{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad414c84-be46-4d2c-be8b-9fc4d24cc672",
   "metadata": {},
   "source": [
    "# Business Data Challenge - Team 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15103481-8d74-404c-aa09-7601fe7730da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee97665c-39af-4c1c-a62b-c9c79feae18f",
   "metadata": {},
   "source": [
    "Configuration de l'accès aux données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d83bb1a-d341-446e-91f6-1c428607f6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filesystem object\n",
    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbd72c5-6f8e-4366-ab66-96c32c6e963a",
   "metadata": {},
   "source": [
    "# Exemple sur Company 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db26e59a-927c-407e-b54b-1815473b0b34",
   "metadata": {},
   "source": [
    "## Chargement données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "699664b9-eee4-4f8d-a207-e524526560c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET = \"bdc2324-data/1\"\n",
    "liste_database = fs.ls(BUCKET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15815/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_in)\n"
     ]
    }
   ],
   "source": [
    "# loop to create dataframes from liste\n",
    "files_path = liste_database\n",
    "\n",
    "client_number = files_path[0].split(\"/\")[1]\n",
    "df_prefix = \"df\" + str(client_number) + \"_\"\n",
    "\n",
    "for i in range(len(files_path)) :\n",
    "    current_path = files_path[i]\n",
    "    with fs.open(current_path, mode=\"rb\") as file_in:\n",
    "        df = pd.read_csv(file_in)\n",
    "        # the pattern of the name is df1xxx\n",
    "        nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n",
    "        globals()[nom_dataframe] = df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4004c8bf-11d9-413d-bb42-2cb8ddde7716",
   "metadata": {},
   "source": [
    "## Cleaning functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d237be96-8c86-4a91-b7a1-487e87a16c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleaning_date(df, column_name):\n",
    "    \"\"\"\n",
    "    Nettoie la colonne spécifiée du DataFrame en convertissant les valeurs en datetime avec le format ISO8601.\n",
    "\n",
    "    Parameters:\n",
    "    - df: DataFrame\n",
    "        Le DataFrame contenant la colonne à nettoyer.\n",
    "    - column_name: str\n",
    "        Le nom de la colonne à nettoyer.\n",
    "\n",
    "    Returns:\n",
    "    - DataFrame\n",
    "        Le DataFrame modifié avec la colonne nettoyée.\n",
    "    \"\"\"\n",
    "    df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398804d8-2225-4fd3-bceb-75ab1588e359",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "568cb180-0dd9-4b27-aecb-05e4c3775ba6",
   "metadata": {},
   "source": [
    "## customer_plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e7b90ce-da54-4f00-bc34-64c543b0858f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_customerplus(customerplus = None):\n",
    "\n",
    "    customerplus_copy = customerplus.copy()\n",
    "    \n",
    "    # Passage en format date\n",
    "    cleaning_date(customerplus_copy, 'first_buying_date')\n",
    "    cleaning_date(customerplus_copy, 'last_visiting_date')\n",
    "    \n",
    "    # Selection des variables\n",
    "    customerplus_copy.drop(['lastname', 'firstname', 'email', 'civility', 'note', 'created_at', 'updated_at', 'deleted_at', 'extra', 'reference', 'extra_field', 'identifier', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode', 'last_visiting_date'], axis = 1, inplace=True)\n",
    "    customerplus_copy.rename(columns = {'id' : 'customer_id'}, inplace = True)\n",
    "\n",
    "    return customerplus_copy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03329e32-00a5-42c8-9470-75f7b6216ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_customerplus_clean = preprocessing_customerplus(df1_customersplus)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bade04b1-0cdf-4d10-bcca-7dc7e4831656",
   "metadata": {},
   "source": [
    "## Ticket area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b95464b1-26bc-4aac-84b4-45da83b92251",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonction de nettoyage et selection\n",
    "def preprocessing_tickets_area(tickets = None, purchases = None, suppliers = None, type_ofs = None):\n",
    "    # Base des tickets\n",
    "    tickets = tickets[['id', 'purchase_id', 'product_id', 'is_from_subscription', 'type_of', 'supplier_id']]\n",
    "    tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n",
    "\n",
    "    # Base des fournisseurs\n",
    "    suppliers = suppliers[['id', 'name']]\n",
    "    suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n",
    "\n",
    "    # Base des types de billets\n",
    "    type_ofs = type_ofs[['id', 'name', 'children']]\n",
    "    type_ofs.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)\n",
    "\n",
    "    # Base des achats\n",
    "    # Nettoyage de la date d'achat\n",
    "    cleaning_date(purchases, 'purchase_date')\n",
    "    # Selection des variables\n",
    "    purchases = purchases[['id', 'purchase_date', 'customer_id']]\n",
    "\n",
    "    # Fusions \n",
    "    # Fusion avec fournisseurs\n",
    "    ticket_information = pd.merge(tickets, suppliers, left_on = 'supplier_id', right_on = 'id', how = 'inner')\n",
    "    ticket_information.drop(['supplier_id', 'id'], axis = 1, inplace=True)\n",
    "    \n",
    "    # Fusion avec type de tickets\n",
    "    ticket_information = pd.merge(ticket_information, type_ofs, left_on = 'type_of', right_on = 'id', how = 'inner')\n",
    "    ticket_information.drop(['type_of', 'id'], axis = 1, inplace=True)\n",
    "    \n",
    "    # Fusion avec achats\n",
    "    ticket_information = pd.merge(ticket_information, purchases, left_on = 'purchase_id', right_on = 'id', how = 'inner')\n",
    "    ticket_information.drop(['purchase_id', 'id'], axis = 1, inplace=True)\n",
    "\n",
    "    return ticket_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e1d2ba7-ff4f-48eb-93a8-2bb648c70396",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15815/1591303091.py:5: 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",
      "  tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n",
      "/tmp/ipykernel_15815/1591303091.py:9: 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",
      "  suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n",
      "/tmp/ipykernel_15815/1591303091.py:13: 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",
      "  type_ofs.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "df1_ticket_information = preprocessing_tickets_area(tickets = df1_tickets, purchases = df1_purchases, suppliers = df1_suppliers, type_ofs = df1_type_ofs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b18edfc-6450-4c6a-9e7b-ee5a5808c8c9",
   "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>ticket_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>is_from_subscription</th>\n",
       "      <th>supplier_name</th>\n",
       "      <th>type_of_ticket_name</th>\n",
       "      <th>children</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13070859</td>\n",
       "      <td>225251</td>\n",
       "      <td>False</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>48187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13070860</td>\n",
       "      <td>224914</td>\n",
       "      <td>False</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>48187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13070861</td>\n",
       "      <td>224914</td>\n",
       "      <td>False</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>48187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13070862</td>\n",
       "      <td>224914</td>\n",
       "      <td>False</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>48187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13070863</td>\n",
       "      <td>224914</td>\n",
       "      <td>False</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>48187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticket_id  product_id  is_from_subscription   supplier_name  \\\n",
       "0   13070859      225251                 False  vente en ligne   \n",
       "1   13070860      224914                 False  vente en ligne   \n",
       "2   13070861      224914                 False  vente en ligne   \n",
       "3   13070862      224914                 False  vente en ligne   \n",
       "4   13070863      224914                 False  vente en ligne   \n",
       "\n",
       "  type_of_ticket_name         children             purchase_date  customer_id  \n",
       "0             Atelier  pricing_formula 2018-12-28 14:47:50+00:00        48187  \n",
       "1             Atelier  pricing_formula 2018-12-28 14:47:50+00:00        48187  \n",
       "2             Atelier  pricing_formula 2018-12-28 14:47:50+00:00        48187  \n",
       "3             Atelier  pricing_formula 2018-12-28 14:47:50+00:00        48187  \n",
       "4             Atelier  pricing_formula 2018-12-28 14:47:50+00:00        48187  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_ticket_information.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096e47f4-1d65-4575-989d-83227eedad2b",
   "metadata": {},
   "source": [
    "## Target area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "baed146a-9d3a-4397-a812-3d50c9a2f038",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_target_area(targets = None, target_types = None, customer_target_mappings = None):\n",
    "    # Target.csv cleaning\n",
    "    targets = targets[[\"id\", \"target_type_id\", \"name\"]]\n",
