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events_theme #2

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ajoubrel-ensae merged 9 commits from events_theme into main 2024-02-05 19:29:12 +01:00
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.ipynb_checkpoints/Clean-Notebook-checkpoint.ipynb
# Created by https://www.toptal.com/developers/gitignore/api/jupyternotebooks,python
# Edit at https://www.toptal.com/developers/gitignore?templates=jupyternotebooks,python
### JupyterNotebooks ###
# gitignore template for Jupyter Notebooks
# website: http://jupyter.org/
.ipynb_checkpoints
*/.ipynb_checkpoints/*
# IPython
profile_default/
ipython_config.py
# Remove previous ipynb_checkpoints
# git rm -r .ipynb_checkpoints/
### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
# IPython
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
### Python Patch ###
# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
poetry.toml
# ruff
.ruff_cache/
# LSP config files
pyrightconfig.json
# End of https://www.toptal.com/developers/gitignore/api/jupyternotebooks,python

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.gitignore.txt
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# Created by https://www.toptal.com/developers/gitignore/api/jupyternotebooks,python
# Edit at https://www.toptal.com/developers/gitignore?templates=jupyternotebooks,python
### JupyterNotebooks ###
# gitignore template for Jupyter Notebooks
# website: http://jupyter.org/
.ipynb_checkpoints
*/.ipynb_checkpoints/*
# IPython
profile_default/
ipython_config.py
# Remove previous ipynb_checkpoints
# git rm -r .ipynb_checkpoints/
### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
# IPython
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
### Python Patch ###
# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
poetry.toml
# ruff
.ruff_cache/
# LSP config files
pyrightconfig.json
# End of https://www.toptal.com/developers/gitignore/api/jupyternotebooks,python

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.ipynb_checkpoints/Notebook_AJ-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3ba1f385-2a2f-4b0c-be79-66f618469a9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['bdc2324-data/1',\n",
" 'bdc2324-data/10',\n",
" 'bdc2324-data/101',\n",
" 'bdc2324-data/11',\n",
" 'bdc2324-data/12',\n",
" 'bdc2324-data/13',\n",
" 'bdc2324-data/14',\n",
" 'bdc2324-data/2',\n",
" 'bdc2324-data/3',\n",
" 'bdc2324-data/4',\n",
" 'bdc2324-data/5',\n",
" 'bdc2324-data/6',\n",
" 'bdc2324-data/7',\n",
" 'bdc2324-data/8',\n",
" 'bdc2324-data/9']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"import s3fs\n",
"\n",
"# 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 = \"bdc2324-data\"\n",
"fs.ls(BUCKET)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "023bfa2b-97c2-4d53-80fb-e2290c73b92f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"version": 3
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"version": "3.10.13"
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},
"nbformat": 4,
"nbformat_minor": 5
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{
"cells": [
{
"cell_type": "markdown",
"id": "8c8e008c-9b92-41f1-88c1-8ec462e4ecab",
"metadata": {},
"source": [
"# Business Data Challenge - Team 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88af2795-8bf9-4df0-a059-be7c28fb4289",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "e05cd2c9-3f76-48e3-b4a6-5055445af2e4",
"metadata": {},
"source": [
"Configuration de l'accès aux données"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ba1f385-2a2f-4b0c-be79-66f618469a9f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import s3fs\n",
"# 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 = \"bdc2324-data\"\n",
"fs.ls(BUCKET)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba9d04ad-6cc1-4bac-b1a0-44bedfb09763",
"metadata": {},
"outputs": [],
"source": [
"# Chargement des fichiers campaign_stats.csv\n",
