python - Vectorized method of converting binary to int for pandas dataframe/series -


everything i've searched has yielded apply solution, know not optimized method. example:

df['c1'].apply(lambda x: int(x2, 2))

i've been looking @ doc pages pd.series can't find far.

is there faster way this?

edit: turn this:

0         11111 1         00000 2         00000 3         00010 4         00011 5         00100 6         00000 7         00001 8         01001 9         00000 10        00111 11        10111 12        11001 13        01001 14        01100 15        01100 16        00000 17        00110 18        10101 19        10101 20        01011 21        01110 22        01110 23        10101 24        00001 25        01001 26        01010 27        00000 28        00000 29        00000           ...   139861    01000 139862    10000 139863    00100

into this:

0         31 1         0  2         0  3         2  4         3  5         4  6         0  7         1  8         9  9         0  10        7  11        23 12        25 13        9  14        12 15        12 16        0  17        6  18        21 19        21 20        11 21        14 22        14 23        21 24        1  25        9  26        10 27        0  28        0  29        0           ..  139861    8  139862    16 139863    4

i don't imagine original...

df['col1'].apply(lambda x: int(x2, 2))

...is going painfully slow.. however, can avoid lambda overhead using args= in apply, eg:

df.col1.apply(int, args=(2,))

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