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
valuessequence

A 1-D sequence. Sequences that aren’t pandas objects are coerced to ndarrays before factorization.

sortbool, default False

Sort uniques and shuffle labels to maintain the relationship.

orderNone

Deprecated since version 0.23.0: This parameter has no effect and is deprecated.

na_sentinelint, default -1

Value to mark “not found”.

size_hintint, optional

Hint to the hashtable sizer.

Returns
labelsndarray

An integer ndarray that’s an indexer into uniques. uniques.take(labels) 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.

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')
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