The categories of this categorical
The codes (integer positions, which point to the categories) of this categorical, read only.
Whether or not this Categorical is ordered.
The instance of CategoricalDtype
storing the categories
and ordered
.
:None:None:`Categoricals` can only take on only a limited, and usually fixed, number of possible values (:None:None:`categories`). In contrast to statistical categorical variables, a Categorical
might have an order, but numerical operations (additions, divisions, ...) are not possible.
All values of the Categorical
are either in :None:None:`categories` or :None:None:`np.nan`. Assigning values outside of :None:None:`categories` will raise a :None:None:`ValueError`. Order is defined by the order of the :None:None:`categories`, not lexical order of the values.
See the :None:None:`user guide
<https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>` for more.
The values of the categorical. If categories are given, values not in categories will be replaced with NaN.
The unique categories for this categorical. If not given, the categories are assumed to be the unique values of :None:None:`values` (sorted, if possible, otherwise in the order in which they appear).
Whether or not this categorical is treated as a ordered categorical. If True, the resulting categorical will be ordered. An ordered categorical respects, when sorted, the order of its :None:None:`categories` attribute (which in turn is the :None:None:`categories` argument, if provided).
An instance of CategoricalDtype
to use for this categorical.
If the categories do not validate.
If an explicit ordered=True
is given but no :None:None:`categories` and the :None:None:`values` are not sortable.
Represent a categorical variable in classic R / S-plus fashion.
CategoricalDtype
Type for categorical data.
CategoricalIndex
An Index with an underlying Categorical
.
>>> pd.Categorical([1, 2, 3, 1, 2, 3]) [1, 2, 3, 1, 2, 3] Categories (3, int64): [1, 2, 3]This example is valid syntax, but we were not able to check execution
>>> pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c']) ['a', 'b', 'c', 'a', 'b', 'c'] Categories (3, object): ['a', 'b', 'c']
Missing values are not included as a category.
This example is valid syntax, but we were not able to check execution>>> c = pd.Categorical([1, 2, 3, 1, 2, 3, np.nan])
... c [1, 2, 3, 1, 2, 3, NaN] Categories (3, int64): [1, 2, 3]
However, their presence is indicated in the :None:None:`codes` attribute by code :None:None:`-1`.
>>> c.codes array([ 0, 1, 2, 0, 1, 2, -1], dtype=int8)
Ordered :None:None:`Categoricals` can be sorted according to the custom order of the categories and can have a min and max value.
>>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,This example is valid syntax, but we were not able to check execution
... categories=['c', 'b', 'a'])
... c ['a', 'b', 'c', 'a', 'b', 'c'] Categories (3, object): ['c' < 'b' < 'a']
>>> c.min() 'c'See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.arrays.categorical.Categorical.mode
pandas.core.dtypes.concat.union_categoricals
pandas.core.arrays.categorical.Categorical
pandas.core.dtypes.dtypes.CategoricalDtype
pandas.core.indexes.category.CategoricalIndex
pandas.core.arrays.categorical.Categorical.min
pandas.core.frame.DataFrame.memory_usage
pandas.core.reshape.tile.cut
pandas.core.arrays.categorical.Categorical.max
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