{ "cells": [ { "cell_type": "markdown", "id": "be628bfc-0bca-48b0-97c9-29063289127e", "metadata": {}, "source": [ "# Statistiques descriptives : compagnies offrant des spectacles" ] }, { "cell_type": "markdown", "id": "0bf5450b-f44d-430a-aed7-d875dc365048", "metadata": {}, "source": [ "## Importations et chargement des données" ] }, { "cell_type": "code", "execution_count": 2, "id": "aa915888-cede-4eb0-8a26-7df573d29a3e", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import s3fs\n", "import warnings\n", "from datetime import date, timedelta, datetime\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "id": "17949e81-c30b-4fdf-9872-d7dc2b22ba9e", "metadata": {}, "outputs": [], "source": [ "# Import KPI construction functions\n", "#exec(open('0_KPI_functions.py').read())\n", "exec(open('../0_KPI_functions.py').read())\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "9c1737a2-bad8-4266-8dec-452085d8cfe7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv',\n", " 'projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv',\n", " 'projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv',\n", " 'projet-bdc2324-team1/0_Input/Company_10/target_information.csv']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create filesystem object\n", "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n", "\n", "BUCKET = \"projet-bdc2324-team1/0_Input/Company_10\"\n", "fs.ls(BUCKET)" ] }, { "cell_type": "code", "execution_count": 5, "id": "a35dc2f6-2017-4b21-abd2-2c4c112c96b2", "metadata": {}, "outputs": [], "source": [ "dic_base=['campaigns_information','customerplus_cleaned','products_purchased_reduced','target_information']\n", "for nom_base in dic_base:\n", " FILE_PATH_S3_fanta = 'projet-bdc2324-team1/0_Input/Company_10/' + nom_base + '.csv'\n", " with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n", " globals()[nom_base] = pd.read_csv(file_in, sep=\",\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "40b705eb-fd18-436b-b150-61611a3c6a84", "metadata": {}, "outputs": [], "source": [ "# fonction permettant d'extraire une table à partir du numéro de la compagnie (directory_path)\n", "\n", "def display_databases(directory_path, file_name, datetime_col = None):\n", " \"\"\"\n", " This function returns the file from s3 storage \n", " \"\"\"\n", " file_path = \"projet-bdc2324-team1\" + \"/0_Input/Company_\" + directory_path + \"/\" + file_name + \".csv\"\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=\",\", parse_dates = datetime_col, date_parser=custom_date_parser) \n", " return df \n" ] }, { "cell_type": "code", "execution_count": 10, "id": "c56decc3-de19-4786-82a4-1386c72a6bfb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_nametarget_type_is_importtarget_type_name
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..................
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69258 rows × 5 columns

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" ], "text/plain": [ " id customer_id target_name target_type_is_import \\\n", "0 1165098 618562 Newsletter mensuelle False \n", "1 1165100 618559 Newsletter mensuelle False \n", "2 1165101 618561 Newsletter mensuelle False \n", "3 1165102 618560 Newsletter mensuelle False \n", "4 1165103 618558 Newsletter mensuelle False \n", "... ... ... ... ... \n", "69253 1698158 18580 Newsletter mensuelle False \n", "69254 1698159 18569 Newsletter mensuelle False \n", "69255 1698160 2962 Newsletter mensuelle False \n", "69256 1698161 3825 Newsletter mensuelle False \n", "69257 1698162 5731 Newsletter mensuelle False \n", "\n", " target_type_name \n", "0 manual_static_filter \n", "1 manual_static_filter \n", "2 manual_static_filter \n", "3 manual_static_filter \n", "4 manual_static_filter \n", "... ... \n", "69253 manual_static_filter \n", "69254 manual_static_filter \n", "69255 manual_static_filter \n", "69256 manual_static_filter \n", "69257 manual_static_filter \n", "\n", "[69258 rows x 5 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target_information" ] }, { "cell_type": "code", "execution_count": 9, "id": "c825d64b-356c-4b71-aa3c-90e0dd7ca092", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ticket_idcustomer_idpurchase_idevent_type_idsupplier_namepurchase_dateamountis_full_pricename_event_typesname_facilitiesname_categoriesname_eventsname_seasonsstart_date_timeend_date_timeopen
01799177369844096132guichet2016-04-28 17:58:26+02:009.0Falsedansele grand tabo t gourmand jeunearinga rossatest 2016/20172016-09-27 00:00:00+02:001901-01-01 00:09:21+00:09True
11799178369844096133guichet2016-04-28 17:58:26+02:009.0Falsecirquele grand tabo t gourmand jeune5èmes hurlantstest 2016/20172016-11-18 00:00:00+01:001901-01-01 00:09:21+00:09True
21799179369844096131guichet2016-04-28 17:58:26+02:009.0Falsethéâtrele grand tabo t gourmand jeunedom juantest 2016/20172016-12-07 00:00:00+01:001901-01-01 00:09:21+00:09True
31799180369844096131guichet2016-04-28 17:58:26+02:009.0Falsethéâtrele grand tabo t gourmand jeunevanishing pointtest 2016/20172017-01-04 00:00:00+01:001901-01-01 00:09:21+00:09True
41799181369844096133guichet2016-04-28 17:58:26+02:0012.0Falsecirquela cite des congresabo t gourmand jeunea o lang photest 2016/20172017-01-03 00:00:00+01:001901-01-01 00:09:21+00:09True
...................................................
49230932522326217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231032522336217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231132522346217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231232522356217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
49231332522366217167100621guichet2023-03-09 12:08:45+01:007.0Falsethéâtrecap norttarif sco co 1 seance scolairesur moi, le temps2022/20232023-03-13 14:00:00+01:001901-01-01 00:09:21+00:09True
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492314 rows × 16 columns

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" ], "text/plain": [ " ticket_id customer_id purchase_id event_type_id supplier_name \\\n", "0 1799177 36984 409613 2 guichet \n", "1 1799178 36984 409613 3 guichet \n", "2 1799179 36984 409613 1 guichet \n", "3 1799180 36984 409613 1 guichet \n", "4 1799181 36984 409613 3 guichet \n", "... ... ... ... ... ... \n", "492309 3252232 621716 710062 1 guichet \n", "492310 3252233 621716 710062 1 guichet \n", "492311 3252234 621716 710062 1 guichet \n", "492312 3252235 621716 710062 1 guichet \n", "492313 3252236 621716 710062 1 guichet \n", "\n", " purchase_date amount is_full_price name_event_types \\\n", "0 2016-04-28 17:58:26+02:00 9.0 False danse \n", "1 2016-04-28 17:58:26+02:00 9.0 False cirque \n", "2 2016-04-28 17:58:26+02:00 9.0 False théâtre \n", "3 2016-04-28 17:58:26+02:00 9.0 False théâtre \n", "4 2016-04-28 17:58:26+02:00 12.0 False cirque \n", "... ... ... ... ... \n", "492309 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", "492310 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", "492311 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", "492312 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", "492313 2023-03-09 12:08:45+01:00 7.0 False théâtre \n", "\n", " name_facilities name_categories \\\n", "0 le grand t abo t gourmand jeune \n", "1 le grand t abo t gourmand jeune \n", "2 le grand t abo t gourmand jeune \n", "3 