xarray.CFTimeIndex.factorize#

CFTimeIndex.factorize(sort=False, use_na_sentinel=True)[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:
  • sort (bool, default False) – Sort uniques and shuffle codes to maintain the relationship.

  • use_na_sentinel (bool, 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:

  • codes (ndarray) – An integer ndarray that’s an indexer into uniques. uniques.take(codes) 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.

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(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"))
>>> 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(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"),
...                               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(np.array(['b', None, 'a', 'c', 'b'], dtype="O"))
>>> 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])