From ba43e87ae609ab4cbf1a46152a02c566f7fa46e9 Mon Sep 17 00:00:00 2001 From: mchambery Date: Mon, 4 Mar 2024 22:46:19 +0100 Subject: [PATCH 1/5] download tmdb datasets from kaggle --- .gitignore | 4 +- Makefile | 5 + README.md | 11 +- poetry.lock | 330 +++++++++++++++++++++++++++++++++++++++++++++---- pyproject.toml | 5 +- 5 files changed, 323 insertions(+), 32 deletions(-) create mode 100644 Makefile diff --git a/.gitignore b/.gitignore index b8fb0eb..9f76b71 100644 --- a/.gitignore +++ b/.gitignore @@ -160,4 +160,6 @@ dmypy.json cython_debug/ # Precommit hooks: ruff cache -.ruff_cache \ No newline at end of file +.ruff_cache + +data/ \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..f9965db --- /dev/null +++ b/Makefile @@ -0,0 +1,5 @@ +download-full-tmdb-tv-shows-dataset: + mkdir data -p ; cd data ; kaggle datasets download -d asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows + +download-tmdb-movies-dataset: + mkdir data -p ; cd data ; kaggle datasets download -d asaniczka/tmdb-movies-dataset-2023-930k-movies diff --git a/README.md b/README.md index 3a03607..7012f31 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,13 @@ -Template DataForGood +Observatoire des imagiaires ================ - -This file will become your README and also the index of your -documentation. +# Requirements -# Contributing +If you want to use kaggle to download datasets, please make sure to have api's credentials in ~/.kaggle/kaggle.json. +How to get .json with kaggle api's credentials : [here](https://github.com/Kaggle/kaggle-api#api-credentials) + +Once you have setup your venv with poetry, you can use commands in the Makefile to download tmdb datasets from kaggle. ## Use a venv diff --git a/poetry.lock b/poetry.lock index 3d82e0a..ef5a134 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,43 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "bleach" +version = "6.1.0" +description = "An easy safelist-based HTML-sanitizing tool." +optional = false +python-versions = ">=3.8" +files = [ + {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"}, + {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"}, +] + +[package.dependencies] +six = ">=1.9.0" +webencodings = "*" + +[package.extras] +css = ["tinycss2 (>=1.1.0,<1.3)"] [[package]] name = "cachetools" -version = "5.3.2" +version = "5.3.3" description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" files = [ - {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, - {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"}, + {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"}, + {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"}, +] + +[[package]] +name = "certifi" +version = "2024.2.2" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -33,6 +62,105 @@ files = [ {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"}, ] +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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"README.md" @@ -16,6 +16,7 @@ python = "^3.10" # pandas = "^1.1.1" # jupyter = "^1.0.0" # ipykernel = "^5.3.4" +kaggle = "^1.6.6" [tool.poetry.group.dev.dependencies] pre-commit = "^2.20.0" From e442cae650041be302ec22963450a85b16b67a68 Mon Sep 17 00:00:00 2001 From: mchambery Date: Mon, 4 Mar 2024 23:58:11 +0100 Subject: [PATCH 2/5] create tmdb movies subset --- notebooks/create_tmdb_subsets.ipynb | 108 + {python_template => observatoire}/__init__.py | 0 observatoire/constants.py | 9 + poetry.lock | 1898 ++++++++++++++++- pyproject.toml | 8 +- python_template/.empty | 0 6 files changed, 2004 insertions(+), 19 deletions(-) create mode 100644 notebooks/create_tmdb_subsets.ipynb rename {python_template => observatoire}/__init__.py (100%) create mode 100644 observatoire/constants.py delete mode 100644 python_template/.empty diff --git a/notebooks/create_tmdb_subsets.ipynb b/notebooks/create_tmdb_subsets.ipynb new file mode 100644 index 0000000..227b786 --- /dev/null +++ b/notebooks/create_tmdb_subsets.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aa58c2c2", + "metadata": {}, + "source": [ + "Before running this notebook, make sure to download datasets from kaggle using commands in Makefile :\n", + "- download-full-tmdb-tv-shows-dataset\n", + "- download-tmdb-movies-dataset\n", + "\n", + "You may need first to get your api's credentials for kaggle first : [here](https://github.com/Kaggle/kaggle-api#api-credentials)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22a2fc9f-6e22-40cc-aa37-e4039572e196", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import zipfile\n", + "\n", + "import pandas as pd\n", + "\n", + "from observatoire.constants import DATA_DIRECTORY, KAGGLE_TMDB_MOVIES_DATASET_PATH, KAGGLE_TMDB_TVSHOWS_DATASET_PATH" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f2b9b36b", + "metadata": {}, + "outputs": [], + "source": [ + "EXTRACT_MOVIES_TO = DATA_DIRECTORY / \"TMDB_MOVIES\"\n", + "EXTRACT_TWSHOWS_TO = DATA_DIRECTORY / \"TMDB_TVSHOWS\"\n", + "\n", + "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies', 'vote_average', 'revenue']\n", + "NB_MOVIES_SUBSET = 5000" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fc3989b0", + "metadata": {}, + "outputs": [], + "source": [ + "with zipfile.ZipFile(KAGGLE_TMDB_MOVIES_DATASET_PATH, 'r') as f:\n", + " f.extractall(path=EXTRACT_MOVIES_TO)\n", + "\n", + "df_movies = pd.read_csv(EXTRACT_MOVIES_TO / os.listdir(EXTRACT_MOVIES_TO)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bf19fe99", + "metadata": {}, + "outputs": [], + "source": [ + "with zipfile.ZipFile(KAGGLE_TMDB_TVSHOWS_DATASET_PATH, 'r') as f:\n", + " f.extractall(path=EXTRACT_TWSHOWS_TO)\n", + "\n", + "df_tvshows = pd.read_csv(EXTRACT_TWSHOWS_TO / os.listdir(EXTRACT_TWSHOWS_TO)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7429796d", + "metadata": {}, + "outputs": [], + "source": [ + "df_movies.dropna(axis=0, how='any', subset=MOVIES_COLUMNS_OF_INTEREST, inplace=True)\n", + "\n", + "df_movies_subset = df_movies[(df_movies['status'] == 'Released') & \n", + " (~df_movies['adult']) &\n", + " (df_movies['release_date'] < '2024-03-01')].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n", + " \n", + "df_movies_subset.to_csv(EXTRACT_MOVIES_TO / \"tmdb_movies_subset.csv\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python_template/__init__.py b/observatoire/__init__.py similarity index 100% rename from python_template/__init__.py rename to observatoire/__init__.py diff --git a/observatoire/constants.py b/observatoire/constants.py new file mode 100644 index 0000000..11fd072 --- /dev/null +++ b/observatoire/constants.py @@ -0,0 +1,9 @@ +from pathlib import Path + + +ROOT_DIRECTORY = Path(__file__).parent.parent.resolve() +DATA_DIRECTORY = ROOT_DIRECTORY / "data" +KAGGLE_TMDB_MOVIES_DATASET_NAME = "tmdb-movies-dataset-2023-930k-movies.zip" +KAGGLE_TMDB_TVSHOWS_DATASET_NAME = "full-tmdb-tv-shows-dataset-2023-150k-shows.zip" +KAGGLE_TMDB_MOVIES_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_MOVIES_DATASET_NAME +KAGGLE_TMDB_TVSHOWS_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_TVSHOWS_DATASET_NAME diff --git a/poetry.lock b/poetry.lock index ef5a134..0736231 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,200 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +[[package]] +name = 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"sphinx-rtd-theme (>=1.1.0)"] +optional = ["python-socks", "wsaccel"] +test = ["websockets"] + +[[package]] +name = "widgetsnbextension" +version = "4.0.10" +description = "Jupyter interactive widgets for Jupyter Notebook" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"}, + {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"}, +] + [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "545f3f3fbe4222bba3b98289593da047617c681d439645dbd33a8b1d926f4b5f" +content-hash = "eeb9ba8f43237f51301a5ec4c62d79a177ae19b81bbf920c0947d53ccca6ec00" diff --git a/pyproject.toml b/pyproject.toml index 8503cee..57841a9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,10 +12,10 @@ readme = "README.md" [tool.poetry.dependencies] python = "^3.10" -# numpy = "^1.21.1" -# pandas = "^1.1.1" -# jupyter = "^1.0.0" -# ipykernel = "^5.3.4" +numpy = "^1.21.1" +pandas = "^1.1.1" +jupyter = "^1.0.0" +ipykernel = "^5.3.4" kaggle = "^1.6.6" [tool.poetry.group.dev.dependencies] diff --git a/python_template/.empty b/python_template/.empty deleted file mode 100644 index e69de29..0000000 From 2d42ba2f3f4c0123ddaf173f2a750f1170e2f1f7 Mon Sep 17 00:00:00 2001 From: mchambery Date: Tue, 5 Mar 2024 21:51:58 +0100 Subject: [PATCH 3/5] simplify and documentation tmdb subsets creation --- Makefile | 6 +- README.md | 86 +++++++++++++++++++++-------- notebooks/create_tmdb_subsets.ipynb | 51 +++++++++++------ observatoire/constants.py | 9 --- 4 files changed, 101 insertions(+), 51 deletions(-) delete mode 100644 observatoire/constants.py diff --git a/Makefile b/Makefile index f9965db..4c2db0c 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ -download-full-tmdb-tv-shows-dataset: - mkdir data -p ; cd data ; kaggle datasets download -d asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows - download-tmdb-movies-dataset: mkdir data -p ; cd data ; kaggle datasets download -d asaniczka/tmdb-movies-dataset-2023-930k-movies + +download-full-tmdb-tv-shows-dataset: + mkdir data -p ; cd data ; kaggle datasets download -d asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows diff --git a/README.md b/README.md index 7012f31..f27b999 100644 --- a/README.md +++ b/README.md @@ -2,53 +2,95 @@ Observatoire des imagiaires ================ -# Requirements +## Installing with poetry -If you want to use kaggle to download datasets, please make sure to have api's credentials in ~/.kaggle/kaggle.json. -How to get .json with kaggle api's credentials : [here](https://github.com/Kaggle/kaggle-api#api-credentials) +### Prerequisites: + +1. Python (≥ `3.10`) installed on your system. +2. Ensure you have `poetry` installed. If not, you can install them using `pip`. + +```bash +pip