le grand t abo t gourmand jeune \n", "4 la cite des congres abo t gourmand jeune \n", "... ... ... \n", "492309 cap nort tarif sco co 1 seance scolaire \n", "492310 cap nort tarif sco co 1 seance scolaire \n", "492311 cap nort tarif sco co 1 seance scolaire \n", "492312 cap nort tarif sco co 1 seance scolaire \n", "492313 cap nort tarif sco co 1 seance scolaire \n", "\n", " name_events name_seasons start_date_time \\\n", "0 aringa rossa test 2016/2017 2016-09-27 00:00:00+02:00 \n", "1 5èmes hurlants test 2016/2017 2016-11-18 00:00:00+01:00 \n", "2 dom juan test 2016/2017 2016-12-07 00:00:00+01:00 \n", "3 vanishing point test 2016/2017 2017-01-04 00:00:00+01:00 \n", "4 a o lang pho test 2016/2017 2017-01-03 00:00:00+01:00 \n", "... ... ... ... \n", "492309 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", "492310 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", "492311 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", "492312 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", "492313 sur moi, le temps 2022/2023 2023-03-13 14:00:00+01:00 \n", "\n", " end_date_time open \n", "0 1901-01-01 00:09:21+00:09 True \n", "1 1901-01-01 00:09:21+00:09 True \n", "2 1901-01-01 00:09:21+00:09 True \n", "3 1901-01-01 00:09:21+00:09 True \n", "4 1901-01-01 00:09:21+00:09 True \n", "... ... ... \n", "492309 1901-01-01 00:09:21+00:09 True \n", "492310 1901-01-01 00:09:21+00:09 True \n", "492311 1901-01-01 00:09:21+00:09 True \n", "492312 1901-01-01 00:09:21+00:09 True \n", "492313 1901-01-01 00:09:21+00:09 True \n", "\n", "[492314 rows x 16 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products_purchased_reduced" ] }, { "cell_type": "code", "execution_count": 7, "id": "afd044b8-ac83-4a35-b959-700cae0b3b41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_10/target_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", ":27: 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_11/customerplus_cleaned.csv\n", "File path : projet-bdc2324-team1/0_Input/Company_11/campaigns_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_11/products_purchased_reduced.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_11/target_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", ":27: 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_12/customerplus_cleaned.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_12/campaigns_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_12/products_purchased_reduced.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", "/tmp/ipykernel_430/3170175140.py:10: DtypeWarning: Columns (4,8,10) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_12/target_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", ":27: 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_13/customerplus_cleaned.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_13/campaigns_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_13/products_purchased_reduced.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_13/target_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", ":27: 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_14/customerplus_cleaned.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_14/campaigns_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_14/products_purchased_reduced.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", "/tmp/ipykernel_430/3170175140.py:10: DtypeWarning: Columns (8,9) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_14/target_information.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3170175140.py:10: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n", ":27: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" ] } ], "source": [ "# création des bases contenant les KPI pour les 5 compagnies de spectacle\n", "\n", "# liste des compagnies de spectacle\n", "nb_compagnie=['10','11','12','13','14']\n", "\n", "# début de la boucle permettant de générer des datasets agrégés pour les 5 compagnies de spectacle\n", "for directory_path in nb_compagnie:\n", " df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n", " df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n", " df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n", " df_target_information = display_databases(directory_path, file_name = \"target_information\")\n", " \n", " df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n", " df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n", " df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)\n", "\n", " \n", "# creation de la colonne Number compagnie, qui permettra d'agréger les résultats\n", " df_tickets_kpi[\"number_compagny\"]=int(directory_path)\n", " df_campaigns_kpi[\"number_compagny\"]=int(directory_path)\n", " df_customerplus_clean[\"number_compagny\"]=int(directory_path)\n", " df_target_information[\"number_compagny\"]=int(directory_path)\n", "\n", " if nb_compagnie.index(directory_path)>=1:\n", " customerplus_clean_spectacle=pd.concat([customerplus_clean_spectacle,df_customerplus_clean],axis=0)\n", " campaigns_information_spectacle=pd.concat([campaigns_information_spectacle,df_campaigns_kpi],axis=0)\n", " products_purchased_reduced_spectacle=pd.concat([products_purchased_reduced_spectacle,df_tickets_kpi],axis=0)\n", " target_information_spectacle=pd.concat([target_information_spectacle,df_target_information],axis=0)\n", " else:\n", " customerplus_clean_spectacle=df_customerplus_clean\n", " campaigns_information_spectacle=df_campaigns_kpi\n", " products_purchased_reduced_spectacle=df_tickets_kpi\n", " target_information_spectacle=df_target_information" ] }, { "cell_type": "code", "execution_count": 37, "id": "b5a4a031-9533-4a50-8569-5f4246691a7a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...purchase_countfirst_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagny
172139NaNNaN0875FalseNaN2False...3NaNNaNother001NaN010
180312319517NaNNaN01556FalseNaN0True...22020-01-01 14:06:52+00:00frfemale1001.0011
2916422757541303.05.01862FalseNaN1True...32016-09-08 14:50:00+00:00frmale0101.0114
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3 rows × 29 columns

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" ], "text/plain": [ " customer_id street_id structure_id mcp_contact_id fidelity \\\n", "17 2 139 NaN NaN 0 \n", "18031 2 319517 NaN NaN 0 \n", "291642 2 757541 303.0 5.0 1 \n", "\n", " tenant_id is_partner deleted_at gender is_email_true ... \\\n", "17 875 False NaN 2 False ... \n", "18031 1556 False NaN 0 True ... \n", "291642 862 False NaN 1 True ... \n", "\n", " purchase_count first_buying_date country gender_label \\\n", "17 3 NaN NaN other \n", "18031 2 2020-01-01 14:06:52+00:00 fr female \n", "291642 3 2016-09-08 14:50:00+00:00 fr male \n", "\n", " gender_female gender_male gender_other country_fr has_tags \\\n", "17 0 0 1 NaN 0 \n", "18031 1 0 0 1.0 0 \n", "291642 0 1 0 1.0 1 \n", "\n", " number_compagny \n", "17 10 \n", "18031 11 \n", "291642 14 \n", "\n", "[3 rows x 29 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "customerplus_clean_spectacle[customerplus_clean_spectacle[\"customer_id\"]==2]" ] }, { "cell_type": "code", "execution_count": 