dask_ml.preprocessing.OrdinalEncoder

dask_ml.preprocessing.OrdinalEncoder

class dask_ml.preprocessing.OrdinalEncoder(columns=None)

Ordinal (integer) encode categorical columns.

Parameters
columnssequence, optional

The columns to encode. Must be categorical dtype. Encodes all categorical dtype columns by default.

Attributes
columns_Index

The columns in the training data before/after encoding

categorical_columns_Index

The categorical columns in the training data

noncategorical_columns_Index

The rest of the columns in the training data

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'])})
>>> enc = OrdinalEncoder()
>>> trn = enc.fit_transform(data)
>>> trn
   A  B
0  1  0
1  2  0
2  3  0
3  4  1
>>> enc.columns_
Index(['A', 'B'], dtype='object')
>>> enc.non_categorical_columns_
Index(['A'], dtype='object')
>>> enc.categorical_columns_
Index(['B'], dtype='object')
>>> enc.dtypes_
{'B': CategoricalDtype(categories=['a', 'b'], ordered=False)}
>>> enc.fit_transform(dd.from_pandas(data, 2))
Dask DataFrame Structure:
                   A     B
npartitions=2
0              int64  int8
2                ...   ...
3                ...   ...
Dask Name: assign, 8 tasks

Methods

fit(X[, y])

Determine the categorical columns to be encoded.

fit_transform(X[, y])

Fit to data, then transform it.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X)

Inverse ordinal-encode the columns in X

set_params(**params)

Set the parameters of this estimator.

transform(X[, y])

Ordinal encode the categorical columns in X

__init__(columns=None)