGCBQvk+dvs7e3l1xIRESF//uzbtw/Dhg1DixYtMHbsWHh7e+cof3q9HnZ2dmjUqJHcdktOTsbPP//8TNe3efPmuHHjhlzu//zzT3Tu3Nkk8/qQ6bm6umLNmjVy+3HmzJlyj7UsebUp7e3t8eDBA8TFxUEIgV9++SXfdDZu3CjXG5999hmmTJlS5Hyz/ZjTy9Z+LPDbadYkPxMnToQQAmq1GuHh4Th+/LgcwAgNDUVISAi8vb2h0+ng5eWF7t27y9umTp0qT1iU9Vg5d3d3TJ48OddHxYSGhiI4OBibN2+GTqeDt7c3evbsifv37xfqRX366acICgrC6tWroVAoMGfOHFStWhUjR47E4MGDYWVlBZVKhdmzZxf2Oj2lUqVKWLx4MT7++GPodDoolUosXLgQNWvWhF6vR8OGDeHl5YV169Zh8+bN6NKlCxQKBVq1aoVy5crl+5ifJ33wwQewtbXFkCFDoFAooNVq8eabb8qTw/j4+ODo0aPw8PBA1apV5W6IGo0G06ZNw/jx46FWq6FQKDBv3jyo1Wq4uLhg8uTJUKvVCAgIQExMDHr37g2j0Yg6depg4cKFz3Q9AgMDMXv2bHh7e0Ov18PV1TXXISpkGtWrV0f79u3RtWtXWFhYwNHREa+99hru3LkDX19fnDp1Cp6enrC2tka1atXg7+9fqEeh2dvb47PPPkNISAgyMjLke6ZOnTo4e/asSV+DhYUFvvjiC8yZM0ee1HfcuHHy/evh4YF+/fph2bJlcjdXjUaDBg0aoG7dus9UhrImUBw3bhwkSUJGRgZef/11fPvtt7CwsEDbtm3x66+/okuXLqhcuTJatmyJmzdvAgCmTp2KuXPn4tNPP4VCocD48eNRtWpVNG/eHOHh4Rg7diyWLFmC27dvw9/fH0II2NvbY/ny5bn2DsvLmDFjsGDBAvj6+soTLhWncqfn44svvsDs2bOxdOlSGI1GfPDBB3jzzTcBAIMHD8agQYMgSRLs7e2xYsUKKJVKBAYGIiAgAF27dkXVqlVz7Y34LOrXr48ZM2bggw8+gNFohLW1NZYvXw5bW1vUq1cPSqUSffv2xdKlS7Fv3z54enpCkiS0b98eCQkJzzR0JDg4GMuWLYOfnx+USiX0ej08PDzkX6AGDhyISZMmoUuXLqhRo4b8a1WlSpUwfPhw+Pv7w9raGmq1GkFBQQAyJxX85JNPAACjR4/G/Pnz4ePjA4PBgGbNmskTsBVWWFgYgoODsX37duh0Ovj6+sLT0xN37tx5pvOQ+ZkxY0aOtoabm5s8QXxhZK93Vq1aBYPBgHHjxhWrW7Wfn1+ebajsba1hw4YhMDBQ7pXg5OQk1zOFzft3332HhQsXwtvbGyqVCmq1GmPGjJHruMmTJyMoKAhr1qxBkyZN5Ik727dvj8OHD8PLywtlypRB+fLl5Tawu7s7Jk6ciJCQECxatAjBwcHyJPPdu3eHr69voctOpUqVsGTJEsybNw86nQ5CCISGhj63pzLRsxk1ahQWLFiAHj16wGg0omHDhpg2bVqOffJqU5YrVw5+fn7o1asXHBwc0L59+zx/FH777bcRExODPn36QKFQoFq1apg/f36R8832Y04vW/tRIYrT75uIiIiIiIiIqIQVb6Dtf8D48eNx69atXLeFhYWhbt26JZ6H2bNn4/Tp07lumzlzJie7JLM2d+7cPLu9BwQEyL9Cl6SVK1fm2WVxxIgR8PT0LPE8EBVX//79kZqamuu2NWvWPJeJ7saOHZvnr6xLly5FrVq1SjwPRM/bzZs3MWHChFy31alT57l0wU5OToa/v3+u2+zs7OSJNIn+K9h+pKJgzwsiIiIiIiIiMmuccpuIiIiIiIiIzBqDF0RERERERERk1hi8ICIiIiIiIiKzxuAFEREREREREZk1Bi+IiIiIiIiIyKwxeEFEREREREREZo3BCyIiIiIiIiIyawxeEBEREREREZFZY/CCiIiIiIiIiMwagxdEREREREREZNYYvCAiIiIiIiIis8bgBRERERERERGZtRc+eHHy5El4eXmVdjaeC51OhyFDhmDXrl25bhdCICwsDB4eHvDx8cHHH38MrVb7nHNJREREREREZFovfPDiZXH27Fn07dsXZ86cyXOfzZs34+DBg9i4cSO2bdsGBwcHLF68+DnmkoiIiIiIiMj0XpjgRWpqKsaOHQsfHx/06NEDM2bMgCRJOfaJjIxE+/bt5S/4v/32G95++234+vrCz88PZ8+eRUJCApo3b460tDQAwKxZs/DOO+/I5/Dw8MCNGzfQsWNHLF26FP3790eHDh1yBAFyOy8A3LhxA35+fujZsyd69OiBNWvW5Lv+SdOnT8ePP/6Y67bVq1fjww8/RNOmTfO8Rn/++Sc6deoEOzs7+bXs3r073+tKREREREREZO5emODF3r17kZqaim3btmHjxo0AgLt378rbT5w4gYCAACxfvhwtWrTA7du3ERYWhpUrV2Lr1q0ICQnBmDFjYGlpiSZNmuDkyZMAMoed3Lp1C6mpqfjrr7+gVqvx6quvAgDS0tKwdu1arFu3Dl9//TXu3r2b53nT0tLw1VdfoWPHjti8eTNWrlyJyMhISJKU5/onzZkzB/369cv19S9atAiurq75XqOmSxMqRgAAGX1JREFUTZvit99+Q3x8PCRJwtatW/Ho0aMiXW8iIiIiIiIic6Eu7QwUlrOzM8LCwuDv74/WrVtj0KBBqF27Nh4+fIiHDx/i/fffR79+/dCgQQMAwLFjx/Do0SMMHjxYPodCoUBUVBTc3d1x+PBh1KpVC1WqVIGjoyNOnz6Nq1evwsPDQ97/rbfeAgBUqVIFFStWRFJSEs6fP5/veadOnYoLFy7AxcUFM2bMgFKpzHO9qfn6+iImJgaDBg1CmTJl0KdPH2g0GpOnQ0RERERERPQ8vTDBi5o1a2Lv3r04efIkTpw4gSFDhiA4OBg2NjZQqVRYuXIlRo0ahS5dusDJyQmSJMHFxSXHcI/o6GhUrlwZ5cuXx4ABA/DKK6+gTZs2sLOzw9GjR3Hx4kUEBQXJ+1taWsp/KxQKCCHyPW+DBg2we/duRERE4Pjx4/j888+xefNmdOjQIdf1VatWNek1SkxMhJeXF0aMGAEAOHPmDGrXrm3SNIiIiIiIiIietxdm2MjatWsREBAAV1dXTJ48Ga6urrh06RIAwMHBAS1atMDUqVMxZcoUpKenw8XFBceOHcONGzcAAIcOHUL37t2RkZGBqlWrokKFCli3bh3atGkDV1dX7NmzB4mJiXLPjbzkd94PP/wQv/76K7p164bAwEDY2toiKioqz/Wm9scff2D06NHQ6/UwGAxYuXIlvL29TZ4OERERERER0fP0wvS88PX1xalTp+Dp6Qlra2tUq1YN/v7+uHLlirxPjx49sHv3bsyfPx9BQUEIDg7GxIkTIYSAWq1GeHg4bGxsAADu7u74+uuv0ahRIyiVSlhZWaFTp04F5uO1117L87yjRo3C9OnT8dNPP0GlUqFTp074v//7P1SsWDHX9U+aPn06Xn/99TznvcjN/v37sW7dOnz55ZdwdXXF6dOn0b17d0iShE6dOuUY3kJERERERET0IlIIIURpZ4KIiIiIiIiIKC8vzLARIiIiIiIiIno5MXhBRERERERERGaNwQsiIiIiIiIiMmsMXhARERERERGRWWPwgoiIiIiIiIjM2jM/KjUhIRWSVPADSipWtEVcXEqRMvVf9bJdE6VSgQoVbEo7G0RERERERPSCe+bghSSJQgUvsvalnHhNiIiIiIiIiJ7NMwcvCuPPW/FwhAKakjg5EREREREREb1USmTOi1U7LmHTgb9K4tRERERERERE9JIpkeBFus6A1HR9SZyaiIiIiIiIiF4yJg9eSEJAp5eQmsHgBREREREREREVn8mDF3q9BABIzzCY+tRERERERERE9BIyefAiQ28EAPa8ICIiIiIiIiKTMHnwQvtP8CKNPS+IiIiIiIiIyARMHrzQ6bKCF+x5QURERERERETFV2I9LzJ0RkiSMPXpiYiIiIiIiOglU2LBCwDI0HHoCBEREREREREVT4kGL9K0DF4QERERERERUfGUbM8LrTGfPYmIiIiIiIiICmb6CTv1kvw3e14QERERERERUXGZvueFjnNeEBEREREREZHpcM4LIiIiIiIiIjJrnPOCiIiIiIiIiMxaiQwbUSkVAIB0DhshIiIiIiIiomIqkZ4XtmU0UCoVSOewESIiIiIiIiIqJrWpT6jVG2GlUcFoKZDOYSNEREREREREVEwmD17o9BIsNSoYRc4njxARERERERERFUWJ9LywsFBBLwkYjJKpT09EREREREREL5kSmfPCUqOCRq2EnsELIiIiIiIiIiqmEgleWGUFLwwMXhARERERERFR8ZTIo1ItNCpYqFUcNkJERERERERExWby4IXOIMFSo4RGrYSBPS+IiIiIiIiIqJhMHrzQGySo1Uqo1UrojcLUpyciIiIiIiKil0yJBC80aiU0KiWHjRARERERERFRsZk0eCFE5uNRNSolJ+wkIiIiIiIiIpMwafDCKGUOE1GrlLDQcMJOIiIiIiIiIio+kwYvsnpaqLN6XjB4QURERERERETFZNrgxT/BCnnOCw4bISIiIiIiIqJiMmnwIitYofnnaSMGPm2EiIiIiIiIiIrJtMELY9awEQUn7CQiIiIiIiIikzDxsJGcE3ZKQkCS2PuCiIiIiIiIiIquxIaNaFSZp+aknURERERERERUHCUzYec/TxsBwMelEhEREREREVGxqE15MkOOR6VmDhfhvBdEREREREREVBymDV5kTdipVkLzT8yCj0slIiIiIiIiouIwafAiq5dF5rCRf9Zx2AgRERERERERFYNpgxfZe17885ARg5FPGyEiIiIiIiKioiuRYSMalQIawQk7iYiIiIiIiKj4TBy8yOxloVYpoQEn7CQiIiIiIiKi4iuROS/UaiU0in/WsecFERERERERERVDiU3YafwneMGnjRARERERERFRcZTMnBdqJbI6XHDOCyIiIiIiIiIqjhIJXqiUCmiUmV0vOGyEiIiIiIiIiIrD5MNG1ColFAoFLFQqeR0RERERERERUVEpTXkyvVGCRp15yqx/s55AQkRERERERERUFCYNXhiMAhpV5nAROXjBnhdEREREREREVAymDV4YJKj/CVqo5Z4XDF4QERERERERUdGZfNiIWpU1bIRzXhARERERERFR8Zm850XWcBGVUgGVUsGnjRARERERERFRsZRYzwsAUKuUHDZCRERERERERMVi4gk7JWhyBC8UMBj4tBEiIiIiIiIiKjrT9rwwSFD/87QRIHPSTr3RaMokiIiIiIiIiOglY/qeF/9M1AkAlhoVdJywk4iIiIiIiIiKwcQ9L0SOnheWGhW0Ova8ICIiIiIiIqKiK4GeF/+e0tJChQwGL4iIiIiIiIioGEpgzot/T2mlUUGrZ/CCiIiIiIiIiIquxHtecNgIERERERERERWHSYMXOoMxR/DCSsNhI0RERERERERUPCYLXgghkKE1wtpCLa+ztOCwESIiIiIiIiIqHpMFL7R6IwQAa8ucwQv2vCAiIiIiIiKi4jBZ8CJdmxmksLJUyeusNCoYjBKMkmSqZIiIiIiIiIjoJWOy4EWGzgAAsLL4N3hhqcn8W6tj8IKIiIiIiIiIisaEwYvMnhdPznkBgPNeEBEREREREVGRmXDYSGbPiyfnvAD+7ZVBRERERERERPSsTD/nhUX2OS8yAxnseUFERERERERERWX6OS9y6Xmh5RNHiIiIiIiIiKiISmDOi2w9L+RhIwxeEBEREREREVHRmHzOC6vsE3ZqOGEnERERERERERWPSXteqFUKaNT/npI9L4iIiIiIiIiouEzX80JnyNHrAgAsNJzzgoiIiIiIiIiKx3Q9L7QGWFuqcqyTe15w2AgRERERERERFZFJh4082fNCrVJCpVRAx+AFERERERERERWRSSfszP6kkSxWFirOeUFERERERERERWbCOS+MsLJUP7XeykKFtAyDqZIhIiIiIiIiopeMiYeNPN3zolI5a8QmpZsqGSIiIiIiIiJ6yZh02MiTc14AQBX7MoiJTzNVMkRERERERET0kjFJ8CItQ4/kVB0qlbN6altV+zJ4nKZHaobeFEkRERERERER0UvGJMGLOw8fAwBeqVb2qW1V7K0BADHxHDpCRERERERERM/u6XEeRXA7K3hR1e6pbVXtywAAYuLTULf609v/iwxGCYmPtYh/rEVyqg56owQAqFE1HZLegPJlLWFXRgOFQlHKOSUiIiIiIiIyfyYJXtx6+BiVylnB1lrz1DaH8tZQKhSIfoHnvRBC4HG6HomPtUhM0SExRYvEx1okpemQnmFAmjbzv3StASnpeiSn6CAKOKettQa1qtiiVpWyqFXZFjWrlEVVe2uolCabhoSIiIiIiIjoP8EkwYu7MY/xSrXce1WoVUrUqGyDoxce4I0GlVG+rKUpksyTEAKSyPxXiKzlf/8WAjmWDUaBtAx9ZgAiIzP4kPBPcCIxRYeEx1okpWphMD4djrCxUsPGSgNrSzWsLVWoXN4adarZwb6sJeztrGBvZ4nyNpbQqJUQANQWGty5n4j4xxm49ygFUY9SsC/yrnxujVqJ6hVtUNZGAxsrDcpYqWFjpYZGpYRGrYJapYBarYRSoYACgEKhgEIBqFQKqJVKqFVKqNX//q1SKTLXqRRQqZT/jhFSAJlnALJ3/sjqCZK1TpFtQSH/D3LaWfQGCeVtLdiThIiIiIiIiEqEQghRUCeBHBISUiFJOQ858WcM/udgg5qVbeV1FSvaIi4uBQAQHZeGz7dchE5vNEGWS56lRgU7GwvYldGgnK0l7MpYwM7WAuX++deujAZ2NhbP3Esi+zXJYpQkxCZk4H5cCh78nYaY+DSkZWT24kjXGpCuM+DZ3qHS4etWF61fr5pjnVKpQIUKNqWUIyIiIiIiIvqveObgBRERERERERHR88QJFoiIiIiIiIjIrDF4QURERERERERmjcELIiIiIiIiIjJrDF4QERERERERkVlj8IKIiIiIiIiIzBqDF0RERERERERk1hi8ICIiIiIiIiKzxuAFEREREREREZk1Bi+IiIiIiIiIyKypTX3C7du3Izw8HAaDAYMGDcKAAQNMncQLx9/fH/Hx8VCrMy93cHAwnJycSjlXRERERERERC8GkwYvYmJiEBYWhs2bN8PCwgJ+fn5o1aoVXnvtNVMm80IRQuD27ds4cOCAHLwgIiIiIiIiosIz6bCRiIgIvPnmmyhfvjzKlCmDzp07Y9euXaZM4oVz8+ZNAMDQoUPRvXt3/PDDD6WcIyIiIiIiIqIXi0m7Ajx69AgODg7ycuXKlXHhwgVTJvHCSU5OhouLC2bOnAm9Xo+BAweiTp06aNOmTWlnjYiIiIiIiOiFYNLghSRJUCgU8rIQIsfyy6h58+Zo3ry5vNy7d28