1, "id": "b9b6ec1f-36fb-4ee9-a1ed-09ff41878005", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'customerplus_clean_spectacle' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcustomerplus_clean_spectacle\u001b[49m[customerplus_clean_spectacle[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcustomer_id\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", "\u001b[0;31mNameError\u001b[0m: name 'customerplus_clean_spectacle' is not defined" ] } ], "source": [ "customerplus_clean_spectacle[customerplus_clean_spectacle[\"customer_id\"]==1]" ] }, { "cell_type": "code", "execution_count": 39, "id": "a12c1b7d-6f6f-483e-b215-6336d7a51057", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customer_id', 'street_id', 'structure_id', 'mcp_contact_id',\n", " 'fidelity', 'tenant_id', 'is_partner', 'deleted_at', 'gender',\n", " 'is_email_true', 'opt_in', 'last_buying_date', 'max_price',\n", " 'ticket_sum', 'average_price', 'average_purchase_delay',\n", " 'average_price_basket', 'average_ticket_basket', 'total_price',\n", " 'purchase_count', 'first_buying_date', 'country', 'gender_label',\n", " 'gender_female', 'gender_male', 'gender_other', 'country_fr',\n", " 'has_tags', 'number_compagny'],\n", " dtype='object')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "customerplus_clean_spectacle.columns" ] }, { "cell_type": "code", "execution_count": null, "id": "6f263e9c-0adf-4f25-8939-7416f3013c04", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 38, "id": "05b9a396-dcd7-4d3d-8b39-5ca48beba4b0", "metadata": {}, "outputs": [], "source": [ "#customerplus_clean_spectacle.isna().sum()\n", "#campaigns_information_spectacle.isna().sum()\n", "#products_purchased_reduced_spectacle.isna().sum()\n", "#target_information_spectacle.isna().sum()" ] }, { "cell_type": "markdown", "id": "fff306c2-1d41-4ef6-867b-ba9a7cf4ee68", "metadata": {}, "source": [ "## Statistiques descriptives" ] }, { "cell_type": "markdown", "id": "0549bdc4-edd7-4511-916e-26e94b5a30f5", "metadata": {}, "source": [ "### 0. Détection du client anonyme (outlier) - utile pour la section 3" ] }, { "cell_type": "code", "execution_count": 45, "id": "5b460061-f8b5-4a6b-ba59-539446d8487f", "metadata": {}, "outputs": [], "source": [ "def outlier_detection(directory_path = \"1\", coupure = 1):\n", " df_tickets = display_databases(directory_path, file_name = 'products_purchased_reduced' , datetime_col = ['purchase_date'])\n", " df_tickets_kpi = tickets_kpi_function(df_tickets)\n", "\n", " if directory_path == \"101\" :\n", " df_tickets_1 = display_databases(directory_path, file_name = 'products_purchased_reduced_1' , datetime_col = ['purchase_date'])\n", " df_tickets_kpi_1 = tickets_kpi_function(df_tickets_1)\n", "\n", " df_tickets_kpi = pd.concat([df_tickets_kpi, df_tickets_kpi_1])\n", " # Part du CA par customer\n", " total_amount_share = df_tickets_kpi.groupby('customer_id')['total_amount'].sum().reset_index()\n", " total_amount_share['total_amount_entreprise'] = total_amount_share['total_amount'].sum()\n", " total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['total_amount_entreprise']\n", " \n", " total_amount_share_index = total_amount_share.set_index('customer_id')\n", " df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False)\n", " \n", " top = df_circulaire[:coupure]\n", " rest = df_circulaire[coupure:]\n", " \n", " # Calculez la somme du reste\n", " rest_sum = rest.sum()\n", " \n", " # Créez une nouvelle série avec les cinq plus grandes parts et 'Autre'\n", " new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])])\n", " \n", " # Créez le graphique circulaire\n", " plt.figure(figsize=(3, 3))\n", " plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5)\n", " plt.axis('equal') # Assurez-vous que le graphique est un cercle\n", " plt.title('Répartition des montants totaux')\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 48, "id": "b6417f09-a6c7-4319-95b3-98c95ec5a3b7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_1173/2987234667.py:8: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n", " df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "outlier_detection(directory_path=\"10\")" ] }, { "cell_type": "markdown", "id": "42f8171c-e80d-4faa-b278-21fcbe3b242c", "metadata": {}, "source": [ "### 1. customerplus_clean" ] }, { "cell_type": "code", "execution_count": 44, "id": "47f98721-53dd-4f8f-85ac-88043ee8d967", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...purchase_countfirst_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagny
0821538139NaNNaN0875FalseNaN2True...0NaNNaNother001NaN010
18091261063NaNNaN0875FalseNaN2True...0NaNfrother0011.0010
2110051063NaNNaN0875FalseNaN2False...14NaNfrother0011.0010
31766312731NaNNaN0875FalseNaN0False...1NaNfrfemale1001.0010
43810012395NaNNaN0875FalseNaN0True...1NaNfrfemale1001.0010
5307036139NaNNaN0875FalseNaN2True...1NaNNaNother001NaN010
629461063NaNNaN0875FalseNaN2False...8NaNfrother0011.0010
71844111139NaNNaN0875FalseNaN2False...3NaNfrother0011.0010
89231139NaNNaN0875FalseNaN0True...1NaNNaNfemale100NaN010
99870139NaNNaN0875FalseNaN2True...1NaNNaNother001NaN010
\n", "

10 rows × 29 columns

\n", "
" ], "text/plain": [ " customer_id street_id structure_id mcp_contact_id fidelity tenant_id \\\n", "0 821538 139 NaN NaN 0 875 \n", "1 809126 1063 NaN NaN 0 875 \n", "2 11005 1063 NaN NaN 0 875 \n", "3 17663 12731 NaN NaN 0 875 \n", "4 38100 12395 NaN NaN 0 875 \n", "5 307036 139 NaN NaN 0 875 \n", "6 2946 1063 NaN NaN 0 875 \n", "7 18441 11139 NaN NaN 0 875 \n", "8 9231 139 NaN NaN 0 875 \n", "9 9870 139 NaN NaN 0 875 \n", "\n", " is_partner deleted_at gender is_email_true ... purchase_count \\\n", "0 False NaN 2 True ... 0 \n", "1 False NaN 2 True ... 0 \n", "2 False NaN 2 False ... 14 \n", "3 False NaN 0 False ... 1 \n", "4 False NaN 0 True ... 1 \n", "5 False NaN 2 True ... 1 \n", "6 False NaN 2 False ... 8 \n", "7 False NaN 2 False ... 3 \n", "8 False NaN 0 True ... 1 \n", "9 False NaN 2 True ... 1 \n", "\n", " first_buying_date country gender_label gender_female gender_male \\\n", "0 NaN NaN other 0 0 \n", "1 NaN fr other 0 0 \n", "2 NaN fr other 0 0 \n", "3 NaN fr female 1 0 \n", "4 NaN fr female 1 0 \n", "5 NaN NaN other 0 0 \n", "6 NaN fr other 0 0 \n", "7 NaN fr other 0 0 \n", "8 NaN NaN female 1 0 \n", "9 NaN NaN other 0 0 \n", "\n", " gender_other country_fr has_tags number_compagny \n", "0 1 NaN 0 10 \n", "1 1 1.0 0 10 \n", "2 1 1.0 0 10 \n", "3 0 1.0 0 10 \n", "4 0 1.0 0 10 \n", "5 1 NaN 0 10 \n", "6 1 1.0 0 10 \n", "7 1 1.0 0 10 \n", "8 0 NaN 0 10 \n", "9 1 NaN 0 10 \n", "\n", "[10 rows x 29 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# visu de la table\n", "customerplus_clean_spectacle.head(10)" ] }, { "cell_type": "code", "execution_count": 43, "id": "738e063b-f84e-4a00-b35d-6d1d657e3c09", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nombre de lignes de la table : 1523688\n" ] }, { "data": { "text/plain": [ "customer_id 0\n", "street_id 0\n", "structure_id 1460624\n", "mcp_contact_id 729167\n", "fidelity 0\n", "tenant_id 0\n", "is_partner 0\n", "deleted_at 1523688\n", "gender 0\n", "is_email_true 0\n", "opt_in 0\n", "last_buying_date 762879\n", "max_price 762879\n", "ticket_sum 0\n", "average_price 667328\n", "average_purchase_delay 762915\n", "average_price_basket 762915\n", "average_ticket_basket 762915\n", "total_price 95551\n", "purchase_count 0\n", "first_buying_date 762879\n", "country 429486\n", "gender_label 0\n", "gender_female 0\n", "gender_male 0\n", "gender_other 0\n", "country_fr 429486\n", "has_tags 0\n", "number_compagny 0\n", "dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# nombre de NaN\n", "print(\"Nombre de lignes de la table : \",customerplus_clean_spectacle.shape[0])\n", "customerplus_clean_spectacle.isna().sum()" ] }, { "cell_type": "code", "execution_count": 148, "id": "296e51c5-30ae-4ade-ba3d-4ba4981a8758", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnycustomer_id