"FILE_PATH_S3 = 'bdc2324-data/1/1campaign_stats.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" campaign_stats_1 = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"FILE_PATH_S3 = 'bdc2324-data/2/2campaign_stats.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" campaign_stats_2 = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"FILE_PATH_S3 = 'bdc2324-data/3/3campaign_stats.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" campaign_stats_3 = pd.read_csv(file_in, sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cacaecc1-4d8a-4e20-8cd3-b452cf17db56",
"metadata": {},
"outputs": [],
"source": [
"# Conversion des dates 'sent_at'\n",
"campaign_stats_1['sent_at'] = pd.to_datetime(campaign_stats_1['sent_at'], format = 'ISO8601', utc = True)\n",
"campaign_stats_2['sent_at'] = pd.to_datetime(campaign_stats_2['sent_at'], format = 'ISO8601', utc = True)\n",
"campaign_stats_3['sent_at'] = pd.to_datetime(campaign_stats_3['sent_at'], format = 'ISO8601', utc = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ec4b583-dc64-43e9-b3ae-6bbaee0bc135",
"metadata": {},
"outputs": [],
"source": [
"# Chaque unites correspond à une période ? --> Non, les dossiers ont juste pour but de réduire la taille des fichiers\n",
"print(campaign_stats_1['sent_at'].max())\n",
"print(campaign_stats_1['sent_at'].min())\n",
"\n",
"print(campaign_stats_2['sent_at'].max())\n",
"print(campaign_stats_2['sent_at'].min())\n",
"\n",
"print(campaign_stats_3['sent_at'].max())\n",
"print(campaign_stats_3['sent_at'].min())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77894273-b3e5-4f29-bd63-9f4df8082b9b",
"metadata": {},
"outputs": [],
"source": [
"campaign_stats_1['sent_at']"
]
},
{
"cell_type": "markdown",
"id": "31f2edbf-5661-4516-9835-06d4da615c13",
"metadata": {},
"source": [
"### Customersplus.csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4223c873-cbd3-46d1-ac96-c9a3b9e97092",
"metadata": {},
"outputs": [],
"source": [
"FILE_PATH_S3 = 'bdc2324-data/1/1customersplus.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" customers_plus_1 = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"FILE_PATH_S3 = 'bdc2324-data/2/2customersplus.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" customers_plus_2 = pd.read_csv(file_in, sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "460f853a-68c0-42a7-9877-b83d3aaec813",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_1.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5a9398f-72fc-4548-9f53-b20b372144b2",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_1.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7467ddbe-0bd4-44cc-8a16-84aa41853638",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_1['id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e15f05f8-3a89-4fc3-84a9-dae70e168440",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_2['id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b40a653e-013f-48d0-8b57-0284587b36c5",
"metadata": {},
"outputs": [],
"source": [
"common_id = set(customers_plus_2['id']).intersection(customers_plus_1['id'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32fa2215-3c79-40b5-8643-755865959fc7",
"metadata": {},
"outputs": [],
"source": [
"common_id = set(customers_plus_2['id']).intersection(customers_plus_1['id'])\n",
"# Exemple id commun = caractéristiques communes\n",
"print(customers_plus_2[customers_plus_2['id'] == list(common_id)[0]])\n",
"\n",
"print(customers_plus_1[customers_plus_1['id'] == list(common_id)[0]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0eb345e4-69f5-4e16-ac57-e33674c6c43d",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"customers_plus_1.isna().mean()*100"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f6ce60d-0912-497d-9108-330acccef394",
"metadata": {},
"outputs": [],
"source": [
"# Chargement de toutes les données\n",
"liste_base = ['customer_target_mappings', 'customersplus', 'target_types', 'tags', 'events', 'tickets', 'representations', 'purchases', 'products']\n",
"\n",
"for nom_base in liste_base:\n",
" FILE_PATH_S3 = 'bdc2324-data/11/11' + nom_base + '.csv'\n",
" with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" globals()[nom_base] = pd.read_csv(file_in, sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fa8ee17d-5092-40ac-8a0a-3790b016dd4e",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Jointure\n",
"merge_1 = pd.merge(purchases, tickets, left_on='id', right_on='purchase_id', how='inner')[['id_x', 'customer_id','product_id', 'purchase_date', 'type_of', 'is_from_subscription']]\n",
