Skip to main content

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.

Metadata

Release files for microimpute 3.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for microimpute 3.1.2
File Size Uploaded
microimpute-3.1.2.tar.gz 146.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for microimpute 3.1.2
File Interpreter ABI Platform
microimpute-3.1.2-py3-none-any.whl Python 3 none any Details

Total release size: 273.9 kB

Release files / microimpute-3.1.2.tar.gz

Download URL microimpute-3.1.2.tar.gz
Size 146.4 kB
Tags Source
SHA-256 checksum
How to use checksums
1738e62df89601037bbd05f8efd67a67fa22c3ea10a993f0fbd222662528390d
BLAKE2b-256 checksum
How to use checksums
bc2e9ea90103da52d86c9da998fd637759830aae6dd9f58dd9b01a9807e601a4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / microimpute-3.1.2-py3-none-any.whl

Download URL microimpute-3.1.2-py3-none-any.whl
Size 127.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d95b537f2fdf4a117d97239823d63b91c32bc007c5cf82edc18b6d695083c8cc
BLAKE2b-256 checksum
How to use checksums
08de7e966cfd2375a0cce1767a7dfe8f25e7934292d1e7bdcfe8624703d0cecd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release history Release notifications | RSS feed

3.1.3

2 release files

This release

3.1.2 This release

2 release files

3.1.1

2 release files

3.1.0

2 release files

3.0.0

2 release files

2.1.1

2 release files

2.1.0

2 release files

2.0.4

2 release files

2.0.3

2 release files

2.0.2

2 release files

1.14.3

2 release files

1.14.2

2 release files

1.14.1

2 release files

1.13.0

2 release files

1.12.0

2 release files

1.9.0

2 release files

1.8.1

2 release files

1.7.0

2 release files

1.6.1

2 release files

1.6.0

2 release files

1.5.2

2 release files

1.5.1

2 release files

1.4.1

2 release files

1.3.0

2 release files

1.2.3

2 release files

1.2.2

2 release files

1.2.1

2 release files

1.2.0

2 release files

1.1.6

2 release files

1.1.5

2 release files

1.1.4

2 release files

1.1.3

2 release files

1.1.2

2 release files

1.1.1

2 release files

1.1.0

2 release files

1.0.2

2 release files

1.0.1

2 release files

0.2.5

2 release files

0.2.4

2 release files

0.2.3

2 release files

0.2.2

2 release files

0.2.1

2 release files

0.2.0

2 release files

0.1.5

2 release files

0.1.4

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page