`dask_ml.preprocessing`.DummyEncoder¶

class dask_ml.preprocessing.DummyEncoder(columns: Optional[Sequence[Any]] = None, drop_first: bool = False)¶

Dummy (one-hot) encode categorical columns.

Parameters:

columns : sequence, optional: The columns to dummy encode. Must be categorical dtype. Dummy encodes all categorical dtype columns by default.
drop_first : bool, default False: Whether to drop the first category in each column.

Attributes:

columns_ : Index

The columns in the training data before dummy encoding

transformed_columns_ : Index

The columns in the training data after dummy encoding

categorical_columns_ : Index

The categorical columns in the training data

noncategorical_columns_ : Index

The rest of the columns in the training data

categorical_blocks_ : dict

Mapping from column names to slice objects. The slices represent the positions in the transformed array that the categorical column ends up at

dtypes_ : dict

Dictionary mapping column name to either

instances of CategoricalDtype (pandas >= 0.21.0)
tuples of (categories, ordered)

Notes

This transformer only applies to dask and pandas DataFrames. For dask DataFrames, all of your categoricals should be known.

The inverse transformation can be used on a dataframe or array.

Examples

>>> data = pd.DataFrame({"A": [1, 2, 3, 4],
...                      "B": pd.Categorical(['a', 'a', 'a', 'b'])})
>>> de = DummyEncoder()
>>> trn = de.fit_transform(data)
>>> trn
A  B_a  B_b
0  1    1    0
1  2    1    0
2  3    1    0
3  4    0    1

>>> de.columns_
Index(['A', 'B'], dtype='object')

>>> de.non_categorical_columns_
Index(['A'], dtype='object')

>>> de.categorical_columns_
Index(['B'], dtype='object')

>>> de.dtypes_
{'B': CategoricalDtype(categories=['a', 'b'], ordered=False)}

>>> de.categorical_blocks_
{'B': slice(1, 3, None)}

>>> de.fit_transform(dd.from_pandas(data, 2))
Dask DataFrame Structure:
                A    B_a    B_b
npartitions=2
0              int64  uint8  uint8
2                ...    ...    ...
3                ...    ...    ...
Dask Name: get_dummies, 4 tasks

Methods

`fit`(X, dask.dataframe.core.DataFrame], y, …)	Determine the categorical columns to be dummy encoded.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X, …)	Inverse dummy-encode the columns in X
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X, dask.dataframe.core.DataFrame], …)	Dummy encode the categorical columns in X

__init__(columns: Optional[Sequence[Any]] = None, drop_first: bool = False)¶: Initialize self. See help(type(self)) for accurate signature.

fit(X: Union[pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None) → dask_ml.preprocessing.data.DummyEncoder¶

Determine the categorical columns to be dummy encoded.

Parameters:	X : pandas.DataFrame or dask.dataframe.DataFrame y : ignored
Returns:	self

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:	X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features) y : ndarray of shape (n_samples,), default=None Target values. **fit_params : dict Additional fit parameters.
Returns:	X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

inverse_transform(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]) → Union[pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]¶

Inverse dummy-encode the columns in X

Parameters:	X : array or dataframe Either the NumPy, dask, or pandas version
Returns:	data : DataFrame Dask array or dataframe will return a Dask DataFrame. Numpy array or pandas dataframe will return a pandas DataFrame

set_params(**params)¶

Set the parameters of this estimator.

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

Parameters:	**params : dict Estimator parameters.
Returns:	self : object Estimator instance.

transform(X: Union[pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None) → Union[pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]¶

Dummy encode the categorical columns in X

Parameters:	X : pd.DataFrame or dd.DataFrame y : ignored
Returns:	transformed : pd.DataFrame or dd.DataFrame Same type as the input

dask_ml.preprocessing.DummyEncoder¶

`dask_ml.preprocessing`.DummyEncoder¶