Pandas text matching like SQL's LIKE? -

is there way similar sql's syntax on pandas text dataframe column, such returns list of indices, or list of booleans can used indexing dataframe? example, able match rows column starts 'prefix_', similar where <col> prefix_% in sql.

you can use series method str.startswith (which takes regex):

in [11]: s = pd.series(['aa', 'ab', 'ca', np.nan])  in [12]: s.str.startswith('a', na=false) out[12]:  0     true 1     true 2    false 3    false dtype: bool 

you can same str.contains (using regex):

in [13]: s.str.contains('^a', na=false) out[13]:  0     true 1     true 2    false 3    false dtype: bool 

so can df[col].str.startswith...

see sql comparison section of docs.

note: (as pointed out op) default nans propagate (and hence cause indexing error if want use result boolean mask), use flag nan should map false.

in [14]: s.str.startswith('a')  # can't use boolean mask out[14]: 0     true 1     true 2    false 3      nan dtype: object