Python 在熊猫中连接两个数据帧的行
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Concatenate rows of two dataframes in pandas
提问by user1140126
I need to concatenate two dataframes df_aanddf_bhaving equal number of rows (nRow) one after another without any consideration of keys. This function is similar to cbindin R programming language. The number of columns in each dataframe may be different.
我需要一个接一个地连接两个数据帧df_a并df_b具有相同数量的行 ( nRow) 而不考虑任何键。此功能类似于cbindin R programming language。每个数据框中的列数可能不同。
The resultant dataframe will have the same number of rows nRowand number of columns equal to the sum of number of columns in both the dataframes. In othe words, this is a blind columnar concatenation of two dataframes.
生成的数据帧将具有相同的行nRow数和列数，等于两个数据帧中列数的总和。换句话说，这是两个数据帧的盲柱状串联。
import pandas as pd
dict_data = {'Treatment': ['C', 'C', 'C'], 'Biorep': ['A', 'A', 'A'], 'Techrep': [1, 1, 1], 'AAseq': ['ELVISLIVES', 'ELVISLIVES', 'ELVISLIVES'], 'mz':[500.0, 500.5, 501.0]}
df_a = pd.DataFrame(dict_data)
dict_data = {'Treatment1': ['C', 'C', 'C'], 'Biorep1': ['A', 'A', 'A'], 'Techrep1': [1, 1, 1], 'AAseq1': ['ELVISLIVES', 'ELVISLIVES', 'ELVISLIVES'], 'inte1':[1100.0, 1050.0, 1010.0]}
df_b = pd.DataFrame(dict_data)
采纳答案by EdChum
call concatand pass param axis=1to concatenate column-wise:
调用concat并传递参数axis=1以按列连接：
In [5]:
pd.concat([df_a,df_b], axis=1)
Out[5]:
AAseq Biorep Techrep Treatment mz AAseq1 Biorep1 Techrep1 \
0 ELVISLIVES A 1 C 500.0 ELVISLIVES A 1
1 ELVISLIVES A 1 C 500.5 ELVISLIVES A 1
2 ELVISLIVES A 1 C 501.0 ELVISLIVES A 1
Treatment1 inte1
0 C 1100
1 C 1050
2 C 1010
There is a useful guide to the various methods of merging, joining and concatenatingonline.
有一个关于在线合并、加入和连接的各种方法的有用指南。
For example, as you have no clashing columns you can mergeand use the indices as they have the same number of rows:
例如，由于您没有冲突的列，您可以merge使用索引，因为它们具有相同的行数：
In [6]:
df_a.merge(df_b, left_index=True, right_index=True)
Out[6]:
AAseq Biorep Techrep Treatment mz AAseq1 Biorep1 Techrep1 \
0 ELVISLIVES A 1 C 500.0 ELVISLIVES A 1
1 ELVISLIVES A 1 C 500.5 ELVISLIVES A 1
2 ELVISLIVES A 1 C 501.0 ELVISLIVES A 1
Treatment1 inte1
0 C 1100
1 C 1050
2 C 1010
And for the same reasons as above a simple joinworks too:
出于与上述相同的原因，一个简单的join工作也可以：
In [7]:
df_a.join(df_b)
Out[7]:
AAseq Biorep Techrep Treatment mz AAseq1 Biorep1 Techrep1 \
0 ELVISLIVES A 1 C 500.0 ELVISLIVES A 1
1 ELVISLIVES A 1 C 500.5 ELVISLIVES A 1
2 ELVISLIVES A 1 C 501.0 ELVISLIVES A 1
Treatment1 inte1
0 C 1100
1 C 1050
2 C 1010
回答by Yury Wallet
Thanks to @EdChum I was struggling with same problem especially when indexes do not match. Unfortunatly in pandas guide this case is missed (when you for example delete some rows)
感谢@EdChum，我一直在努力解决同样的问题，尤其是当索引不匹配时。不幸的是，在熊猫指南中，这种情况被遗漏了（例如，当您删除一些行时）
import pandas as pd
t=pd.DataFrame()
t['a']=[1,2,3,4]
t=t.loc[t['a']>1] #now index starts from 1
u=pd.DataFrame()
u['b']=[1,2,3] #index starts from 0
#option 1
#keep index of t
u.index = t.index
#option 2
#index of t starts from 0
t.reset_index(drop=True, inplace=True)
#now concat will keep number of rows
r=pd.concat([t,u], axis=1)
