rename multiple columns of pandas dataframe based on condition

Question

I have a df in which I need to rename 40 column names to empty string. this can be achieved by using .rename(), but I need to provide all the column names in dict, which needs to be renamed. I was searching for some better way to rename columns by some pattern matching. wherever it finds NULL/UNNAMED in column name, replace that with empty string.

df1: original df (In actual df, i have around 20 columns as NULL1-NULL20 and 20 columns as UNNAMED1-UNNAMED20)

    NULL1   NULL2   C1  C2  UNNAMED1    UNNAMED2
0   1   11  21  31  41  51
1   2   22  22  32  42  52
2   3   33  23  33  43  53
3   4   44  24  34  44  54

desired output df:

            C1  C2      
0   1   11  21  31  41  51
1   2   22  22  32  42  52
2   3   33  23  33  43  53
3   4   44  24  34  44  54

This can be achieved by

df.rename(columns={'NULL1':'', 'NULL2':'', 'UNNAMED1':'', 'UNNAMED2':''}, inplace=True)

But I dont want to create the long dictionary of 40 elements

Answer 1

If you want to stick with rename:

def renaming_fun(x):
    if "NULL" in x or "UNNAMED" in x:
        return "" # or None
    return x

df = df.rename(columns=renaming_fun)

It can be handy if the mapping function gets more complex. Otherwise, list comprehensions will do:

df.columns = [renaming_fun(col) for col in cols]

Another possibility:

df.columns = map(renaming_fun, df.columns)

But as it was already mentioned, renaming with empty strings is not something you would usually do.