01045264
11135313
212216105
313388731
414101642
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" ], "text/plain": [ " number_compagny customer_id\n", "0 10 45264\n", "1 11 35313\n", "2 12 216105\n", "3 13 388731\n", "4 14 101642" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# nombre de clients de la compagnie (pas les clients visés par une campagne mais ceux ayant acheté)\n", "# on rq le nbre de clients est très variable : de 35k à 389k\n", "company_nb_clients = customerplus_clean_spectacle[customerplus_clean_spectacle[\"purchase_count\"]>0].groupby(\"number_compagny\")[\"customer_id\"].count().reset_index()\n", "company_nb_clients" ] }, { "cell_type": "code", "execution_count": 151, "id": "5845aedf-78ca-4d3d-ad61-3561d4fc1886", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(company_nb_clients[\"number_compagny\"], company_nb_clients[\"customer_id\"]/1000)\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Nombre de clients (milliers)\")\n", "plt.title(\"Nombre de clients de chaque compagnie de spectacle\")\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 152, "id": "fd11c547-7128-4ef6-ad7b-4b7c2a30cd9e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnymax_price
01013823.0
111108.0
2125000.0
3133180.0
414456.0
\n", "
" ], "text/plain": [ " number_compagny max_price\n", "0 10 13823.0\n", "1 11 108.0\n", "2 12 5000.0\n", "3 13 3180.0\n", "4 14 456.0" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# prix maximal payé par un client pour chaque compagnie - très variable : de 108 à 13823\n", "\n", "company_max_price = customerplus_clean_spectacle.groupby(\"number_compagny\")[\"max_price\"].max().reset_index()\n", "company_max_price" ] }, { "cell_type": "code", "execution_count": 153, "id": "b8f8f162-4153-4cfe-bfaa-d981d414510d", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(company_max_price[\"number_compagny\"], company_max_price[\"max_price\"])\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Prix maximal d'un billet vendu\")\n", "plt.title(\"Prix maximal de vente observé par compagnie de spectacle\")\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 71, "id": "bff23e5d-d7ed-4092-ae3c-5df503e54a6d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 762879.000000\n", "mean 0.079068\n", "std 3.969729\n", "min 0.000000\n", "25% 0.000000\n", "50% 0.000000\n", "75% 0.000000\n", "max 3334.000000\n", "Name: purchase_count, dtype: float64" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "customerplus_clean_spectacle[customerplus_clean_spectacle[\"first_buying_date\"].isna()][\"purchase_count\"].describe()" ] }, { "cell_type": "code", "execution_count": 72, "id": "89466dbd-14d2-4ede-9ca0-b9c32b764e25", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 7.608090e+05\n", "mean 3.863940e+00\n", "std 1.685825e+03\n", "min 1.000000e+00\n", "25% 1.000000e+00\n", "50% 1.000000e+00\n", "75% 2.000000e+00\n", "max 1.469325e+06\n", "Name: purchase_count, dtype: float64" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "customerplus_clean_spectacle[~customerplus_clean_spectacle[\"first_buying_date\"].isna()][\"purchase_count\"].describe()" ] }, { "cell_type": "code", "execution_count": 77, "id": "5f9feae4-35f4-43b6-adeb-f75773900a2d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...first_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagnyalready_purchased
0821538139NaNNaN0875FalseNaN2True...NaNNaNother001NaN010False
18091261063NaNNaN0875FalseNaN2True...NaNfrother0011.0010False
2110051063NaNNaN0875FalseNaN2False...NaNfrother0011.0010False
31766312731NaNNaN0875FalseNaN0False...NaNfrfemale1001.0010False
43810012395NaNNaN0875FalseNaN0True...NaNfrfemale1001.0010False
..................................................................
3431214667645122NaN1534181.00862FalseNaN2True...NaNNaNother001NaN014False
3431224667649122NaN1534177.00862FalseNaN2True...NaNNaNother001NaN014False
3431234667660122NaN1534165.00862FalseNaN0True...NaNNaNfemale100NaN014False
3431244667679122NaN1534132.00862FalseNaN2True...NaNNaNother001NaN014False
3431254667686122NaN1567949.00862FalseNaN0True...NaNNaNfemale100NaN014False
\n", "

1523688 rows × 30 columns

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" ], "text/plain": [ " customer_id street_id structure_id mcp_contact_id fidelity \\\n", "0 821538 139 NaN NaN 0 \n", "1 809126 1063 NaN NaN 0 \n", "2 11005 1063 NaN NaN 0 \n", "3 17663 12731 NaN NaN 0 \n", "4 38100 12395 NaN NaN 0 \n", "... ... ... ... ... ... \n", "343121 4667645 122 NaN 1534181.0 0 \n", "343122 4667649 122 NaN 1534177.0 0 \n", "343123 4667660 122 NaN 1534165.0 0 \n", "343124 4667679 122 NaN 1534132.0 0 \n", "343125 4667686 122 NaN 1567949.0 0 \n", "\n", " tenant_id is_partner deleted_at gender is_email_true ... \\\n", "0 875 False NaN 2 True ... \n", "1 875 False NaN 2 True ... \n", "2 875 False NaN 2 False ... \n", "3 875 False NaN 0 False ... \n", "4 875 False NaN 0 True ... \n", "... ... ... ... ... ... ... \n", "343121 862 False NaN 2 True ... \n", "343122 862 False NaN 2 True ... \n", "343123 862 False NaN 0 True ... \n", "343124 862 False NaN 2 True ... \n", "343125 862 False NaN 0 True ... \n", "\n", " first_buying_date country gender_label gender_female gender_male \\\n", "0 NaN NaN other 0 0 \n", "1 NaN fr other 0 0 \n", "2 NaN fr other 0 0 \n", "3 NaN fr female 1 0 \n", "4 NaN fr female 1 0 \n", "... ... ... ... ... ... \n", "343121 NaN NaN other 0 0 \n", "343122 NaN NaN other 0 0 \n", "343123 NaN NaN female 1 0 \n", "343124 NaN NaN other 0 0 \n", "343125 NaN NaN female 1 0 \n", "\n", " gender_other country_fr has_tags number_compagny already_purchased \n", "0 1 NaN 0 10 False \n", "1 1 1.0 0 10 False \n", "2 1 1.0 0 10 False \n", "3 0 1.0 0 10 False \n", "4 0 1.0 0 10 False \n", "... ... ... ... ... ... \n", "343121 1 NaN 0 14 False \n", "343122 1 NaN 0 14 False \n", "343123 0 NaN 0 14 False \n", "343124 1 NaN 0 14 False \n", "343125 0 NaN 0 14 False \n", "\n", "[1523688 rows x 30 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "customerplus_clean_spectacle[\"already_purchased\"] = customerplus_clean_spectacle[\"first_buying_date\"].isna()==False\n", "customerplus_clean_spectacle" ] }, { "cell_type": "code", "execution_count": 83, "id": "cec4f1eb-cec8-409d-8b2c-1e01f1bf81ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...first_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagnyalready_purchased
2110051063NaNNaN0875FalseNaN2False...NaNfrother0011.0010False
31766312731NaNNaN0875FalseNaN0False...NaNfrfemale1001.0010False
43810012395NaNNaN0875FalseNaN0True...NaNfrfemale1001.0010False
5307036139NaNNaN0875FalseNaN2True...NaNNaNother001NaN010False
629461063NaNNaN0875FalseNaN2False...NaNfrother0011.0010False
..................................................................
