1466 lines
53 KiB
Plaintext
1466 lines
53 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ad414c84-be46-4d2c-be8b-9fc4d24cc672",
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"metadata": {},
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"source": [
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"# Business Data Challenge - Team 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "15103481-8d74-404c-aa09-7601fe7730da",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import s3fs\n",
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"import re"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee97665c-39af-4c1c-a62b-c9c79feae18f",
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"metadata": {},
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"source": [
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"Configuration de l'accès aux données"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5d83bb1a-d341-446e-91f6-1c428607f6d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create filesystem object\n",
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"S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
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"fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9cbd72c5-6f8e-4366-ab66-96c32c6e963a",
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"metadata": {},
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"source": [
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"# Exemple sur Company 1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db26e59a-927c-407e-b54b-1815473b0b34",
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"metadata": {},
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"source": [
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"## Chargement données"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "699664b9-eee4-4f8d-a207-e524526560c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"BUCKET = \"bdc2324-data/1\"\n",
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"liste_database = fs.ls(BUCKET)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "aaf64d60-bf92-470c-8210-d09abd6a653e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['bdc2324-data/1/1campaign_stats.csv',\n",
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" 'bdc2324-data/1/1campaigns.csv',\n",
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" 'bdc2324-data/1/1categories.csv',\n",
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" 'bdc2324-data/1/1countries.csv',\n",
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" 'bdc2324-data/1/1currencies.csv',\n",
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" 'bdc2324-data/1/1customer_target_mappings.csv',\n",
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" 'bdc2324-data/1/1customersplus.csv',\n",
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" 'bdc2324-data/1/1event_types.csv',\n",
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" 'bdc2324-data/1/1events.csv',\n",
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" 'bdc2324-data/1/1facilities.csv',\n",
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" 'bdc2324-data/1/1link_stats.csv',\n",
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" 'bdc2324-data/1/1pricing_formulas.csv',\n",
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" 'bdc2324-data/1/1product_packs.csv',\n",
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" 'bdc2324-data/1/1products.csv',\n",
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" 'bdc2324-data/1/1products_groups.csv',\n",
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" 'bdc2324-data/1/1purchases.csv',\n",
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" 'bdc2324-data/1/1representation_category_capacities.csv',\n",
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" 'bdc2324-data/1/1representations.csv',\n",
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" 'bdc2324-data/1/1seasons.csv',\n",
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" 'bdc2324-data/1/1structure_tag_mappings.csv',\n",
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" 'bdc2324-data/1/1suppliers.csv',\n",
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" 'bdc2324-data/1/1tags.csv',\n",
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" 'bdc2324-data/1/1target_types.csv',\n",
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" 'bdc2324-data/1/1targets.csv',\n",
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" 'bdc2324-data/1/1tickets.csv',\n",
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" 'bdc2324-data/1/1type_of_categories.csv',\n",
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" 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n",
