iskandr/fancyimpute

Health v2 · maintenance only

Adoption not part of the score

1281 stars · 178 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library offering a variety of multivariate imputation and matrix completion algorithms such as KNN, SoftImpute, and NuclearNormMinimization for filling in missing values in data matrices. It is now in bare maintenance mode, with IterativeImputer delegated to scikit-learn.

Use cases

• fill in missing values in a dataset with NaN entries
• complete a partially observed matrix using low-rank methods
• impute missing features using k-nearest neighbors
• compare imputation methods by mean squared error
• handle missing data before training a machine learning model

When to choose

• you need a ready-made set of matrix completion and imputation algorithms in Python
• your data has missing numeric entries and you want alternatives to simple mean/median filling
• you want KNN, SoftImpute, or nuclear norm minimization implementations without writing them yourself

When to avoid

• you need actively developed imputation features or new algorithms
• you only need IterativeImputer, which is better obtained directly from scikit-learn
• you need guaranteed long-term support, since the project is in bare maintenance mode

Facets

library · maturity maintenance

machine-learning data-science data-science machine-learning python missing-data imputation matrix-completion numpy scikit-learn

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