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']