详解 Pandas 的 reset_index 函数

Pandas 的 reset_index 函数主要分为 Series 对象的 reset_index() 和 DataFrame 对象的 reset_index()，功能是用来重置索引的，二者的用法有所不同。

一、Series 的 reset_index()

1. 数据准备

import pandas as pdexaminations = pd.DataFrame({"student_id": [1, 1, 1, 2, 1, 1, 13, 13, 13, 2, 1],"subject_name": ["Math", "Physics", "Programming", "Programming", "Physics", "Math", "Math", "Programming","Physics", "Math", "Math"]
})
print(df)

    student_id subject_name
0            1         Math
1            1      Physics
2            1  Programming
3            2  Programming
4            1      Physics
5            1         Math
6           13         Math
7           13  Programming
8           13      Physics
9            2         Math
10           1         Math

2. 方法声明

def reset_index(self,level: IndexLabel | None = None, # 复合索引时，指定删除哪一级，默认删除所有*,drop: bool = False, # 是否删除原索引列name: Level = lib.no_default, # 重命名 Series 数值列inplace: bool = False, # 是否在原对象上操作allow_duplicates: bool = False, # 是否允许重复) -> DataFrame | Series | None

3. 基本使用

# 1.将 DataFrame 聚合成 Series
res = examinations.groupby(by=["student_id", "subject_name"]).size()
print(res, type(res)) # MultiIndex(names=['student_id', 'subject_name'])

student_id  subject_name
1           Math            3Physics         2Programming     1
2           Math            1Programming     1
13          Math            1Physics         1Programming     1
dtype: int64 <class 'pandas.core.series.Series'>

# 2.重置索引，使用默认参数
print(res.reset_index(), type(res.reset_index()))

   student_id subject_name  0
0           1         Math  3
1           1      Physics  2
2           1  Programming  1
3           2         Math  1
4           2  Programming  1
5          13         Math  1
6          13      Physics  1
7          13  Programming  1<class 'pandas.core.frame.DataFrame'>

# 3.重置索引，并命名数值列
print(res.reset_index(name="attended_exams"))

   student_id subject_name  attended_exams
0           1         Math               3
1           1      Physics               2
2           1  Programming               1
3           2         Math               1
4           2  Programming               1
5          13         Math               1
6          13      Physics               1
7          13  Programming               1

# 4.重置索引，并删除原索引列
print(res.reset_index(drop=True), type(res.reset_index(drop=True)))

0    3
1    2
2    1
3    1
4    1
5    1
6    1
7    1
dtype: int64 <class 'pandas.core.series.Series'>

# 5.指定重置复合索引中的某一级索引
print(res.reset_index(level="student_id"))

              student_id  0
subject_name               
Math                   1  3
Physics                1  2
Programming            1  1
Math                   2  1
Programming            2  1
Math                  13  1
Physics               13  1
Programming           13  1

二、DataFrame 的 reset_index()

1. 数据准备

import pandas as pddf = pd.DataFrame(data={"Jan": [1, 2, 3],"Feb": [4, 5, 6],"Mar": [7, 8, 9]
}, index=["a", "b", "c"])print(df)

   Jan  Feb  Mar
a    1    4    7
b    2    5    8
c    3    6    9

2. 方法声明

def reset_index(self,level: IndexLabel | None = None, # 复合索引时，指定删除哪一级，默认删除所有*,drop: bool = False, # 是否删除原索引列inplace: bool = False, # 是否在原对象上操作col_level: Hashable = 0,col_fill: Hashable = "",allow_duplicates: bool | lib.NoDefault = lib.no_default,names: Hashable | Sequence[Hashable] | None = None, # 重命名原索引列，默认名为 Index) -> DataFrame | None

3. 基本使用

# 1.重置索引，使用默认参数
print(df.reset_index())

  index  Jan  Feb  Mar
0     a    1    4    7
1     b    2    5    8
2     c    3    6    9

# 2.重置索引，并删除原索引列
print(df.reset_index(drop=True))

   Jan  Feb  Mar
0    1    4    7
1    2    5    8
2    3    6    9

# 3.重置索引，并重命名原索引列
print(df.reset_index(names="A"))

   A  Jan  Feb  Mar
0  a    1    4    7
1  b    2    5    8
2  c    3    6    9