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BDC-team-1/0_Cleaning_and_merge.ipynb

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{
"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_8302/4081512283.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",
" suppliers['supplier_name'] = suppliers['supplier_name'].fillna('')\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_8302/3092893564.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_8302/3092893564.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_8302/3092893564.py:10: 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",
" suppliers['supplier_name'] = suppliers['supplier_name'].fillna('')\n",
"/tmp/ipykernel_8302/3092893564.py:14: 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_8302/3848597476.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": {
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"</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": {
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"</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_8302/1967867975.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_8302/1967867975.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_8302/1967867975.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_8302/3700263836.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": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>nb_campaigns</th>\n",
" <th>nb_campaigns_opened</th>\n",
" <th>time_to_open</th>\n",
" </tr>\n",
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" <tbody>\n",
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" <th>0</th>\n",
" <td>2</td>\n",
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" <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",
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" <td>1 days 04:31:01.428571428</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
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" </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"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id_products</th>\n",
" <th>representation_id</th>\n",
" <th>pricing_formula_id</th>\n",
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" <td>478</td>\n",
" <td>273</td>\n",
" <td>131</td>\n",
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" <td>471</td>\n",
" <td>1</td>\n",
" <td>12.0</td>\n",
" <td>9.5</td>\n",
" <td>False</td>\n",
" <td>indiv entrées tp</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>12.0</td>\n",
" <td>11.5</td>\n",
" <td>False</td>\n",
" <td>indiv entrées tp</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>NaN</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>indiv entrées tr</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>12.0</td>\n",
" <td>8.5</td>\n",
" <td>False</td>\n",
" <td>indiv entrées tp</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\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 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>mucem</td>\n",
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" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" </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",
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"3 37 2515 269 10\n",
"4 37 383 269 1"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"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"
]
},
{
"data": {
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" <td>billet mucem picasso</td>\n",
" <td>2016</td>\n",
" <td>offre muséale individuel</td>\n",
" <td>mucem</td>\n",
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" <tr>\n",
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" <td>mucem</td>\n",
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" <td>5</td>\n",
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" <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",
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" <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",
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" <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",
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" <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": 33,
"id": "3d8b0875-b409-44ce-b688-d9d6758782d3",
"metadata": {},
"outputs": [
{
"data": {
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" <th>ticket_id</th>\n",
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" <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",
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" <td>Atelier</td>\n",
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" <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",
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" <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",
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" <td>Billet en nombre</td>\n",
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" <td>pricing_formula</td>\n",
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" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
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" <td>NaN</td>\n",
" <td>2022</td>\n",
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" <td>Billet en nombre</td>\n",
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" <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",
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],
"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": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Importance des suppliers\n",
"df1_products_purchased_reduced"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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": 74,
"id": "043303fe-e90f-4689-a2a9-5d690555a045",
"metadata": {},
"outputs": [],
"source": [
"def tickets_kpi_function(tickets_information = None):\n",
"\n",
" tickets_information_copy = tickets_information.copy()\n",
"\n",
" # Dummy : Canal de vente en ligne\n",
" liste_mots = ['en ligne', 'internet', 'web', 'net', 'vad', 'online'] # vad = vente à distance\n",
" tickets_information_copy['vente_internet'] = tickets_information_copy['supplier_name'].str.contains('|'.join(liste_mots), case=False).astype(int)\n",
"\n",
" # Proportion de vente en ligne\n",
" prop_vente_internet = tickets_information_copy[tickets_information_copy['vente_internet'] == 1].groupby('customer_id')['ticket_id'].count().reset_index()\n",