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" 'bdc2324-data/1/1type_ofs.csv']"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"liste_database"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_50143/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" df = pd.read_csv(file_in)\n"
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]
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}
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],
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"source": [
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"# loop to create dataframes from liste\n",
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"files_path = liste_database\n",
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"\n",
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"client_number = files_path[0].split(\"/\")[1]\n",
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"df_prefix = \"df\" + str(client_number) + \"_\"\n",
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"\n",
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"for i in range(len(files_path)) :\n",
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" current_path = files_path[i]\n",
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" with fs.open(current_path, mode=\"rb\") as file_in:\n",
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" df = pd.read_csv(file_in)\n",
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" # the pattern of the name is df1xxx\n",
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" nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n",
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" globals()[nom_dataframe] = df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4004c8bf-11d9-413d-bb42-2cb8ddde7716",
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"metadata": {},
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"source": [
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"## Cleaning functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "d237be96-8c86-4a91-b7a1-487e87a16c3d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def cleaning_date(df, column_name):\n",
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" \"\"\"\n",
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" Nettoie la colonne spécifiée du DataFrame en convertissant les valeurs en datetime avec le format ISO8601.\n",
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"\n",
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" Parameters:\n",
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" - df: DataFrame\n",
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" Le DataFrame contenant la colonne à nettoyer.\n",
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" - column_name: str\n",
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" Le nom de la colonne à nettoyer.\n",
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"\n",
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" Returns:\n",
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" - DataFrame\n",
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" Le DataFrame modifié avec la colonne nettoyée.\n",
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" \"\"\"\n",
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" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
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" return df"
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]
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},
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{
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"cell_type": "markdown",
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|||
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"id": "398804d8-2225-4fd3-bceb-75ab1588e359",
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"metadata": {},
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"source": [
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"## Preprocessing"
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]
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},
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{
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"cell_type": "markdown",
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|||
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"id": "568cb180-0dd9-4b27-aecb-05e4c3775ba6",
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"metadata": {},
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"source": [
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"## customer_plus"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e7b90ce-da54-4f00-bc34-64c543b0858f",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "bade04b1-0cdf-4d10-bcca-7dc7e4831656",
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"metadata": {},
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"source": [
