Pandas的合并

最新推荐文章于 2024-09-02 16:47:03 发布

原创最新推荐文章于 2024-09-02 16:47:03 发布 · 123 阅读

0 ·

CC 4.0 BY-SA版权

Pandas数据分析库专栏收录该内容

8 篇文章

订阅专栏

import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'D'])
df2 = pd.DataFrame(np.arange(12, 24).reshape(3, 4), columns=['A', 'B', 'C', 'D'])
df3 = pd.DataFrame(np.arange(24, 36).reshape(3, 4), columns=['A', 'B', 'C', 'D'])
print(df1)
print()
print(df2)
print()
print(df3)
print()
df4 = pd.concat([df1, df2, df3], axis=0)  # 纵向合并
print(df4)
print()
df5 = pd.concat([df1, df2, df3], axis=0, ignore_index=True)  # 不考虑原来的index
print(df5)
print()
df6 = pd.concat([df1, df2, df3], axis=1)  # 横向合并
print(df6)
print()
df1 = pd.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'F'])
df2 = pd.DataFrame(np.arange(12, 24).reshape(3, 4), columns=['A', 'C', 'D', 'E'])
print(df1)
print()
print(df2)
print()
# 合并两个表，缺少的部分填充NaN
df7 = pd.concat([df1, df2], join='outer', ignore_index=True)
print(df7)
print()
# 合并两个表共同部分
df8 = pd.concat([df1, df2], join='inner', ignore_index=True)
print(df8

   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

    A   B   C   D
0  12  13  14  15
1  16  17  18  19
2  20  21  22  23

    A   B   C   D
0  24  25  26  27
1  28  29  30  31
2  32  33  34  35

    A   B   C   D
0   0   1   2   3
1   4   5   6   7
2   8   9  10  11
0  12  13  14  15
1  16  17  18  19
2  20  21  22  23
0  24  25  26  27
1  28  29  30  31
2  32  33  34  35

    A   B   C   D
0   0   1   2   3
1   4   5   6   7
2   8   9  10  11
3  12  13  14  15
4  16  17  18  19
5  20  21  22  23
6  24  25  26  27
7  28  29  30  31
8  32  33  34  35

   A  B   C   D   A   B   C   D   A   B   C   D
0  0  1   2   3  12  13  14  15  24  25  26  27
1  4  5   6   7  16  17  18  19  28  29  30  31
2  8  9  10  11  20  21  22  23  32  33  34  35

   A  B   C   F
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

    A   C   D   E
0  12  13  14  15
1  16  17  18  19
2  20  21  22  23

    A    B   C     F     D     E
0   0  1.0   2   3.0   NaN   NaN
1   4  5.0   6   7.0   NaN   NaN
2   8  9.0  10  11.0   NaN   NaN
3  12  NaN  13   NaN  14.0  15.0
4  16  NaN  17   NaN  18.0  19.0
5  20  NaN  21   NaN  22.0  23.0

    A   C
0   0   2
1   4   6
2   8  10
3  12  13
4  16  17
5  20  21