python - Numpy 字符串分区 : Perform Multiple Splits

Question

我有一组字符串，每个字符串包含一个或多个单词。我想在分隔符(在我的例子中为空白)上拆分/分区数组，拆分次数与包含最多分隔符的元素中的分隔符一样多。 numpy.char.partition但是只执行一次拆分，不管分隔符出现的频率如何:

我有:

>>> a = np.array(['word', 'two words', 'and three words'])
>>> np.char.partition(a, ' ')

>>> array([['word', '', ''],
       ['two', ' ', 'words'],
       ['and', ' ', 'three words']], dtype='<U8')

我想要:

>>> array([['word', '', '', '', ''],
       ['two', ' ', 'words', '', ''],
       ['and', ' ', 'three', ' ', 'words']], dtype='<U8')

Answer 1

方法 #1

那些 partition 函数似乎并没有对所有事件进行分区。为了解决我们的问题，我们可以使用 np.char.split 来获取拆分字符串，然后使用 masking，array-assignment，就像这样-

def partitions(a, sep):
    # Split based on sep
    s = np.char.split(a,sep)

    # Get concatenated split strings
    cs = np.concatenate(s)

    # Get params
    N = len(a)
    l = np.array(list(map(len,s)))
    el = 2*l-1
    ncols = el.max()

    out = np.zeros((N,ncols),dtype=cs.dtype)

    # Setup valid mask that starts at fist col until the end for each row
    mask = el[:,None] > np.arange(el.max())

    # Assign sepeter into valid ones
    out[mask] = sep

    # Setup valid mask that has True at postions where words are to be assigned
    mask[:,1::2] = 0

    # Assign words
    out[mask] = cs
    return out

样本运行-

In [32]: a = np.array(['word', 'two words', 'and three words'])

In [33]: partitions(a, sep=' ')
Out[33]: 
array([['word', '', '', '', ''],
       ['two', ' ', 'words', '', ''],
       ['and', ' ', 'three', ' ', 'words']], dtype='<U5')

In [44]: partitions(a, sep='ord')
Out[44]: 
array([['w', 'ord', ''],
       ['two w', 'ord', 's'],
       ['and three w', 'ord', 's']], dtype='<U11')

方法 #2

这是另一个带循环的，以节省内存 -

def partitions_loopy(a, sep):
    # Get params
    N = len(a)
    l = np.char.count(a, sep)+1
    ncols = 2*l.max()-1
    out = np.zeros((N,ncols),dtype=a.dtype)
    for i,(a_i,L) in enumerate(zip(a,l)):
        ss = a_i.split(sep)
        out[i,1:2*L-1:2] = sep
        out[i,:2*L:2] = ss
    return out