pandas.factorize

pandas.factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None) [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:

values : sequence A 1-D sequence. Sequences that aren’t pandas objects are coerced to ndarrays before factorization. sort : bool, default False Sort uniques and shuffle labels to maintain the relationship. order : None Deprecated since version 0.23.0: This parameter has no effect and is deprecated. na_sentinel : int, default -1 Value to mark “not found”. size_hint : int, optional Hint to the hashtable sizer.

Returns:

labels : ndarray An integer ndarray that’s an indexer into uniques. uniques.take(labels) will have the same values as values. uniques : ndarray, 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.

Examples

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

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

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

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

Missing values are indicated in labels with na_sentinel (-1 by default). Note that missing values are never included in uniques.

>>> labels, uniques = pd.factorize(['b', None, 'a', 'c', 'b'])
>>> labels
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'])
>>> labels, uniques = pd.factorize(cat)
>>> labels
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'])
>>> labels, uniques = pd.factorize(cat)
>>> labels
array([0, 0, 1])
>>> uniques
Index(['a', 'c'], dtype='object')