"merge_2 = pd.merge(products, merge_1, left_on='id', right_on='product_id', how='inner')[['id_x', 'customer_id', 'representation_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price']]\n",
"merge_3 = pd.merge(representations, merge_2, left_on='id', right_on='representation_id', how='inner')[['id_x', 'customer_id', 'event_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time']]\n",
"merge_4 = pd.merge(events, merge_3, left_on='id', right_on='event_id', how='inner')[['id_x', 'customer_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time', 'name']]\n",
"merge_4 = merge_4.rename(columns={'name': 'event_name'})\n",
"df_customer_event = pd.merge(customersplus, merge_4, left_on = 'id', right_on = 'customer_id', how = 'inner')[['id_x', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time', 'event_name']]\n",
"df_customer_event"
]
},
{
"cell_type": "markdown",
"id": "f1d4aeb8-ec74-4d49-989a-9116e01afe2f",
"metadata": {},
"source": [
"# Fusion et exploration"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "22bfad2b-d52a-4077-9b39-bee35004e01c",
"metadata": {},
"outputs": [],
"source": [
"# Jointure\n",
"var_choosed = ['id_x', 'customer_id','product_id', 'purchase_date', 'type_of', 'is_from_subscription']\n",
"merge_1 = pd.merge(purchases, tickets, left_on='id', right_on='purchase_id', how='inner')[var_choosed]\n",
"\n",
"var_choosed.extend(['amount', 'is_full_price', 'representation_id'])\n",
"merge_2 = pd.merge(products, merge_1, left_on='id', right_on='product_id', how='inner')[var_choosed]\n",
"\n",
"var_choosed.remove('representation_id')\n",
"var_choosed.extend(['start_date_time', 'event_id'])\n",
"merge_3 = pd.merge(representations, merge_2, left_on='id', right_on='representation_id', how='inner')[var_choosed]\n",
"\n",
"var_choosed.remove('event_id')\n",
"var_choosed.extend(['name', 'customer_id'])\n",
"merge_4 = pd.merge(events, merge_3, left_on='id', right_on='event_id', how='inner')[var_choosed]\n",
"\n",
"# Changement de nom\n",
"merge_4 = merge_4.rename(columns={'name': 'event_name'})\n",
"var_choosed[var_choosed.index('name')] = \"event_name\"\n",
"\n",
"# Base finale\n",
"var_choosed.extend(['age', 'gender', 'country', 'fidelity', 'profession'])\n",
"df_customer_event = pd.merge(customersplus, merge_4, left_on = 'id', right_on = 'customer_id', how = 'inner')[var_choosed]\n",
"df_customer_event"
]
},
{
"cell_type": "markdown",
"id": "4cb08d7a-ff04-4951-863d-20aaf33f0b31",
"metadata": {},
"source": [
"## Type de client au globale"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f47ba14a-8601-4b91-9712-223a5ed8a1d1",
"metadata": {},
"outputs": [],
"source": [
"# Client\n",
"print(customer_target_mappings.columns)\n",
"print(customer_target_mappings.shape)\n",
"customer_target_mappings.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f11f829e-66b1-4fd0-a46f-5ae7cb78073f",
"metadata": {},
"outputs": [],
"source": [
"customer_target_mappings['extra_field'].unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c240ab80-c746-4a64-ac6a-be8382c4f0ec",
"metadata": {},
"outputs": [],
"source": [
"customer_target_mappings['name'].unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c03c0597-3f21-4673-8a0f-24d7d9bc5ce4",
"metadata": {},
"outputs": [],
"source": [
"# Segmentation existante\n",
"print(target_types.columns)\n",
"print(target_types.shape)\n",
"target_types.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5adb1773-648d-4683-bc08-d1f2298c1283",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"target_types"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d65f74e-47fc-4296-b493-a1ebefb91cde",
"metadata": {},
"outputs": [],
"source": [
"# Tags = clients\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11tags.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" tags = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(tags.columns)\n",
"print(tags.shape)\n",
"tags.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a689a63-165b-4c4e-bbb0-695b661048d9",
"metadata": {},
"outputs": [],
"source": [
"tags"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69e38c52-0570-4531-aebb-9deb6db8c40b",
"metadata": {},
"outputs": [],
"source": [
