十分钟搞定 Pandas - 十、 Categorical

从 0.15 版本开始，pandas 可以在DataFrame中支持 Categorical 类型的数据，详细 介绍参看：Categorical 简介和API documentation。

In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

1、 将原始的grade转换为 Categorical 数据类型：

In [128]: df["grade"] = df["raw_grade"].astype("category")
In [129]: df["grade"]
Out[129]: 
0    a
1    b
2    b
3    a
4    a
5    e
Name: grade, dtype: category
Categories (3, object): [a, b, e]

2、 将 Categorical 类型数据重命名为更有意义的名称：

In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]

3、 对类别进行重新排序，增加缺失的类别：

In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
In [132]: df["grade"]
Out[132]: 
0    very good
1         good
2         good
3    very good
4    very good
5     very bad
Name: grade, dtype: category
Categories (5, object): [very bad, bad, medium, good, very good]

4、 排序是按照 Categorical 的顺序进行的而不是按照字典顺序进行：

In [133]: df.sort_values(by="grade")
Out[133]: 
   id raw_grade      grade
5   6         e   very bad
1   2         b       good
2   3         b       good
0   1         a  very good
3   4         a  very good
4   5         a  very good

5、 对 Categorical 列进行排序时存在空的类别：

In [134]: df.groupby("grade").size()
Out[134]: 
grade
very bad     1
bad          0
medium       0
good         2
very good    3
dtype: int64