cOnSIwQsiIiIiIiKiQjLpsJGqVasiNjZWXo6NjUXlypVNmcQLJzIyEsePH5eXhRCc+4KIiIiIiIjoGZg0eNG6dWscP34c8fHxSE9Px549e9C2bVtTJvHCefz4MRYuXAitVouUlBRs2bIF7u7upZ0tIiIiIiIioheGSbsAVKlSBRMmTMDAgQOh1+vRu3dvNG3a1JRJvHA6dOiA8+fPw9fXF5IkoX///jmGkRARERERERFR/hRCCFHamSAiIiIiIiIiyotJh40QEREREREREZkagxdEREREREREZNYYvCAiIiIiIiIis8bgBRERERERERGZNQYviIiIiIiIiMisMXhBRERERERERGbNLIIXFy9exNixY016zq1bt8LHxwc+Pj5444034ObmJi9HRkbme2xAQACuXLmS7z537txBy5YtC5WXyMhIDBs2DL6+vvDy8sKIESPw119/Ffq1PCkqKgrjxo0r8vFERERERERELxJ1aWcAAJo0aYIlS5aY9Jy+vr7w9fUFAEybNg316tXDsGHDCnXssWPHMHDgQJPk48SJEwgICMDnn3+ORo0aAQC2bNmCAQMGYNeuXahQocIzn/PevXu4ffu2SfJHREREREREZO5KPHhx8uRJLFq0CNWqVcOtW7dgbW2N9957D6tXr8atW7fg4eGBt956CyEhIdixYwciIyMxf/58SJIEABgxYgQ6d+6M1NRUzJ49G2fOnIFKpUKnTp0wYcIEKBSKIuXrwYMHCAoKwoMHDwAAPXv2xJAhQxAaGor4+HiMHz8eoaGh0Ol0WLRoEbRaLWJjY9G2bVuEhIQUOp0lS5Zg9OjRcuACAHr06AFra2tIkoSIiAgsWLAA27ZtA4Acy9evX8fMmTOh0+kghEDfvn3Rs2dPBAYGIiYmBsOHD8eXX36J3bt3Izw8HJIkwdbWFgEBAWjSpAnCwsLw8OFDPHjwALGxsWjatCmcnZ2xbds23L9/H1OnToWnpyeEEPjiiy+wb98+SJKEmjVrIjAwEA4ODujXrx8qVaqEGzduYMCAARgwYECRrjcRERERERFRUT2XYSMXL17Ee++9h23btsHW1hYrV67EihUrsHnzZqxduxaPHj2S9126dCmGDBmCzZs3Y+7cuThx4gSAzCCAVqvFr7/+iq1bt+LMmTM4depUkfM0ceJEtGnTBtu3b8eaNWuwefNm7Nq1C5MmTYK9vT0WL16MJk2a4Pvvv8eECROwceNG/PLLL9i9e3eBQ0qy++OPP9CiRYun1nfp0gUVK1bM99hVq1bB3d0dmzdvxvLly3Hq1Cmo1WoEBQWhTp06+PLLL3H9+nWEhIRg2bJl+PnnnzFq1CiMHDkSqampAIAzZ84gPDwcW7Zswf79+xEVFYW1a9fio48+wrJlywAAmzZtws2bN7FhwwZs27YNLi4umDlzppyPChUq4Ndff2XggoiIiIiIiErFcxk2UqNGDbnnQa1atVC2bFlYWFjA3t4eNjY2SEpKkvft2rUrgoOD8dtvv6F169aYOHEigMweCQEBAVCpVFCpVPjhhx+KnJ+UlBRcvHgRq1evBgDY2dnBx8cHhw8fRpcuXXLs+8knn+DQoUMIDw/HzZs3odVqkZqaCmtr60KlpVQq5V4kz8rd3R0fffQRzp07BxcXF8yYMQNKZc540/Hjx9GmTRvUqFEDAODq6opy5crh8uXLAIDWrVvD1tYWAODg4AA3NzcAme9D1nU/ePAgLl26hF69egEAJEmCTqeT03B2di5S/omIiIiIiIhM4bkELywsLHImqs47WT8/P3To0AHHjh3DkSNHsGzZMuzatQtqtTrHEJHo6GhYWVkVac6I3IIJkiTBYDDkWCeEgJ+fH15//XW4ubmhW7duOHv2LIQQhU7LyckJ58+fx6uvvppjfWBgILp06QKFQpHjfHq9Xv67U6dOaNGiBSIiIhAREYFly5Zh69atT+X7yaEzkiTJ5ynMtTcajXj//ffRp08fAIBWq0VycrK83cbGptCvl4iIiIiIiMjUzOJpI9n5+fnh8uXL6NmzJ0JCQpCcnIzY2Fi4uLhgy5Ytcq+AsWPH4vTp00VKw87ODo0aNcKPP/4IAEhOTsbPP/+M1q1bA8j8gq/X65GQkIArV65g8uTJcHd3x/3793Hv3r1n6kkxatQoLFmyBJcuXZLXbdiwAfv370f9+vVRoUIF3L9/H/Hx8RBC4JdffpH3GzduHPbu3QsvLy98/PHHsLa2xt27d6FSqeRAi4uLCw4dOoR79+4BAI4ePYq///4bTZo0KXQeXV1dsX79eqSkpAAAwsLCEBAQUOjjiYiIiIiIiEqSWTxtJLtJkyZh7ty5WLx4MRQKBUaPHo0aNWpg9OjRmDNnDnx8fGA0GuHp6QkPD48ip7No0SIEBwdjw4YN0Ov16N69u/x0End3d0ycOBEhISEYNmwYfHx8YG1tjWrVqqF58+a4c+cOqlSpUqh0WrVqhaCgIAQHByM9PR06nQ61a9fGd999B3t7e9jb26NXr17o1asXKlWqhHbt2uHq1asAgNGjR2PmzJlYs2YNVCoVunbtCmdnZ8THx0OpVKJv37746aefMGPGDHzwwQcwGo2wtrbG8uXL5aEihdGvXz88evQIffv2BQBUr14dc+fOfcYrSkRERERERFQyFOJZxkAQERERERERET1nZtfz4lmsWrUK27dvz3XbsGHD0L179xLPw9atW/HNN9/kuq1Hjx4YPHhwieeBiIiIiIiI6L+MPS+IiIiIiIiIyKyZ3YSdRERERERERETZMXhBRERERERERGaNwQsiIiIiIiIiMmsMXhARERERERGRWWPwgoiIiIiIiIjMGoMXRERERERERGTWGLwgIiIiIiIiIrPG4AURERERERERmTUGL4iIiIiIiIj+vx07FgAAAAAY5G89jR2FEWvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArMkLAAAAYE1eAAAAAGvyAgAAAFiTFwAAAMCavAAAAADW5AUAAACwJi8AAACANXkBAAAArAUkENMJ7GU/4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "densite(df_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 - [Export](#62)<a class=\"anchor\" id=\"62\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93104, 27)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Base_Export = pd.concat([df_quali, df_other,\n",
    "                                  df_log, df_rfm], axis=1, join='outer')\n",
    "Customer_Base_Export.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_Base_Export.to_csv(path+\"Customer_Base_Export.csv\",\n",
    "                            index=False, sep=',', encoding='utf-8')"
   ]
  }
 ],
 "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
}