    "    targets.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n",
    "    \n",
    "    # target_type cleaning\n",
    "    target_types = target_types[[\"id\",\"is_import\",\"name\"]].add_prefix(\"target_type_\")\n",
    "    \n",
    "    #customer_target_mappings cleaning\n",
    "    customer_target_mappings = customer_target_mappings[[\"id\", \"customer_id\", \"target_id\"]]\n",
    "    \n",
    "    # Merge target et target_type\n",
    "    targets_full = pd.merge(targets, target_types, left_on='target_type_id', right_on='target_type_id', how='inner')\n",
    "    targets_full.drop(['target_type_id'], axis = 1, inplace=True)\n",
    "    \n",
    "    # Merge\n",
    "    targets_full = pd.merge(customer_target_mappings, targets_full, left_on='target_id', right_on='target_id', how='inner')\n",
    "    targets_full.drop(['target_id'], axis = 1, inplace=True)\n",
    "\n",
    "    return targets_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5fbfd88b-b94c-489c-9201-670e96e453e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15815/3848597476.py:4: 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",
      "  targets.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "df1_target_information = preprocessing_target_area(targets = df1_targets, target_types = df1_target_types, customer_target_mappings = df1_customer_target_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4f05142-2a22-42ef-a60d-f23cc4b5cb09",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>consentement optin mediation specialisee</th>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consentement optin jeune public</th>\n",
       "      <td>149979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consentement optin b2c</th>\n",
       "      <td>108909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arenametrix_bascule tel vers sib</th>\n",
       "      <td>35216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consentement optout b2c</th>\n",
       "      <td>34523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          customer_id\n",
       "target_name                                          \n",
       "consentement optin mediation specialisee       150000\n",
       "consentement optin jeune public                149979\n",
       "consentement optin b2c                         108909\n",
       "Arenametrix_bascule tel vers sib                35216\n",
       "consentement optout b2c                         34523"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_target_information[['target_name', 'customer_id']].groupby('target_name').count().sort_values(by='customer_id', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4417ff51-f501-4ab9-a192-4ab75764a8ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arenametrix_bascule tel vers sib</th>\n",
       "      <td>35216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Autres_interet_exposition</th>\n",
       "      <td>1021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COM Inscrits NL générale (historique)</th>\n",
       "      <td>23005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contacts_prenomsdoubles</th>\n",
       "      <td>11643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDCP MD Procès du Siècle</th>\n",
       "      <td>1684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       customer_id\n",
       "target_name                                       \n",
       "Arenametrix_bascule tel vers sib             35216\n",
       "Autres_interet_exposition                     1021\n",
       "COM Inscrits NL générale (historique)        23005\n",
       "Contacts_prenomsdoubles                      11643\n",
       "DDCP MD Procès du Siècle                      1684"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_target_information_reduced = df1_target_information[['target_name', 'customer_id']].groupby('target_name').count()\n",
    "df1_target_information_reduced[df1_target_information_reduced['customer_id'] >= 1000].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdbb48b4-5e16-4ef4-8791-ed213d68d52f",
   "metadata": {},
   "source": [
    "## Campaings area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d883cc7b-ac43-4485-b86f-eaf595fbad85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_campaigns_area(campaign_stats = None, campaigns = None):\n",
    "    # campaign_stats cleaning \n",
    "    campaign_stats = campaign_stats[[\"id\", \"campaign_id\", \"customer_id\", \"opened_at\", \"sent_at\", \"delivered_at\"]]\n",
    "    cleaning_date(campaign_stats, 'opened_at')\n",
    "    cleaning_date(campaign_stats, 'sent_at')\n",
    "    cleaning_date(campaign_stats, 'delivered_at')\n",
    "    \n",
    "    # campaigns cleaning\n",
    "    campaigns = campaigns[[\"id\", \"name\", \"service_id\", \"sent_at\"]].add_prefix(\"campaign_\")\n",
    "    cleaning_date(campaigns, 'campaign_sent_at')\n",
    "    \n",
    "    # Merge \n",
    "    campaigns_full = pd.merge(campaign_stats, campaigns, on = \"campaign_id\", how = \"left\")\n",
    "    campaigns_full.drop(['campaign_id'], axis = 1, inplace=True)\n",
    "\n",
    "    return campaigns_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c8552dd6-52c5-4431-b43d-3cd6c578fd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15815/1967867975.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
      "/tmp/ipykernel_15815/1967867975.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
      "/tmp/ipykernel_15815/1967867975.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n"
     ]
    }
   ],
   "source": [
    "df1_campaigns_information = preprocessing_campaigns_area(campaign_stats = df1_campaign_stats, campaigns = df1_campaigns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c24457e7-3cad-451a-a65b-7373b656bd6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>opened_at</th>\n",
       "      <th>sent_at</th>\n",
       "      <th>delivered_at</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_service_id</th>\n",
       "      <th>campaign_sent_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19793</td>\n",
       "      <td>112597</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:01:09+00:00</td>\n",
       "      <td>2021-03-28 16:24:18+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14211</td>\n",
       "      <td>113666</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:01:09+00:00</td>\n",
       "      <td>2021-03-28 16:21:02+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13150</td>\n",
       "      <td>280561</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:00:59+00:00</td>\n",
       "      <td>2021-03-28 16:08:45+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7073</td>\n",
       "      <td>101007</td>\n",
       "      <td>2021-03-28 18:11:06+00:00</td>\n",
       "      <td>2021-03-28 16:00:59+00:00</td>\n",
       "      <td>2021-03-28 16:09:47+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5175</td>\n",
       "      <td>103972</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:01:06+00:00</td>\n",
       "      <td>2021-03-28 16:05:03+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  customer_id                 opened_at                   sent_at  \\\n",
       "0  19793       112597                       NaT 2021-03-28 16:01:09+00:00   \n",
       "1  14211       113666                       NaT 2021-03-28 16:01:09+00:00   \n",
       "2  13150       280561                       NaT 2021-03-28 16:00:59+00:00   \n",
       "3   7073       101007 2021-03-28 18:11:06+00:00 2021-03-28 16:00:59+00:00   \n",
       "4   5175       103972                       NaT 2021-03-28 16:01:06+00:00   \n",
       "\n",
       "               delivered_at                            campaign_name  \\\n",
       "0 2021-03-28 16:24:18+00:00  Le Mucem chez vous, gardons le lien #22   \n",
       "1 2021-03-28 16:21:02+00:00  Le Mucem chez vous, gardons le lien #22   \n",
       "2 2021-03-28 16:08:45+00:00  Le Mucem chez vous, gardons le lien #22   \n",
       "3 2021-03-28 16:09:47+00:00  Le Mucem chez vous, gardons le lien #22   \n",
       "4 2021-03-28 16:05:03+00:00  Le Mucem chez vous, gardons le lien #22   \n",
       "\n",
       "   campaign_service_id          campaign_sent_at  \n",
       "0                  404 2021-03-27 23:00:00+00:00  \n",
       "1                  404 2021-03-27 23:00:00+00:00  \n",
       "2                  404 2021-03-27 23:00:00+00:00  \n",
       "3                  404 2021-03-27 23:00:00+00:00  \n",
       "4                  404 2021-03-27 23:00:00+00:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_campaigns_information.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2c88552-b863-47a2-be23-8d2898fb28bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def campaigns_kpi_function(campaigns_information = None):\n",
    "    # Nombre de campagnes de mails\n",
    "    nb_campaigns = campaigns_information[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index()\n",
    "    nb_campaigns.rename(columns = {'campaign_name' : 'nb_campaigns'}, inplace = True)\n",
    "    # Temps d'ouverture en min moyen    \n",
    "    campaigns_information['time_to_open'] = campaigns_information['opened_at'] - campaigns_information['delivered_at']\n",
    "    time_to_open = campaigns_information[['customer_id', 'time_to_open']].groupby('customer_id').mean().reset_index()\n",
    "\n",
    "    # Nombre de mail ouvert    \n",
    "    opened_campaign = campaigns_information[['customer_id', 'campaign_name', 'opened_at']]\n",
    "    opened_campaign.dropna(subset=['opened_at'], inplace=True)\n",
    "    opened_campaign = opened_campaign[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index()\n",
    "    opened_campaign.rename(columns = {'campaign_name' : 'nb_campaigns_opened' }, inplace = True)\n",
    "\n",
    "    # Fusion des indicateurs\n",
    "    campaigns_reduced = pd.merge(nb_campaigns, opened_campaign, on = 'customer_id', how = 'left')\n",
    "    campaigns_reduced = pd.merge(campaigns_reduced, time_to_open, on = 'customer_id', how = 'left')\n",
    "\n",
    "    # Remplir les NaN : nb_campaigns_opened\n",
    "    campaigns_reduced['nb_campaigns_opened'].fillna(0, inplace=True)\n",
    "\n",
    "    # Remplir les NaT : time_to_open (??)\n",
    "\n",
    "    return campaigns_reduced\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24537647-bc29-4777-9848-ac4120a4aa60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15815/3700263836.py:11: 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",
      "  opened_campaign.dropna(subset=['opened_at'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df1_campaigns_kpi = campaigns_kpi_function(campaigns_information = df1_campaigns_information) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6be2a9a6-056b-4e19-8c26-a18ba3df36b3",
   "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>nb_campaigns</th>\n",
       "      <th>nb_campaigns_opened</th>\n",
       "      <th>time_to_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1 days 00:28:30.169354838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1 days 04:31:01.428571428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  nb_campaigns  nb_campaigns_opened              time_to_open\n",
       "0            2             4                  0.0                       NaT\n",
       "1            3           222                124.0 1 days 00:28:30.169354838\n",
       "2            4             7                  7.0 1 days 04:31:01.428571428\n",
       "3            5             4                  0.0                       NaT\n",
       "4            6            20                  0.0                       NaT"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_campaigns_kpi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56520a97-ede8-4920-a211-3b5b136af33d",
   "metadata": {},
   "source": [
    "## Create Products Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9782e9d3-ba20-46bf-8562-bd0969972ddc",
   "metadata": {},
   "source": [
    "Some useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30488a40-1b38-4b9a-9d3b-26a0597c5e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET = \"bdc2324-data\"\n",
    "directory_path = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "607eb4b4-eed9-4b50-b823-f75c116dd37c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_databases(file_name):\n",
    "    \"\"\"\n",
    "    This function returns the file from s3 storage\n",
    "    \"\"\"\n",
    "    file_path = BUCKET + \"/\" + directory_path + \"/\" + file_name\n",
    "    print(\"File path : \", file_path)\n",
    "    with fs.open(file_path, mode=\"rb\") as file_in:\n",
    "        df = pd.read_csv(file_in, sep=\",\")\n",
    "        \n",
    "    print(\"Shape : \", df.shape)\n",
    "    return df\n",
    "\n",
    "\n",
    "def remove_horodates(df):\n",
    "    \"\"\"\n",
    "    this function remove horodate columns like created_at and updated_at\n",
    "    \"\"\"\n",
    "    df = df.drop(columns = [\"created_at\", \"updated_at\"])\n",
    "    return df\n",
    "\n",
    "\n",
    "def order_columns_id(df):\n",
    "    \"\"\"\n",
    "    this function puts all id columns at the beginning in order to read the dataset easier\n",
    "    \"\"\"\n",
    "    substring = 'id'\n",
    "    id_columns = [col for col in df.columns if substring in col]\n",
    "    remaining_col = [col for col in df.columns if substring not in col]\n",
    "    new_order = id_columns + remaining_col\n",
    "    return df[new_order]\n",
    "\n",
    "\n",
    "def process_df_2(df):\n",
    "    \"\"\"\n",
    "    This function organizes dataframe\n",
    "    \"\"\"\n",
    "    df = remove_horodates(df)\n",
    "    print(\"Number of columns : \", len(df.columns))\n",
    "    df = order_columns_id(df)\n",
    "    print(\"Columns : \", df.columns)\n",
    "    return df\n",
    "\n",
    "def load_dataset(name):\n",
    "    \"\"\"\n",
    "    This function loads csv file\n",
    "    \"\"\"\n",
    "    df = display_databases(name)\n",
    "    df = process_df_2(df)\n",
    "    # drop na :\n",
    "    #df = df.dropna(axis=1, thresh=len(df))\n",
    "    # if identifier in table : delete it\n",
    "    if 'identifier' in df.columns:\n",
    "        df = df.drop(columns = 'identifier')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23f28c0-bc95-438b-8d14-5b7bb6e267bd",
   "metadata": {},
   "source": [
    "Create theme tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "350b09b9-451f-4d47-81fe-f34b892db027",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_products_table():\n",
    "    # first merge products and categories\n",
    "    print(\"first merge products and categories\")\n",
    "    products = load_dataset(\"1products.csv\")\n",
    "    categories = load_dataset(\"1categories.csv\")\n",
    "    # Drop useless columns\n",
    "    products = products.drop(columns = ['apply_price', 'extra_field', 'amount_consumption'])\n",
    "    categories = categories.drop(columns = ['extra_field', 'quota'])\n",
    "\n",
    "    #Merge\n",
    "    products_theme = products.merge(categories, how = 'left', left_on = 'category_id',\n",
    "                                    right_on = 'id', suffixes=('_products', '_categories'))\n",
    "    products_theme = products_theme.rename(columns = {\"name\" : \"name_categories\"})\n",
    "    \n",
    "    # Second merge products_theme and type of categories\n",
    "    print(\"Second merge products_theme and type of categories\")\n",
    "    type_of_categories = load_dataset(\"1type_of_categories.csv\")\n",
    "    type_of_categories = type_of_categories.drop(columns = 'id')\n",
    "    products_theme = products_theme.merge(type_of_categories, how = 'left', left_on = 'category_id',\n",
    "                                          right_on = 'category_id' )\n",
    "\n",
    "    # Index cleaning\n",
    "    products_theme = products_theme.drop(columns = ['id_categories'])\n",
    "    products_theme  = order_columns_id(products_theme)\n",
    "    return products_theme\n",
    "\n",
    "\n",
    "def create_events_table():\n",
    "    # first merge events and seasons : \n",
    "    print(\"first merge events and seasons : \")\n",
    "    events = load_dataset(\"1events.csv\")\n",
    "    seasons = load_dataset(\"1seasons.csv\")\n",
    "\n",
    "    # Drop useless columns\n",
    "    events = events.drop(columns = ['manual_added', 'is_display'])\n",
    "    seasons = seasons.drop(columns = ['start_date_time'])\n",
    "        \n",
    "    events_theme = events.merge(seasons, how = 'left', left_on = 'season_id', right_on = 'id', suffixes=('_events', '_seasons'))\n",
    "\n",
    "    # Secondly merge events_theme and event_types\n",
    "    print(\"Secondly merge events_theme and event_types : \")\n",
    "    event_types = load_dataset(\"1event_types.csv\")\n",
    "    event_types = event_types.drop(columns = ['fidelity_delay'])\n",
    "    \n",
    "    events_theme = events_theme.merge(event_types, how = 'left', left_on = 'event_type_id', right_on = 'id', suffixes=('_events', '_event_type'))\n",
    "    events_theme = events_theme.rename(columns = {\"name\" : \"name_event_types\"})\n",
    "    events_theme = events_theme.drop(columns = 'id')\n",
    "\n",
    "    # thirdly merge events_theme and facilities\n",
    "    print(\"thirdly merge events_theme and facilities : \")\n",
    "    facilities = load_dataset(\"1facilities.csv\")\n",
    "    facilities = facilities.drop(columns = ['fixed_capacity'])\n",
    "    \n",
    "    events_theme = events_theme.merge(facilities, how = 'left', left_on = 'facility_id', right_on = 'id', suffixes=('_events', '_facility'))\n",
    "    events_theme = events_theme.rename(columns = {\"name\" : \"name_facilities\", \"id_events\" : \"event_id\"})\n",
    "    events_theme = events_theme.drop(columns = 'id')\n",
    "\n",
    "    # Index cleaning\n",
    "    events_theme = events_theme.drop(columns = ['id_seasons'])\n",
    "    events_theme  = order_columns_id(events_theme)\n",
    "    return events_theme\n",
    "\n",
    "\n",
    "def create_representations_table():\n",
    "    representations = load_dataset(\"1representations.csv\")\n",
    "    representations = representations.drop(columns = ['serial', 'open', 'satisfaction', 'is_display', 'expected_filling',\n",
    "                                                     'max_filling', 'extra_field', 'start_date_time', 'end_date_time', 'name',\n",
    "                                                     'representation_type_id'])\n",
    "    \n",
    "    representations_capacity = load_dataset(\"1representation_category_capacities.csv\")\n",
    "    representations_capacity = representations_capacity.drop(columns = ['expected_filling', 'max_filling'])\n",
    "\n",
    "    representations_theme = representations.merge(representations_capacity, how='left',\n",
    "                                                  left_on='id', right_on='representation_id',\n",
    "                                                  suffixes=('_representation', '_representation_cap'))\n",
    "    # index cleaning\n",
    "    representations_theme = representations_theme.drop(columns = [\"id_representation\"])\n",
    "    representations_theme = order_columns_id(representations_theme)\n",
