I have a pandas dataframe like:
_Ab _Bc C D _Ef _Fg
0 a 4 7 1 5 a
1 b 5 8 3 3 a
2 c 4 9 5 6 a
3 d 5 4 7 9 b
4 e 5 2 1 2 b
5 f 4 3 0 4 b
I want to remove the starting _ present in some of the column names. Obviously I can replace them one at a time but that would be very inefficient as I have a lot of columns. So how can I do this efficiently?
Use list comprehension with if-else and startswith:
df.columns = [x.lstrip('_') if x.startswith('_') else x for x in df.columns]
Another solutions try left strip _ from all columns:
df.columns = [x.lstrip('_') for x in df.columns]
Or with Index.str.lstrip:
df.columns = df.columns.str.lstrip('_')
Or with rename:
df = df.rename(columns=lambda x: x.lstrip('_'))
print (df)
Ab Bc C D Ef Fg
0 a 4 7 1 5 a
1 b 5 8 3 3 a
2 c 4 9 5 6 a
3 d 5 4 7 9 b
4 e 5 2 1 2 b
5 f 4 3 0 4 b
df.columns = df.columns.lstrip('_') instead df.columns = df.columns.str.lstrip('_') - missing str?
df.columns.lstrip(.) write df.columns.str.lstrip(.) (full stop in method invocation standing in for assorted parameters)df.columns = [x.lstrip('_') if x.startswith('_') else x for x in df.columns] ?This should work for you:
df.columns = [c.lstrip("_") for c in df.columns]
