GrowthCleanPy

GrowthCleanPy is a Python package for cleaning pediatric growth data. It identifies implausible height and weight measurements — such as unit errors, swapped height/weight values, duplicates, carried-forward values, and extreme outliers — by comparing each measurement against standard growth references (WHO, CDC, NHANES, and Tanner height-velocity data). It adds an exclude column marking the status of each measurement so that only trustworthy values are retained for analysis.

GrowthCleanPy is a Python port of the R growthcleanr algorithm. This is a version 0 (minimum viable) release providing the core pediatric cleaning capability; future releases are expected to add functionality and enhancements.

The growth reference data is bundled with the package — no external data files need to be downloaded or configured.

Installation

GrowthCleanPy requires Python 3.10 or newer.

Install from the distributed wheel:

pip install growthcleanpy-0.1.0-py3-none-any.whl

Dependencies (pandas, numpy) are installed automatically. The same wheel works on Windows, macOS, Linux, and Databricks.

Confirm the installation:

python -c "import growthcleanpy; print(growthcleanpy.__version__)"

Input data

The input is a pandas DataFrame (or CSV) in long format with one measurement per row and the following columns:

Usage

import pandas as pd
from growthcleanpy import cleangrowth

# load your data
df = pd.read_csv("my_growth_data.csv")

# run the cleaner
result = cleangrowth(df)

# review what was flagged
print(result["exclude"].value_counts())

# keep only valid measurements
clean = result[result["exclude"] == "Include"]

# save results
result.to_csv("cleaned_results.csv", index=False)

The result is the input data with an added exclude column. Rows marked Include passed all checks; other labels indicate why a measurement was flagged.

Common exclude labels

Repository contents

Running the tests

The automated regression suite verifies that the packaged library reproduces the validated baseline output exactly. Run against a source checkout:

pip install -e .[dev]
python -m pytest tests/

Or run the full build-and-test cycle against a freshly built wheel:

bash run_tests.sh

Releases

Built wheel packages are attached to tagged Releases. Download the .whl from the desired release and install as shown above.

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This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

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