add_suffix到基于位置的列名

Question

I have a dataset where I want to add a suffix to column names based on their positions. For ex- 1st to 4th columns should be named 'abc_1', then 5th to 8th columns as 'abc_2' and so on.

I have tried using dataframe.rename
but it is a time consuming process. What would be the most efficient way to achieve this?

Answer 1

I think here is good choice create MultiIndex for avoid duplicated columns names - create first level by floor divide by 4 and add prefix by f-string s:

np.random.seed(123)

df = pd.DataFrame(np.random.randint(10, size=(5, 10)))

df.columns = [[f'abc_{i+1}' for i in df.columns // 4], df.columns]
print (df)
  abc_1          abc_2          abc_3   
      0  1  2  3     4  5  6  7     8  9
0     2  2  6  1     3  9  6  1     0  1
1     9  0  0  9     3  4  0  0     4  1
2     7  3  2  4     7  2  4  8     0  7
3     9  3  4  6     1  5  6  2     1  8
4     3  5  0  2     6  2  4  4     6  3

More general solution if no RangeIndex in column names:

cols = [f'abc_{i+1}' for i in np.arange(len(df.columns)) // 4]
df.columns = [cols, df.columns]
print (df)
  abc_1          abc_2          abc_3   
      0  1  2  3     4  5  6  7     8  9
0     2  2  6  1     3  9  6  1     0  1
1     9  0  0  9     3  4  0  0     4  1
2     7  3  2  4     7  2  4  8     0  7
3     9  3  4  6     1  5  6  2     1  8
4     3  5  0  2     6  2  4  4     6  3

Also is possible specify MultiIndex levels names by MultiIndex.from_arrays :

df.columns = pd.MultiIndex.from_arrays([cols, df.columns], names=('level0','level1'))
print (df)
level0 abc_1          abc_2          abc_3   
level1     0  1  2  3     4  5  6  7     8  9
0          2  2  6  1     3  9  6  1     0  1
1          9  0  0  9     3  4  0  0     4  1
2          7  3  2  4     7  2  4  8     0  7
3          9  3  4  6     1  5  6  2     1  8
4          3  5  0  2     6  2  4  4     6  3

Then is possible select each level by xs :

print (df.xs('abc_2', axis=1))
   4  5  6  7
0  3  9  6  1
1  3  4  0  0
2  7  2  4  8
3  1  5  6  2
4  6  2  4  4