DataForgeML
DataForgeML is an automated, end-to-end pipeline for turning raw tabular datasets into ML-ready data — without hand-writing schema logic, imputation rules, or splitting code for every new dataset.
The library is built as a one-stop solution: the goal is that a user should not need to manually assemble fragmented libraries steps or write custom glue code around it. Every phase — profiling, imputation, splitting — is designed to compose directly, driven by configuration objects rather than bespoke per-dataset scripts.
Supported input formats: CSV, TSV, Parquet, JSON, NDJSON, JSONL, XLSX, XLS, Arrow, and Feather, with automatic encoding and delimiter detection.
Installation
Requires Python 3.11+.
uv add dataforge-ml
or, from PyPI with pip:
pip install dataforge-ml
Quick start — profile a dataset
Structurally profile a raw dataset: inferred semantic types, per-column stats, missingness, correlations, and nonlinearity signals — all driven by config.
from dataforge_ml import (
PipelineConfig,
ProfileConfig,
SemanticType,
StructuralProfiler,
)
pipeline_config = PipelineConfig(
profiling=ProfileConfig(
compute_correlation=True,
compute_nonlinearity=True,
),
random_seed=42,
)
# Nudge the profiler where you know better than the type detector.
pipeline_config.set_column_type(column="Year", semantic_type=SemanticType.Categorical)
profiler = StructuralProfiler(config=pipeline_config)
result = profiler.profile(data=df) # df is a polars.DataFrame
# The lossless Profile Report — every to_dict() field rendered as Markdown.
# On a wide dataset (~82 columns) this is roughly a megabyte of text; for a
# bounded view, read result.to_dict() and select the fields you care about.
print(result.to_markdown())
Examples
To see how to use DataForgeML end to end, browse the full set of runnable, self-contained example scripts — each with bundled datasets and step-by-step walkthroughs — in the dedicated examples repository:
https://github.com/DEVunderdog/dataforgeml-examples
Video
Documentation
Full documentation — including the complete API reference, configuration guide, and the domain concepts behind profiling, imputation, and splitting — is available at:
https://devunderdog.github.io/DataForgeML/
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for dataforge-ml 3.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|---|
| dataforge_ml-3.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 443.5 kB
Release files / dataforge_ml-3.3.0.tar.gz
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