    "    return representations_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0fccc8ef-e575-4857-a401-94a7274394df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first merge products and categories\n",
      "File path :  bdc2324-data/1/1products.csv\n",
      "Shape :  (94803, 14)\n",
      "Number of columns :  12\n",
      "Columns :  Index(['id', 'representation_id', 'pricing_formula_id', 'category_id',\n",
      "       'products_group_id', 'product_pack_id', 'identifier', 'amount',\n",
      "       'is_full_price', 'apply_price', 'extra_field', 'amount_consumption'],\n",
      "      dtype='object')\n",
      "File path :  bdc2324-data/1/1categories.csv\n",
      "Shape :  (27, 7)\n",
      "Number of columns :  5\n",
      "Columns :  Index(['id', 'identifier', 'name', 'extra_field', 'quota'], dtype='object')\n",
      "Second merge products_theme and type of categories\n",
      "File path :  bdc2324-data/1/1type_of_categories.csv\n",
      "Shape :  (5, 6)\n",
      "Number of columns :  4\n",
      "Columns :  Index(['id', 'type_of_id', 'category_id', 'identifier'], dtype='object')\n"
     ]
    },
    {
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       "      <th>id_products</th>\n",
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      ],
      "text/plain": [
       "   id_products  representation_id  pricing_formula_id  category_id  \\\n",
       "0        10682                914                 114           41   \n",
       "1          478                273                 131            1   \n",
       "2        20873                275                 137            1   \n",
       "3       157142              82519                   9            5   \n",
       "4         1341                  9                  93            1   \n",
       "\n",
       "   products_group_id  product_pack_id  type_of_id  amount  is_full_price  \\\n",
       "0              10655                1         NaN     9.0          False   \n",
       "1                471                1        12.0     9.5          False   \n",
       "2              20825                1        12.0    11.5          False   \n",
       "3             156773                1         NaN     8.0          False   \n",
       "4               1175                1        12.0     8.5          False   \n",
       "\n",
       "     name_categories  \n",
       "0  indiv activité tr  \n",
       "1   indiv entrées tp  \n",
       "2   indiv entrées tp  \n",
       "3   indiv entrées tr  \n",
       "4   indiv entrées tp  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_theme = create_products_table()\n",
    "products_theme.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "779d8aaf-6668-4f66-8852-847304407ea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first merge events and seasons : \n",
      "File path :  bdc2324-data/1/1events.csv\n",
      "Shape :  (1232, 12)\n",
      "Number of columns :  10\n",
      "Columns :  Index(['id', 'season_id', 'facility_id', 'event_type_id', 'event_type_key_id',\n",
      "       'facility_key_id', 'identifier', 'name', 'manual_added', 'is_display'],\n",
      "      dtype='object')\n",
      "File path :  bdc2324-data/1/1seasons.csv\n",
      "Shape :  (13, 6)\n",
      "Number of columns :  4\n",
      "Columns :  Index(['id', 'identifier', 'name', 'start_date_time'], dtype='object')\n",
      "Secondly merge events_theme and event_types : \n",
      "File path :  bdc2324-data/1/1event_types.csv\n",
      "Shape :  (9, 6)\n",
      "Number of columns :  4\n",
      "Columns :  Index(['id', 'fidelity_delay', 'identifier', 'name'], dtype='object')\n",
      "thirdly merge events_theme and facilities : \n",
      "File path :  bdc2324-data/1/1facilities.csv\n",
      "Shape :  (2, 7)\n",
      "Number of columns :  5\n",
      "Columns :  Index(['id', 'street_id', 'identifier', 'name', 'fixed_capacity'], dtype='object')\n"
     ]
    },
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       "      <td>1</td>\n",
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       "      <td>2023</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   event_id  season_id  facility_id  event_type_id  event_type_key_id  \\\n",
       "0       192         16            1              4                  4   \n",
       "1     30329       2767            1              5                  5   \n",
       "2       161         16            1              2                  2   \n",
       "3      5957        582            1              4                  4   \n",
       "4      8337        582            1              4                  4   \n",
       "\n",
       "   facility_key_id  street_id  \\\n",
       "0                1          1   \n",
       "1                1          1   \n",
       "2                1          1   \n",
       "3                1          1   \n",
       "4                1          1   \n",
       "\n",
       "                                        name_events name_seasons  \\\n",
       "0                                        frontières         2018   \n",
       "1  visite guidée une autre histoire du monde (1h00)         2023   \n",
       "2           visite contée les chercheurs d'or indiv         2018   \n",
       "3     we dreamt of utopia and we woke up screaming.         2021   \n",
       "4                             jeff koons épisodes 4         2021   \n",
       "\n",
       "           name_event_types name_facilities  \n",
       "0          spectacle vivant           mucem  \n",
       "1      offre muséale groupe           mucem  \n",
       "2  offre muséale individuel           mucem  \n",
       "3          spectacle vivant           mucem  \n",
       "4          spectacle vivant           mucem  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_theme= create_events_table()\n",
    "events_theme.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7714fa32-303b-4ea7-b174-3fd0fcab5af0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File path :  bdc2324-data/1/1representations.csv\n",
      "Shape :  (36095, 16)\n",
      "Number of columns :  14\n",
      "Columns :  Index(['id', 'event_id', 'representation_type_id', 'identifier', 'serial',\n",
      "       'start_date_time', 'open', 'satisfaction', 'end_date_time', 'name',\n",
      "       'is_display', 'expected_filling', 'max_filling', 'extra_field'],\n",
      "      dtype='object')\n",
      "File path :  bdc2324-data/1/1representation_category_capacities.csv\n",
      "Shape :  (65241, 7)\n",
      "Number of columns :  5\n",
      "Columns :  Index(['id', 'representation_id', 'category_id', 'expected_filling',\n",
      "       'max_filling'],\n",
      "      dtype='object')\n"
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       "   event_id  id_representation_cap  representation_id  category_id\n",
       "0     12384                 123058              84820            2\n",
       "1        37                   2514                269            2\n",
       "2        37                    384                269            5\n",
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       "4        37                    383                269            1"
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     },
     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "representation_theme = create_representations_table()\n",
    "representation_theme.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa191d5-c867-4d4d-bbab-f29d7d91ce6a",
   "metadata": {},
   "source": [
    "Create uniform product database "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "15a62ed6-35e4-4abc-aeef-a7daeec0a4ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def uniform_product_df():\n",
    "    \"\"\"\n",
    "    This function returns the uniform product dataset\n",
    "    \"\"\"\n",
    "    print(\"Products theme columns : \", products_theme.columns)\n",
    "    print(\"\\n Representation theme columns : \", representation_theme.columns)\n",
    "    print(\"\\n Events theme columns : \", events_theme.columns)\n",
    "\n",
    "    products_global = products_theme.merge(representation_theme, how='left',\n",
    "                                           on= [\"representation_id\", \"category_id\"])\n",
    "    \n",
    "    products_global = products_global.merge(events_theme, how='left', on='event_id',\n",
    "                                            suffixes = (\"_representation\", \"_event\"))\n",
    "    \n",
    "    products_global = order_columns_id(products_global)\n",
    "\n",
    "    # remove useless columns \n",
    "    products_global = products_global.drop(columns = ['type_of_id']) # 'name_events', 'name_seasons', 'name_categories'\n",
    "    return products_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89dc9685-1de9-4ce3-a6c0-8d7f1931a951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Products theme columns :  Index(['id_products', 'representation_id', 'pricing_formula_id', 'category_id',\n",
      "       'products_group_id', 'product_pack_id', 'type_of_id', 'amount',\n",
      "       'is_full_price', 'name_categories'],\n",
      "      dtype='object')\n",
      "\n",
      " Representation theme columns :  Index(['event_id', 'id_representation_cap', 'representation_id',\n",
      "       'category_id'],\n",
      "      dtype='object')\n",
      "\n",
      " Events theme columns :  Index(['event_id', 'season_id', 'facility_id', 'event_type_id',\n",
      "       'event_type_key_id', 'facility_key_id', 'street_id', 'name_events',\n",
      "       'name_seasons', 'name_event_types', 'name_facilities'],\n",
      "      dtype='object')\n"
     ]
    },
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       "      <th>season_id</th>\n",
       "      <th>facility_id</th>\n",
