Microimpute
Microimpute is a Python package for imputing variables from one survey dataset onto another. It wraps five imputation methods behind a common interface so you can benchmark them on your data and pick the one that works best, rather than defaulting to a single approach.
Methods
- Statistical Matching: distance-based matching to find similar donor observations
- Ordinary Least Squares (OLS): linear regression imputation
- Quantile Regression: models conditional quantiles instead of the conditional mean
- Quantile Regression Forests (QRF): non-parametric, tree-based quantile estimation
- Mixture Density Networks (MDN): neural network with a Gaussian mixture output
Autoimpute
The autoimpute function tunes hyperparameters, runs cross-validation across all five methods, and selects the best performer based on quantile loss (for numerical targets) or log loss (for categorical targets). It handles numerical, categorical, and boolean variables.
API
All models follow a fit() / predict() interface. The package supports sample weights to account for survey design, and validates inputs automatically. Adding a custom imputation method is straightforward since new models just need to implement the same interface.
Documentation and paper
- Documentation with examples and interactive notebooks
- Paper presenting microimpute and demonstrating it for SCF-to-CPS net worth imputation
Dashboard
An interactive dashboard for exploring imputation results is available at https://microimpute-dashboard.vercel.app/. It supports file upload, URL loading, direct GitHub artifact integration, and sample data.
Installation
pip install microimpute
For image export (PNG/JPG):
pip install microimpute[images]
Contributing
Pull requests are welcome. If you find a bug or have a feature idea, open an issue or submit a PR.
1 maintainer
Metadata
1 maintainer
Release files for microimpute 3.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| microimpute-3.1.4.tar.gz | 149.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| microimpute-3.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 278.9 kB
Release files / microimpute-3.1.4.tar.gz
| Download URL | microimpute-3.1.4.tar.gz |
|---|---|
| Size | 149.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ba2fc484affea5ade1bea7f2f8ebf14059aaff85c5dd37727c736e1b4184de42
|
|
BLAKE2b-256 checksum How to use checksums |
3c6af95b4ecf8e2b134ea78afaf47ba5f031967d2f2da8e470924b5f991ffde9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 12, 2026.
Transparency logRelease files / microimpute-3.1.4-py3-none-any.whl
| Download URL | microimpute-3.1.4-py3-none-any.whl |
|---|---|
| Size | 129.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
002a678639993f7634fe1263635e594c4a3c3330f0d912b6dcd174202aab62de
|
|
BLAKE2b-256 checksum How to use checksums |
c374eb0365218bad70019c2ad532792631e226bfa5bd82304fef2f2416069339
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 12, 2026.
Transparency log
