# pandas.api.types.union_categoricals

`pandas.api.types.``union_categoricals`(to_union, sort_categories=False, ignore_order=False)

  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]

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