MissingPy is a Python library providing tools for missing data imputation, offering an API consistent with scikit-learn. It primarily supports k-Nearest Neighbors (KNNImputer) and Random Forest-based (MissForest) imputation algorithms. The current version is 0.2.0, released in December 2018. Due to infrequent updates since its last release and limited recent activity on its GitHub repository, the library is considered to be in a maintenance state, with no active development or new releases anticipated.
Install & Compatibility
Where this runs
tested against v0.2.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.920 runs
installs and imports cleanly · install 0.0s · import 0.000s · 18.1MB
glibcpy 3.10–3.920 runs
installs and imports cleanly · install 1.6s · import 0.000s · 19MB
16MB installed
● package 16MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
from missingpy import KNNImputer
This quickstart demonstrates how to use `MissForest` to impute missing values (represented by `np.nan`) in a NumPy array. It includes the necessary workaround for `scikit-learn` compatibility that is commonly required. Ensure categorical variables are one-hot encoded before passing them to the imputer.
import numpy as np
# Workaround for scikit-learn compatibility
import sklearn.neighbors._base
import sys
sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base
from missingpy import MissForest
nan = np.nan
X = np.array([
[1, 2, nan],
[3, 4, 3],
[nan, 6, 5],
[8, 8, 7]
])
imputer = MissForest(random_state=42)
X_imputed = imputer.fit_transform(X)
print("Original Data with NaNs:\n", X)
print("Imputed Data:\n", X_imputed)
Upgrade
Version history
Breaking-change detection hasn't run for this library yet.
Audit
Security & dependencies
CVE tracking and dependency tree are planned for a later release.