install poetry +``` + +### Steps: + +1. **Clone the GitHub Repository:** + + Clone the GitHub repository you want to install locally using the `git clone` command. + + ```bash + git clone https://github.com/dataforgoodfr/12_observatoire_des_imaginaires.git + ``` -Once you have setup your venv with poetry, you can use commands in the Makefile to download tmdb datasets from kaggle. +2. **Navigate to the Repository Directory:** + Use the `cd` command to navigate into the repository directory. -## Use a venv + ```bash + cd 12_observatoire_des_imaginaires/ + ``` - python3 -m venv name-of-your-venv +3. **Configure `poetry` to create a Virtual Environment inside the project:** - source name-of-your-venv/bin/activate + Ensure that poetry will create a `.venv` directory into the project with the command: + ```bash + poetry config virtualenvs.in-project true + ``` -## Utiliser Poetry +4. **Install Project Dependencies using `poetry`:** -[Installer Poetry](https://python-poetry.org/docs/): + Use `poetry` to install the project dependencies. - python3 -m pip install "poetry==1.4.0" + ```bash + poetry install + ``` -Installer les dépendances: + This will read the `pyproject.toml` file in the repository and install all the dependencies specified. - poetry install +5. **Activate the Virtual Environment:** -Ajouter une dépendance: + Activate the virtual environment to work within its isolated environment. - poetry add pandas + On Unix or MacOS: -Mettre à jour les dépendances: + ```bash + poetry shell + ``` + +6. **Run & edit notebooks**: + + ```bash + jupyter notebook + ``` + +## Download datasets from kaggle + +If you want to use kaggle to download datasets, please make sure to have api's credentials in ~/.kaggle/kaggle.json. + +How to get .json with kaggle api's credentials : [here](https://github.com/Kaggle/kaggle-api#api-credentials) - poetry update +Once you have setup your venv with poetry, you can use commands in the Makefile to download tmdb datasets from kaggle : -## Utiliser Jupyter Notebook +```bash +make download-tmdb-movies-dataset +make download-full-tmdb-tv-shows-dataset +``` - jupyter notebook -and check your browser ! +Alternatively you can download directly the datasets from kaggle website : +- [tmdb-movies-dataset](https://www.kaggle.com/datasets/asaniczka/tmdb-movies-dataset-2023-930k-movies) +- [full-tmdb-tv-shows-dataset](https://www.kaggle.com/datasets/asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows) -## Lancer les precommit-hook localement +## Run precommit-hook locally -[Installer les precommit](https://pre-commit.com/) +[Install precommits](https://pre-commit.com/) pre-commit run --all-files -## Utiliser Tox pour tester votre code +## Use Tox to test your code tox -vv \ No newline at end of file diff --git a/notebooks/create_tmdb_subsets.ipynb b/notebooks/create_tmdb_subsets.ipynb index 227b786..49529e5 100644 --- a/notebooks/create_tmdb_subsets.ipynb +++ b/notebooks/create_tmdb_subsets.ipynb @@ -6,36 +6,53 @@ "metadata": {}, "source": [ "Before running this notebook, make sure to download datasets from kaggle using commands in Makefile :\n", - "- download-full-tmdb-tv-shows-dataset\n", "- download-tmdb-movies-dataset\n", + "- download-full-tmdb-tv-shows-dataset\n", + "\n", + "You may need first to get your api's credentials for kaggle first : [here](https://github.com/Kaggle/kaggle-api#api-credentials)\n", + "\n", + "Alternatively you can download directly the datasets from kaggle website :\n", + "- [tmdb-movies-dataset](https://www.kaggle.com/datasets/asaniczka/tmdb-movies-dataset-2023-930k-movies)\n", + "- [full-tmdb-tv-shows-dataset](https://www.kaggle.com/datasets/asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows)\n", "\n", - "You may need first to get your api's credentials for kaggle first : [here](https://github.com/Kaggle/kaggle-api#api-credentials)" + "And put downloaded zip files into ./data folder and change KAGGLE_TMDB_MOVIES_DATASET_NAME & KAGGLE_TMDB_TVSHOWS_DATASET_NAME values if needed." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "22a2fc9f-6e22-40cc-aa37-e4039572e196", "metadata": {}, "outputs": [], "source": [ "import os\n", "import zipfile\n", + "from pathlib import Path\n", "\n", - "import pandas as pd\n", - "\n", - "from observatoire.constants import DATA_DIRECTORY, KAGGLE_TMDB_MOVIES_DATASET_PATH, KAGGLE_TMDB_TVSHOWS_DATASET_PATH" + "import pandas as pd" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "id": "f2b9b36b", "metadata": {}, "outputs": [], "source": [ - "EXTRACT_MOVIES_TO = DATA_DIRECTORY / \"TMDB_MOVIES\"\n", - "EXTRACT_TWSHOWS_TO = DATA_DIRECTORY / \"TMDB_TVSHOWS\"\n", + "DATA_DIRECTORY = Path(\"../data\").resolve()\n", + "\n", + "KAGGLE_TMDB_MOVIES_DATASET_NAME = \"tmdb-movies-dataset-2023-930k-movies.zip\"\n", + "KAGGLE_TMDB_TVSHOWS_DATASET_NAME = \"full-tmdb-tv-shows-dataset-2023-150k-shows.zip\"\n", + "\n", + "KAGGLE_TMDB_MOVIES_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_MOVIES_DATASET_NAME\n", + "KAGGLE_TMDB_TVSHOWS_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_TVSHOWS_DATASET_NAME\n", + "\n", + "EXTRACT_MOVIES_ZIP_TO = DATA_DIRECTORY / \"tmdb_movies\"\n", + "EXTRACT_MOVIES_ZIP_TO.mkdir(exist_ok=True, parents=True)\n", + "EXTRACT_TWSHOWS_ZIP_TO = DATA_DIRECTORY / \"tmdb_tvshows\"\n", + "EXTRACT_TWSHOWS_ZIP_TO.mkdir(exist_ok=True, parents=True)\n", + "EXPORT_TMDB_SUBSETS_TO = DATA_DIRECTORY / \"subsets\"\n", + "EXPORT_TMDB_SUBSETS_TO.mkdir(exist_ok=True, parents=True)\n", "\n", "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies', 'vote_average', 'revenue']\n", "NB_MOVIES_SUBSET = 5000" @@ -43,33 +60,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "fc3989b0", "metadata": {}, "outputs": [], "source": [ "with zipfile.ZipFile(KAGGLE_TMDB_MOVIES_DATASET_PATH, 'r') as f:\n", - " f.extractall(path=EXTRACT_MOVIES_TO)\n", + " f.extractall(path=EXTRACT_MOVIES_ZIP_TO)\n", "\n", - "df_movies = pd.read_csv(EXTRACT_MOVIES_TO / os.listdir(EXTRACT_MOVIES_TO)[0])" + "df_movies = pd.read_csv(EXTRACT_MOVIES_ZIP_TO / os.listdir(EXTRACT_MOVIES_ZIP_TO)[0])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "id": "bf19fe99", "metadata": {}, "outputs": [], "source": [ "with zipfile.ZipFile(KAGGLE_TMDB_TVSHOWS_DATASET_PATH, 'r') as f:\n", - " f.extractall(path=EXTRACT_TWSHOWS_TO)\n", + " f.extractall(path=EXTRACT_TWSHOWS_ZIP_TO)\n", "\n", - "df_tvshows = pd.read_csv(EXTRACT_TWSHOWS_TO / os.listdir(EXTRACT_TWSHOWS_TO)[0])" + "df_tvshows = pd.read_csv(EXTRACT_TWSHOWS_ZIP_TO / os.listdir(EXTRACT_TWSHOWS_ZIP_TO)[0])" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "id": "7429796d", "metadata": {}, "outputs": [], @@ -80,7 +97,7 @@ " (~df_movies['adult']) &\n", " (df_movies['release_date'] < '2024-03-01')].