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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",
"fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},key ='WKTGH4YGUBAT3TR0OSUR', secret = 'g8ozi6ZUrBy8DzaAip4F7zOizbr4DKf4RgYNseqU', token = 'eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3NLZXkiOiJXS1RHSDRZR1VCQVQzVFIwT1NVUiIsImFjciI6IjAiLCJhbGxvd2VkLW9yaWdpbnMiOlsiaHR0cHM6Ly9vbnl4aWEubGFiLmdyb3VwZS1nZW5lcy5mciJdLCJhdWQiOlsibWluaW8iLCJhY2NvdW50Il0sImF1dGhfdGltZSI6MTcwNzU4NjUwMCwiYXpwIjoib255eGlhLW1pbmlvIiwiZW1haWwiOiJhbnRvaW5lLmpvdWJyZWxAZW5zYWUuZnIiLCJlbWFpbF92ZXJpZmllZCI6dHJ1ZSwiZXhwIjoxNzA3NjczMDQ3LCJmYW1pbHlfbmFtZSI6IkpPVUJSRUwiLCJnaXZlbl9uYW1lIjoiQW50b2luZSIsImdyb3VwcyI6WyJiZGMyMzI0LXRlYW0xIl0sImlhdCI6MTcwNzU4NjY0NywiaXNzIjoiaHR0cHM6Ly9hdXRoLmdyb3VwZS1nZW5lcy5mci9yZWFsbXMvZ2VuZXMiLCJqdGkiOiI1MjQ2MDZmMS1lYWM3LTQxZDgtYTEzMy04MGZjMDk0MGVlNzEiLCJuYW1lIjoiQW50b2luZSBKT1VCUkVMIiwicG9saWN5Ijoic3Rzb25seSIsInByZWZlcnJlZF91c2VybmFtZSI6ImFqb3VicmVsLWVuc2FlIiwicmVhbG1fYWNjZXNzIjp7InJvbGVzIjpbIm9mZmxpbmVfYWNjZXNzIiwiZGVmYXVsdC1yb2xlcy1nZW5lcyIsInVtYV9hdXRob3JpemF0aW9uIl19LCJyZXNvdXJjZV9hY2Nlc3MiOnsiYWNjb3VudCI6eyJyb2xlcyI6WyJtYW5hZ2UtYWNjb3VudCIsIm1hbmFnZS1hY2NvdW50LWxpbmtzIiwidmlldy1wcm9maWxlIl19fSwic2NvcGUiOiJvcGVuaWQgcHJvZmlsZSBlbWFpbCIsInNlc3Npb25fc3RhdGUiOiI1OTk2MWNkYy0xNmFiLTQ4MTAtYWE4Zi1iZGUyMjkwNjhiNzUiLCJzaWQiOiI1OTk2MWNkYy0xNmFiLTQ4MTAtYWE4Zi1iZGUyMjkwNjhiNzUiLCJzdWIiOiIwNWYwZDk3Mi1jNWM4LTQyNmYtODAwZC00NmQ0OGU4NjkwMzUiLCJ0eXAiOiJCZWFyZXIifQ.-imw-N4bk1uCcQGobkxhsRoeBAqxC9rT7PifElbC7ODOStnwIulc7HRR2fmtiqI2PdyrfnVvzfmIPK1g056HbA')"
]
},
{
"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_42764/4081512283.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_42764/3092893564.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_42764/3092893564.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_42764/3092893564.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_42764/3092893564.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_42764/3848597476.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": "markdown",
"id": "cdbb48b4-5e16-4ef4-8791-ed213d68d52f",
"metadata": {},
"source": [
"## Campaings area"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 14,
"id": "c8552dd6-52c5-4431-b43d-3cd6c578fd9f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_42764/1967867975.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_42764/1967867975.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_42764/1967867975.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": 15,
"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": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_campaigns_information.head()"
]
},
{
"cell_type": "markdown",
"id": "56520a97-ede8-4920-a211-3b5b136af33d",
"metadata": {},
"source": [
"## Product area"
]
},
{
"cell_type": "markdown",
"id": "9782e9d3-ba20-46bf-8562-bd0969972ddc",
"metadata": {},
"source": [
"Some useful functions"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "30488a40-1b38-4b9a-9d3b-26a0597c5e6d",
"metadata": {},
"outputs": [],
"source": [
"BUCKET = \"bdc2324-data\"\n",
"directory_path = '1'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"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": 19,
"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": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id_products</th>\n",
" <th>representation_id</th>\n",
" <th>pricing_formula_id</th>\n",
" <th>category_id</th>\n",
" <th>products_group_id</th>\n",
" <th>product_pack_id</th>\n",
" <th>type_of_id</th>\n",
" <th>amount</th>\n",
" <th>is_full_price</th>\n",
" <th>name_categories</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>NaN</td>\n",
" <td>9.0</td>\n",
" <td>False</td>\n",
" <td>indiv activité tr</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>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": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products_theme = create_products_table()\n",
"products_theme.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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"
]
},
{
"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>event_id</th>\n",
" <th>season_id</th>\n",
" <th>facility_id</th>\n",
" <th>event_type_id</th>\n",
" <th>event_type_key_id</th>\n",
" <th>facility_key_id</th>\n",
" <th>street_id</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>192</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>frontières</td>\n",
" <td>2018</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30329</td>\n",