"# Structure = clients\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11structure_tag_mappings.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" structure_tag_mappings = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(structure_tag_mappings.columns)\n",
"print(structure_tag_mappings.shape)\n",
"structure_tag_mappings.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74dc34ad-375b-48df-a900-40d92c5fff13",
"metadata": {},
"outputs": [],
"source": [
"structure_tag_mappings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a479ceeb-0135-4899-9cbc-90ed7bf941fe",
"metadata": {},
"outputs": [],
"source": [
"# Tags = clients\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11customersplus.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" customersplus = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(customersplus.columns)\n",
"print(customersplus.shape)\n",
"customersplus.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "383e892c-606a-45ce-bdd6-b503b3e0be33",
"metadata": {},
"outputs": [],
"source": [
"customersplus"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70324d06-b855-4386-a7de-eef1eb13dfdf",
"metadata": {},
"outputs": [],
"source": [
"# But : lier les caractéristiques socio-demo et les comportements d'achat\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bbd743d-51fe-4786-8ad3-5a4a4d09439c",
"metadata": {},
"outputs": [],
"source": [
"# tickets\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11tickets.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" tickets = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(tickets.columns)\n",
"print(tickets.shape)\n",
"tickets.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea83ea5c-3d47-4a66-a523-04b69b149a20",
"metadata": {},
"outputs": [],
"source": [
"tickets"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba15708e-eb84-4b5d-a86c-05ebed188cf6",
"metadata": {},
"outputs": [],
"source": [
"tickets['type_of'].unique()"
]
},
{
"cell_type": "markdown",
"id": "bc192b08-30a5-486a-8bea-93e765dbfce6",
"metadata": {},
"source": [
"## Types d'évenement et client"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e14dcf62-2def-4ed5-834b-cf21abbc2894",
"metadata": {},
"outputs": [],
"source": [
"# Evenement = events.csv\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11events.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" events = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(events.columns)\n",
"print(events.shape)\n",
"events.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1a1d63c-d7de-4b63-93a8-1c734eb5b316",
"metadata": {},
"outputs": [],
"source": [
"events"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af80eee8-f717-4159-a0fd-09d47ec96621",
"metadata": {},
"outputs": [],
"source": [
"events['name'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6afc6f3d-4292-4a92-a4d6-14f1edc25df2",
"metadata": {},
"outputs": [],
"source": [
"# Représentation des évenements = representations.csv\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11representations.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" representations = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(representations.columns)\n",
"print(representations.shape)\n",
"representations.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1487402a-a49b-4737-b7d7-40c764d2f0b4",
"metadata": {},
"outputs": [],
"source": [
"representations"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99b27418-2c15-4a6e-bcf5-d329ca492085",
"metadata": {},
"outputs": [],
"source": [
"# Produits vendues = products.csv\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11products.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" products = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(products.columns)\n",
"print(products.shape)\n",
"products.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c49bcd47-672f-4e0f-aee9-a7475151b97f",
"metadata": {},
"outputs": [],
"source": [
"products"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4aec5ce-d0c9-4625-bb29-9ac154818621",
"metadata": {},
"outputs": [],
"source": [
"# Lieu = facilities.csv\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11facilities.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" facilities = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(facilities.columns)\n",
