Converting a long list of sequence of 0's and 1's into a numpy array or pandas dataframe

Question

I have a very long list of sequences(suppose of length 16 each) consisting of 0 and 1. eg

s = ['0100100000010111', '1100100010010101', '1100100000010000', '0111100011110111', '1111100011010111']

Now I want to treat each bit as a feature so I need to convert it into numpy array or pandas dataframe. In order to do that I need to comma separate all the bits present in the sequences which is impossible for big datasets.

So what I have tried is to generate all the positions in the string:

slices = []
for j in range(len(s[0])):
    slices.append((j,j+1)) 

print(slices)
[(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (12, 13), (13, 14), (14, 15), (15, 16)]


new = []
for i in range(len(s)):
    seq = s[i]
    for j in range(len(s[i])):
    ## I have tried both of these LOC but couldn't figure out 
    ## how it could be done        
    new.append([s[slice(*slc)] for slc in slices])
    new.append(s[j:j+1])
print(new)

Expected o/p:

new = [[0,1,0,0,1,0,0,0,0,0,0,1,0,1,1,1], [1,1,0,0,1,0,0,0,1,0,0,1,0,1,0,1], [1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0], [0,1,1,1,1,0,0,0,1,1,1,1,0,1,1,1], [1,1,1,1,1,0,0,0,1,1,0,1,0,1,1,1]]

Thanks in advance!!

Answer 1

Using the np.array constructor and a list comprehension:

np.array([list(row) for row in s], dtype=int)

array([[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1],
       [1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1],
       [1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],
       [1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1]])

Answer 2

In one line, without for loops:

np.array(s).view('<U1').astype(int).reshape(len(s), -1)

array([[0, 1, 0, ..., 1, 1, 1],
       [1, 1, 0, ..., 1, 0, 1],
       [1, 1, 0, ..., 0, 0, 0],
       [0, 1, 1, ..., 1, 1, 1],
       [1, 1, 1, ..., 1, 1, 1]])

Still a bit slower than list comprehension though