6.Pandas的數據合併

0 引言

Pandas對多個表格可以進行合併操作，主要分爲縱向合併和橫向合併。

1 縱向合併

import pandas as pd
import numpy as np

生成多個DataFrame表格

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(df2)
print(df3)

   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

調用 .concat 函數，axis=0表示列，即進行縱向合併

# 縱向合併
df4 = pd.concat([df1,df2,df3],axis=0)
df4

	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

.concat 函數有個參數可以不考慮原來的索引 index

df4 = pd.concat([df1,df2,df3],axis=0,ignore_index=True) # 不考慮原來的index
df4

	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

2 橫向合併

調用 .concat 函數，參數axis=1表示行，也就是橫向合併

# 橫向合併
df5 = pd.concat([df1,df2,df3],axis=1)
df5

	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

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(df2)

   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

.concat 函數中參數join設置爲’outer’ 並且 ignore_index設置爲True可以把表格缺少部分填充爲NaN

df6 = pd.concat([df1,df2],join='outer',ignore_index=True) # 合併兩個表，缺少的部分填充 NaN
df6

D:\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=True'.

To retain the current behavior and silence the warning, pass sort=False

  """Entry point for launching an IPython kernel.

	a	b	c	d	e	f
0	0	1.0	2	NaN	NaN	3.0
1	4	5.0	6	NaN	NaN	7.0
2	8	9.0	10	NaN	NaN	11.0
3	12	NaN	13	14.0	15.0	NaN
4	16	NaN	17	18.0	19.0	NaN
5	20	NaN	21	22.0	23.0	NaN

.concat 函數也可以實現只合並兩個表相同索引 index 的部分

df6 = pd.concat([df1,df2],join='inner',ignore_index=True) # 只合並兩個表相同的index部分,缺少的部分去掉
df6

	a	c
0	0	2
1	4	6
2	8	10
3	12	13
4	16	17
5	20	21

df1 = pd.DataFrame(np.arange(12).reshape((3,4)),columns=['a','b','c','f'])
df2 = pd.DataFrame(np.arange(12,24).reshape((4,3)),columns=['a','c','d'])
print(df1)
print(df2)

   a  b   c   f
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
    a   c   d
0  12  13  14
1  15  16  17
2  18  19  20
3  21  22  23

橫向合併，並可以指定使用某個表格的索引 index

df8 = pd.concat([df1,df2],axis=1,join_axes=[df1.index]) # 橫向合併，index使用df1的index
df8

	a	b	c	f	a	c	d
0	0	1	2	3	12	13	14
1	4	5	6	7	15	16	17
2	8	9	10	11	18	19	20

df8 = pd.concat([df1,df2],axis=1) # 橫向合併
df8

	a	b	c	f	a	c	d
0	0.0	1.0	2.0	3.0	12	13	14
1	4.0	5.0	6.0	7.0	15	16	17
2	8.0	9.0	10.0	11.0	18	19	20
3	NaN	NaN	NaN	NaN	21	22	23