" <td>2767</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>visite guidée une autre histoire du monde (1h00)</td>\n",
" <td>2023</td>\n",
" <td>offre muséale groupe</td>\n",
" <td>mucem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>161</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>visite contée les chercheurs d'or indiv</td>\n",
" <td>2018</td>\n",
" <td>offre muséale individuel</td>\n",
" <td>mucem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5957</td>\n",
" <td>582</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>we dreamt of utopia and we woke up screaming.</td>\n",
" <td>2021</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8337</td>\n",
" <td>582</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>jeff koons épisodes 4</td>\n",
" <td>2021</td>\n",
" <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": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events_theme= create_events_table()\n",
"events_theme.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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",
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"3 37 2515 269 10\n",
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},
"execution_count": 21,
"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": 22,
"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": 23,
"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>2016</td>\n",
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" <td>mucem</td>\n",
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" <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": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products_global = uniform_product_df()\n",
"products_global.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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')\n",
"\n",
"# Selection des variables d'intérêts\n",
"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": "markdown",
"id": "d7c3668a-c016-4bd0-837e-04af328ff14f",
"metadata": {},
"source": [
"# Construction des variables explicatives"
]
},
{
"cell_type": "markdown",
"id": "314f1b7f-ae48-4c6f-8469-9ce879043243",
"metadata": {},
"source": [
"## KPI campaigns"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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": 29,
"id": "24537647-bc29-4777-9848-ac4120a4aa60",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_42764/3700263836.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": 30,
"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",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
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" </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",
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" </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": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_campaigns_kpi.head()"
]
},
{
"cell_type": "markdown",
"id": "d4dcfbe0-c6ce-497e-b75e-dc9e938801b2",
"metadata": {},
"source": [
"## KPI tickets"
]
},
{
"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_reduced['name_event_types'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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', 'event_type_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",
" \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',\n",
" 'event_type_id_' : 'event_type_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', 'event_type_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": 34,
"id": "5882234a-1ed5-4269-87a6-0d75613476e3",
"metadata": {},
"outputs": [],
"source": [
"df1_tickets_kpi = tickets_kpi_function(tickets_information = df1_products_purchased_reduced)"
]
},
{
"cell_type": "markdown",
"id": "597b241e-a83d-4b7c-8ad7-eec50295dff2",
"metadata": {},
"source": [
"#### Exportation"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "a4a2311d-8a72-4030-afd5-218004d5d2a5",
"metadata": {},
"outputs": [],
"source": [
"# Exportation vers 'projet-bdc2324-team1'\n",
"BUCKET_OUT = \"projet-bdc2324-team1\"\n",
"FILE_KEY_OUT_S3 = \"0_Temp/Company 1 - Purchasing behaviour.csv\"\n",
"FILE_PATH_OUT_S3 = BUCKET_OUT + \"/\" + FILE_KEY_OUT_S3\n",
"\n",
"with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n",
" df1_tickets_kpi.to_csv(file_out, index = False)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>event_type_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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>453242</td>\n",