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n", " \n", - "df_movies_subset.to_csv(EXTRACT_MOVIES_TO / \"tmdb_movies_subset.csv\")" + "df_movies_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_movies_subset.csv\")" ] } ], diff --git a/observatoire/constants.py b/observatoire/constants.py deleted file mode 100644 index 11fd072..0000000 --- a/observatoire/constants.py +++ /dev/null @@ -1,9 +0,0 @@ -from pathlib import Path - - -ROOT_DIRECTORY = Path(__file__).parent.parent.resolve() -DATA_DIRECTORY = ROOT_DIRECTORY / "data" -KAGGLE_TMDB_MOVIES_DATASET_NAME = "tmdb-movies-dataset-2023-930k-movies.zip" -KAGGLE_TMDB_TVSHOWS_DATASET_NAME = "full-tmdb-tv-shows-dataset-2023-150k-shows.zip" -KAGGLE_TMDB_MOVIES_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_MOVIES_DATASET_NAME -KAGGLE_TMDB_TVSHOWS_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_TVSHOWS_DATASET_NAME From 9046263acad787f483b0d25b1c3b32b9bf42fa27 Mon Sep 17 00:00:00 2001 From: mchambery Date: Tue, 5 Mar 2024 22:22:13 +0100 Subject: [PATCH 4/5] create tmdb tvshows subset --- notebooks/create_tmdb_subsets.ipynb | 68 ++++++++++++++++++++--------- 1 file changed, 47 insertions(+), 21 deletions(-) diff --git a/notebooks/create_tmdb_subsets.ipynb b/notebooks/create_tmdb_subsets.ipynb index 49529e5..d432166 100644 --- a/notebooks/create_tmdb_subsets.ipynb +++ b/notebooks/create_tmdb_subsets.ipynb @@ -20,13 +20,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "22a2fc9f-6e22-40cc-aa37-e4039572e196", "metadata": {}, "outputs": [], "source": [ "import os\n", - "import zipfile\n", + "from zipfile import ZipFile\n", "from pathlib import Path\n", "\n", "import pandas as pd" @@ -34,38 +34,46 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "f2b9b36b", "metadata": {}, "outputs": [], "source": [ - "DATA_DIRECTORY = Path(\"../data\").resolve()\n", + "KAGGLE_TMDB_MOVIES_DATASET_PATH = Path(\"../data/tmdb-movies-dataset-2023-930k-movies.zip\").resolve()\n", + "KAGGLE_TMDB_TVSHOWS_DATASET_PATH = Path(\"../data/full-tmdb-tv-shows-dataset-2023-150k-shows.zip\").resolve()\n", + "\n", + "EXTRACT_MOVIES_ZIP_TO = Path(\"../data/tmdb_movies\").resolve()\n", + "EXTRACT_TWSHOWS_ZIP_TO = Path(\"../data/tmdb_tvshows\").resolve()\n", "\n", - "KAGGLE_TMDB_MOVIES_DATASET_NAME = \"tmdb-movies-dataset-2023-930k-movies.zip\"\n", - "KAGGLE_TMDB_TVSHOWS_DATASET_NAME = \"full-tmdb-tv-shows-dataset-2023-150k-shows.zip\"\n", + "EXPORT_TMDB_SUBSETS_TO = Path(\"../data/tmdb_subsets\").resolve()\n", "\n", - "KAGGLE_TMDB_MOVIES_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_MOVIES_DATASET_NAME\n", - "KAGGLE_TMDB_TVSHOWS_DATASET_PATH = DATA_DIRECTORY / KAGGLE_TMDB_TVSHOWS_DATASET_NAME\n", + "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies', 'vote_average', 'revenue']\n", + "NB_MOVIES_SUBSET = 5000\n", "\n", - "EXTRACT_MOVIES_ZIP_TO = DATA_DIRECTORY / \"tmdb_movies\"\n", + "TVSHOWS_COLUMNS_OF_INTEREST = ['name', 'original_name', 'first_air_date', 'production_countries', 'genres', 'production_companies', 'vote_average']\n", + "NB_TVSHOWS_SUBSET = 5000" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f35eec9c", + "metadata": {}, + "outputs": [], + "source": [ "EXTRACT_MOVIES_ZIP_TO.mkdir(exist_ok=True, parents=True)\n", - "EXTRACT_TWSHOWS_ZIP_TO = DATA_DIRECTORY / \"tmdb_tvshows\"\n", "EXTRACT_TWSHOWS_ZIP_TO.mkdir(exist_ok=True, parents=True)\n", - "EXPORT_TMDB_SUBSETS_TO = DATA_DIRECTORY / \"subsets\"\n", - "EXPORT_TMDB_SUBSETS_TO.mkdir(exist_ok=True, parents=True)\n", - "\n", - "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies', 'vote_average', 'revenue']\n", - "NB_MOVIES_SUBSET = 5000" + "EXPORT_TMDB_SUBSETS_TO.mkdir(exist_ok=True, parents=True)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "fc3989b0", "metadata": {}, "outputs": [], "source": [ - "with zipfile.ZipFile(KAGGLE_TMDB_MOVIES_DATASET_PATH, 'r') as f:\n", + "with ZipFile(KAGGLE_TMDB_MOVIES_DATASET_PATH, 'r') as f:\n", " f.extractall(path=EXTRACT_MOVIES_ZIP_TO)\n", "\n", "df_movies = pd.read_csv(EXTRACT_MOVIES_ZIP_TO / os.listdir(EXTRACT_MOVIES_ZIP_TO)[0])" @@ -73,12 +81,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "bf19fe99", "metadata": {}, "outputs": [], "source": [ - "with zipfile.ZipFile(KAGGLE_TMDB_TVSHOWS_DATASET_PATH, 'r') as f:\n", + "with ZipFile(KAGGLE_TMDB_TVSHOWS_DATASET_PATH, 'r') as f:\n", " f.extractall(path=EXTRACT_TWSHOWS_ZIP_TO)\n", "\n", "df_tvshows = pd.read_csv(EXTRACT_TWSHOWS_ZIP_TO / os.listdir(EXTRACT_TWSHOWS_ZIP_TO)[0])" @@ -86,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "7429796d", "metadata": {}, "outputs": [], @@ -95,10 +103,28 @@ "\n", "df_movies_subset = df_movies[(df_movies['status'] == 'Released') & \n", " (~df_movies['adult']) &\n", - " (df_movies['release_date'] < '2024-03-01')].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n", + " (df_movies['release_date'] < '2024-03-01') &\n", + " (df_movies['original_language'].isin(['fr', 'en']))].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n", " \n", "df_movies_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_movies_subset.csv\")" ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c3a4ecc2", + "metadata": {}, + "outputs": [], + "source": [ + "df_tvshows.dropna(axis=0, how='any', subset=TVSHOWS_COLUMNS_OF_INTEREST, inplace=True)\n", + "\n", + "df_tvshows_subset = df_tvshows[(df_tvshows['status'] == 'Ended') & \n", + " (~df_tvshows['adult']) &\n", + " (df_tvshows['last_air_date'] < '2024-03-01') &\n", + " (df_tvshows['original_language'].isin(['fr', 'en']))].sort_values(by='last_air_date', ascending=False).iloc[0:NB_TVSHOWS_SUBSET]\n", + " \n", + "df_tvshows_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_tvshows_subset.csv\")" + ] } ], "metadata": { From c5b73e21f1fcbd9584b32e97dd737a336ff0f27d Mon Sep 17 00:00:00 2001 From: mchambery Date: Wed, 6 Mar 2024 21:35:50 +0100 Subject: [PATCH 5/5] check if movies can be identify by original_title and release_year --- notebooks/create_tmdb_subsets.ipynb | 55 ++++++++++++++++++++++------- 1 file changed, 42 insertions(+), 13 deletions(-) diff --git a/notebooks/create_tmdb_subsets.ipynb b/notebooks/create_tmdb_subsets.ipynb index d432166..e62e131 100644 --- a/notebooks/create_tmdb_subsets.ipynb +++ b/notebooks/create_tmdb_subsets.ipynb @@ -47,10 +47,10 @@ "\n", "EXPORT_TMDB_SUBSETS_TO = Path(\"../data/tmdb_subsets\").resolve()\n", "\n", - "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies', 'vote_average', 'revenue']\n", + "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies']\n", "NB_MOVIES_SUBSET = 5000\n", "\n", - "TVSHOWS_COLUMNS_OF_INTEREST = ['name', 'original_name', 'first_air_date', 'production_countries', 