3389333625705648752NaN1253864.00862FalseNaN0True...NaNfrfemale1001.0014False
3389543627626636890NaN1253887.00862FalseNaN0True...NaNfrfemale1001.0014False
3389593628124653042NaN1253899.00862FalseNaN0True...NaNfrfemale1001.0014False
3389863631189648423NaN1253928.00862FalseNaN0True...NaNfrfemale1001.0014False
3390393635380659417NaN1253975.00862FalseNaN1True...NaNfrmale0101.0014False
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26246 rows × 30 columns

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" ], "text/plain": [ " customer_id street_id structure_id mcp_contact_id fidelity \\\n", "2 11005 1063 NaN NaN 0 \n", "3 17663 12731 NaN NaN 0 \n", "4 38100 12395 NaN NaN 0 \n", "5 307036 139 NaN NaN 0 \n", "6 2946 1063 NaN NaN 0 \n", "... ... ... ... ... ... \n", "338933 3625705 648752 NaN 1253864.0 0 \n", "338954 3627626 636890 NaN 1253887.0 0 \n", "338959 3628124 653042 NaN 1253899.0 0 \n", "338986 3631189 648423 NaN 1253928.0 0 \n", "339039 3635380 659417 NaN 1253975.0 0 \n", "\n", " tenant_id is_partner deleted_at gender is_email_true ... \\\n", "2 875 False NaN 2 False ... \n", "3 875 False NaN 0 False ... \n", "4 875 False NaN 0 True ... \n", "5 875 False NaN 2 True ... \n", "6 875 False NaN 2 False ... \n", "... ... ... ... ... ... ... \n", "338933 862 False NaN 0 True ... \n", "338954 862 False NaN 0 True ... \n", "338959 862 False NaN 0 True ... \n", "338986 862 False NaN 0 True ... \n", "339039 862 False NaN 1 True ... \n", "\n", " first_buying_date country gender_label gender_female gender_male \\\n", "2 NaN fr other 0 0 \n", "3 NaN fr female 1 0 \n", "4 NaN fr female 1 0 \n", "5 NaN NaN other 0 0 \n", "6 NaN fr other 0 0 \n", "... ... ... ... ... ... \n", "338933 NaN fr female 1 0 \n", "338954 NaN fr female 1 0 \n", "338959 NaN fr female 1 0 \n", "338986 NaN fr female 1 0 \n", "339039 NaN fr male 0 1 \n", "\n", " gender_other country_fr has_tags number_compagny already_purchased \n", "2 1 1.0 0 10 False \n", "3 0 1.0 0 10 False \n", "4 0 1.0 0 10 False \n", "5 1 NaN 0 10 False \n", "6 1 1.0 0 10 False \n", "... ... ... ... ... ... \n", "338933 0 1.0 0 14 False \n", "338954 0 1.0 0 14 False \n", "338959 0 1.0 0 14 False \n", "338986 0 1.0 0 14 False \n", "339039 0 1.0 0 14 False \n", "\n", "[26246 rows x 30 columns]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# attention, on a des cas où le client a pas de première date d'achat alors qu'il compte plusieurs achats\n", "# on peut donc avoir une date de première achat valant NaN non pas parce que l'individu n'a jamais acheté \n", "# mais simplement car elle n'est pas renseignée\n", "\n", "customerplus_clean_spectacle[(customerplus_clean_spectacle[\"already_purchased\"]==False) &\n", "(customerplus_clean_spectacle[\"purchase_count\"]>0)]" ] }, { "cell_type": "code", "execution_count": 80, "id": "b5904039-a967-47d5-ba13-1b805bcd76ca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idstreet_idstructure_idmcp_contact_idfidelitytenant_idis_partnerdeleted_atgenderis_email_true...first_buying_datecountrygender_labelgender_femalegender_malegender_othercountry_frhas_tagsnumber_compagnyalready_purchased
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0 rows × 30 columns

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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [customer_id, street_id, structure_id, mcp_contact_id, fidelity, tenant_id, is_partner, deleted_at, gender, is_email_true, opt_in, last_buying_date, max_price, ticket_sum, average_price, average_purchase_delay, average_price_basket, average_ticket_basket, total_price, purchase_count, first_buying_date, country, gender_label, gender_female, gender_male, gender_other, country_fr, has_tags, number_compagny, already_purchased]\n", "Index: []\n", "\n", "[0 rows x 30 columns]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cpdt, si un client a un nombre d'achats nul, il a bien une date de premier achat valant NaN, OK\n", "customerplus_clean_spectacle[(customerplus_clean_spectacle[\"already_purchased\"]) &\n", "(customerplus_clean_spectacle[\"purchase_count\"]==0)]" ] }, { "cell_type": "code", "execution_count": 89, "id": "e940bfcf-29cc-4d4c-ae5e-e2a8cecf28af", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "number_compagny already_purchased\n", "10 False 0.234840\n", " True 0.236236\n", "11 False 0.141746\n", " True 0.002804\n", "12 False 0.485950\n", " True 0.244779\n", "13 False 0.084057\n", " True 0.177213\n", "14 False 0.885553\n", " True 0.308859\n", "Name: opt_in, dtype: float64" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# différence de consentement aux campagnes de mails (opt in)\n", "\n", "# en se restreignant au personnes n'ayant pas acheté, on a quand même des individus acceptant d'être ciblés\n", "customerplus_clean_spectacle[customerplus_clean_spectacle[\"first_buying_date\"].isna()][\"opt_in\"].unique()\n", "\n", "# taux de consentement variés\n", "customerplus_clean_spectacle[\"already_purchased\"] = customerplus_clean_spectacle[\"purchase_count\"] > 0\n", "customerplus_clean_spectacle.groupby([\"number_compagny\", \"already_purchased\"])[\"opt_in\"].mean()" ] }, { "cell_type": "code", "execution_count": 94, "id": "a5e79beb-9ba0-4c89-b084-e27ff0d65dcc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnyalready_purchasedopt_in
010False0.234840
110True0.236236
211False0.141746
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\n", "
" ], "text/plain": [ " number_compagny already_purchased opt_in\n", "0 10 False 0.234840\n", "1 10 True 0.236236\n", "2 11 False 0.141746\n", "3 11 True 0.002804\n", "4 12 False 0.485950\n", "5 12 True 0.244779\n", "6 13 False 0.084057\n", "7 13 True 0.177213\n", "8 14 False 0.885553\n", "9 14 True 0.308859" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_graph = customerplus_clean_spectacle.groupby([\"number_compagny\", \"already_purchased\"])[\"opt_in\"].mean().reset_index()\n", "df_graph" ] }, { "cell_type": "code", "execution_count": 127, "id": "5be56c41-7697-481a-84ea-f77a2041484b", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot groupé\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", "categories = df_graph[\"number_compagny\"].unique()\n", "bar_width = 0.35\n", "bar_positions = np.arange(len(categories))\n", "\n", "# Grouper les données par label et créer les barres groupées\n", "for label in df_graph[\"already_purchased\"].unique():\n", " label_data = df_graph[df_graph['already_purchased'] == label]\n", " values = [label_data[label_data['number_compagny'] == category]['opt_in'].values[0]*100 for category in categories]\n", "\n", " label_printed = \"purchased\" if label else \"no purchase\"\n", " ax.bar(bar_positions, values, bar_width, label=label_printed)\n", "\n", " # Mise à jour des positions des barres pour le prochain groupe\n", " bar_positions = [pos + bar_width for pos in bar_positions]\n", "\n", "# Ajout des étiquettes, de la légende, etc.\n", "ax.set_xlabel('Numero de compagnie')\n", "ax.set_ylabel('Part de consentement (%)')\n", "ax.set_title('Part de consentement au mailing selon les compagnies')\n", "ax.set_xticks([pos + bar_width / 2 for pos in np.arange(len(categories))])\n", "ax.set_xticklabels(categories)\n", "ax.legend()\n", "\n", "# Affichage du plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 124, "id": "32960530-cb46-4eeb-a6d2-1dcf5fb640d8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnygender_malegender_femalegender_other
0100.1815800.3438370.474583
1110.1795200.3144430.506037
2120.3463800.4540360.199584
3130.3181080.5030920.178800
4140.3319540.3161810.351865
\n", "
