Select dataframes header and edit Pandas

Question

I'm currently working with r number of dataframes with identical headers. I would like to select all except one (eg. except 'Apple') and change them by adding the dataframe number next to them.

In this case, all my r dataframes have:

                Apple             Orange            Pear       ...
0                  70                  5               3       ...
1                  15                  1               4       ...
2                  17                  1               6       ...
3                  26                  2               7       ...
4                  12                  1               2       ...
5                  16                  1               0       ...
6                  13                  1               1       ...

I have tried:

for r in range(len(csv_files)):
     dfs[r].columns = [col_name + ' R{}'.format(r+1) for col_name in dfs[r].columns]

For dataframe 1,

             Apple R1          Orange R1         Pear R1       ...
0                  70                  5               3       ...
1                  15                  1               4       ...
2                  17                  1               6       ...
3                  26                  2               7       ...
4                  12                  1               2       ...
5                  16                  1               0       ...
6                  13                  1               1       ...

For dataframe 2,

             Apple R2          Orange R2         Pear R2       ...
0                  70                  5               3       ...
1                  15                  1               4       ...
2                  17                  1               6       ...
3                  26                  2               7       ...
4                  12                  1               2       ...
5                  16                  1               0       ...
6                  13                  1               1       ...

For dataframe 3,

             Apple R3          Orange R3         Pear R3       ...
0                  70                  5               3       ...
1                  15                  1               4       ...
2                  17                  1               6       ...
3                  26                  2               7       ...
4                  12                  1               2       ...
5                  16                  1               0       ...
6                  13                  1               1       ...

Which gives me almost what I want but I can't find how to not include ['Apple'] in this action like for example on dataframe 3:

                Apple          Orange R3         Pear R3       ...
0                  70                  5               3       ...
1                  15                  1               4       ...
2                  17                  1               6       ...
3                  26                  2               7       ...
4                  12                  1               2       ...
5                  16                  1               0       ...
6                  13                  1               1       ...

Thank you :)

jezrael · Accepted Answer · 2017-10-19 06:06:17Z

1

If same positions of columns and Apple is always first:

for r in range(len(csv_files)):
     dfs[r].columns = ['Apple'] + 
                      [col_name + ' R{}'.format(r+1) for col_name in dfs[r].columns[1:]]

More general solution without define position of column:

for r in range(len(csv_files)):
     dfs[r].columns = [c if c == 'Apple' else c + ' R{}'.format(r+1) for c in dfs[r].columns]

edited Oct 19, 2017 at 6:06

answered Oct 19, 2017 at 5:31

jezrael

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Zero · Accepted Answer · 2017-10-19 06:04:52Z

0

You could use

for r in range(len(csv_files)):
     newcols = dfs[r].columns[1:] + ' R{}'.format(r)
     dfs[r].columns = ['Apple'] + newcols.tolist()

Or, use map like

for r in range(len(csv_files)):
     newcols = dfs[r].columns[1:].map(lambda x: '{} R{}'.format(x, r))
     dfs[r].columns = ['Apple'] + newcols.tolist()

answered Oct 19, 2017 at 6:04

Zero

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