       "      <th>...</th>\n",
       "      <th>event_type_key_id</th>\n",
       "      <th>facility_key_id</th>\n",
       "      <th>street_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>is_full_price</th>\n",
       "      <th>name_categories</th>\n",
       "      <th>name_events</th>\n",
       "      <th>name_seasons</th>\n",
       "      <th>name_event_types</th>\n",
       "      <th>name_facilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10682</td>\n",
       "      <td>914</td>\n",
       "      <td>114</td>\n",
       "      <td>41</td>\n",
       "      <td>10655</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>8789</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>False</td>\n",
       "      <td>indiv activité tr</td>\n",
       "      <td>visite-jeu \"le classico des minots\" (1h30)</td>\n",
       "      <td>2017</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>mucem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>478</td>\n",
       "      <td>273</td>\n",
       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>390</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>False</td>\n",
       "      <td>indiv entrées tp</td>\n",
       "      <td>billet mucem picasso</td>\n",
       "      <td>2016</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>mucem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20873</td>\n",
       "      <td>275</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>20825</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>395</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>False</td>\n",
       "      <td>indiv entrées tp</td>\n",
       "      <td>billet mucem picasso</td>\n",
       "      <td>2016</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>mucem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157142</td>\n",
       "      <td>82519</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>156773</td>\n",
       "      <td>1</td>\n",
       "      <td>12365</td>\n",
       "      <td>120199</td>\n",
       "      <td>1754</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>False</td>\n",
       "      <td>indiv entrées tr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>mucem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1341</td>\n",
       "      <td>9</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>1175</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>False</td>\n",
       "      <td>indiv entrées tp</td>\n",
       "      <td>non défini</td>\n",
       "      <td>2017</td>\n",
       "      <td>non défini</td>\n",
       "      <td>mucem</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_products  representation_id  pricing_formula_id  category_id  \\\n",
       "0        10682                914                 114           41   \n",
       "1          478                273                 131            1   \n",
       "2        20873                275                 137            1   \n",
       "3       157142              82519                   9            5   \n",
       "4         1341                  9                  93            1   \n",
       "\n",
       "   products_group_id  product_pack_id  event_id  id_representation_cap  \\\n",
       "0              10655                1       132                   8789   \n",
       "1                471                1        37                    390   \n",
       "2              20825                1        37                    395   \n",
       "3             156773                1     12365                 120199   \n",
       "4               1175                1         8                     21   \n",
       "\n",
       "   season_id  facility_id  ...  event_type_key_id  facility_key_id  street_id  \\\n",
       "0          4            1  ...                  5                1          1   \n",
       "1          2            1  ...                  2                1          1   \n",
       "2          2            1  ...                  2                1          1   \n",
       "3       1754            1  ...                  4                1          1   \n",
       "4          4            1  ...                  6                1          1   \n",
       "\n",
       "   amount  is_full_price    name_categories  \\\n",
       "0     9.0          False  indiv activité tr   \n",
       "1     9.5          False   indiv entrées tp   \n",
       "2    11.5          False   indiv entrées tp   \n",
       "3     8.0          False   indiv entrées tr   \n",
       "4     8.5          False   indiv entrées tp   \n",
       "\n",
       "                                  name_events name_seasons  \\\n",
       "0  visite-jeu \"le classico des minots\" (1h30)         2017   \n",
       "1                        billet mucem picasso         2016   \n",
       "2                        billet mucem picasso         2016   \n",
       "3                                         NaN          NaN   \n",
       "4                                  non défini         2017   \n",
       "\n",
       "           name_event_types name_facilities  \n",
       "0  offre muséale individuel           mucem  \n",
       "1  offre muséale individuel           mucem  \n",
       "2  offre muséale individuel           mucem  \n",
       "3  offre muséale individuel           mucem  \n",
       "4                non défini           mucem  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_global = uniform_product_df()\n",
    "products_global.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "98f78cd5-b694-4cc6-b033-20170aa13e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fusion liée au product\n",
    "df1_products_purchased = pd.merge(df1_ticket_information, products_global, left_on = 'product_id', right_on = 'id_products', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52db7bcb-3fb7-48e5-b612-4e22bdab4a94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d4dcfbe0-c6ce-497e-b75e-dc9e938801b2",
   "metadata": {},
   "source": [
    "### KPI tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "665a5925-9c0e-425a-8f11-c33a0a9ec444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ticket_id', 'product_id', 'is_from_subscription', 'supplier_name',\n",
       "       'type_of_ticket_name', 'children', 'purchase_date', 'customer_id',\n",
       "       'id_products', 'representation_id', 'pricing_formula_id', 'category_id',\n",
       "       'products_group_id', 'product_pack_id', 'event_id',\n",
       "       'id_representation_cap', 'season_id', 'facility_id', 'event_type_id',\n",
       "       'event_type_key_id', 'facility_key_id', 'street_id', 'amount',\n",
       "       'is_full_price', 'name_categories', 'name_events', 'name_seasons',\n",
       "       'name_event_types', 'name_facilities'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_products_purchased.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b913a69e-3146-4919-b5f6-a6108532bffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['spectacle vivant', 'offre muséale individuel', 'formule adhésion',\n",
       "       'offre muséale groupe'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_products_purchased['name_event_types'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e01e8cf9-1187-4a4b-993d-b7b4321cd8f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_products_purchased_reduced = df1_products_purchased[['ticket_id', 'customer_id',  'event_type_id', 'supplier_name', 'purchase_date', 'type_of_ticket_name', 'amount', 'children', 'is_full_price', 'name_event_types', 'name_facilities', 'name_categories', 'name_events', 'name_seasons']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3d8b0875-b409-44ce-b688-d9d6758782d3",
   "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>ticket_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>event_type_id</th>\n",
       "      <th>supplier_name</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>type_of_ticket_name</th>\n",
       "      <th>amount</th>\n",
       "      <th>children</th>\n",
       "      <th>is_full_price</th>\n",
       "      <th>name_event_types</th>\n",
       "      <th>name_facilities</th>\n",
       "      <th>name_categories</th>\n",
       "      <th>name_events</th>\n",
       "      <th>name_seasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13070859</td>\n",
       "      <td>48187</td>\n",
       "      <td>4</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv prog enfant</td>\n",
       "      <td>l'école des magiciens</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13070855</td>\n",
       "      <td>48187</td>\n",
       "      <td>4</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv prog enfant</td>\n",
       "      <td>l'école des magiciens</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13070856</td>\n",
       "      <td>48187</td>\n",
       "      <td>4</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv prog enfant</td>\n",
       "      <td>l'école des magiciens</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13070857</td>\n",
       "      <td>48187</td>\n",
       "      <td>4</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv prog enfant</td>\n",
       "      <td>l'école des magiciens</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13070858</td>\n",
       "      <td>48187</td>\n",
       "      <td>4</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>2018-12-28 14:47:50+00:00</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv prog enfant</td>\n",
       "      <td>l'école des magiciens</td>\n",
       "      <td>2018</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826667</th>\n",