"print(facilities.shape)\n",
"facilities.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3642483-2879-442a-ad69-efcd2331a200",
"metadata": {},
"outputs": [],
"source": [
"facilities"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da1e9807-2a8d-4be7-a785-55cffd734f36",
"metadata": {},
"outputs": [],
"source": [
"# Saisons = seasons.csv période sur deux années consécutives\n",
"FILE_PATH_S3 = 'bdc2324-data/11/11seasons.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" seasons = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(seasons.columns)\n",
"print(seasons.shape)\n",
"seasons.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec8a37b5-2d78-4b1c-aa47-bd923fdc2ba9",
"metadata": {},
"outputs": [],
"source": [
"seasons['name'].unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abb3aa20-774b-4761-983a-df5eb2bc51c6",
"metadata": {},
"outputs": [],
"source": [
"# Achats = purchases.csv \n",
"FILE_PATH_S3 = 'bdc2324-data/11/11purchases.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" purchases = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"print(purchases.columns)\n",
"print(purchases.shape)\n",
"purchases.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30e204ab-4f63-430c-a818-5c8035b6e17b",
"metadata": {},
"outputs": [],
"source": [
"purchases"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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@ -1,823 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8c8e008c-9b92-41f1-88c1-8ec462e4ecab",
"metadata": {},
"source": [
"# Business Data Challenge - Team 1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "88af2795-8bf9-4df0-a059-be7c28fb4289",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "e05cd2c9-3f76-48e3-b4a6-5055445af2e4",
"metadata": {},
"source": [
"Configuration de l'accès aux données"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3ba1f385-2a2f-4b0c-be79-66f618469a9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['bdc2324-data/1',\n",
" 'bdc2324-data/10',\n",
" 'bdc2324-data/101',\n",
" 'bdc2324-data/11',\n",
" 'bdc2324-data/12',\n",
" 'bdc2324-data/13',\n",
" 'bdc2324-data/14',\n",
" 'bdc2324-data/2',\n",
" 'bdc2324-data/3',\n",
" 'bdc2324-data/4',\n",
" 'bdc2324-data/5',\n",
" 'bdc2324-data/6',\n",
" 'bdc2324-data/7',\n",
" 'bdc2324-data/8',\n",
" 'bdc2324-data/9']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"import s3fs\n",
"# 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 = \"bdc2324-data\"\n",
"fs.ls(BUCKET)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ba9d04ad-6cc1-4bac-b1a0-44bedfb09763",
"metadata": {},
"outputs": [],
"source": [
"# Chargement des fichiers campaign_stats.csv\n",
"FILE_PATH_S3 = 'bdc2324-data/1/1campaign_stats.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" campaign_stats_1 = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"FILE_PATH_S3 = 'bdc2324-data/2/2campaign_stats.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" campaign_stats_2 = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"FILE_PATH_S3 = 'bdc2324-data/3/3campaign_stats.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" campaign_stats_3 = pd.read_csv(file_in, sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cacaecc1-4d8a-4e20-8cd3-b452cf17db56",
"metadata": {},
"outputs": [],
"source": [
"# Conversion des dates 'sent_at'\n",
"campaign_stats_1['sent_at'] = pd.to_datetime(campaign_stats_1['sent_at'], format = 'ISO8601', utc = True)\n",
"campaign_stats_2['sent_at'] = pd.to_datetime(campaign_stats_2['sent_at'], format = 'ISO8601', utc = True)\n",
"campaign_stats_3['sent_at'] = pd.to_datetime(campaign_stats_3['sent_at'], format = 'ISO8601', utc = True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2ec4b583-dc64-43e9-b3ae-6bbaee0bc135",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-09 18:10:45+00:00\n",
"2020-06-02 08:24:08+00:00\n",
"2023-10-12 01:39:48+00:00\n",
"2023-10-10 17:06:29+00:00\n",
"2023-11-01 09:20:48+00:00\n",
"2021-03-31 14:59:02+00:00\n"
]
}
],
"source": [
"# Chaque unites correspond à une période ? --> Non, les dossiers ont juste pour but de réduire la taille des fichiers\n",