" <td>3248965.5</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2013-09-23 14:45:01+00:00</td>\n",
" <td>2023-11-03 14:11:01+00:00</td>\n",
" <td>3692 days 23:26:00</td>\n",
" <td>2988.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>384226</td>\n",
" <td>2686540.5</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2014-12-03 14:55:37+00:00</td>\n",
" <td>2023-11-04 15:12:16+00:00</td>\n",
" <td>3258 days 00:16:39</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>217356</td>\n",
" <td>1435871.5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2017-01-01 02:20:08+00:00</td>\n",
" <td>2019-12-31 02:20:06+00:00</td>\n",
" <td>1093 days 23:59:58</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>201750</td>\n",
" <td>1459190.0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2013-06-10 10:37:58+00:00</td>\n",
" <td>2023-11-08 15:59:45+00:00</td>\n",
" <td>3803 days 05:21:47</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>6733</td>\n",
" <td>6</td>\n",
" <td>14208</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2017-01-11 15:00:54+00:00</td>\n",
" <td>2019-11-27 09:47:06+00:00</td>\n",
" <td>1049 days 18:46:12</td>\n",
" <td>13497.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id event_type_id nb_tickets total_amount nb_suppliers \\\n",
"1 1 4 453242 3248965.5 6 \n",
"0 1 2 384226 2686540.5 7 \n",
"3 1 6 217356 1435871.5 5 \n",
"2 1 5 201750 1459190.0 6 \n",
"5032 6733 6 14208 0.0 3 \n",
"\n",
" vente_internet_max purchase_date_min purchase_date_max \\\n",
"1 1 2013-09-23 14:45:01+00:00 2023-11-03 14:11:01+00:00 \n",
"0 1 2014-12-03 14:55:37+00:00 2023-11-04 15:12:16+00:00 \n",
"3 1 2017-01-01 02:20:08+00:00 2019-12-31 02:20:06+00:00 \n",
"2 1 2013-06-10 10:37:58+00:00 2023-11-08 15:59:45+00:00 \n",
"5032 1 2017-01-11 15:00:54+00:00 2019-11-27 09:47:06+00:00 \n",
"\n",
" time_between_purchase nb_tickets_internet \n",
"1 3692 days 23:26:00 2988.0 \n",
"0 3258 days 00:16:39 51.0 \n",
"3 1093 days 23:59:58 5.0 \n",
"2 3803 days 05:21:47 9.0 \n",
"5032 1049 days 18:46:12 13497.0 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_tickets_kpi.sort_values(by='nb_tickets', ascending=False).head(5)"
]
},
{
"cell_type": "markdown",
"id": "f1d7f7ba-361b-467d-b375-b09c149185f7",
"metadata": {},
"source": [
"## Alexis' work"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "4ab1c0d2-0097-4669-b984-b6822c976740",
"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>event_type_id</th>\n",
" <th>avg_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>6.150659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>7.762474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>4.452618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6</td>\n",
" <td>6.439463</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" event_type_id avg_amount\n",
"0 2 6.150659\n",
"1 4 7.762474\n",
"2 5 4.452618\n",
"3 6 6.439463"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_amount = (df1_products_purchased_reduced.groupby([\"event_type_id\"])\n",
" .agg({\"amount\" : \"mean\"}).reset_index()\n",
" .rename(columns = {'amount' : 'avg_amount'}))\n",
"\n",
"avg_amount"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "a9c62b39-389e-4dac-89a6-ac8a59fea58a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>event_type_id</th>\n",
" <th>nb_tickets</th>\n",
" <th>avg_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>384226</td>\n",
" <td>6.150659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>453242</td>\n",
" <td>7.762474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>201750</td>\n",
" <td>4.452618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>217356</td>\n",
" <td>6.439463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>143</td>\n",
" <td>6.150659</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id event_type_id nb_tickets avg_amount\n",
"0 1 2 384226 6.150659\n",
"1 1 4 453242 7.762474\n",
"2 1 5 201750 4.452618\n",
"3 1 6 217356 6.439463\n",
"4 2 2 143 6.150659"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb_tickets = (df1_products_purchased_reduced.groupby([\"customer_id\", \"event_type_id\"])\n",
" .agg({\"ticket_id\" : \"count\"}).reset_index()\n",
" .rename(columns = {'ticket_id' : 'nb_tickets'})\n",
" .merge(avg_amount, how='left', on='event_type_id'))\n",
"nb_tickets.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "8710611c-7eb8-45ca-bdcc-009f4081f9e2",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" .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>birthdate</th>\n",
