Module impute (2.17.0)

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
<BLANKLINE>
[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.

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Classes

SimpleImputer

Module impute (2.17.0)