"print(campaign_stats_1['sent_at'].max())\n",
"print(campaign_stats_1['sent_at'].min())\n",
"\n",
"print(campaign_stats_2['sent_at'].max())\n",
"print(campaign_stats_2['sent_at'].min())\n",
"\n",
"print(campaign_stats_3['sent_at'].max())\n",
"print(campaign_stats_3['sent_at'].min())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "77894273-b3e5-4f29-bd63-9f4df8082b9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2021-03-28 16:01:09+00:00\n",
"1 2021-03-28 16:01:09+00:00\n",
"2 2021-03-28 16:00:59+00:00\n",
"3 2021-03-28 16:00:59+00:00\n",
"4 2021-03-28 16:01:06+00:00\n",
" ... \n",
"6214803 2023-10-23 09:32:33+00:00\n",
"6214804 2023-10-23 09:32:49+00:00\n",
"6214805 2023-10-23 09:33:28+00:00\n",
"6214806 2023-10-23 09:31:53+00:00\n",
"6214807 2023-10-23 09:33:54+00:00\n",
"Name: sent_at, Length: 6214808, dtype: datetime64[ns, UTC]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"campaign_stats_1['sent_at']"
]
},
{
"cell_type": "markdown",
"id": "31f2edbf-5661-4516-9835-06d4da615c13",
"metadata": {},
"source": [
"### Customersplus.csv"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4223c873-cbd3-46d1-ac96-c9a3b9e97092",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1362/4118060109.py:9: DtypeWarning: Columns (20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" customers_plus_2 = pd.read_csv(file_in, sep=\",\")\n"
]
}
],
"source": [
"FILE_PATH_S3 = 'bdc2324-data/1/1customersplus.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" customers_plus_1 = pd.read_csv(file_in, sep=\",\")\n",
"\n",
"FILE_PATH_S3 = 'bdc2324-data/2/2customersplus.csv'\n",
"\n",
"with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" customers_plus_2 = pd.read_csv(file_in, sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "460f853a-68c0-42a7-9877-b83d3aaec813",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['id', 'lastname', 'firstname', 'birthdate', 'email', 'street_id',\n",
" 'created_at', 'updated_at', 'civility', 'is_partner', 'extra',\n",
" 'deleted_at', 'reference', 'gender', 'is_email_true', 'extra_field',\n",
" 'identifier', 'opt_in', 'structure_id', 'note', 'profession',\n",
" 'language', 'mcp_contact_id', 'need_reload', 'last_buying_date',\n",
" 'max_price', 'ticket_sum', 'average_price', 'fidelity',\n",
" 'average_purchase_delay', 'average_price_basket',\n",
" 'average_ticket_basket', 'total_price', 'preferred_category',\n",
" 'preferred_supplier', 'preferred_formula', 'purchase_count',\n",
" 'first_buying_date', 'last_visiting_date', 'zipcode', 'country', 'age',\n",
" 'tenant_id'],\n",
" dtype='object')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customers_plus_1.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5a9398f-72fc-4548-9f53-b20b372144b2",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_1.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7467ddbe-0bd4-44cc-8a16-84aa41853638",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_1['id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e15f05f8-3a89-4fc3-84a9-dae70e168440",
"metadata": {},
"outputs": [],
"source": [
"customers_plus_2['id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b40a653e-013f-48d0-8b57-0284587b36c5",
"metadata": {},
"outputs": [],
"source": [
"common_id = set(customers_plus_2['id']).intersection(customers_plus_1['id'])"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "32fa2215-3c79-40b5-8643-755865959fc7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"common_id = set(customers_plus_2['id']).intersection(customers_plus_1['id'])\n",
"# Exemple id commun = caractéristiques communes\n",
"print(customers_plus_2[customers_plus_2['id'] == list(common_id)[0]])\n",
"\n",
"print(customers_plus_1[customers_plus_1['id'] == list(common_id)[0]])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "0eb345e4-69f5-4e16-ac57-e33674c6c43d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0.000000\n",
"lastname 43.461341\n",
"firstname 44.995588\n",
"birthdate 96.419870\n",
"email 8.622075\n",
"street_id 0.000000\n",
"created_at 0.000000\n",
"updated_at 0.000000\n",
"civility 100.000000\n",
"is_partner 0.000000\n",
"extra 100.000000\n",
"deleted_at 100.000000\n",
"reference 100.000000\n",
"gender 0.000000\n",