'genres', 'production_companies', 'vote_average']\n", + "TVSHOWS_COLUMNS_OF_INTEREST = ['name', 'original_name', 'first_air_date', 'production_countries', 'genres', 'production_companies']\n", "NB_TVSHOWS_SUBSET = 5000" ] }, @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "bf19fe99", "metadata": {}, "outputs": [], @@ -92,6 +92,35 @@ "df_tvshows = pd.read_csv(EXTRACT_TWSHOWS_ZIP_TO / os.listdir(EXTRACT_TWSHOWS_ZIP_TO)[0])" ] }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ad152395", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6497 movies are note uniquely identify by original_title & release_year on 797541 movies (0.81%)\n" + ] + } + ], + "source": [ + "df = df_movies[(df_movies['status'] == 'Released') & \n", + " (~df_movies['adult']) &\n", + " (~df_movies['release_date'].isna())].copy()\n", + "\n", + "df['release_year'] = df['release_date'].apply(lambda date : date[0:4]).astype(int)\n", + "\n", + "df_by_title_year = df.groupby(by=['original_title', 'release_year']).id.count()\n", + "\n", + "nb_duplicates_title_year = df_by_title_year[df_by_title_year > 1].shape[0]\n", + "nb_total_movies = df.shape[0]\n", + "print(f\"{nb_duplicates_title_year} movies are note uniquely identify by original_title & release_year on {nb_total_movies} movies ({(100 * nb_duplicates_title_year / nb_total_movies):.2f}%)\")\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -99,12 +128,12 @@ "metadata": {}, "outputs": [], "source": [ - "df_movies.dropna(axis=0, how='any', subset=MOVIES_COLUMNS_OF_INTEREST, inplace=True)\n", + "df_movies_subset = df_movies.dropna(axis=0, how='any', subset=MOVIES_COLUMNS_OF_INTEREST)\n", "\n", - "df_movies_subset = df_movies[(df_movies['status'] == 'Released') & \n", - " (~df_movies['adult']) &\n", - " (df_movies['release_date'] < '2024-03-01') &\n", - " (df_movies['original_language'].isin(['fr', 'en']))].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n", + "df_movies_subset = df_movies_subset[(df_movies_subset['status'] == 'Released') & \n", + " (~df_movies_subset['adult']) &\n", + " (df_movies_subset['release_date'] < '2024-03-01') &\n", + " (df_movies_subset['original_language'].isin(['fr', 'en']))].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n", " \n", "df_movies_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_movies_subset.csv\")" ] @@ -116,12 +145,12 @@ "metadata": {}, "outputs": [], "source": [ - "df_tvshows.dropna(axis=0, how='any', subset=TVSHOWS_COLUMNS_OF_INTEREST, inplace=True)\n", + "df_tvshows_subset = df_tvshows.dropna(axis=0, how='any', subset=TVSHOWS_COLUMNS_OF_INTEREST)\n", "\n", - "df_tvshows_subset = df_tvshows[(df_tvshows['status'] == 'Ended') & \n", - " (~df_tvshows['adult']) &\n", - " (df_tvshows['last_air_date'] < '2024-03-01') &\n", - " (df_tvshows['original_language'].isin(['fr', 'en']))].sort_values(by='last_air_date', ascending=False).iloc[0:NB_TVSHOWS_SUBSET]\n", + "df_tvshows_subset = df_tvshows_subset[(df_tvshows_subset['status'] == 'Ended') & \n", + " (~df_tvshows_subset['adult']) &\n", + " (df_tvshows_subset['last_air_date'] < '2024-03-01') &\n", + " (df_tvshows_subset['original_language'].isin(['fr', 'en']))].sort_values(by='last_air_date', ascending=False).iloc[0:NB_TVSHOWS_SUBSET]\n", " \n", "df_tvshows_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_tvshows_subset.csv\")" ]