pandas.CategoricalIndex — pandas 2.2.3 documentation (original) (raw)
class pandas.CategoricalIndex(data=None, categories=None, ordered=None, dtype=None, copy=False, name=None)[source]#
Index based on an underlying Categorical.
CategoricalIndex, like Categorical, can only take on a limited, and usually fixed, number of possible values (categories). Also, like Categorical, it might have an order, but numerical operations (additions, divisions, …) are not possible.
Parameters:
dataarray-like (1-dimensional)
The values of the categorical. If categories are given, values not incategories will be replaced with NaN.
categoriesindex-like, optional
The categories for the categorical. Items need to be unique. If the categories are not given here (and also not in dtype), they will be inferred from the data.
orderedbool, optional
Whether or not this categorical is treated as an ordered categorical. If not given here or in dtype, the resulting categorical will be unordered.
dtypeCategoricalDtype or “category”, optional
If CategoricalDtype, cannot be used together withcategories or ordered.
copybool, default False
Make a copy of input ndarray.
nameobject, optional
Name to be stored in the index.
Attributes
| codes | The category codes of this categorical index. |
|---|---|
| categories | The categories of this categorical. |
| ordered | Whether the categories have an ordered relationship. |
Methods
| rename_categories(*args, **kwargs) | Rename categories. |
|---|---|
| reorder_categories(*args, **kwargs) | Reorder categories as specified in new_categories. |
| add_categories(*args, **kwargs) | Add new categories. |
| remove_categories(*args, **kwargs) | Remove the specified categories. |
| remove_unused_categories(*args, **kwargs) | Remove categories which are not used. |
| set_categories(*args, **kwargs) | Set the categories to the specified new categories. |
| as_ordered(*args, **kwargs) | Set the Categorical to be ordered. |
| as_unordered(*args, **kwargs) | Set the Categorical to be unordered. |
| map(mapper[, na_action]) | Map values using input an input mapping or function. |
Raises:
ValueError
If the categories do not validate.
TypeError
If an explicit ordered=True is given but no categories and thevalues are not sortable.
Notes
See the user guidefor more.
Examples
pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"]) CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'], categories=['a', 'b', 'c'], ordered=False, dtype='category')
CategoricalIndex can also be instantiated from a Categorical:
c = pd.Categorical(["a", "b", "c", "a", "b", "c"]) pd.CategoricalIndex(c) CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'], categories=['a', 'b', 'c'], ordered=False, dtype='category')
Ordered CategoricalIndex can have a min and max value.
ci = pd.CategoricalIndex( ... ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"] ... ) ci CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'], categories=['c', 'b', 'a'], ordered=True, dtype='category') ci.min() 'c'