python - How to split a pandas dataframe based on column prefix -

i have dataframe imported csv. goes this:

df a.1  b.1  a.2  b.2 1    1    1    1 2    2    2    2 

my question is, efficient way turn seperate data frames comprised of a's , b's

df_a a.1  a.2 1    1 2    2  df_b b.1  b.2 1    1 2    2 

i not picky far column names, fine having them stripped 1 , 2 etc haven't been able find way this. open other/better ways accomplish trying in case doesn't make sense more knowledgable. thanks!

you use df.filter regex patterns:

df_a, df_b = df.filter(regex=r'^a'), df.filter(regex=r'^b') 

or

df_a, df_b = df.filter(like='a'), df.filter(like='b') 

note if use like='a' columns name contains 'a' selected. if use regex=r'^a' columns name begins a selected.

---

in [7]: df out[7]:     a.1  b.1  a.2  b.2 0    1    1    1    1 1    2    2    2    2  in [8]: df_a, df_b = df.filter(regex=r'^a'), df.filter(regex=r'^b')  in [9]: df_a out[9]:     a.1  a.2 0    1    1 1    2    2  in [10]: df_b out[10]:     b.1  b.2 0    1    1 1    2    2