pandas.api.types.union_categoricals¶
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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.
- Parameters
- to_unionlist-like
Categorical, CategoricalIndex, or Series with dtype=’category’.
- sort_categoriesbool, default False
If true, resulting categories will be lexsorted, otherwise they will be ordered as they appear in the data.
- ignore_orderbool, default False
If true, the ordered attribute of the Categoricals will be ignored. Results in an unordered categorical.
- Returns
- 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]) Traceback (most recent call last): ... 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']