missingpy
Missing Data Imputation for Python
Decision gist · record as of 2026-08-14
Yes, if you need a lightweight, zero-dependency imputation tool and can tolerate dormant maintenance. The scikit-learn API is familiar and the code is straightforward. No, if you require active maintenance, compatibility with recent library versions, or timely bug fixes. Test thoroughly on your specific data and Python version before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with no runtime dependencies.
- Maintenance is dormant—last release was 2018-12-10 and last commit 2024-02-29, so expect no active development or timely bug fixes.
License · maintenance · safety
copyleft license (copyleft) — Licensed under GPLv3 (copyleft). Any derivative work or bundled distribution must also be open-source under compatible terms.
last release 2018-12-10 (2804 days) · last repo commit 2024-02-29 · 246 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 296,221 downloads/mo, #7,901 on PyPI
Alternatives
Verify before relying
pip install missingpy
from missingpy import KNNImputer
X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]]
imputer = KNNImputer(n_neighbors=2)
X_imputed = imputer.fit_transform(X)- Whether the package works with current versions of common numerical libraries (last release predates many ecosystem updates)
- Performance characteristics on large datasets or high-dimensional data
- Whether categorical variable support in MissForest is fully documented and stable
What it is and what it does
missingpy provides two methods for imputing missing values in numerical data arrays: k-Nearest Neighbors (KNNImputer) and Random Forest (MissForest). Both follow scikit-learn's fit/transform interface, making them familiar to users of that ecosystem. KNNImputer replaces missing values by averaging values from the k nearest neighbors; MissForest uses iterative random forest predictions, starting with the column containing the fewest missing values and working outward. The library handles both numerical and categorical variables (in MissForest) and allows configuration of neighbor counts, distance metrics, and missing-value thresholds.
The package has no external runtime dependencies, making installation straightforward. However, it has been dormant since late 2018—the latest release is 0.2.0 from December 2018, and while the repository shows a commit in February 2024, there have been no new releases. This means the codebase may not be compatible with recent versions of common libraries, and bug reports or feature requests are unlikely to receive timely attention.
Use it for
- Preprocess datasets with missing values before feeding them into machine learning pipelines that require complete data.
- Impute missing entries in time-series or sensor data where nearest-neighbor patterns reflect realistic local structure.
- Handle mixed numerical and categorical missing data in tabular datasets using MissForest's iterative approach.
- Replace missing values in microarray or genomic data where the k-NN methodology has established validity.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a lightweight, zero-dependency imputation tool and can tolerate dormant maintenance.
The scikit-learn API is familiar and the code is straightforward. No, if you require active maintenance, compatibility with recent library versions, or timely bug fixes. Test thoroughly on your specific data and Python version before production use.
Install
missingpy on PyPI
Before you install
Low install friction with no runtime dependencies. Maintenance is dormant—last release was 2018-12-10 and last commit 2024-02-29, so expect no active development or timely bug fixes.
License in practice
Licensed under GPLv3 (copyleft). Any derivative work or bundled distribution must also be open-source under compatible terms.
Quickstart
pip install missingpy
from missingpy import KNNImputer
X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]]
imputer = KNNImputer(n_neighbors=2)
X_imputed = imputer.fit_transform(X)Verify before relying
- Whether the package works with current versions of common numerical libraries (last release predates many ecosystem updates)
- Performance characteristics on large datasets or high-dimensional data
- Whether categorical variable support in MissForest is fully documented and stable
Package facts
| License | copyleft license copyleft |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 2,804 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 296,221 / month, #7,901 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: GNU General Public License v3 (GPLv3)Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: missingpy-0.2.0-py3-none-any.whl
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