" prop_vente_internet.rename(columns = {'ticket_id' : 'nb_tickets_internet'}, inplace = True)\n",
" \n",
" tickets_kpi = (tickets_information_copy[['event_type_id', 'customer_id', 'ticket_id','supplier_name', 'purchase_date', 'amount', 'vente_internet']]\n",
" .groupby([ 'customer_id']) # 'event_type_id',\n",
" .agg({'ticket_id': 'count', \n",
" 'amount' : 'sum',\n",
" 'supplier_name': 'nunique',\n",
" 'vente_internet' : 'max',\n",
" 'purchase_date' : ['min', 'max']})\n",
" .reset_index()\n",
" )\n",
" tickets_kpi.columns = tickets_kpi.columns.map('_'.join)\n",
" \n",
" tickets_kpi.rename(columns = {'ticket_id_count' : 'nb_tickets', \n",
" 'amount_sum' : 'total_amount',\n",
" 'supplier_name_nunique' : 'nb_suppliers', \n",
" 'customer_id_' : 'customer_id'}, inplace = True)\n",
" \n",
" tickets_kpi['time_between_purchase'] = tickets_kpi['purchase_date_max'] - tickets_kpi['purchase_date_min']\n",
"\n",
" tickets_kpi = tickets_kpi.merge(prop_vente_internet, on = 'customer_id', how = 'left')\n",
" tickets_kpi['nb_tickets_internet'] = tickets_kpi['nb_tickets_internet'].fillna(0)\n",
" \n",
" return tickets_kpi\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 75,
"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": 76,
"id": "c78f5ade-c721-49d9-a474-73c217686ed1",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>customer_id</th>\n",
" <th>nb_tickets</th>\n",
" <th>total_amount</th>\n",
" <th>nb_suppliers</th>\n",
" <th>vente_internet_max</th>\n",
" <th>purchase_date_min</th>\n",
" <th>purchase_date_max</th>\n",
" <th>time_between_purchase</th>\n",
" <th>nb_tickets_internet</th>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>3.0</td>\n",
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" <tr>\n",
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" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73517</th>\n",
" <td>1256137</td>\n",
" <td>2</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2023-11-08 19:30:28+00:00</td>\n",
" <td>2023-11-08 19:30:28+00:00</td>\n",
" <td>0 days 00:00:00</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>73518 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" customer_id nb_tickets total_amount nb_suppliers \\\n",
"0 1 1256574 8830567.5 7 \n",
"1 2 307 0.0 1 \n",
"2 3 6 110.0 1 \n",
"3 4 4 41.0 1 \n",
"4 5 2 19.0 1 \n",
"... ... ... ... ... \n",
"73513 1256133 3 33.0 1 \n",
"73514 1256134 4 44.0 1 \n",
"73515 1256135 1 11.0 1 \n",
"73516 1256136 2 22.0 1 \n",
"73517 1256137 2 22.0 1 \n",
"\n",
" vente_internet_max purchase_date_min purchase_date_max \\\n",
"0 1 2013-06-10 10:37:58+00:00 2023-11-08 15:59:45+00:00 \n",
"1 0 2018-04-07 12:55:07+00:00 2020-03-08 12:06:43+00:00 \n",
"2 1 2019-09-19 15:15:01+00:00 2023-09-27 09:13:09+00:00 \n",
"3 1 2019-09-19 15:43:49+00:00 2021-09-02 18:42:19+00:00 \n",
"4 1 2019-09-19 15:45:36+00:00 2019-09-19 15:45:36+00:00 \n",
"... ... ... ... \n",
"73513 1 2023-11-08 16:51:19+00:00 2023-11-08 16:51:19+00:00 \n",
"73514 1 2023-11-08 17:17:51+00:00 2023-11-08 17:17:51+00:00 \n",
"73515 1 2023-11-08 17:23:54+00:00 2023-11-08 17:23:54+00:00 \n",
"73516 1 2023-11-08 18:32:18+00:00 2023-11-08 18:32:18+00:00 \n",
"73517 1 2023-11-08 19:30:28+00:00 2023-11-08 19:30:28+00:00 \n",
"\n",
" time_between_purchase nb_tickets_internet \n",
"0 3803 days 05:21:47 3053.0 \n",
"1 700 days 23:11:36 0.0 \n",
"2 1468 days 17:58:08 6.0 \n",
"3 714 days 02:58:30 4.0 \n",
"4 0 days 00:00:00 2.0 \n",
"... ... ... \n",
"73513 0 days 00:00:00 3.0 \n",
"73514 0 days 00:00:00 4.0 \n",
"73515 0 days 00:00:00 1.0 \n",
"73516 0 days 00:00:00 2.0 \n",
"73517 0 days 00:00:00 2.0 \n",
"\n",
"[73518 rows x 9 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_tickets_kpi"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "de1ecf15-1aa1-4aa2-9467-8ad8e9be5856",
"metadata": {},
"outputs": [],
"source": [
" df_tickets_information_copy = df1_products_purchased_reduced.copy()\n",
"\n",
" # Dummy : Canal de vente en ligne\n",
" liste_mots = ['en ligne', 'internet', 'web', 'net', 'vad', 'online'] # vad = vente à distance\n",
" df_tickets_information_copy['vente_internet'] = df_tickets_information_copy['supplier_name'].str.contains('|'.join(liste_mots), case=False).astype(int)\n",
"\n",
" # Proportion de vente en ligne\n",
" prop_vente_internet = df_tickets_information_copy[df_tickets_information_copy['vente_internet'] == 1].groupby('customer_id')['ticket_id'].count().reset_index()\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "9cd36178-11dc-409c-b148-fb1d208c2faf",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>ticket_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3053</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>2</td>\n",
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" <td>6</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56744</th>\n",
" <td>1256133</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56745</th>\n",
" <td>1256134</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56746</th>\n",
" <td>1256135</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56747</th>\n",
" <td>1256136</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56748</th>\n",
" <td>1256137</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>56749 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" customer_id ticket_id\n",
"0 1 3053\n",
"1 3 6\n",
"2 4 4\n",
"3 5 2\n",
"4 6 2\n",
"... ... ...\n",
"56744 1256133 3\n",
"56745 1256134 4\n",
"56746 1256135 1\n",
"56747 1256136 2\n",
"56748 1256137 2\n",
"\n",
"[56749 rows x 2 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prop_vente_internet"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7a452a6-cd5e-4c8b-b250-8a7d26e48fad",
"metadata": {},
"outputs": [],
"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": null,
"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": null,
"id": "9740d64a-e5eb-4967-a534-ca6177546465",
"metadata": {},
"outputs": [],
"source": [
"df1_customer.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5c4418c-ad2e-4bb9-bd5c-3b769e9c87d4",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_columns', None)\n",
"\n",
"\n",
"df1_customer[df1_customer['customer_id'] == 1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"id": "d950f24d-a5d1-4f1e-aeaa-ca826470365f",
"metadata": {},
"outputs": [],
"source": [
"# df1_customer_product"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"version": 3
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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