# FitObjectiveDataset
FitObjectiveDataset est un package Python qui permet de récupérer des produits alimentaires avec leurs données nutritionnelles, présentées sous forme de DataFrames Pandas.
Ce package minimaliste offre une gestion simplifiée mais efficace des fichiers. Il inclut des fonctionnalités essentielles ainsi qu'un premier tri des données. Il est également possible de récupérer l'intégralité de la DataFrame brute si nécessaire.
Raw data
{
"_id": null,
"home_page": "https://gitlab.com/misternobody01/fitobjective-dataset.git",
"name": "fitobjectivedataset",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "nutrition dataset pandas fitobjective",
"author": "Monsieur Nobody",
"author_email": "monsieurnobody01@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/a6/d0/644334173363e91d7c97b33bd9bbec1a8c120fe28fd26f26779fb2f2c1e0/fitobjectivedataset-1.2.0.tar.gz",
"platform": null,
"description": "# FitObjectiveDataset\n\nFitObjectiveDataset est un package Python qui permet de r\u00e9cup\u00e9rer des produits alimentaires avec leurs donn\u00e9es nutritionnelles, pr\u00e9sent\u00e9es sous forme de DataFrames Pandas.\n\nCe package minimaliste offre une gestion simplifi\u00e9e mais efficace des fichiers. Il inclut des fonctionnalit\u00e9s essentielles ainsi qu'un premier tri des donn\u00e9es. Il est \u00e9galement possible de r\u00e9cup\u00e9rer l'int\u00e9gralit\u00e9 de la DataFrame brute si n\u00e9cessaire.\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Un package Python pour g\u00e9rer des datasets nutritionnels dans le cadre du projet FitObjective.",
"version": "1.2.0",
"project_urls": {
"Homepage": "https://gitlab.com/misternobody01/fitobjective-dataset.git"
},
"split_keywords": [
"nutrition",
"dataset",
"pandas",
"fitobjective"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0f144a91a9df5c17957c167aed86c33355e7e467bfc4da141154666597ec60e2",
"md5": "1fe2d172174600dd6019081d920fa90a",
"sha256": "543f16c2805fdef014ff5b94a03988f9433680bb37f7dbf87f4f9b240857a79c"
},
"downloads": -1,
"filename": "fitobjectivedataset-1.2.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "1fe2d172174600dd6019081d920fa90a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 20937,
"upload_time": "2025-01-09T10:46:43",
"upload_time_iso_8601": "2025-01-09T10:46:43.819949Z",
"url": "https://files.pythonhosted.org/packages/0f/14/4a91a9df5c17957c167aed86c33355e7e467bfc4da141154666597ec60e2/fitobjectivedataset-1.2.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a6d0644334173363e91d7c97b33bd9bbec1a8c120fe28fd26f26779fb2f2c1e0",
"md5": "e4e66b10117537f7155027d6dc43e997",
"sha256": "e473fcd295d74e4a763d890d1455c727a6b2b2e216031cd734c219826c888d21"
},
"downloads": -1,
"filename": "fitobjectivedataset-1.2.0.tar.gz",
"has_sig": false,
"md5_digest": "e4e66b10117537f7155027d6dc43e997",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 20374,
"upload_time": "2025-01-09T10:46:48",
"upload_time_iso_8601": "2025-01-09T10:46:48.160621Z",
"url": "https://files.pythonhosted.org/packages/a6/d0/644334173363e91d7c97b33bd9bbec1a8c120fe28fd26f26779fb2f2c1e0/fitobjectivedataset-1.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-09 10:46:48",
"github": false,
"gitlab": true,
"bitbucket": false,
"codeberg": false,
"gitlab_user": "misternobody01",
"gitlab_project": "fitobjective-dataset",
"lcname": "fitobjectivedataset"
}