Interpret columns of zeros and ones as binary and store as an integer column

Question

I have a dataframe of zeros and ones. I want to treat each column as if its values were a binary representation of an integer. What is easiest way to make this conversion?

I want this:

df = pd.DataFrame([[1, 0, 1], [1, 1, 0], [0, 1, 1], [0, 0, 1]])

print df

   0  1  2
0  1  0  1
1  1  1  0
2  0  1  1
3  0  0  1

converted to:

0    12
1     6
2    11
dtype: int64

As efficiently as possible.

Answer 1

Similar solution, but more faster:

print (df.T.dot(1 << np.arange(df.shape[0] - 1, -1, -1)))
0    12
1     6
2    11
dtype: int64

Timings:

In [81]: %timeit df.apply(lambda col: int(''.join(str(v) for v in col), 2))
The slowest run took 5.66 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 264 µs per loop

In [82]: %timeit (df.T*(1 << np.arange(df.shape[0]-1, -1, -1))).sum(axis=1)
1000 loops, best of 3: 492 µs per loop

In [83]: %timeit (df.T.dot(1 << np.arange(df.shape[0] - 1, -1, -1)))
The slowest run took 6.14 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 204 µs per loop