pandas.factorize#
- pandas.factorize(values, sort=False, na_sentinel=_NoDefault.no_default, use_na_sentinel=_NoDefault.no_default, 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 methodSeries.factorize()
andIndex.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 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.
- size_hintint, optional
Hint to the hashtable sizer.
- 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.
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 likeSeries.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 inuniques.categories
, despite not being present incat.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])