将多个 csv 文件按列与 header 作为文件名组合

Question

I couldn't find the exact code on the platform which is why posting it for suggestions.

I have multiple CSV files (about 100) with the same data formats and header names.

 ,Mean,SD
1,96.432,13.899
2,96.432,13.899
3,96.432,13.899
4,96.432,13.899
5,96.432,13.899

I want to append all files column-wise so that I have them in one file. Also, the header of each data should be the file name so that I can follow which data belongs to which file. For example, above mean, sd--> another row of the file name.

Please guide me, as I am new to Python.

Thank you and regards, Khan.

Answer 1

The question was vague about formatting, so this may vary from the desired output.

filenames = [...]
dfs = []
for f in filenames:
    newdf = pd.read_csv(f)
    newdf.rename(columns={'Mean': 'Mean ' + f, 'SD': 'SD ' + f})
    dfs.append(newdf)
df = pd.concat(dfs)

Answer 2

You can use pandas to read and concatenate the files, together with glob and a dictionary comprehension:

from glob import glob
import pandas as pd

files = glob('/tmp/*.csv') # change the location/pattern accordingly
# if you have a list of files, use: files=['file1.csv', 'file2.csv'...]

df = pd.concat({fname.rsplit('/')[-1]: pd.read_csv(fname, index_col=0)
                for fname in files}, axis=1)

output:

>>> print(df)
  file1.csv         file2.csv        
       Mean      SD      Mean      SD
                                     
1    96.432  13.899    96.432  13.899
2    96.432  13.899    96.432  13.899
3    96.432  13.899    96.432  13.899
4    96.432  13.899    96.432  13.899
5    96.432  13.899    96.432  13.899

Saving to new file:

df.to_csv('concatenated_file.csv')

output:

,file1.csv,file1.csv,file2.csv,file2.csv
,Mean,SD,Mean,SD
 ,,,,
1,96.432,13.899,96.432,13.899
2,96.432,13.899,96.432,13.899
3,96.432,13.899,96.432,13.899
4,96.432,13.899,96.432,13.899
5,96.432,13.899,96.432,13.899

Answer 3

you can use pandas to work with

In [3]: import pandas  

                                                                                                                           

In [4]: import pandas as pd                                                                                                                           

In [13]: ls                                                                                                                                           
abc1.csv  abc.csv

In [14]: df = pd.read_csv('abc.csv')                                                                                                                  

In [15]: df1 = pd.read_csv('abc1.csv')                                                                                                                

In [16]: df                                                                                                                                           
Out[16]: 
        Mean      SD
0  1  96.432  13.899
1  2  96.432  13.899


In [16]: df                                                                                                                                           
Out[16]: 
        Mean      SD
0  1  96.432  13.899
1  2  96.432  13.899

In [17]: df1                                                                                                                                          
Out[17]: 
        Mean      SD
0  3  96.432  13.899
1  4  96.432  13.899
2  5  96.432  13.899

In [18]: df.append(df1)                                                                                                                               
Out[18]: 
        Mean      SD
0  1  96.432  13.899
1  2  96.432  13.899
0  3  96.432  13.899
1  4  96.432  13.899
2  5  96.432  13.899

In [19]: ds = df.append(df1)                                                                                                                          

In [20]: ds                                                                                                                                           
Out[20]: 
        Mean      SD
0  1  96.432  13.899
1  2  96.432  13.899
0  3  96.432  13.899
1  4  96.432  13.899
2  5  96.432  13.899



In [21]: ds.to_csv('file1.csv')  


In [23]: ls                                                                                                                                           
abc1.csv  abc.csv  file1.csv