       "      <td>18643494</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>vad</td>\n",
       "      <td>2022-08-02 12:18:16+00:00</td>\n",
       "      <td>Billet en nombre</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>en nb entrées tr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826668</th>\n",
       "      <td>18643495</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>vad</td>\n",
       "      <td>2022-08-02 12:18:16+00:00</td>\n",
       "      <td>Billet en nombre</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>en nb entrées tr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826669</th>\n",
       "      <td>18643496</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>vad</td>\n",
       "      <td>2022-08-02 12:18:16+00:00</td>\n",
       "      <td>Billet en nombre</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>en nb entrées tr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826670</th>\n",
       "      <td>18643497</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>vad</td>\n",
       "      <td>2022-08-02 12:18:16+00:00</td>\n",
       "      <td>Billet en nombre</td>\n",
       "      <td>11.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>en nb entrées tr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826671</th>\n",
       "      <td>19853111</td>\n",
       "      <td>62763</td>\n",
       "      <td>4</td>\n",
       "      <td>vad</td>\n",
       "      <td>2022-11-04 14:25:42+00:00</td>\n",
       "      <td>Billet en nombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>False</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>mucem</td>\n",
       "      <td>indiv entrées gr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1826672 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ticket_id  customer_id  event_type_id   supplier_name  \\\n",
       "0         13070859        48187              4  vente en ligne   \n",
       "1         13070855        48187              4  vente en ligne   \n",
       "2         13070856        48187              4  vente en ligne   \n",
       "3         13070857        48187              4  vente en ligne   \n",
       "4         13070858        48187              4  vente en ligne   \n",
       "...            ...          ...            ...             ...   \n",
       "1826667   18643494           81              4             vad   \n",
       "1826668   18643495           81              4             vad   \n",
       "1826669   18643496           81              4             vad   \n",
       "1826670   18643497           81              4             vad   \n",
       "1826671   19853111        62763              4             vad   \n",
       "\n",
       "                    purchase_date type_of_ticket_name  amount  \\\n",
       "0       2018-12-28 14:47:50+00:00             Atelier     8.0   \n",
       "1       2018-12-28 14:47:50+00:00             Atelier     8.0   \n",
       "2       2018-12-28 14:47:50+00:00             Atelier     8.0   \n",
       "3       2018-12-28 14:47:50+00:00             Atelier     8.0   \n",
       "4       2018-12-28 14:47:50+00:00             Atelier     8.0   \n",
       "...                           ...                 ...     ...   \n",
       "1826667 2022-08-02 12:18:16+00:00    Billet en nombre    11.0   \n",
       "1826668 2022-08-02 12:18:16+00:00    Billet en nombre    11.0   \n",
       "1826669 2022-08-02 12:18:16+00:00    Billet en nombre    11.0   \n",
       "1826670 2022-08-02 12:18:16+00:00    Billet en nombre    11.0   \n",
       "1826671 2022-11-04 14:25:42+00:00    Billet en nombre     0.0   \n",
       "\n",
       "                children  is_full_price  name_event_types name_facilities  \\\n",
       "0        pricing_formula          False  spectacle vivant           mucem   \n",
       "1        pricing_formula          False  spectacle vivant           mucem   \n",
       "2        pricing_formula          False  spectacle vivant           mucem   \n",
       "3        pricing_formula          False  spectacle vivant           mucem   \n",
       "4        pricing_formula          False  spectacle vivant           mucem   \n",
       "...                  ...            ...               ...             ...   \n",
       "1826667  pricing_formula          False  spectacle vivant           mucem   \n",
       "1826668  pricing_formula          False  spectacle vivant           mucem   \n",
       "1826669  pricing_formula          False  spectacle vivant           mucem   \n",
       "1826670  pricing_formula          False  spectacle vivant           mucem   \n",
       "1826671  pricing_formula          False  spectacle vivant           mucem   \n",
       "\n",
       "           name_categories            name_events name_seasons  \n",
       "0        indiv prog enfant  l'école des magiciens         2018  \n",
       "1        indiv prog enfant  l'école des magiciens         2018  \n",
       "2        indiv prog enfant  l'école des magiciens         2018  \n",
       "3        indiv prog enfant  l'école des magiciens         2018  \n",
       "4        indiv prog enfant  l'école des magiciens         2018  \n",
       "...                    ...                    ...          ...  \n",
       "1826667   en nb entrées tr                    NaN         2022  \n",
       "1826668   en nb entrées tr                    NaN         2022  \n",
       "1826669   en nb entrées tr                    NaN         2022  \n",
       "1826670   en nb entrées tr                    NaN         2022  \n",
       "1826671   indiv entrées gr                    NaN         2022  \n",
       "\n",
       "[1826672 rows x 14 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importance des suppliers\n",
    "df1_products_purchased_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2bda0b97-b28b-4070-a57d-aeab0e2f7dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nombre de client assistant à plus de 2 type d'événement\n",
    "nb_event_types = df1_products_purchased_reduced[['customer_id', 'name_event_types']].groupby('customer_id').nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "043303fe-e90f-4689-a2a9-5d690555a045",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tickets_kpi_function(tickets_information = None):\n",
    "    tickets_information_copy = tickets_information.copy()\n",
    "    tickets_information_copy['purchase_date_max'] = tickets_information_copy['purchase_date']\n",
    "    tickets_kpi = (tickets_information_copy[['event_type_id', 'customer_id', 'ticket_id','supplier_name', 'purchase_date', 'purchase_date_max', 'amount']]\n",
    "                   .groupby([ 'customer_id']) # 'event_type_id',\n",
    "                   .agg({'ticket_id': 'count', \n",
    "                         'amount' : 'sum',\n",
    "                         'supplier_name': 'nunique',\n",
    "                         'purchase_date_max' : 'max',\n",
    "                         'purchase_date' : 'min'})\n",
    "                   .reset_index()\n",
    "                  )\n",
    "    \n",
    "    tickets_kpi.rename(columns = {'ticket_id' : 'nb_tickets', \n",
    "                                  'amount' : 'total_amount',\n",
    "                                  'supplier_name' : 'nb_suppliers', \n",
    "                                  'purchase_date' : 'purchase_date_min'}, inplace = True)\n",
    "    \n",
    "    tickets_kpi['time_between_purchase'] = tickets_kpi['purchase_date_max'] - tickets_kpi['purchase_date_min']\n",
    "    \n",
    "    return tickets_kpi\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5882234a-1ed5-4269-87a6-0d75613476e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_tickets_kpi = tickets_kpi_function(tickets_information = df1_products_purchased_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a7a452a6-cd5e-4c8b-b250-8a7d26e48fad",
   "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>nb_tickets</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>nb_suppliers</th>\n",
       "      <th>purchase_date_max</th>\n",
       "      <th>purchase_date_min</th>\n",
       "      <th>time_between_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1256574</td>\n",
       "      <td>8830567.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2023-11-08 15:59:45+00:00</td>\n",
       "      <td>2013-06-10 10:37:58+00:00</td>\n",
       "      <td>3803 days 05:21:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>6733</td>\n",
       "      <td>35527</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-11-03 09:42:40+00:00</td>\n",
       "      <td>2015-09-09 13:48:38+00:00</td>\n",
       "      <td>2976 days 19:54:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>41</td>\n",
       "      <td>16263</td>\n",
       "      <td>37642.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-10-25 09:13:16+00:00</td>\n",
       "      <td>2014-01-23 16:56:57+00:00</td>\n",
       "      <td>3561 days 16:16:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>5871</td>\n",
       "      <td>38767.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-11-04 13:46:59+00:00</td>\n",
       "      <td>2018-04-04 07:46:31+00:00</td>\n",
       "      <td>2040 days 06:00:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32809</th>\n",
       "      <td>63488</td>\n",
       "      <td>5851</td>\n",
       "      <td>64350.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25 13:08:38+00:00</td>\n",
       "      <td>2020-08-18 08:32:57+00:00</td>\n",
       "      <td>737 days 04:35:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3708</th>\n",
       "      <td>6916</td>\n",
       "      <td>5482</td>\n",
       "      <td>51489.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-08-26 12:49:17+00:00</td>\n",
