Run same operation on multiple columns with assign?

I am looking for a short way to run the same operation on multiple columns – typically by running it through .assign.

import pandas as pd

df = pd.DataFrame({'a':[' abc ', 'efg', ' hij'], 'b': [' kkk ', 'eee', 'uuu ']})

In this example I have two columns with strings, some of which have leading and trailing spaces. If I want to remove them I would do something like this:

df.assign(a=lambda x: x["a"].str.strip(), b=lambda x: x["b"].str.strip())

basically repeating the same lambda expression for every column. Is there a more convenient way? I do not want to run a loop, because that cannot be method-chained.

My first idea was something like this, following the "don’t repeat yourself" principle:

df.assign({col : lambda x: x[col].str.strip() for col in ['a', 'b']})

which of course does not work.

Any suggestions very welcome!

>Solution :

You would need to unpack the dictionary to parameters using **:

df.assign(**{col : lambda x: x[col].str.strip() for col in ['a', 'b']})

output:

     a    b
0  kkk  kkk
1  eee  eee
2  uuu  uuu

ref: PEP 448 for dictionary unpacking