{"library":"prince","title":"Prince - Factor Analysis in Python","description":"Prince is a Python library for various factor analysis methods, including Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), Multiple Factor Analysis (MFA), Factor Analysis of Mixed Data (FAMD), Generalized Procrustes Analysis (GPA), and Procrustes Global Analysis (PGA). As of version 0.17.0, it offers a scikit-learn compatible API, making it easy to integrate into existing data science workflows. The project is actively maintained with a relatively steady release cadence, incorporating new features and improvements.","language":"python","status":"active","last_verified":"Fri Apr 17","install":{"commands":["pip install prince"],"cli":null},"imports":["from prince import PCA","from prince import MCA","from prince import CA","from prince import MFA","from prince import FAMD"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pandas as pd\nfrom prince import PCA\n\n# Sample data for PCA\nX = pd.DataFrame({\n    'feature_a': [1, 2, 3, 4, 5],\n    'feature_b': [2, 3, 4, 5, 6],\n    'feature_c': [3, 4, 5, 6, 7]\n})\n\n# Initialize and fit PCA model\npca = PCA(n_components=2)\npca.fit(X)\n\n# Transform the data\nX_transformed = pca.transform(X)\n\nprint(\"Original data head:\\n\", X.head())\nprint(\"\\nTransformed data head (2 components):\\n\", X_transformed.head())\nprint(\"\\nExplained inertia per component:\", pca.explained_inertia_)\n","lang":"python","description":"This quickstart demonstrates how to use Prince's PCA implementation. It initializes a PCA model with 2 components, fits it to a sample Pandas DataFrame, and then transforms the data. Finally, it prints the transformed data and the explained inertia for each component.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}