python - 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

Is it possible, but be carefull - then if need select one empty column get all empty columns, because duplicated columns names:

print (df[''])

0  1  11  41  51
1  2  22  42  52
2  3  33  43  53
3  4  44  44  54

Use startswith for get all columns by tuples in list comprehension:

df.columns = ['' if c.startswith(('NULL','UNNAMED')) else c for c in df.columns]

Your solution should be changed:

d = dict.fromkeys(df.columns[df.columns.str.startswith(('NULL','UNNAMED'))], '')
print (d)
{'NULL1': '', 'NULL2': '', 'UNNAMED1': '', 'UNNAMED2': ''}
df = df.rename(columns=d)