{"id":20579942,"url":"https://github.com/fredgainza/generateacp","last_synced_at":"2026-06-05T19:31:21.828Z","repository":{"id":63576797,"uuid":"567784677","full_name":"FredGainza/generateACP","owner":"FredGainza","description":"Génération data et graphs d'une 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generateACP\n\n[![Badge KoPaTiK](https://img.shields.io/badge/KoPaTiK-Agency-blue)](https://fgainza.fr) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/FredGainza/generateACP.git/HEAD)\n\n*** \n\n## \u003cp style=\"color:#00FFFF;\"\u003eFonction générant les calculs et les graphiques d'une ACP.\u003c/p\u003e\n\n***\n\n### Paramètre principal à renseigner =\u003e dataframe avec :\n\n- en index : la variable d'observation\n- en colonnes : les variables explicatives quantitatives\n- en ligne : les individus / observations\n\n\n### Structure de l'analyse réalisée :\n\n#### Information sur les données\n        * données initiales\n        * données centrées réduites\n#### Recherche du nombre de facteurs à retenir\n        * graphique eboulis des valeurs propres\n        * calcul de la proportion de variance expliquée\n        * test des bâtons brisés\n#### Représentation des individus\n        * coordonnées factorielles des individus\n        * qualité de la représentation des individus (cos² de chaque individu par axe)\n        * contribution des individus aux axes\n#### Représentation des variables\n        * les vecteurs propres\n        * corrélations par facteur\n        * qualité de la représentation des variables (cos² de chaque variable par axe)\n        * contribution des variables aux axes\n#### Traitement des variables supplémentaires\n        * variables illustratives quantitatives\n        * variables illustratives qualitatives\n#### Représentation graphique (pour chaque plan factoriel)\n        * Projection des individus\n        * Cercle des corrélations\n\n\n## 2. Installation\n\n### Exemple avec Anaconda\n\n```bash\n    ### Créer un dossier pour le projet\n    $ mkdir /vers/dossier/testACP\n    ### Se déplacer dans le dossier\n    $ cd /vers/dossier/testACP\n\t\n    ### Créer un nouvel environnement\n    $ conda create -n envAcp python=3.9\n    ### Activer le nouvel environnement\n    $ conda activate envAcp\n\t\n    ### Installer le module generateAcp\n    $ pip install git+https://github.com/FredGainza/generateACP.git\n```\n\nPour utiliser le module dans un notebook, il faut importer la fonction **acp_global()** :\n\n```python\n    from generateACP import acp_global\n```\n\nLes modules suivants seront automatiquement installés :\n\n- pandas\n- numpy\n- scikit-learn\n- matplotlib\n- jupyter\n- adjustText\n- pdfservices-sdk\n- openpyxl\n\n## Documentation\n\nRDV [ICI](https://FredGainza.github.io/generateACP/)\n\n## Exemple\n\n* Notebook disponible au format [html](https://kopadata.fr/data/generateACP/analyse_acp_exemple.html)\n* Notebook exécuté *analyse_acp_exemple.ipynb* présent dans le dossier [/docs/](https://github.com/FredGainza/generateACP/tree/main/docs)\n* Notebook dispo sur [binder](https://mybinder.org/v2/gh/FredGainza/generateACP.git/HEAD) (exécutez le notebook *binder_exemple_module_acp.ipynb* présent dans le dossier /docs/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffredgainza%2Fgenerateacp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffredgainza%2Fgenerateacp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffredgainza%2Fgenerateacp/lists"}