"is_email_true 0.000000\n",
"extra_field 100.000000\n",
"identifier 0.000000\n",
"opt_in 0.000000\n",
"structure_id 88.072380\n",
"note 99.403421\n",
"profession 95.913503\n",
"language 99.280945\n",
"mcp_contact_id 34.876141\n",
"need_reload 0.000000\n",
"last_buying_date 51.653431\n",
"max_price 51.653431\n",
"ticket_sum 0.000000\n",
"average_price 8.639195\n",
"fidelity 0.000000\n",
"average_purchase_delay 51.653431\n",
"average_price_basket 51.653431\n",
"average_ticket_basket 51.653431\n",
"total_price 43.014236\n",
"preferred_category 100.000000\n",
"preferred_supplier 100.000000\n",
"preferred_formula 100.000000\n",
"purchase_count 0.000000\n",
"first_buying_date 51.653431\n",
"last_visiting_date 100.000000\n",
"zipcode 71.176564\n",
"country 5.459418\n",
"age 96.419870\n",
"tenant_id 0.000000\n",
"dtype: float64\n"
]
}
],
"source": [
"pd.DataFrame(customers_plus_1.isna().mean()*100)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6f6ce60d-0912-497d-9108-330acccef394",
"metadata": {},
"outputs": [],
"source": [
"# Chargement de toutes les données\n",
"liste_base = ['customer_target_mappings', 'customersplus', 'target_types', 'tags', 'events', 'tickets', 'representations', 'purchases', 'products']\n",
"\n",
"for nom_base in liste_base:\n",
" FILE_PATH_S3 = 'bdc2324-data/11/11' + nom_base + '.csv'\n",
" with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
" globals()[nom_base] = pd.read_csv(file_in, sep=\",\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "fa8ee17d-5092-40ac-8a0a-3790b016dd4e",
"metadata": {},
"outputs": [
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"text/plain": [
" id lastname firstname birthdate email \\\n",
"0 405082 lastname405082 NaN NaN NaN \n",
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"... ... ... ... ... ... \n",
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"\n",
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"0 2023-01-12 06:30:31.197484+01:00 NaN False ... \n",
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"... ... ... ... ... \n",
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"318965 2023-09-12 18:27:36.904104+02:00 NaN False ... \n",
"318966 2023-09-12 18:27:36.904104+02:00 NaN False ... \n",
"318967 2023-09-12 18:27:36.904104+02:00 NaN False ... \n",
"318968 2023-09-12 18:27:36.904104+02:00 NaN False ... \n",
"\n",
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"... ... ... ... ... ... \n",
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"\n",
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" event_name \n",
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"\n",
"[318969 rows x 52 columns]"
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},
"execution_count": 12,
"metadata": {},
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}
],
"source": [
"# Jointure\n",
"merge_1 = pd.merge(purchases, tickets, left_on='id', right_on='purchase_id', how='inner')[['id_x', 'customer_id','product_id', 'purchase_date', 'type_of', 'is_from_subscription']]\n",
"merge_2 = pd.merge(products, merge_1, left_on='id', right_on='product_id', how='inner')[['id_x', 'customer_id', 'representation_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price']]\n",
"merge_3 = pd.merge(representations, merge_2, left_on='id', right_on='representation_id', how='inner')[['id_x', 'customer_id', 'event_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time']]\n",
"merge_4 = pd.merge(events, merge_3, left_on='id', right_on='event_id', how='inner')[['id_x', 'customer_id', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time', 'name']]\n",
"merge_4 = merge_4.rename(columns={'name': 'event_name'})\n",
"df_customer_event = pd.merge(customersplus, merge_4, left_on = 'id', right_on = 'customer_id', how = 'inner')[['id_x', 'purchase_date', 'type_of', 'is_from_subscription', 'amount', 'is_full_price', 'start_date_time', 'event_name']]\n",
"df_customer_event"
]
}
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400
Notebook_AR.ipynb
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@ -6103,6 +6103,403 @@
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]
},
{
"cell_type": "markdown",
"id": "e274e3cc-1b41-43e0-8412-1563166060cb",
"metadata": {},
