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
columnssequence, optional

The columns to dummy encode. Must be categorical dtype. Dummy encodes all categorical dtype columns by default.

drop_firstbool, 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[, 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[, y])

Dummy encode the categorical columns in X

__init__(columns: Optional[Sequence[Any]] = None, drop_first: bool = False)