将一长串 0 和 1 的序列转换为 numpy 数组或 Pandas 数据帧

Question

我有一个很长的序列列表（假设每个长度为 16），由 0 和 1 组成。例如

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，这对于大数据集是不可能的。

所以我尝试的是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]]

提前致谢！！

Answer 1

使用np.array构造函数和列表推导式：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

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],&nbsp; &nbsp; &nbsp; &nbsp;[1, 1, 0, ..., 1, 0, 1],&nbsp; &nbsp; &nbsp; &nbsp;[1, 1, 0, ..., 0, 0, 0],&nbsp; &nbsp; &nbsp; &nbsp;[0, 1, 1, ..., 1, 1, 1],&nbsp; &nbsp; &nbsp; &nbsp;[1, 1, 1, ..., 1, 1, 1]])虽然仍然比列表理解慢一点