" ], "text/plain": [ " number_compagny gender_male gender_female gender_other\n", "0 10 0.181580 0.343837 0.474583\n", "1 11 0.179520 0.314443 0.506037\n", "2 12 0.346380 0.454036 0.199584\n", "3 13 0.318108 0.503092 0.178800\n", "4 14 0.331954 0.316181 0.351865" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# genre \n", "\n", "company_genders = customerplus_clean_spectacle.groupby(\"number_compagny\")[[\"gender_male\", \"gender_female\", \"gender_other\"]].mean().reset_index()\n", "company_genders" ] }, { "cell_type": "code", "execution_count": 126, "id": "1b4a49d7-7bfe-4e80-aa7e-c9c6d4bc46e2", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(company_genders[\"number_compagny\"], company_genders[\"gender_male\"], label = \"Homme\")\n", "plt.bar(company_genders[\"number_compagny\"], company_genders[\"gender_female\"], \n", " bottom = company_genders[\"gender_male\"], label = \"Femme\")\n", "\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Part de clients de chaque sexe\")\n", "plt.title(\"Sexe des clients de chaque compagnie de spectacle\")\n", "plt.legend()\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 144, "id": "ed6374e5-f36c-4f8e-9dba-602715b726f1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnycountry_fr
0100.996136
1110.994838
2120.002119
3130.831795
4140.993978
\n", "
" ], "text/plain": [ " number_compagny country_fr\n", "0 10 0.996136\n", "1 11 0.994838\n", "2 12 0.002119\n", "3 13 0.831795\n", "4 14 0.993978" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pays d'origine (France VS reste du monde)\n", "\n", "company_country_fr = customerplus_clean_spectacle.groupby(\"number_compagny\")[\"country_fr\"].mean().reset_index()\n", "company_country_fr" ] }, { "cell_type": "code", "execution_count": 147, "id": "8d95cdd9-2ab3-4c9a-8442-bb9b98e0dd18", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(company_country_fr[\"number_compagny\"], company_country_fr[\"country_fr\"])\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Part de clients français\")\n", "plt.title(\"Nationalité des clients de chaque compagnie de spectacle\")\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ecfd112e-270a-4223-b80f-7e95e57d199d", "metadata": {}, "source": [ "### 2. campaigns_information" ] }, { "cell_type": "code", "execution_count": 189, "id": "b37e7ddf-321a-4ebe-9742-9e760a541d29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nombre de lignes de la table : 688953\n" ] }, { "data": { "text/plain": [ "customer_id 0\n", "nb_campaigns 0\n", "nb_campaigns_opened 0\n", "time_to_open 301495\n", "number_compagny 0\n", "dtype: int64" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# nombre de nan\n", "print(\"Nombre de lignes de la table : \",campaigns_information_spectacle.shape[0])\n", "campaigns_information_spectacle.isna().sum()" ] }, { "cell_type": "code", "execution_count": 192, "id": "de1ecaac-25bb-4853-b8ab-3ef2ca6917ed", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idnb_campaignsnb_campaigns_openedtime_to_opennumber_compagnyno_campaign_opened
02940.0NaT10True
13730.0NaT10True
23941.00 days 05:16:3810False
34141.00 days 01:12:2910False
44440.0NaT10True
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254699683776911.00 days 23:42:1514False
254700687503810.0NaT14True
254701687506610.0NaT14True
254702687509910.0NaT14True
254703687514311.00 days 01:17:0114False
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688953 rows × 6 columns

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" ], "text/plain": [ " customer_id nb_campaigns nb_campaigns_opened time_to_open \\\n", "0 29 4 0.0 NaT \n", "1 37 3 0.0 NaT \n", "2 39 4 1.0 0 days 05:16:38 \n", "3 41 4 1.0 0 days 01:12:29 \n", "4 44 4 0.0 NaT \n", "... ... ... ... ... \n", "254699 6837769 1 1.0 0 days 23:42:15 \n", "254700 6875038 1 0.0 NaT \n", "254701 6875066 1 0.0 NaT \n", "254702 6875099 1 0.0 NaT \n", "254703 6875143 1 1.0 0 days 01:17:01 \n", "\n", " number_compagny no_campaign_opened \n", "0 10 True \n", "1 10 True \n", "2 10 False \n", "3 10 False \n", "4 10 True \n", "... ... ... \n", "254699 14 False \n", "254700 14 True \n", "254701 14 True \n", "254702 14 True \n", "254703 14 False \n", "\n", "[688953 rows x 6 columns]" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# part de clients n'ouvrant jamais les mails par compagnie\n", "\n", "campaigns_information_spectacle[\"no_campaign_opened\"] = pd.isna(campaigns_information_spectacle[\"time_to_open\"])\n", "campaigns_information_spectacle" ] }, { "cell_type": "code", "execution_count": 197, "id": "b5a0060f-a9dd-435b-844f-b24674b8bc27", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnyno_campaign_opened
0100.605656
1110.294001
2120.475719
3130.353820
4140.428148
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" ], "text/plain": [ " number_compagny no_campaign_opened\n", "0 10 0.605656\n", "1 11 0.294001\n", "2 12 0.475719\n", "3 13 0.353820\n", "4 14 0.428148" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "company_lazy_customers = campaigns_information_spectacle.groupby(\"number_compagny\")[\"no_campaign_opened\"].mean().reset_index()\n", "company_lazy_customers" ] }, { "cell_type": "code", "execution_count": 198, "id": "788c90e0-f13a-4804-ace7-e5159fddd7fd", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(company_lazy_customers[\"number_compagny\"], company_lazy_customers[\"no_campaign_opened\"])\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Part de clients n'ayant ouvert aucun mail\")\n", "plt.title(\"Part de clients n'ayant ouvert aucun mail pour les compagnies de spectacle\")\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 203, "id": "c48015c2-6451-4089-93b7-6d55d3b2e553", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnynb_campaignsnb_campaigns_openedratio_campaigns_opened
010734772126151.00.171687
111342396129833.00.379190
2123168123810722.00.255900
3133218569793581.00.246563
4142427043723846.00.298242
\n", "
" ], "text/plain": [ " number_compagny nb_campaigns nb_campaigns_opened ratio_campaigns_opened\n", "0 10 734772 126151.0 0.171687\n", "1 11 342396 129833.0 0.379190\n", "2 12 3168123 810722.0 0.255900\n", "3 13 3218569 793581.0 0.246563\n", "4 14 2427043 723846.0 0.298242" ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# taux d'ouverture des campaigns\n", "\n", "company_campaigns_stats = campaigns_information_spectacle.groupby(\"number_compagny\")[[\"nb_campaigns\", \"nb_campaigns_opened\"]].sum().reset_index()\n", "company_campaigns_stats[\"ratio_campaigns_opened\"] = company_campaigns_stats[\"nb_campaigns_opened\"] / company_campaigns_stats[\"nb_campaigns\"]\n", "company_campaigns_stats" ] }, { "cell_type": "code", "execution_count": 213, "id": "d06ab865-4832-4fe9-918b-e5ff72bebee4", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(company_campaigns_stats[\"number_compagny\"], 100 * company_campaigns_stats[\"ratio_campaigns_opened\"])\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Taux d'ouverture (%)\")\n", "plt.title(\"Taux d'ouverture des campagnes de mails pour les compagnies de spectacle\")\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 219, "id": "5c37e063-a717-4a8c-828e-b386b87e8409", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# création d'un barplot permettant de visualiser les 2 indicateurs sur le même graphique\n", "\n", "# Création du premier barplot\n", "plt.bar(company_campaigns_stats[\"number_compagny\"], 100 * company_campaigns_stats[\"ratio_campaigns_opened\"],\n", " label = \"taux d'ouverture\", alpha = 0.7)\n", "\n", "# Création du deuxième barplot à côté du premier\n", "bar_width = 0.4 # Largeur des barres\n", "indices2 = company_campaigns_stats[\"number_compagny\"] + bar_width\n", "plt.bar(indices2, 100 * (1 - company_lazy_customers[\"no_campaign_opened\"]), \n", " label='Part de clients ouvrant des mails', alpha=0.7, width=bar_width)\n", "\n", "# Ajout des étiquettes et de la légende\n", "plt.xlabel('Compagnie')\n", "plt.ylabel('Taux (%)')\n", "plt.title('Lien entre taux d ouverture des mails et nombre de clients actifs')\n", "plt.legend()\n", "\n", "# Affichage du graphique\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "783f6fb2-5f26-42a9-a22d-f4ece44bfaf2", "metadata": {}, "source": [ "### 3. products_purchased_reduced" ] }, { "cell_type": "code", "execution_count": 48, "id": "643e75f7-5ff2-45e8-b6b9-808d2c8d40c4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idnb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetnumber_compagny
0194828829872.0212643.092500718.1493981924.9431028.010
1194843262.0101745.0217361743.0450351.9767010.010
219485131211878.0212649.04474585.2408452563.80390084.010
31948610496.0101944.0776041742.794225201.2833800.010
4194872133.0101742.8777661742.8777660.0000000.010
....................................