       "      <td>2018-03-26 11:13:43+00:00</td>\n",
       "      <td>1249 days 01:35:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32616</th>\n",
       "      <td>63194</td>\n",
       "      <td>4507</td>\n",
       "      <td>13232.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-09-07 12:55:33+00:00</td>\n",
       "      <td>2017-11-28 13:52:15+00:00</td>\n",
       "      <td>1743 days 23:03:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>81</td>\n",
       "      <td>3562</td>\n",
       "      <td>38746.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-30 11:51:34+00:00</td>\n",
       "      <td>2017-01-05 13:04:58+00:00</td>\n",
       "      <td>2062 days 22:46:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35295</th>\n",
       "      <td>84002</td>\n",
       "      <td>3403</td>\n",
       "      <td>19830.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-11-06 15:59:22+00:00</td>\n",
       "      <td>2021-05-28 10:22:33+00:00</td>\n",
       "      <td>892 days 05:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>5618</td>\n",
       "      <td>3294</td>\n",
       "      <td>31684.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-24 07:47:20+00:00</td>\n",
       "      <td>2018-10-25 11:04:24+00:00</td>\n",
       "      <td>1217 days 20:42:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30011</th>\n",
       "      <td>59259</td>\n",
       "      <td>2591</td>\n",
       "      <td>4350.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-06-12 14:05:19+00:00</td>\n",
       "      <td>2019-11-25 08:52:48+00:00</td>\n",
       "      <td>1295 days 05:12:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34937</th>\n",
       "      <td>74876</td>\n",
       "      <td>2571</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-10-02 08:13:05+00:00</td>\n",
       "      <td>2018-02-08 12:54:01+00:00</td>\n",
       "      <td>2061 days 19:19:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>295</td>\n",
       "      <td>2570</td>\n",
       "      <td>17678.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-10-16 10:19:22+00:00</td>\n",
       "      <td>2014-01-24 15:16:17+00:00</td>\n",
       "      <td>3551 days 19:03:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>1221</td>\n",
       "      <td>2320</td>\n",
       "      <td>9652.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-09-19 12:55:15+00:00</td>\n",
       "      <td>2017-03-29 08:00:09+00:00</td>\n",
       "      <td>2000 days 04:55:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>1429</td>\n",
       "      <td>2249</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-11-06 08:30:37+00:00</td>\n",
       "      <td>2014-12-03 14:56:38+00:00</td>\n",
       "      <td>3259 days 17:33:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>7249</td>\n",
       "      <td>1827</td>\n",
       "      <td>13385.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-10-26 12:28:40+00:00</td>\n",
       "      <td>2019-05-07 12:34:56+00:00</td>\n",
       "      <td>902 days 23:53:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54425</th>\n",
       "      <td>1070539</td>\n",
       "      <td>1800</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-25 12:49:27+00:00</td>\n",
       "      <td>2022-05-02 16:09:03+00:00</td>\n",
       "      <td>83 days 20:40:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69520</th>\n",
       "      <td>1216801</td>\n",
       "      <td>1623</td>\n",
       "      <td>12562.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-09-29 16:34:38+00:00</td>\n",
       "      <td>2023-06-16 14:16:04+00:00</td>\n",
       "      <td>105 days 02:18:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30056</th>\n",
       "      <td>59330</td>\n",
       "      <td>1551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-11-06 10:22:14+00:00</td>\n",
       "      <td>2018-02-02 08:53:51+00:00</td>\n",
       "      <td>2103 days 01:28:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3243</th>\n",
       "      <td>5441</td>\n",
       "      <td>1544</td>\n",
       "      <td>14133.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-09-22 08:21:47+00:00</td>\n",
       "      <td>2017-12-14 12:50:23+00:00</td>\n",
       "      <td>1742 days 19:31:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55195</th>\n",
       "      <td>1084435</td>\n",
       "      <td>1500</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-09-27 14:32:13+00:00</td>\n",
       "      <td>2022-05-18 08:04:41+00:00</td>\n",
       "      <td>132 days 06:27:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28983</th>\n",
       "      <td>57816</td>\n",
       "      <td>1485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-05-22 07:30:55+00:00</td>\n",
       "      <td>2019-01-21 14:19:18+00:00</td>\n",
       "      <td>1581 days 17:11:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>2942</td>\n",
       "      <td>1307</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-29 09:33:58+00:00</td>\n",
       "      <td>2017-10-25 15:06:58+00:00</td>\n",
       "      <td>2072 days 18:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-10-19 07:20:48+00:00</td>\n",
       "      <td>2015-09-30 16:07:52+00:00</td>\n",
       "      <td>2940 days 15:12:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4513</th>\n",
       "      <td>9592</td>\n",
       "      <td>1211</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-10-17 09:39:40+00:00</td>\n",
       "      <td>2018-02-25 07:17:19+00:00</td>\n",
       "      <td>2060 days 02:22:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>5059</td>\n",
       "      <td>1186</td>\n",
       "      <td>6308.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-05-22 13:41:22+00:00</td>\n",
       "      <td>2018-02-01 11:16:51+00:00</td>\n",
       "      <td>1936 days 02:24:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11484</th>\n",
       "      <td>25100</td>\n",
       "      <td>1123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-07-13 07:39:57+00:00</td>\n",
       "      <td>2015-12-21 15:38:05+00:00</td>\n",
       "      <td>2030 days 16:01:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>1326</td>\n",
       "      <td>1098</td>\n",
       "      <td>798.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-02-01 08:39:45+00:00</td>\n",
       "      <td>2018-02-13 13:13:48+00:00</td>\n",
       "      <td>1813 days 19:25:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30156</th>\n",
       "      <td>59490</td>\n",
       "      <td>1088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-10-05 08:23:50+00:00</td>\n",
       "      <td>2019-12-06 12:59:20+00:00</td>\n",
       "      <td>1398 days 19:24:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36478</th>\n",
       "      <td>251268</td>\n",
       "      <td>1086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-30 07:22:46+00:00</td>\n",
       "      <td>2018-02-02 09:06:22+00:00</td>\n",
       "      <td>1973 days 22:16:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  nb_tickets  total_amount  nb_suppliers  \\\n",
       "0                1     1256574     8830567.5             7   \n",
       "3615          6733       35527        1188.0             4   \n",
       "39              41       16263       37642.0             6   \n",
       "11              12        5871       38767.0             2   \n",
       "32809        63488        5851       64350.0             1   \n",
       "3708          6916        5482       51489.5             2   \n",
       "32616        63194        4507       13232.0             3   \n",
       "78              81        3562       38746.0             1   \n",
       "35295        84002        3403       19830.0             4   \n",
       "3377          5618        3294       31684.5             1   \n",
       "30011        59259        2591        4350.0             3   \n",
       "34937        74876        2571        2600.0             2   \n",
       "270            295        2570       17678.5             6   \n",
       "866           1221        2320        9652.0             2   \n",
       "1022          1429        2249        3500.0             4   \n",
       "3922          7249        1827       13385.0             1   \n",
       "54425      1070539        1800       19800.0             1   \n",
       "69520      1216801        1623       12562.0             2   \n",
       "30056        59330        1551           0.0             1   \n",
       "3243          5441        1544       14133.0             2   \n",
       "55195      1084435        1500       16500.0             1   \n",
       "28983        57816        1485           0.0             2   \n",
       "2231          2942        1307         100.0             2   \n",
       "23              24        1266           0.0             2   \n",
       "4513          9592        1211          62.0             4   \n",
       "2936          5059        1186        6308.0             3   \n",
       "11484        25100        1123           0.0             1   \n",
       "934           1326        1098         798.0             3   \n",
       "30156        59490        1088           0.0             1   \n",
       "36478       251268        1086           0.0             2   \n",
       "\n",
       "              purchase_date_max         purchase_date_min  \\\n",
       "0     2023-11-08 15:59:45+00:00 2013-06-10 10:37:58+00:00   \n",
       "3615  2023-11-03 09:42:40+00:00 2015-09-09 13:48:38+00:00   \n",
       "39    2023-10-25 09:13:16+00:00 2014-01-23 16:56:57+00:00   \n",
