合并两个csv文件并使用python提取有用的信息

Question

I have two .csv files that look like following:

file_1:

id  a b c
10  1 2 3
11  2 3 4

file_2:

id   d e 
10   2 3
11   2 3
12   2 3

My expected output is:

id  a b c d e
10  1 2 3 2 3
11  2 3 4 2 3

I wish to merge these two files by comparing the id number. If the id number matched, the id and the corresponding rows need to be merged and extracted. If not matched, the corresponding id number's row is ignored. My code look like this:

import pandas as pd
s1=pd.read_csv("file_1.csv")
s2=pd.read_csv("file_2.csv")
if s1['id']==s2['id']:
    merged=s1.merge(s2, on="id", how="outer")
else:
    pass
merged
merged.to_csv("output.csv")

After running this coding, I cannot get my expected output. Anyone can help me? Thanks.

Answer 1

Try using pd.DataFrame.merge :

print(file_1.merge(file_2, on='id'))

Output:

   a  b  c  id  d  e
0  1  2  3  10  2  3
1  2  3  4  11  2  3

If you care about the order of the columns do:

print(file_1.merge(file_2, on='id')[['id', 'a', 'b', 'c', 'd', 'e']])

Output:

   id  a  b  c  d  e
0  10  1  2  3  2  3
1  11  2  3  4  2  3

Answer 2

You are not merging the dataframes properly. Try this:

import pandas as pd
s1=pd.read_csv("file_1.csv")
s2=pd.read_csv("file_2.csv")

merged=s1.merge(s2, on="id")
# Set the index back to id
merged = merged.set_index("id")
merged.to_csv("output.csv")

Answer 3

pandas.join or pandas.merge should work here:

import pandas as pd
s1=pd.DataFrame({'id':[10, 11], 'a':[1,2], 'b':[2,3], 'c':[3,4]})
s2=pd.DataFrame({'id':[10, 11, 12], 'd':[2,2, 2], 'e':[3,3, 3]})
merged = s1.merge(s2, on='id', how='inner')
# join works as well
# merged = s1.join(s2.set_index('id'), on='id', how='inner')
merged

The output:

    id  a   b   c   d   e
0   10  1   2   3   2   3
1   11  2   3   4   2   3

Answer 4

As you didn't have mentioned weather your id is index name or a column name, so i took as index.

import pandas as pd
s1=pd.DataFrame({'id':[10, 11], 'a':[1,2], 'b':[2,3], 'c':[3,4]})
s2=pd.DataFrame({'id':[10, 11, 12], 'd':[2,2, 2], 'e':[3,3, 3]})
merg = pd.merge(left=s1,right=s2,on='id').set_index('id')
print merg

Here is your Output--

    a  b  c  d  e
id               
10  1  2  3  2  3
11  2  3  4  2  3