Install & Compatibility
Where this runs
tested against v1.6.1 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
py 3.10
✕ build_error
1/2 runs
py 3.11
✕ build_error
1/2 runs
py 3.12
✕ build_error
1/2 runs
py 3.13
✕ build_error
1/2 runs
py 3.9
✕ build_error
1/2 runs
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
PipelineFeatureGenerator
✓ from autogluon.features import PipelineFeatureGenerator
✗ from autogluon.features import PipelineFeatureGenerator
This quickstart demonstrates how to instantiate and use a `PipelineFeatureGenerator` from `autogluon-features` to apply common transformations like categorical encoding and datetime feature extraction to a pandas DataFrame.
import pandas as pd
from autogluon.features.generators import PipelineFeatureGenerator, CategoryFeatureGenerator, DatetimeFeatureGenerator
# Create a sample DataFrame
data = {
'numeric_col': [1, 2, 3, 4, 5],
'categorical_col': ['A', 'B', 'A', 'C', 'B'],
'datetime_col': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],
'text_col': ['hello world', 'foo bar', 'hello again', 'world peace', 'bar foo']
}
df = pd.DataFrame(data)
# Initialize a pipeline of feature generators
# CategoryFeatureGenerator converts object/category dtypes
# DatetimeFeatureGenerator extracts year, month, day, etc. from datetime columns
pipeline_generator = PipelineFeatureGenerator(
[CategoryFeatureGenerator(), DatetimeFeatureGenerator(fillna_limit=0)]
)
# Fit and transform the DataFrame
df_transformed = pipeline_generator.fit_transform(X=df)
print("Original DataFrame:")
print(df)
print("\nTransformed DataFrame features (first 5 rows):")
print(df_transformed.head())
print("\nFeature metadata after transformation (output features):")
print(pipeline_generator.feature_metadata_out.pretty_print())
Debug
Known issues
breakingLoading models trained with a different AutoGluon version (which includes `autogluon-features` components) is not supported and can lead to crashes, incorrect predictions, or unexpected behavior due to internal API changes and serialization formats.fixAlways train and predict with the same AutoGluon (and therefore `autogluon-features`) version. When upgrading AutoGluon, retrain your models with the new version.
affects: All versions, explicitly highlighted in v0.8.2 onwards.
gotchaDirectly using `autogluon-features` components (like `FeatureGenerator` subclasses) is primarily for advanced customization. Most users should leverage feature engineering through AutoGluon's high-level APIs like `TabularPredictor`, which manage feature generation automatically.fixUnless you have a specific need to customize the feature generation pipeline, prefer using `AutoGluon.TabularPredictor.fit()` which handles feature processing implicitly.
affects: All versions.
breakingPython version compatibility has changed. Support for Python 3.8 was dropped in AutoGluon v1.2.0, and support for Python 3.12 was added. Current versions require Python >=3.10 and <3.14.fixEnsure your Python environment is within the supported range (currently 3.10-3.13) before installing or upgrading AutoGluon. Check `requires_python` on PyPI for the most up-to-date information.
affects: >=1.2.0
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Version history
1.6.1latest on PyPI · released Aug 6, 2026
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Dependencies
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