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"## Ticket area"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "b95464b1-26bc-4aac-84b4-45da83b92251",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fonction de nettoyage et selection\n",
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"def preprocessing_tickets_area(tickets = None, purchases = None, suppliers = None, type_ofs = None):\n",
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" # Base des tickets\n",
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" tickets = tickets[['id', 'purchase_id', 'product_id', 'is_from_subscription', 'type_of', 'supplier_id']]\n",
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" tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n",
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"\n",
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" # Base des fournisseurs\n",
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" suppliers = suppliers[['id', 'name']]\n",
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" suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n",
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"\n",
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" # Base des types de billets\n",
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" # type_ofs = type_ofs[['id', 'name', 'children']]\n",
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" # type_ofs.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)\n",
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"\n",
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" # Base des achats\n",
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" # Nettoyage de la date d'achat\n",
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" cleaning_date(purchases, 'purchase_date')\n",
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" # Selection des variables\n",
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" purchases = purchases[['id', 'purchase_date', 'customer_id']]\n",
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"\n",
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" # Fusions \n",
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" # Fusion avec fournisseurs\n",
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" ticket_information = pd.merge(tickets, suppliers, left_on = 'supplier_id', right_on = 'id', how = 'inner')\n",
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" ticket_information.drop(['supplier_id', 'id'], axis = 1, inplace=True)\n",
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" \n",
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" # # Fusion avec type de tickets\n",
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" # ticket_information = pd.merge(ticket_information, type_ofs, left_on = 'type_of', right_on = 'id', how = 'inner')\n",
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" # ticket_information.drop(['type_of', 'id'], axis = 1, inplace=True)\n",
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" \n",
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" # Fusion avec achats\n",
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" ticket_information = pd.merge(ticket_information, purchases, left_on = 'purchase_id', right_on = 'id', how = 'inner')\n",
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" ticket_information.drop(['purchase_id', 'id'], axis = 1, inplace=True)\n",
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"\n",
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" return ticket_information"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "3e1d2ba7-ff4f-48eb-93a8-2bb648c70396",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_50143/1320335767.py:5: SettingWithCopyWarning: \n",
|
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|
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"A value is trying to be set on a copy of a slice from a DataFrame\n",
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"\n",
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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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" tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n",
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"/tmp/ipykernel_50143/1320335767.py:9: SettingWithCopyWarning: \n",
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"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
|||
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"\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",
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" suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n"
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]
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|||