"source": [
"## Price Table"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "c52621e7-01de-48dc-b572-2974542a8be5",
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
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" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>extra_field</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>41909</td>\n",
" <td>visite mécènes 1h30</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>502</td>\n",
" <td>entree mucem tp( expo picasso)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>504</td>\n",
" <td>nombre de personnes cinema</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>117</td>\n",
" <td>spectacle tarif e famille tr</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1496</td>\n",
" <td>billet nb famille mecene 1a</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name extra_field\n",
"0 41909 visite mécènes 1h30 NaN\n",
"1 502 entree mucem tp( expo picasso) NaN\n",
"2 504 nombre de personnes cinema NaN\n",
"3 117 spectacle tarif e famille tr NaN\n",
"4 1496 billet nb famille mecene 1a NaN"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pricing_formula = load_dataset(\"1pricing_formulas.csv\")\n",
"pricing_formula.head()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"id": "247b5c45-a18a-4cfd-86b4-d3453e157bcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File path : bdc2324-data/1/1type_of_pricing_formulas.csv\n",
"Shape : (568, 6)\n",
"Number of columns : 4\n",
"Columns : Index(['id', 'type_of_id', 'pricing_formula_id', 'identifier'], dtype='object')\n"
]
},
{
"data": {
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"</table>\n",
"</div>"
],
"text/plain": [
" id type_of_id pricing_formula_id\n",
"0 1 1 127\n",
"1 2 1 2425\n",
"2 3 1 2937\n",
"3 4 1 48\n",
"4 5 1 7"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type_pricing_formula = load_dataset(\"1type_of_pricing_formulas.csv\")\n",
"type_pricing_formula.head()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "4b48f7b3-0f06-4ef6-9355-5016af82f49c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File path : bdc2324-data/1/1products_groups.csv\n",
"Shape : (92973, 9)\n",
"Number of columns : 7\n",
"Columns : Index(['id', 'category_id', 'pricing_formula_id', 'representation_id',\n",
" 'percent_price', 'max_price', 'min_price'],\n",
" dtype='object')\n"
]
},
{
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" <th>percent_price</th>\n",
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" <th>0</th>\n",
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" <td>100.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>156773</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>82519</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>14387</td>\n",
" <td>16</td>\n",
" <td>79</td>\n",
" <td>8046</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2770</td>\n",
" <td>2</td>\n",
" <td>37</td>\n",
" <td>1563</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>27179</td>\n",
" <td>13</td>\n",
" <td>119</td>\n",
" <td>14192</td>\n",
" <td>100.0</td>\n",
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" <td>0.0</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id category_id pricing_formula_id representation_id percent_price \\\n",
"0 2735 8 97 1534 100.0 \n",
"1 156773 5 9 82519 100.0 \n",
"2 14387 16 79 8046 100.0 \n",
"3 2770 2 37 1563 100.0 \n",
"4 27179 13 119 14192 100.0 \n",
"\n",
" max_price min_price \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 "
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product_groups = load_dataset(\"1products_groups.csv\")\n",
"product_groups.head()"
]
},
{
"cell_type": "markdown",
"id": "71c26a38-6818-42df-8aee-0135681a5563",
@ -6741,6 +7138,9 @@
"outputs": [],
"source": [
"def uniform_product_df():\n",
" \"\"\"\n",
" This function returns the uniform product dataset\n",
" \"\"\"\n",
" print(\"Products theme columns : \", products_theme.columns)\n",
" print(\"\\n Representation theme columns : \", representation_theme.columns)\n",
" print(\"\\n Events theme columns : \", events_theme.columns)\n",

1760
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1537
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