How can I count the number of consecutive TRUEs in a DataFrame?

Question

I have a dataset made of True and False.

Sample Table:
       A      B      C
0  False   True  False
1  False  False  False
2   True   True  False
3   True           


        

Answer 1

We would basically leverage two philosophies - Catching shifts on compared array and Offsetting each column results so that we could vectorize it.

So, with that intention set, here's one way to achieve the desired results -

def maxisland_start_len_mask(a, fillna_index = -1, fillna_len = 0):
    # a is a boolean array

    pad = np.zeros(a.shape[1],dtype=bool)
    mask = np.vstack((pad, a, pad))

    mask_step = mask[1:] != mask[:-1]
    idx = np.flatnonzero(mask_step.T)
    island_starts = idx[::2]
    island_lens = idx[1::2] - idx[::2]
    n_islands_percol = mask_step.sum(0)//2

    bins = np.repeat(np.arange(a.shape[1]),n_islands_percol)
    scale = island_lens.max()+1

    scaled_idx = np.argsort(scale*bins + island_lens)
    grp_shift_idx = np.r_[0,n_islands_percol.cumsum()]
    max_island_starts = island_starts[scaled_idx[grp_shift_idx[1:]-1]]

    max_island_percol_start = max_island_starts%(a.shape[0]+1)

    valid = n_islands_percol!=0
    cut_idx = grp_shift_idx[:-1][valid]
    max_island_percol_len = np.maximum.reduceat(island_lens, cut_idx)

    out_len = np.full(a.shape[1], fillna_len, dtype=int)
    out_len[valid] = max_island_percol_len
    out_index = np.where(valid,max_island_percol_start,fillna_index)
    return out_index, out_len

Sample run -

# Generic case to handle all 0s columns
In [112]: a
Out[112]: 
array([[False, False, False],
       [False, False, False],
       [ True, False, False],
       [ True, False,  True],
       [False, False, False],
       [ True, False,  True],
       [ True, False, False],
       [ True, False,  True],
       [False, False,  True],
       [ True, False, False]])

In [117]: starts,lens = maxisland_start_len_mask(a, fillna_index=-1, fillna_len=0)

In [118]: starts
Out[118]: array([ 5, -1,  7])

In [119]: lens
Out[119]: array([3, 0, 2])