feature-engineering-polars 0.2.1

Feature engineering done with Polars

Feature Engineering with Polars

Feature engineering done with Polars

How to install

pip install feature-engineering-polars

How to use it

import polars as pl
from fe_polars.imputing.base_imputing import Imputer
from fe_polars.encoding.target_encoding import TargetEncoder

dataframe = pl.DataFrame(
        {
            "City": ["A", "A", "B", "B", "B", "C", "C", "C"],
            "Rain": [103, None, 90, 75, None, 200, 155, 127],
            "Temperature": [30.5, 32, 25, 38, 40, 29.6, 21.3, 24.9],
        }
    )

imputer = Imputer(features_to_impute=["Rain"], strategy="mean")
encoder = TargetEncoder(smoothing=2, features_to_encode=["City"])

temp = imputer.fit_transform(x=dataframe)
encoder.fit_transform(x=temp, y=dataframe['Temperature'])

shape: (8, 3)
City	Temperature	Rain
f64	    f64	        f64

30.706	    30.5	103.0
30.706	    32.0	125.0
32.665	    25.0	90.0
32.665	    38.0	75.0
32.665	    40.0	125.0
27.225	    29.6	200.0
27.225	    21.3	155.0
27.225	    24.9	127.0

Available transformers

• Encoding:
  • Target encoding
  • One hot encoding
• Imputing:
  • Base imputing:
    • Mean imputing
    • Median imputing
    • Mode imputing
    • Max imputing
    • Min imputing