pandas.Index.factorize#

Index.factorize(sort=False, na_sentinel=_NoDefault.no_default, use_na_sentinel=_NoDefault.no_default)[source]#

Encode the object as an enumerated type or categorical variable.

This method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values. factorize is available as both a top-level function pandas.factorize(), and as a method Series.factorize() and Index.factorize().

Parameters

sortbool, default False: Sort uniques and shuffle codes to maintain the relationship.
na_sentinelint or None, default -1: Value to mark “not found”. If None, will not drop the NaN from the uniques of the values.

Deprecated since version 1.5.0: The na_sentinel argument is deprecated and will be removed in a future version of pandas. Specify use_na_sentinel as either True or False.

Changed in version 1.1.2.
use_na_sentinelbool, default True: If True, the sentinel -1 will be used for NaN values. If False, NaN values will be encoded as non-negative integers and will not drop the NaN from the uniques of the values.

New in version 1.5.0.

Returns

codesndarray: An integer ndarray that’s an indexer into uniques. uniques.take(codes) will have the same values as values.
uniquesndarray, Index, or Categorical: The unique valid values. When values is Categorical, uniques is a Categorical. When values is some other pandas object, an Index is returned. Otherwise, a 1-D ndarray is returned.

Note

Even if there’s a missing value in values, uniques will not contain an entry for it.

See also

cut: Discretize continuous-valued array.
unique: Find the unique value in an array.

Notes

Reference the user guide for more examples.

Examples

These examples all show factorize as a top-level method like pd.factorize(values). The results are identical for methods like Series.factorize().

>>> codes, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'])
>>> codes
array([0, 0, 1, 2, 0]...)
>>> uniques
array(['b', 'a', 'c'], dtype=object)

With sort=True, the uniques will be sorted, and codes will be shuffled so that the relationship is the maintained.

>>> codes, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'], sort=True)
>>> codes
array([1, 1, 0, 2, 1]...)
>>> uniques
array(['a', 'b', 'c'], dtype=object)

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

Thus far, we’ve only factorized lists (which are internally coerced to NumPy arrays). When factorizing pandas objects, the type of uniques will differ. For Categoricals, a Categorical is returned.

>>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
>>> codes, uniques = pd.factorize(cat)
>>> codes
array([0, 0, 1]...)
>>> uniques
['a', 'c']
Categories (3, object): ['a', 'b', 'c']

Notice that 'b' is in uniques.categories, despite not being present in cat.values.

For all other pandas objects, an Index of the appropriate type is returned.

>>> cat = pd.Series(['a', 'a', 'c'])
>>> codes, uniques = pd.factorize(cat)
>>> codes
array([0, 0, 1]...)
>>> uniques
Index(['a', 'c'], dtype='object')

If NaN is in the values, and we want to include NaN 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 = pd.factorize(values)  # default: use_na_sentinel=True
>>> codes
array([ 0,  1,  0, -1])
>>> uniques
array([1., 2.])

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