pandas.Series.cat¶

Series.cat()[source]¶

Accessor object for categorical properties of the Series values.

Be aware that assigning to categories is a inplace operation, while all methods return new categorical data per default (but can be called with inplace=True).

Parameters

dataSeries or CategoricalIndex

Examples

>>> s = pd.Series(list("abbccc")).astype("category")
>>> s
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (3, object): ['a', 'b', 'c']

>>> s.cat.categories
Index(['a', 'b', 'c'], dtype='object')

>>> s.cat.rename_categories(list("cba"))
0    c
1    b
2    b
3    a
4    a
5    a
dtype: category
Categories (3, object): ['c', 'b', 'a']

>>> s.cat.reorder_categories(list("cba"))
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (3, object): ['c', 'b', 'a']

>>> s.cat.add_categories(["d", "e"])
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (5, object): ['a', 'b', 'c', 'd', 'e']

>>> s.cat.remove_categories(["a", "c"])
0    NaN
1      b
2      b
3    NaN
4    NaN
5    NaN
dtype: category
Categories (1, object): ['b']

>>> s1 = s.cat.add_categories(["d", "e"])
>>> s1.cat.remove_unused_categories()
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (3, object): ['a', 'b', 'c']

>>> s.cat.set_categories(list("abcde"))
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (5, object): ['a', 'b', 'c', 'd', 'e']

>>> s.cat.as_ordered()
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (3, object): ['a' < 'b' < 'c']

>>> s.cat.as_unordered()
0    a
1    b
2    b
3    c
4    c
5    c
dtype: category
Categories (3, object): ['a', 'b', 'c']

pandas.Series.bool pandas.Series.clip