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}
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|||
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],
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|||
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"source": [
|
|||
|
|
"df1_ticket_information = preprocessing_tickets_area(tickets = df1_tickets, purchases = df1_purchases, suppliers = df1_suppliers, type_ofs = df1_type_ofs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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|||
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"id": "4b18edfc-6450-4c6a-9e7b-ee5a5808c8c9",
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|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
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|||
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|
{
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"data": {
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"text/html": [
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|||
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"<div>\n",
|
|||
|
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"<style scoped>\n",
|
|||
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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|||
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"\n",
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|||
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" .dataframe tbody tr th {\n",
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|||
|
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" vertical-align: top;\n",
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|||
|
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" }\n",
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"\n",
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|||
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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|||
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>ticket_id</th>\n",
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" <th>product_id</th>\n",
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" <th>is_from_subscription</th>\n",
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" <th>type_of</th>\n",
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" <th>supplier_name</th>\n",
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|
|
" <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>1</td>\n",
|
|||
|
|
" <td>vente en ligne</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>1</td>\n",
|
|||
|
|
" <td>vente en ligne</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>1</td>\n",
|
|||
|
|
" <td>vente en ligne</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>1</td>\n",
|
|||
|
|
" <td>vente en ligne</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>1</td>\n",
|
|||
|
|
" <td>vente en ligne</td>\n",
|
|||
|
|
" <td>2018-12-28 14:47:50+00:00</td>\n",
|
|||
|
|
" <td>48187</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1826667</th>\n",
|
|||
|
|
" <td>20662815</td>\n",
|
|||
|
|
" <td>405689</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>vente en ligne</td>\n",
|
|||
|
|
" <td>2023-11-08 17:23:54+00:00</td>\n",
|
|||
|
|
" <td>1256135</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1826668</th>\n",
|
|||
|
|
" <td>20662816</td>\n",
|
|||
|
|
" <td>403658</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>vente en ligne</td>\n",
|
|||
|
|
" <td>2023-11-08 18:32:18+00:00</td>\n",
|
|||
|
|
" <td>1256136</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1826669</th>\n",
|
|||
|
|
" <td>20662817</td>\n",
|
|||
|
|
" <td>403658</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>vente en ligne</td>\n",
|
|||
|
|
" <td>2023-11-08 18:32:18+00:00</td>\n",
|
|||
|
|
" <td>1256136</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1826670</th>\n",
|
|||
|
|
" <td>20662818</td>\n",
|
|||
|
|
" <td>403658</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>vente en ligne</td>\n",
|
|||
|
|
" <td>2023-11-08 19:30:28+00:00</td>\n",
|
|||
|
|
" <td>1256137</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1826671</th>\n",
|
|||
|
|
" <td>20662819</td>\n",
|
|||
|
|
" <td>403658</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>vente en ligne</td>\n",
|
|||
|
|
" <td>2023-11-08 19:30:28+00:00</td>\n",
|
|||
|
|
" <td>1256137</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>1826672 rows × 7 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" ticket_id product_id is_from_subscription type_of supplier_name \\\n",
|
|||
|
|
"0 13070859 225251 False 1 vente en ligne \n",
|
|||
|
|
"1 13070860 224914 False 1 vente en ligne \n",
|
|||
|
|
"2 13070861 224914 False 1 vente en ligne \n",
|
|||
|
|
"3 13070862 224914 False 1 vente en ligne \n",
|
|||
