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cudf.factorize#

cudf.factorize(
values,
sort: bool = False,
use_na_sentinel: bool = True,
size_hint: int | None = None,
) → tuple[cp.ndarray, cp.ndarray | Index][source]#

Encode the input values as integer labels

Parameters:
values: Series, Index, or CuPy array

The data to be factorized.

sortbool, default True

Sort uniques and shuffle codes to maintain the relationship.

use_na_sentinelbool, default True

If True, the sentinel -1 will be used for NA values. If False, NA values will be encoded as non-negative integers and will not drop the NA from the uniques of the values.

Returns:
(labels, cats)(cupy.ndarray, cupy.ndarray or Index)

  • labels contains the encoded values

  • cats contains the categories in order that the N-th

    item corresponds to the (N-1) code.

See also

cudf.Series.factorize

Encode the input values of Series.

Examples

>>> import cudf
>>> import numpy as np
>>> data = cudf.Series(['a', 'c', 'c'])
>>> codes, uniques = cudf.factorize(data)
>>> codes
array([0, 1, 1])
>>> uniques
Index(['a', 'c'], dtype='str')

When use_na_sentinel=True (the default), missing values are indicated in the codes with the sentinel value -1 and missing values are not included in uniques.

>>> codes, uniques = cudf.factorize(['b', None, 'a', 'c', 'b'])
>>> codes
array([ 0, -1,  1,  2,  0])
>>> uniques
Index(['b', 'a', 'c'], dtype='str')

If NA is in the values, and we want to include NA in the uniques of the values, it can be achieved by setting use_na_sentinel=False.

>>> values = np.array([1, 2, 1, np.nan])
>>> codes, uniques = cudf.factorize(values)
>>> codes
array([ 0,  1,  0, -1])
>>> uniques
array([1., 2.])
>>> codes, uniques = cudf.factorize(values, use_na_sentinel=False)
>>> codes
array([0, 1, 0, 2])
>>> uniques
array([ 1.,  2., nan])
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