       "11    2023-11-04 13:46:59+00:00 2018-04-04 07:46:31+00:00   \n",
       "32809 2022-08-25 13:08:38+00:00 2020-08-18 08:32:57+00:00   \n",
       "3708  2021-08-26 12:49:17+00:00 2018-03-26 11:13:43+00:00   \n",
       "32616 2022-09-07 12:55:33+00:00 2017-11-28 13:52:15+00:00   \n",
       "78    2022-08-30 11:51:34+00:00 2017-01-05 13:04:58+00:00   \n",
       "35295 2023-11-06 15:59:22+00:00 2021-05-28 10:22:33+00:00   \n",
       "3377  2022-02-24 07:47:20+00:00 2018-10-25 11:04:24+00:00   \n",
       "30011 2023-06-12 14:05:19+00:00 2019-11-25 08:52:48+00:00   \n",
       "34937 2023-10-02 08:13:05+00:00 2018-02-08 12:54:01+00:00   \n",
       "270   2023-10-16 10:19:22+00:00 2014-01-24 15:16:17+00:00   \n",
       "866   2022-09-19 12:55:15+00:00 2017-03-29 08:00:09+00:00   \n",
       "1022  2023-11-06 08:30:37+00:00 2014-12-03 14:56:38+00:00   \n",
       "3922  2021-10-26 12:28:40+00:00 2019-05-07 12:34:56+00:00   \n",
       "54425 2022-07-25 12:49:27+00:00 2022-05-02 16:09:03+00:00   \n",
       "69520 2023-09-29 16:34:38+00:00 2023-06-16 14:16:04+00:00   \n",
       "30056 2023-11-06 10:22:14+00:00 2018-02-02 08:53:51+00:00   \n",
       "3243  2022-09-22 08:21:47+00:00 2017-12-14 12:50:23+00:00   \n",
       "55195 2022-09-27 14:32:13+00:00 2022-05-18 08:04:41+00:00   \n",
       "28983 2023-05-22 07:30:55+00:00 2019-01-21 14:19:18+00:00   \n",
       "2231  2023-06-29 09:33:58+00:00 2017-10-25 15:06:58+00:00   \n",
       "23    2023-10-19 07:20:48+00:00 2015-09-30 16:07:52+00:00   \n",
       "4513  2023-10-17 09:39:40+00:00 2018-02-25 07:17:19+00:00   \n",
       "2936  2023-05-22 13:41:22+00:00 2018-02-01 11:16:51+00:00   \n",
       "11484 2021-07-13 07:39:57+00:00 2015-12-21 15:38:05+00:00   \n",
       "934   2023-02-01 08:39:45+00:00 2018-02-13 13:13:48+00:00   \n",
       "30156 2023-10-05 08:23:50+00:00 2019-12-06 12:59:20+00:00   \n",
       "36478 2023-06-30 07:22:46+00:00 2018-02-02 09:06:22+00:00   \n",
       "\n",
       "      time_between_purchase  \n",
       "0        3803 days 05:21:47  \n",
       "3615     2976 days 19:54:02  \n",
       "39       3561 days 16:16:19  \n",
       "11       2040 days 06:00:28  \n",
       "32809     737 days 04:35:41  \n",
       "3708     1249 days 01:35:34  \n",
       "32616    1743 days 23:03:18  \n",
       "78       2062 days 22:46:36  \n",
       "35295     892 days 05:36:49  \n",
       "3377     1217 days 20:42:56  \n",
       "30011    1295 days 05:12:31  \n",
       "34937    2061 days 19:19:04  \n",
       "270      3551 days 19:03:05  \n",
       "866      2000 days 04:55:06  \n",
       "1022     3259 days 17:33:59  \n",
       "3922      902 days 23:53:44  \n",
       "54425      83 days 20:40:24  \n",
       "69520     105 days 02:18:34  \n",
       "30056    2103 days 01:28:23  \n",
       "3243     1742 days 19:31:24  \n",
       "55195     132 days 06:27:32  \n",
       "28983    1581 days 17:11:37  \n",
       "2231     2072 days 18:27:00  \n",
       "23       2940 days 15:12:56  \n",
       "4513     2060 days 02:22:21  \n",
       "2936     1936 days 02:24:31  \n",
       "11484    2030 days 16:01:52  \n",
       "934      1813 days 19:25:57  \n",
       "30156    1398 days 19:24:30  \n",
       "36478    1973 days 22:16:24  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_tickets_kpi.sort_values(by='nb_tickets', ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c3211a5-a851-43bc-a1f0-b39d51857fb7",
   "metadata": {},
   "source": [
    "# Fusion des bases locales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "46de1912-4a66-46e5-8b9e-7768b2d2723b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fusion avec KPI liés au customer\n",
    "df1_customer = pd.merge(df1_customerplus_clean, df1_campaigns_kpi, on = 'customer_id', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9740d64a-e5eb-4967-a534-ca6177546465",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>street_id</th>\n",
       "      <th>is_partner</th>\n",
       "      <th>gender</th>\n",
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       "      <th>profession</th>\n",
       "      <th>language</th>\n",
       "      <th>...</th>\n",
       "      <th>average_ticket_basket</th>\n",
       "      <th>total_price</th>\n",
       "      <th>purchase_count</th>\n",
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       "      <th>age</th>\n",
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       "      <td>fr</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1311</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 19:53:02.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id birthdate  street_id  is_partner  gender  is_email_true  \\\n",
       "0        12751       NaN          2       False       1           True   \n",
       "1        12825       NaN          2       False       2           True   \n",
       "2        11261       NaN          2       False       1           True   \n",
       "3        13071       NaN          2       False       2           True   \n",
       "4       653061       NaN         10       False       2           True   \n",
       "\n",
       "   opt_in  structure_id profession language  ...  average_ticket_basket  \\\n",
       "0    True           NaN        NaN      NaN  ...                    NaN   \n",
       "1    True           NaN        NaN      NaN  ...                    NaN   \n",
       "2    True           NaN        NaN      NaN  ...                    NaN   \n",
       "3    True           NaN        NaN      NaN  ...                    NaN   \n",
       "4   False           NaN        NaN      NaN  ...                    NaN   \n",
       "\n",
       "  total_price  purchase_count  first_buying_date  country  age  tenant_id  \\\n",
       "0         NaN               0                NaT       fr  NaN       1311   \n",
       "1         NaN               0                NaT       fr  NaN       1311   \n",
       "2         NaN               0                NaT       fr  NaN       1311   \n",
       "3         NaN               0                NaT       fr  NaN       1311   \n",
       "4         NaN               0                NaT      NaN  NaN       1311   \n",
       "\n",
       "   nb_campaigns  nb_campaigns_opened           time_to_open  \n",
       "0           NaN                  NaN                    NaT  \n",
       "1           NaN                  NaN                    NaT  \n",
       "2           NaN                  NaN                    NaT  \n",
       "3           NaN                  NaN                    NaT  \n",
       "4          80.0                  2.0 0 days 19:53:02.500000  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b5c4418c-ad2e-4bb9-bd5c-3b769e9c87d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>street_id</th>\n",
       "      <th>is_partner</th>\n",
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       "      <th>language</th>\n",
       "      <th>mcp_contact_id</th>\n",
       "      <th>last_buying_date</th>\n",
       "      <th>max_price</th>\n",
       "      <th>ticket_sum</th>\n",
       "      <th>average_price</th>\n",
       "      <th>fidelity</th>\n",
       "      <th>average_purchase_delay</th>\n",
       "      <th>average_price_basket</th>\n",
       "      <th>average_ticket_basket</th>\n",
       "      <th>total_price</th>\n",
       "      <th>purchase_count</th>\n",
       "      <th>first_buying_date</th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58201</th>\n",
       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-11-08 03:20:07</td>\n",
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       "      <td>1254775</td>\n",
       "      <td>7.030122</td>\n",
       "      <td>330831</td>\n",
       "      <td>-67.790969</td>\n",
       "      <td>13.75153</td>\n",
       "      <td>1.956087</td>\n",
       "      <td>8821221.5</td>\n",
       "      <td>641472</td>\n",
       "      <td>2013-06-10 10:37:58+00:00</td>\n",
       "      <td>fr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id birthdate  street_id  is_partner  gender  is_email_true  \\\n",
       "58201            1       NaN          2       False       2           True   \n",
       "\n",
       "       opt_in  structure_id profession language  mcp_contact_id  \\\n",
       "58201   False           NaN        NaN      NaN             NaN   \n",
       "\n",
       "          last_buying_date  max_price  ticket_sum  average_price  fidelity  \\\n",
       "58201  2023-11-08 03:20:07       45.0     1254775       7.030122    330831   \n",
       "\n",
       "       average_purchase_delay  average_price_basket  average_ticket_basket  \\\n",
       "58201              -67.790969              13.75153               1.956087   \n",
       "\n",
       "       total_price  purchase_count         first_buying_date country  age  \\\n",
       "58201    8821221.5          641472 2013-06-10 10:37:58+00:00      fr  NaN   \n",
       "\n",
       "       tenant_id  nb_campaigns  nb_campaigns_opened time_to_open  \n",
       "58201       1311           NaN                  NaN          NaT  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "\n",
    "df1_customer[df1_customer['customer_id'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1e42a790-b215-4107-a969-85005da06ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fusion avec KPI liés au comportement d'achat\n",
    "# df1_customer_product = pd.merge(df1_products_purchased_reduced, df1_products_purchased, on = 'customer_id', how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d950f24d-a5d1-4f1e-aeaa-ca826470365f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1_customer_product"
   ]
  }
 ],
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