|
|
"4 13070863 224914 False 1 vente en ligne \n",
|
|||
|
|
"... ... ... ... ... ... \n",
|
|||
|
|
"1826667 20662815 405689 False 1 vente en ligne \n",
|
|||
|
|
"1826668 20662816 403658 False 1 vente en ligne \n",
|
|||
|
|
"1826669 20662817 403658 False 1 vente en ligne \n",
|
|||
|
|
"1826670 20662818 403658 False 1 vente en ligne \n",
|
|||
|
|
"1826671 20662819 403658 False 1 vente en ligne \n",
|
|||
|
|
"\n",
|
|||
|
|
" purchase_date customer_id \n",
|
|||
|
|
"0 2018-12-28 14:47:50+00:00 48187 \n",
|
|||
|
|
"1 2018-12-28 14:47:50+00:00 48187 \n",
|
|||
|
|
"2 2018-12-28 14:47:50+00:00 48187 \n",
|
|||
|
|
"3 2018-12-28 14:47:50+00:00 48187 \n",
|
|||
|
|
"4 2018-12-28 14:47:50+00:00 48187 \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"1826667 2023-11-08 17:23:54+00:00 1256135 \n",
|
|||
|
|
"1826668 2023-11-08 18:32:18+00:00 1256136 \n",
|
|||
|
|
"1826669 2023-11-08 18:32:18+00:00 1256136 \n",
|
|||
|
|
"1826670 2023-11-08 19:30:28+00:00 1256137 \n",
|
|||
|
|
"1826671 2023-11-08 19:30:28+00:00 1256137 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[1826672 rows x 7 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 70,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df1_ticket_information"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "096e47f4-1d65-4575-989d-83227eedad2b",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Target area"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"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": 10,
|
|||
|
|
"id": "5fbfd88b-b94c-489c-9201-670e96e453e7",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/tmp/ipykernel_50143/3848597476.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": 19,
|
|||
|
|
"id": "b4f05142-2a22-42ef-a60d-f23cc4b5cb09",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>customer_id</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>target_name</th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin mediation specialisee</th>\n",
|
|||
|
|
" <td>150000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin jeune public</th>\n",
|
|||
|
|
" <td>149979</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin b2c</th>\n",
|
|||
|
|
" <td>108909</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Arenametrix_bascule tel vers sib</th>\n",
|
|||
|
|
" <td>35216</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout b2c</th>\n",
|
|||
|
|
" <td>34523</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Automation_parrainage_newsletter_handicap_visuel</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout mediation specialisee</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Inscrits NL LSF formulaire</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Market auto - contacts inactifs post-scénario</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Inactifs - fin du scénario</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>283 rows × 1 columns</p>\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\n",
|
|||
|
|
"... ...\n",
|
|||
|
|
"Automation_parrainage_newsletter_handicap_visuel 1\n",
|
|||
|
|
"consentement optout mediation specialisee 1\n",
|
|||
|
|
"Inscrits NL LSF formulaire 1\n",
|
|||
|
|
"Market auto - contacts inactifs post-scénario 1\n",
|
|||
|
|
"Inactifs - fin du scénario 1\n",
|
|||
|
|
"\n",
|
|||
|
|
"[283 rows x 1 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df1_target_information[['target_name', 'customer_id']].groupby('target_name').count().sort_values(by='customer_id', ascending=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"id": "4417ff51-f501-4ab9-a192-4ab75764a8ed",
|
|||
|
|
"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>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",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP Newsletter centres de loisirs</th>\n",
|
|||
|
|
" <td>1032</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP Newsletter enseignants</th>\n",
|
|||
|
|
" <td>4510</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP Newsletter jeune public</th>\n",
|
|||
|
|
" <td>3862</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP Newsletter relais champ social</th>\n",
|
|||
|
|
" <td>2270</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP PROMO Participants ateliers (adultes et enfants)</th>\n",
|
|||
|
|
" <td>1954</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP billets famille</th>\n",
|
|||
|
|
" <td>3609</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP promo MD pass musées dps oct 2018</th>\n",
|
|||
|
|
" <td>1785</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP promo Plan B 2019 (concerts)</th>\n",
|
|||
|
|
" <td>1948</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP promo spectateurs prog 21-22 (spectacles, ciné, ateliers)</th>\n",
|
|||
|
|
" <td>1293</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP rentrée culturelle 2023</th>\n",
|
|||
|
|
" <td>1757</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DDCP_marseille_jazz_2023</th>\n",
|
|||
|
|
" <td>1043</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DRE Festival Jean Rouch</th>\n",
|
|||
|
|
" <td>1502</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DRE MucemLab</th>\n",
|
|||
|
|
" <td>2302</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DRE chercheurs</th>\n",
|
|||
|
|
" <td>1557</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>DRE institutionnels</th>\n",
|
|||
|
|
" <td>2229</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>FORMATION _ acheteurs optin last year</th>\n",
|
|||
|
|
" <td>10485</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Inscrits NL générale (export_291019 + operation_videomaton)</th>\n",
|
|||
|
|
" <td>14086</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Inscrits NL générale site web</th>\n",
|
|||
|
|
" <td>3732</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Inscrits NL jeune public site web</th>\n",
|
|||
|
|
" <td>1249</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Votre première liste</th>\n",
|
|||
|
|
" <td>3715</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin b2b</th>\n",
|
|||
|
|
" <td>12735</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin b2c</th>\n",
|
|||
|
|
" <td>108909</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin dre</th>\n",
|
|||
|
|
" <td>4527</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin jeune public</th>\n",
|
|||
|
|
" <td>149979</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin mediation specialisee</th>\n",
|
|||
|
|
" <td>150000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin newsletter generale</th>\n",
|
|||
|
|
" <td>22095</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optin scolaires</th>\n",
|
|||
|
|
" <td>4849</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout b2b</th>\n",
|
|||
|
|
" <td>14219</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout b2c</th>\n",
|
|||
|
|
" <td>34523</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout dre</th>\n",
|
|||
|
|
" <td>14328</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout newsletter generale</th>\n",
|
|||
|
|
" <td>18855</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>consentement optout scolaires</th>\n",
|
|||
|
|
" <td>15744</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>ddcp_md_scene_ouverte_au_talent</th>\n",
|
|||
|
|
" <td>1577</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>ddcp_promo_MD_billet_musée_oct_2019_agarder2</th>\n",
|
|||
|
|
" <td>5482</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>ddcp_promo_md_musée_dps 011019</th>\n",
|
|||
|
|
" <td>6010</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>ddcp_promo_visiteurs occasionnels_musee_8mois</th>\n",
|
|||
|
|
" <td>6640</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>ddcp_visiteurs dps 010622</th>\n",
|
|||
|
|
" <td>12355</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>festival_jean_rouch</th>\n",
|
|||
|
|
" <td>1502</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>rappel po barvalo</th>\n",
|
|||
|
|
" <td>1248</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>structures_etiquette champ social</th>\n",
|
|||
|
|
" <td>1488</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\n",
|
|||
|
|
"DDCP Newsletter centres de loisirs 1032\n",
|
|||
|
|
"DDCP Newsletter enseignants 4510\n",
|
|||
|
|
"DDCP Newsletter jeune public 3862\n",
|
|||
|
|
"DDCP Newsletter relais champ social 2270\n",
|
|||
|
|
"DDCP PROMO Participants ateliers (adultes et en... 1954\n",
|
|||
|
|
"DDCP billets famille 3609\n",
|
|||
|
|
"DDCP promo MD pass musées dps oct 2018 1785\n",
|
|||
|
|
"DDCP promo Plan B 2019 (concerts) 1948\n",
|
|||
|
|
"DDCP promo spectateurs prog 21-22 (spectacles, ... 1293\n",
|
|||
|
|
"DDCP rentrée culturelle 2023 1757\n",
|
|||
|
|
"DDCP_marseille_jazz_2023 1043\n",
|
|||
|
|
"DRE Festival Jean Rouch 1502\n",
|
|||
|
|
"DRE MucemLab 2302\n",
|
|||
|
|
"DRE chercheurs 1557\n",
|
|||
|
|
"DRE institutionnels 2229\n",
|
|||
|
|
"FORMATION _ acheteurs optin last year 10485\n",
|
|||
|
|
"Inscrits NL générale (export_291019 + operation... 14086\n",
|
|||
|
|
"Inscrits NL générale site web 3732\n",
|
|||
|
|
"Inscrits NL jeune public site web 1249\n",
|
|||
|
|
"Votre première liste 3715\n",
|
|||
|
|
"consentement optin b2b 12735\n",
|
|||
|
|
"consentement optin b2c 108909\n",
|
|||
|
|
"consentement optin dre 4527\n",
|
|||
|
|
"consentement optin jeune public 149979\n",
|
|||
|
|
"consentement optin mediation specialisee 150000\n",
|
|||
|
|
"consentement optin newsletter generale 22095\n",
|
|||
|
|
"consentement optin scolaires 4849\n",
|
|||
|
|
"consentement optout b2b 14219\n",
|
|||
|
|
"consentement optout b2c 34523\n",
|
|||
|
|
"consentement optout dre 14328\n",
|
|||
|
|
"consentement optout newsletter generale 18855\n",
|
|||
|
|
"consentement optout scolaires 15744\n",
|
|||
|
|
"ddcp_md_scene_ouverte_au_talent 1577\n",
|
|||
|
|
"ddcp_promo_MD_billet_musée_oct_2019_agarder2 5482\n",
|
|||
|
|
"ddcp_promo_md_musée_dps 011019 6010\n",
|
|||
|
|
"ddcp_promo_visiteurs occasionnels_musee_8mois 6640\n",
|
|||
|
|
"ddcp_visiteurs dps 010622 12355\n",
|
|||
|
|
"festival_jean_rouch 1502\n",
|
|||
|
|
"rappel po barvalo 1248\n",
|
|||
|
|
"structures_etiquette champ social 1488"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"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]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "cdbb48b4-5e16-4ef4-8791-ed213d68d52f",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Campaings area"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"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": 12,
|
|||
|
|
"id": "c8552dd6-52c5-4431-b43d-3cd6c578fd9f",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/tmp/ipykernel_50143/1967867975.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_50143/1967867975.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_50143/1967867975.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": 27,
|
|||
|
|
"id": "c24457e7-3cad-451a-a65b-7373b656bd6e",
|
|||
|
|
"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>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",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6214803</th>\n",
|
|||
|
|
" <td>8302994</td>\n",
|
|||
|
|
" <td>266155</td>\n",
|
|||
|
|
" <td>2023-10-23 09:43:25+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:32:33+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:32:34+00:00</td>\n",
|
|||
|
|
" <td>dre_nov_2023</td>\n",
|
|||
|
|
" <td>1318</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:17+00:00</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6214804</th>\n",
|
|||
|
|
" <td>8303307</td>\n",
|
|||
|
|
" <td>21355</td>\n",
|
|||
|
|
" <td>2023-10-23 09:44:02+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:32:49+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:32:49+00:00</td>\n",
|
|||
|
|
" <td>dre_nov_2023</td>\n",
|
|||
|
|
" <td>1318</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:17+00:00</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6214805</th>\n",
|
|||
|
|
" <td>8304346</td>\n",
|
|||
|
|
" <td>21849</td>\n",
|
|||
|
|
" <td>2023-10-23 09:45:52+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:33:28+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:33:29+00:00</td>\n",
|
|||
|
|
" <td>dre_nov_2023</td>\n",
|
|||
|
|
" <td>1318</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:17+00:00</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6214806</th>\n",
|
|||
|
|
" <td>8302037</td>\n",
|
|||
|
|
" <td>667789</td>\n",
|
|||
|
|
" <td>2023-10-23 09:47:32+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:53+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:54+00:00</td>\n",
|
|||
|
|
" <td>dre_nov_2023</td>\n",
|
|||
|
|
" <td>1318</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:17+00:00</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6214807</th>\n",
|
|||
|
|
" <td>8304939</td>\n",
|
|||
|
|
" <td>294154</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" <td>2023-10-23 09:33:54+00:00</td>\n",
|
|||
|
|
" <td>2023-10-23 09:33:55+00:00</td>\n",
|
|||
|
|
" <td>dre_nov_2023</td>\n",
|
|||
|
|
" <td>1318</td>\n",
|
|||
|
|
" <td>2023-10-23 09:31:17+00:00</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>6214808 rows × 8 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" id customer_id opened_at \\\n",
|
|||
|
|
"0 19793 112597 NaT \n",
|
|||
|
|
"1 14211 113666 NaT \n",
|
|||
|
|
"2 13150 280561 NaT \n",
|
|||
|
|
"3 7073 101007 2021-03-28 18:11:06+00:00 \n",
|
|||
|
|
"4 5175 103972 NaT \n",
|
|||
|
|
"... ... ... ... \n",
|
|||
|
|
"6214803 8302994 266155 2023-10-23 09:43:25+00:00 \n",
|
|||
|
|
"6214804 8303307 21355 2023-10-23 09:44:02+00:00 \n",
|
|||
|
|
"6214805 8304346 21849 2023-10-23 09:45:52+00:00 \n",
|
|||
|
|
"6214806 8302037 667789 2023-10-23 09:47:32+00:00 \n",
|
|||
|
|
"6214807 8304939 294154 NaT \n",
|
|||
|
|
"\n",
|
|||
|
|
" sent_at delivered_at \\\n",
|
|||
|
|
"0 2021-03-28 16:01:09+00:00 2021-03-28 16:24:18+00:00 \n",
|
|||
|
|
"1 2021-03-28 16:01:09+00:00 2021-03-28 16:21:02+00:00 \n",
|
|||
|
|
"2 2021-03-28 16:00:59+00:00 2021-03-28 16:08:45+00:00 \n",
|
|||
|
|
"3 2021-03-28 16:00:59+00:00 2021-03-28 16:09:47+00:00 \n",
|
|||
|
|
"4 2021-03-28 16:01:06+00:00 2021-03-28 16:05:03+00:00 \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"6214803 2023-10-23 09:32:33+00:00 2023-10-23 09:32:34+00:00 \n",
|
|||
|
|
"6214804 2023-10-23 09:32:49+00:00 2023-10-23 09:32:49+00:00 \n",
|
|||
|
|
