Transformers for missing value imputation. This module is styled after scikit-learn's preprocessing module: https://scikit-learn.org/stable/modules/impute.html.
Classes
SimpleImputer
SimpleImputer(strategy: typing.Literal["mean", "median", "most_frequent"] = "mean")Univariate imputer for completing missing values with simple strategies.
Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column.
Examples:
>>> import bigframes.pandas as bpd
>>> from bigframes.ml.impute import SimpleImputer
>>> bpd.options.display.progress_bar = None
>>> X_train = bpd.DataFrame({"feat0": [7.0, 4.0, 10.0], "feat1": [2.0, None, 5.0], "feat2": [3.0, 6.0, 9.0]})
>>> imp_mean = SimpleImputer().fit(X_train)
>>> X_test = bpd.DataFrame({"feat0": [None, 4.0, 10.0], "feat1": [2.0, None, None], "feat2": [3.0, 6.0, 9.0]})
>>> imp_mean.transform(X_test)
imputer_feat0 imputer_feat1 imputer_feat2
0 7.0 2.0 3.0
1 4.0 3.5 6.0
2 10.0 3.5 9.0
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[3 rows x 3 columns]
| Parameter | |
|---|---|
| Name | Description |
strategy |
{'mean', 'median', 'most_frequent'}, default='mean'
The imputation strategy. 'mean': replace missing values using the mean along the axis. 'median':replace missing values using the median along the axis. 'most_frequent', replace missing using the most frequent value along the axis. |