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。

  1. 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：

  1. In [128]: df["grade"] = df["raw_grade"].astype("category")
  3. In [129]: df["grade"]
  4. Out[129]: 
  5. 0    a
  6. 1    b
  7. 2    b
  8. 3    a
  9. 4    a
  10. 5    e
  11. Name: grade, dtype: category
  12. Categories (3, object): [a, b, e]

2、 将 Categorical 类型数据重命名为更有意义的名称：

  1. 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：

  1. In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
  3. In [132]: df["grade"]
  4. Out[132]: 
  5. 0    very good
  6. 1         good
  7. 2         good
  8. 3    very good
  9. 4    very good
  10. 5     very bad
  11. Name: grade, dtype: category
  12. 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：

  1. In [133]: df.sort_values(by="grade")
  2. Out[133]: 
  3.    id raw_grade      grade
  4. 5   6         e   very bad
  5. 1   2         b       good
  6. 2   3         b       good
  7. 0   1         a  very good
  8. 3   4         a  very good
  9. 4   5         a  very good

5、 对 Categorical 列进行排序时存在空的类别：

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