GetDummies

class scikitplot.preprocessing.GetDummies(columns=None, data_sep=', ', col_name_sep='_', dtype=<class 'float'>, sparse_output=False, handle_unknown='ignore', drop_first=None)

Multi-column multi-label string column one-hot encoder.

Custom transformer to expand string columns that contain multiple labels separated by data_sep into one-hot encoded columns by pandas.get_dummies.

Compatible with sklearn pipelines, set_output API, and supports both dense and sparse output.

Parameters:

columnsstr, list of str or None, default=None

Column(s) to encode. If str, single column. If list, multiple columns. If None, automatically detect object (string) columns containing data_sep.

data_sepstr, default=”,”

Separator used inside string cells, e.g., “a,b,c”.

col_name_sepstr, default=”_”

Separator for new dummy column names, e.g., “tags_a”.

dtypedata-type, default=float

Data type for the output values. (sklearn default is float)

sparse_outputbool, default=False

If True, return a SciPy sparse CSR matrix. If False, return pandas DataFrame (or numpy array if set_output=”default”).

handle_unknown{“ignore”, “error”}, default=”ignore”

Strategy for unknown categories at transform time.

drop_first{“first”, True, None}, default=None

Drop the first dummy in each feature (sorted order) to avoid collinearity.

See also

pandas.Series.str.get_dummies

Convert Series of strings to dummy codes.

pandas.from_dummies

Convert dummy codes back to categorical DataFrame.

sklearn.preprocessing.OneHotEncoder

General-purpose one-hot encoder.

sklearn.preprocessing.MultiLabelBinarizer

Multi-label binarizer for iterable of iterables.

References

[1]

Çelik, M. (2023, December 9). How to converting pandas column of comma-separated strings into dummy variables?.

Medium. https://medium.com/@celik-muhammed/how-to-converting-pandas-column-of-comma-separated-strings-into-dummy-variables-762c02282a6c

fit(X, y=None)

Learn dummy categories from training data.

Stores column order, prefixes, and categories for later alignment.

fit_transform(X, y=None, **fit_params)

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_feature_names_out(input_features=None)

Return feature names after transformation.

get_metadata_routing()

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)

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)

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)

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, y=None)

Transform new data into dummy-expanded format.

Steps: - Align columns with fit. - Drop unknown categories or raise error. - Return dense/pandas or sparse output.