" <th>street_id</th>\n",
" <th>is_partner</th>\n",
" <th>gender</th>\n",
" <th>is_email_true</th>\n",
" <th>opt_in</th>\n",
" <th>structure_id</th>\n",
" <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",
" <th>first_buying_date</th>\n",
" <th>country</th>\n",
" <th>age</th>\n",
" <th>tenant_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>12751</td>\n",
" <td>NaN</td>\n",
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" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaT</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",
" <tr>\n",
" <th>1</th>\n",
" <td>12825</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaT</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",
" <tr>\n",
" <th>2</th>\n",
" <td>11261</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaT</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",
" <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",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaT</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",
" <tr>\n",
" <th>4</th>\n",
" <td>653061</td>\n",
" <td>NaN</td>\n",
" <td>10</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaT</td>\n",
" <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": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fusion avec KPI campaigns liés au customer\n",
"df1_customer = pd.merge(df1_customerplus_clean, df1_campaigns_kpi, on = 'customer_id', how = 'left')\n",
"df1_customer.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "a89fad43-ee68-4081-9384-3e9f08ec6a59",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shape : (156289, 31)\n"
]
},
{
"data": {
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" <th>language</th>\n",
" <th>...</th>\n",
" <th>first_buying_date</th>\n",
" <th>country</th>\n",
" <th>age</th>\n",
" <th>tenant_id</th>\n",
" <th>nb_campaigns</th>\n",
" <th>nb_campaigns_opened</th>\n",
" <th>time_to_open</th>\n",
" <th>event_type_id</th>\n",
" <th>nb_tickets</th>\n",
" <th>avg_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12751</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaT</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",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12825</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaT</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",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>11261</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaT</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",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaT</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",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>653061</td>\n",
" <td>NaN</td>\n",
" <td>10</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaT</td>\n",
" <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",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 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 ... first_buying_date country \\\n",
"0 True NaN NaN NaN ... NaT fr \n",
"1 True NaN NaN NaN ... NaT fr \n",
"2 True NaN NaN NaN ... NaT fr \n",
"3 True NaN NaN NaN ... NaT fr \n",
"4 False NaN NaN NaN ... NaT NaN \n",
"\n",
" age tenant_id nb_campaigns nb_campaigns_opened time_to_open \\\n",
"0 NaN 1311 NaN NaN NaT \n",
"1 NaN 1311 NaN NaN NaT \n",
"2 NaN 1311 NaN NaN NaT \n",
"3 NaN 1311 NaN NaN NaT \n",
"4 NaN 1311 80.0 2.0 0 days 19:53:02.500000 \n",
"\n",
" event_type_id nb_tickets avg_amount \n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_customer_product = pd.merge(df1_customer, nb_tickets, on = 'customer_id', how = 'left')\n",
"print(\"shape : \", df1_customer_product.shape)\n",
"df1_customer_product.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "a19fec00-4ece-400c-937c-ce5cd8daccfd",
"metadata": {},
"outputs": [],
"source": [
"# df1_customer_product.to_csv(\"customer_product.csv\", index = False)"
]
},
{
"cell_type": "markdown",
"id": "7c3211a5-a851-43bc-a1f0-b39d51857fb7",
"metadata": {},
"source": [
"# Fusion des bases locales"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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": 43,
"id": "1e42a790-b215-4107-a969-85005da06ebd",
"metadata": {},
"outputs": [],
"source": [
"# Fusion avec KPI liés au comportement d'achat\n",
"df1_customer_product = pd.merge(df1_tickets_kpi, df1_customer, 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": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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