表1

image.png

表2

image.png

把两个表横向排列（通过axis=1实现）

import pandas as pd

df_1 = pd.read_excel(io='exls/columns.xlsx', sheet_name='Sheet1')
df_2 = pd.read_excel(io='exls/columns.xlsx', sheet_name='Sheet2')
# 把两个表横向排列（通过axis=1实现）
df_new = pd.concat([df_1, df_2], axis=1).reset_index(drop=True)
print(df_new)

output

   ID NAME  SCORES  ID NAME  SCORES
0   1   S1      71  11  S11      61
1   2   S2      72  12  S12      62
2   3   S3      73  13  S13      63
3   4   S4      74  14  S14      64
4   5   S5      75  15  S15      65
5   6   S6      76  16  S16      66
6   7   S7      77  17  S17      67
7   8   S8      78  18  S18      68
8   9   S9      79  19  S19      69
9  10  S10      80  20  S20      70

实际工作中意义不大

添加一列

df_new = pd.concat([df_1, df_2]).reset_index(drop=True)
# 添加一列
df_new['AGE'] = 25

output

    ID NAME  SCORES  AGE
0    1   S1      71   25
1    2   S2      72   25
2    3   S3      73   25
3    4   S4      74   25
4    5   S5      75   25
5    6   S6      76   25
6    7   S7      77   25
7    8   S8      78   25
8    9   S9      79   25
9   10  S10      80   25
10  11  S11      61   25
11  12  S12      62   25
12  13  S13      63   25
13  14  S14      64   25
14  15  S15      65   25
15  16  S16      66   25
16  17  S17      67   25
17  18  S18      68   25
18  19  S19      69   25
19  20  S20      70   25

添加一个序列

import numpy as np

# 添加一个序列
df_new['Series'] = np.arange(0, len(df_new))

output

    ID NAME  SCORES  AGE  Series
0    1   S1      71   25       0
1    2   S2      72   25       1
2    3   S3      73   25       2
3    4   S4      74   25       3
4    5   S5      75   25       4
5    6   S6      76   25       5
6    7   S7      77   25       6
7    8   S8      78   25       7
8    9   S9      79   25       8
9   10  S10      80   25       9
10  11  S11      61   25      10
11  12  S12      62   25      11
12  13  S13      63   25      12
13  14  S14      64   25      13
14  15  S15      65   25      14
15  16  S16      66   25      15
16  17  S17      67   25      16
17  18  S18      68   25      17
18  19  S19      69   25      18
19  20  S20      70   25      19

删除列

df_new.drop(columns=['AGE', 'SCORES'], inplace=True)

output

    ID NAME  Series
0    1   S1       0
1    2   S2       1
2    3   S3       2
3    4   S4       3
4    5   S5       4
5    6   S6       5
6    7   S7       6
7    8   S8       7
8    9   S9       8
9   10  S10       9
10  11  S11      10
11  12  S12      11
12  13  S13      12
13  14  S14      13
14  15  S15      14
15  16  S16      15
16  17  S17      16
17  18  S18      17
18  19  S19      18
19  20  S20      19

插入列

df_new.insert(1, column='INSERT', value=np.repeat('insert', len(df_new)))

output

    ID  INSERT NAME  Series
0    1  insert   S1       0
1    2  insert   S2       1
2    3  insert   S3       2
3    4  insert   S4       3
4    5  insert   S5       4
5    6  insert   S6       5
6    7  insert   S7       6
7    8  insert   S8       7
8    9  insert   S9       8
9   10  insert  S10       9
10  11  insert  S11      10
11  12  insert  S12      11
12  13  insert  S13      12
13  14  insert  S14      13
14  15  insert  S15      14
15  16  insert  S16      15
16  17  insert  S17      16
17  18  insert  S18      17
18  19  insert  S19      18
19  20  insert  S20      19

修改列名

df_new.rename(columns={'INSERT': 'Insert', 'NAME': 'Name'}, inplace=True)

output

    ID  Insert Name  Series
0    1  insert   S1       0
1    2  insert   S2       1
2    3  insert   S3       2