将0和1序列的长列表转换为numpy数组或datafram

Question

我有一个非常长的序列列表（假设每个长度为16），由0和1组成。e、 g

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

现在我想把每一位都当作一个特征，所以我需要把它转换成numpy数组或pandas数据帧。为了做到这一点，我需要用逗号分隔序列中存在的所有位，这对于大型数据集是不可能的。你知道吗

所以我试着生成字符串中的所有位置：

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)

预期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]]

提前谢谢！！你知道吗

Answer 1

在一行中，没有for循环：

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]])

还是比列表理解慢一点

Answer 2

使用np.array构造函数和列表理解：

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]])