9958068847472140.0100.1937500.1937500.0000000.014
9958168847482140.0100.1868060.1868060.0000000.014
9958268847504180.0100.1361110.1361110.0000000.014
9958368847512140.0100.1229170.1229170.0000000.014
9958468847532140.0100.0472220.0472220.0000000.014
\n", "

764880 rows × 11 columns

\n", "
" ], "text/plain": [ " customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n", "0 19482 88 29 872.0 2 \n", "1 19484 3 2 62.0 1 \n", "2 19485 131 21 1878.0 2 \n", "3 19486 10 4 96.0 1 \n", "4 19487 2 1 33.0 1 \n", "... ... ... ... ... ... \n", "99580 6884747 2 1 40.0 1 \n", "99581 6884748 2 1 40.0 1 \n", "99582 6884750 4 1 80.0 1 \n", "99583 6884751 2 1 40.0 1 \n", "99584 6884753 2 1 40.0 1 \n", "\n", " vente_internet_max purchase_date_min purchase_date_max \\\n", "0 1 2643.092500 718.149398 \n", "1 0 1745.021736 1743.045035 \n", "2 1 2649.044745 85.240845 \n", "3 0 1944.077604 1742.794225 \n", "4 0 1742.877766 1742.877766 \n", "... ... ... ... \n", "99580 0 0.193750 0.193750 \n", "99581 0 0.186806 0.186806 \n", "99582 0 0.136111 0.136111 \n", "99583 0 0.122917 0.122917 \n", "99584 0 0.047222 0.047222 \n", "\n", " time_between_purchase nb_tickets_internet number_compagny \n", "0 1924.943102 8.0 10 \n", "1 1.976701 0.0 10 \n", "2 2563.803900 84.0 10 \n", "3 201.283380 0.0 10 \n", "4 0.000000 0.0 10 \n", "... ... ... ... \n", "99580 0.000000 0.0 14 \n", "99581 0.000000 0.0 14 \n", "99582 0.000000 0.0 14 \n", "99583 0.000000 0.0 14 \n", "99584 0.000000 0.0 14 \n", "\n", "[764880 rows x 11 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products_purchased_reduced_spectacle" ] }, { "cell_type": "code", "execution_count": 47, "id": "74534ded-8121-43fb-8cf8-af353bed2c77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nombre de lignes de la table : 764880\n" ] }, { "data": { "text/plain": [ "customer_id 0\n", "nb_tickets 0\n", "nb_purchases 0\n", "total_amount 0\n", "nb_suppliers 0\n", "vente_internet_max 0\n", "purchase_date_min 0\n", "purchase_date_max 0\n", "time_between_purchase 0\n", "nb_tickets_internet 0\n", "number_compagny 0\n", "dtype: int64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# nombre de nan\n", "print(\"Nombre de lignes de la table : \",products_purchased_reduced_spectacle.shape[0])\n", "products_purchased_reduced_spectacle.isna().sum()" ] }, { "cell_type": "code", "execution_count": 22, "id": "d64979ba-fccf-45f2-8a15-40ef1b49c74f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_430/3239820253.py:6: DtypeWarning: Columns (39) have mixed types. Specify dtype option on import or set low_memory=False.\n", " dataset_train = pd.read_csv(file_in, sep=\",\")\n" ] } ], "source": [ "#base d'entrainement\n", "\n", "#FILE_PATH_S3='projet-bdc2324-team1/Generalization/musique/Train_test/dataset_train14.csv'\n", "\n", "#with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n", " #dataset_train = pd.read_csv(file_in, sep=\",\")" ] }, { "cell_type": "code", "execution_count": 36, "id": "635d60cd-2dbc-49da-b0f4-94e16667882f", "metadata": {}, "outputs": [], "source": [ "#Creation de la variable dependante fictive: 1 si l'individu a effectué un achat au cours de la periode de train et 0 sinon\n", "\n", "#dataset_train_modif=dataset_train\n", "\n", "#dataset_train_modif[\"y_purchase_fictive\"]=np.random.randint(2, size=dataset_train_modif.shape[0])" ] }, { "cell_type": "code", "execution_count": 40, "id": "ea63e5d6-70f9-4685-8b08-673a47108954", "metadata": {}, "outputs": [], "source": [ "#dataset_train_modif[\"y_purchase_fictive\"].value_counts(normalize=True)" ] }, { "cell_type": "code", "execution_count": 41, "id": "98f7645f-ffe6-4d7b-8032-15e65f36af87", "metadata": {}, "outputs": [], "source": [ "\n", "#dataset_train_modif[\"y_purchase_fictive\"]=dataset_train_modif[\"y_purchase_fictive\"].replace([0,1],[\"Purchase_train\",\"no_purchase_train\"])" ] }, { "cell_type": "code", "execution_count": 44, "id": "6db089d5-5517-4aee-a5fd-53f20ae3f0d7", "metadata": {}, "outputs": [], "source": [ "#importation librairies\n", "import warnings\n", "warnings.simplefilter(\"ignore\")\n", "import pandas as pd\n", "import numpy as np\n", "import statsmodels\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy.stats import shapiro\n", "from numpy.random import randn\n", "import scipy.stats as st\n", "%matplotlib inline\n", "\n", "#col_purchase=[\"nb_tickets\",\"nb_purchases\",\"total_amount\",\"nb_suppliers\",\"time_between_purchase\",\"nb_tickets_internet\"]" ] }, { "cell_type": "code", "execution_count": null, "id": "c532f884-3f44-4ce7-8924-9b4542bc3c10", "metadata": {}, "outputs": [], "source": [ "#histogrames des variable quantitatives\n", "col_purchase=[\"nb_tickets\",\"nb_purchases\",\"total_amount\",\"nb_suppliers\",\"time_between_purchase\",\"nb_tickets_internet\"]\n", "for col in col_purchase:\n", " plt.figure()\n", " sns.histplot(products_purchased_reduced_spectacle[col], kde=True, color='red')" ] }, { "cell_type": "code", "execution_count": 61, "id": "eb6355e0-3f8c-47d9-a5ee-d349040dcf51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"Boite à moustache du chiffre d'affaire selon les compagnies de spectacles\")" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#repartion Chiffre d'affaire selon le numero de la compagnie\n", "\n", "sns.boxplot(data=products_purchased_reduced_spectacle, y=\"total_amount\",x=\"number_compagny\",showfliers=False,showmeans=True)\n", "plt.title(\"Boite à moustache du chiffre d'affaire selon les compagnies de spectacles\")" ] }, { "cell_type": "code", "execution_count": 54, "id": "aacf2c34-f7ea-4d6e-935b-c5db01f03bbe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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number_compagnynb_ticketsnb_tickets_internetTaux_ticket_internet
010492314126262.025.646640
11131896916348.05.125263
21259102842045.07.113876
31370242271247482.017.759705
414335741125638.037.421107
\n", "