"6214805 2023-10-23 09:33:28+00:00 2023-10-23 09:33:29+00:00 \n",
|
|||
|
|
"6214806 2023-10-23 09:31:53+00:00 2023-10-23 09:31:54+00:00 \n",
|
|||
|
|
"6214807 2023-10-23 09:33:54+00:00 2023-10-23 09:33:55+00:00 \n",
|
|||
|
|
"\n",
|
|||
|
|
" campaign_name campaign_service_id \\\n",
|
|||
|
|
"0 Le Mucem chez vous, gardons le lien #22 404 \n",
|
|||
|
|
"1 Le Mucem chez vous, gardons le lien #22 404 \n",
|
|||
|
|
"2 Le Mucem chez vous, gardons le lien #22 404 \n",
|
|||
|
|
"3 Le Mucem chez vous, gardons le lien #22 404 \n",
|
|||
|
|
"4 Le Mucem chez vous, gardons le lien #22 404 \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"6214803 dre_nov_2023 1318 \n",
|
|||
|
|
"6214804 dre_nov_2023 1318 \n",
|
|||
|
|
"6214805 dre_nov_2023 1318 \n",
|
|||
|
|
"6214806 dre_nov_2023 1318 \n",
|
|||
|
|
"6214807 dre_nov_2023 1318 \n",
|
|||
|
|
"\n",
|
|||
|
|
" campaign_sent_at \n",
|
|||
|
|
"0 2021-03-27 23:00:00+00:00 \n",
|
|||
|
|
"1 2021-03-27 23:00:00+00:00 \n",
|
|||
|
|
"2 2021-03-27 23:00:00+00:00 \n",
|
|||
|
|
"3 2021-03-27 23:00:00+00:00 \n",
|
|||
|
|
"4 2021-03-27 23:00:00+00:00 \n",
|
|||
|
|
"... ... \n",
|
|||
|
|
"6214803 2023-10-23 09:31:17+00:00 \n",
|
|||
|
|
"6214804 2023-10-23 09:31:17+00:00 \n",
|
|||
|
|
"6214805 2023-10-23 09:31:17+00:00 \n",
|
|||
|
|
"6214806 2023-10-23 09:31:17+00:00 \n",
|
|||
|
|
"6214807 2023-10-23 09:31:17+00:00 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[6214808 rows x 8 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df1_campaigns_information"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 67,
|
|||
|
|
"id": "e2c88552-b863-47a2-be23-8d2898fb28bc",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def campaigns_kpi(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": 69,
|
|||
|
|
"id": "24537647-bc29-4777-9848-ac4120a4aa60",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/tmp/ipykernel_50143/2679359833.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",
|
|||
|
|
"/tmp/ipykernel_50143/2679359833.py:20: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
|
|||
|
|
"The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
|
|||
|
|
"\n",
|
|||
|
|
"For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
" campaigns_reduced['nb_campaigns_opened'].fillna(0, inplace=True)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df1_campaigns_kpi = campaigns_kpi(campaigns_information = df1_campaigns_information) "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 66,
|
|||
|
|
"id": "6be2a9a6-056b-4e19-8c26-a18ba3df36b3",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>customer_id</th>\n",
|
|||
|
|
" <th>nb_campaigns</th>\n",
|
|||
|
|
" <th>nb_campaigns_opened</th>\n",
|
|||
|
|
" <th>time_to_open</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>222</td>\n",
|
|||
|
|
" <td>124.0</td>\n",
|
|||
|
|
" <td>1 days 00:28:30.169354838</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>7.0</td>\n",
|
|||
|
|
" <td>1 days 04:31:01.428571428</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>6</td>\n",
|
|||
|
|
" <td>20</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>130467</th>\n",
|
|||
|
|
" <td>1256097</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>0 days 02:11:15</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>130468</th>\n",
|
|||
|
|
" <td>1256098</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>130469</th>\n",
|
|||
|
|
" <td>1256099</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>130470</th>\n",
|
|||
|
|
" <td>1256100</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>130471</th>\n",
|
|||
|
|
" <td>1256101</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>NaT</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>130472 rows × 4 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" customer_id nb_campaigns nb_campaigns_opened \\\n",
|
|||
|
|
"0 2 4 0.0 \n",
|
|||
|
|
"1 3 222 124.0 \n",
|
|||
|
|
"2 4 7 7.0 \n",
|
|||
|
|
"3 5 4 0.0 \n",
|
|||
|
|
"4 6 20 0.0 \n",
|
|||
|
|
"... ... ... ... \n",
|
|||
|
|
"130467 1256097 1 1.0 \n",
|
|||
|
|
"130468 1256098 1 0.0 \n",
|
|||
|
|
"130469 1256099 1 0.0 \n",
|
|||
|
|
"130470 1256100 1 0.0 \n",
|
|||
|
|
"130471 1256101 1 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" time_to_open \n",
|
|||
|
|
"0 NaT \n",
|
|||
|
|
"1 1 days 00:28:30.169354838 \n",
|
|||
|
|
"2 1 days 04:31:01.428571428 \n",
|
|||
|
|
"3 NaT \n",
|
|||
|
|
"4 NaT \n",
|
|||
|
|
"... ... \n",
|
|||
|
|
"130467 0 days 02:11:15 \n",
|
|||
|
|
"130468 NaT \n",
|
|||
|
|
"130469 NaT \n",
|
|||
|
|
"130470 NaT \n",
|
|||
|
|
"130471 NaT \n",
|
|||
|
|
"\n",
|
|||
|
|
"[130472 rows x 4 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 66,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df1_campaigns_kpi"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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
|
|||
|
|
}
|