SelectCols#

class skrub.SelectCols(cols)[source]#

Select a subset of a DataFrame’s columns.

A ValueError is raised if any of the provided column names are not in the dataframe.

Accepts pandas.DataFrame and polars.DataFrame inputs.

Parameters:
colslist of str or str

The columns to select. A single column name can be passed as a str: "col_name" is the same as ["col_name"].

Examples

>>> import pandas as pd
>>> from skrub import SelectCols
>>> df = pd.DataFrame({"A": [1, 2], "B": [10, 20], "C": ["x", "y"]})
>>> df
   A   B  C
0  1  10  x
1  2  20  y
>>> SelectCols(["C", "A"]).fit_transform(df)
   C  A
0  x  1
1  y  2
>>> SelectCols(["X", "A"]).fit_transform(df)
Traceback (most recent call last):
    ...
ValueError: The following columns are requested for selection but missing from dataframe: ['X']

Methods

fit(X[, y])

Fit the transformer.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Transform a dataframe by selecting columns.
fit(X, y=None)[source]#

Fit the transformer.

Parameters:
XDataFrame or None

If X is a DataFrame, the transformer checks that all the column names provided in self.cols can be found in X.

yNone

Unused.

Returns:
SelectCols

The transformer itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray_like of shape (n_samples, n_features)

Input samples.

yarray_like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

transform(X)[source]#

Transform a dataframe by selecting columns.

Parameters:
XDataFrame

The DataFrame on which to apply the selection.

Returns:
DataFrame

The input DataFrame X after selecting only the columns listed in self.cols (in the provided order).