" ], "text/plain": [ " number_compagny nb_tickets nb_tickets_internet Taux_ticket_internet\n", "0 10 492314 126262.0 25.646640\n", "1 11 318969 16348.0 5.125263\n", "2 12 591028 42045.0 7.113876\n", "3 13 7024227 1247482.0 17.759705\n", "4 14 335741 125638.0 37.421107" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Taux de ticket payé par internet selon les compagnies\n", "\n", "purchase_spectacle = products_purchased_reduced_spectacle.groupby(\"number_compagny\")[[\"nb_tickets\", \"nb_tickets_internet\"]].sum().reset_index()\n", "purchase_spectacle[\"Taux_ticket_internet\"] = purchase_spectacle[\"nb_tickets_internet\"]*100 / purchase_spectacle[\"nb_tickets\"]\n", "purchase_spectacle" ] }, { "cell_type": "code", "execution_count": 57, "id": "f71bb53d-724b-454d-8743-305d20eec2b0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Création du barplot\n", "plt.bar(purchase_spectacle[\"number_compagny\"], purchase_spectacle[\"Taux_ticket_internet\"])\n", "\n", "# Ajout de titres et d'étiquettes\n", "plt.xlabel('Company')\n", "plt.ylabel(\"Taux d'achat de tickets en ligne (%)\")\n", "plt.title(\"Taux d'achat des tickets en ligne selon les compagnies de spectacle\")\n", "\n", "# Affichage du barplot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 65, "id": "254875ac-95e4-44fa-9f02-6cec144e4bde", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "la p-value associé à la stat de fisher est superieure à 5% donc il n y a pas de lien entre les entreprise et le taux de ticket acheté en ligne\n" ] } ], "source": [ "#test anova entre les entreprise de spectacle et taux d'achat de ticket en ligne\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import ols\n", "model = ols('Taux_ticket_internet ~ number_compagny', data=purchase_spectacle).fit()\n", "anova_table = sm.stats.anova_lm(model, typ=2)\n", "print(\"la p-value associé à la stat de fisher est superieure à 5% donc il n y a pas de lien entre les entreprise et le taux de ticket acheté en ligne\")\n" ] }, { "cell_type": "code", "execution_count": 66, "id": "59a95248-0261-4970-9e91-e43d50cf4d69", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Boite à moustache du temps ecoulés entre le premier et le dernier achat selon les compagnies de spectacles')" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#repartion Chiffre d'affaire selon le numero de la compagnie\n", "\n", "sns.boxplot(data=products_purchased_reduced_spectacle, y=\"time_between_purchase\",x=\"number_compagny\",showfliers=False,showmeans=True)\n", "plt.title(\"Boite à moustache du temps ecoulés entre le premier et le dernier achat selon les compagnies de spectacles\")" ] }, { "cell_type": "code", "execution_count": null, "id": "58cbe8a5-3899-4aa3-91ab-48bed9124fbd", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 72, "id": "58f49748-e55f-4d1b-b58b-102d02a9e0eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sum_sq df F PR(>F)\n", "number_compagny 4.108441e+09 1.0 23548.336165 0.0\n", "Residual 1.334471e+11 764878.0 NaN NaN la p-value associé à la stat de fisher est inferieure à 5% donc il y a un lien entre les entreprise et le temps écoulés entre le premier et le dernier achat\n" ] } ], "source": [ "#test anova entre les entreprise de spectacle et taux d'achat de ticket en ligne\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import ols\n", "model = ols('time_between_purchase ~ number_compagny', data=products_purchased_reduced_spectacle).fit()\n", "anova_table = sm.stats.anova_lm(model, typ=2)\n", "anova_table\n", "print(anova_table,\"la p-value associé à la stat de fisher est inferieure à 5% donc il y a un lien entre les entreprise et le temps écoulés entre le premier et le dernier achat\" )\n" ] }, { "cell_type": "code", "execution_count": null, "id": "33ef5773-a09d-4b8c-918f-1b64a9790422", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b9e84af4-a02b-4f83-81ae-b7a73475d060", "metadata": {}, "source": [ "### 4. target_information" ] }, { "cell_type": "code", "execution_count": 222, "id": "2867eceb-1f72-406c-adc2-adfedcaf60e6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nombre de lignes de la table : 6240166\n" ] }, { "data": { "text/plain": [ "id 0\n", "customer_id 0\n", "target_name 0\n", "target_type_is_import 0\n", "target_type_name 0\n", "number_compagny 0\n", "dtype: int64" ] }, "execution_count": 222, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# nombre de nan\n", "print(\"Nombre de lignes de la table : \",target_information_spectacle.shape[0])\n", "target_information_spectacle.isna().sum()" ] }, { "cell_type": "code", "execution_count": 11, "id": "985b6403-3c75-420e-a4a4-d3045213e9ef", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_nametarget_type_is_importtarget_type_namenumber_compagny
01165098618562Newsletter mensuelleFalsemanual_static_filter10
11165100618559Newsletter mensuelleFalsemanual_static_filter10
21165101618561Newsletter mensuelleFalsemanual_static_filter10
31165102618560Newsletter mensuelleFalsemanual_static_filter10
41165103618558Newsletter mensuelleFalsemanual_static_filter10
.....................
77965342070826764876INSCRIPTION NL VOYAGES HUMAFalsemanual_static_filter14
77965442070836764877Inscriptions newsletters (depuis 2019)Falsemanual_static_filter14
77965542070846801322Inscriptions newsletters (depuis 2019)Falsemanual_static_filter14
77965642070856837768Inscriptions newsletters (depuis 2019)Falsemanual_static_filter14
77965742070866837769Inscriptions newsletters (depuis 2019)Falsemanual_static_filter14
\n", "

6240166 rows × 6 columns

\n", "
" ], "text/plain": [ " id customer_id target_name \\\n", "0 1165098 618562 Newsletter mensuelle \n", "1 1165100 618559 Newsletter mensuelle \n", "2 1165101 618561 Newsletter mensuelle \n", "3 1165102 618560 Newsletter mensuelle \n", "4 1165103 618558 Newsletter mensuelle \n", "... ... ... ... \n", "779653 4207082 6764876 INSCRIPTION NL VOYAGES HUMA \n", "779654 4207083 6764877 Inscriptions newsletters (depuis 2019) \n", "779655 4207084 6801322 Inscriptions newsletters (depuis 2019) \n", "779656 4207085 6837768 Inscriptions newsletters (depuis 2019) \n", "779657 4207086 6837769 Inscriptions newsletters (depuis 2019) \n", "\n", " target_type_is_import target_type_name number_compagny \n", "0 False manual_static_filter 10 \n", "1 False manual_static_filter 10 \n", "2 False manual_static_filter 10 \n", "3 False manual_static_filter 10 \n", "4 False manual_static_filter 10 \n", "... ... ... ... \n", "779653 False manual_static_filter 14 \n", "779654 False manual_static_filter 14 \n", "779655 False manual_static_filter 14 \n", "779656 False manual_static_filter 14 \n", "779657 False manual_static_filter 14 \n", "\n", "[6240166 rows x 6 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target_information_spectacle" ] }, { "cell_type": "code", "execution_count": null, "id": "93a8ae1f-6fbd-4210-a857-728ae472d1c5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "904cbf32-77b6-49dd-a96c-9e7e5a0175c3", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }