pandas.api.types.union_categoricals

pandas.api.types.union_categoricals(to_union, sort_categories=False, ignore_order=False) [source]

Combine list-like of Categorical-like, unioning categories. All categories must have the same dtype.

New in version 0.19.0.

Parameters:	to_union : list-like of Categorical, CategoricalIndex, or Series with dtype=’category’ sort_categories : boolean, default False If true, resulting categories will be lexsorted, otherwise they will be ordered as they appear in the data. ignore_order: boolean, default False If true, the ordered attribute of the Categoricals will be ignored. Results in an unordered categorical. New in version 0.20.0.
Returns:	result : Categorical
Raises:	TypeError all inputs do not have the same dtype all inputs do not have the same ordered property all inputs are ordered and their categories are not identical sort_categories=True and Categoricals are ordered ValueError Empty list of categoricals passed

Notes

To learn more about categories, see link

Examples

>>> from pandas.api.types import union_categoricals

If you want to combine categoricals that do not necessarily have the same categories, union_categoricals will combine a list-like of categoricals. The new categories will be the union of the categories being combined.

>>> a = pd.Categorical(["b", "c"])
>>> b = pd.Categorical(["a", "b"])
>>> union_categoricals([a, b])
[b, c, a, b]
Categories (3, object): [b, c, a]

By default, the resulting categories will be ordered as they appear in the categories of the data. If you want the categories to be lexsorted, use sort_categories=True argument.

>>> union_categoricals([a, b], sort_categories=True)
[b, c, a, b]
Categories (3, object): [a, b, c]

union_categoricals also works with the case of combining two categoricals of the same categories and order information (e.g. what you could also append for).

>>> a = pd.Categorical(["a", "b"], ordered=True)
>>> b = pd.Categorical(["a", "b", "a"], ordered=True)
>>> union_categoricals([a, b])
[a, b, a, b, a]
Categories (2, object): [a < b]

Raises TypeError because the categories are ordered and not identical.

>>> a = pd.Categorical(["a", "b"], ordered=True)
>>> b = pd.Categorical(["a", "b", "c"], ordered=True)
>>> union_categoricals([a, b])
TypeError: to union ordered Categoricals, all categories must be the same

New in version 0.20.0

Ordered categoricals with different categories or orderings can be combined by using the ignore_ordered=True argument.

>>> a = pd.Categorical(["a", "b", "c"], ordered=True)
>>> b = pd.Categorical(["c", "b", "a"], ordered=True)
>>> union_categoricals([a, b], ignore_order=True)
[a, b, c, c, b, a]
Categories (3, object): [a, b, c]

union_categoricals also works with a CategoricalIndex, or Series containing categorical data, but note that the resulting array will always be a plain Categorical

>>> a = pd.Series(["b", "c"], dtype='category')
>>> b = pd.Series(["a", "b"], dtype='category')
>>> union_categoricals([a, b])
[b, c, a, b]
Categories (3, object): [b, c, a]