Fast, python-ish way of ranking chunks of 1's in numpy array?

Question

I have a numpy array consisting of 0\'s and 1\'s. Each sequence of 1\'s within the array stands for occurrence of one event. I want to

Answer 1

You want to label and luckily, there's one with SciPy, scipy.ndimage.label -

In [43]: from scipy.ndimage import label

In [47]: out = label(arr)[0]

In [48]: np.where(arr==0,np.nan,out-1)
Out[48]: 
array([nan, nan, nan,  0.,  0.,  0., nan, nan, nan,  1.,  1., nan, nan,
       nan,  2.,  2.,  2.,  2.])

Another with some NumPy work -

def rank_chunks(arr):
    m = np.r_[False,arr.astype(bool)]
    idx = np.flatnonzero(m[:-1] < m[1:])
    id_ar = np.zeros(len(arr),dtype=float)
    id_ar[idx[1:]] = 1
    out = id_ar.cumsum()
    out[arr==0] = np.nan
    return out

Another with masking + np.repeat -

def rank_chunks_v2(arr):
    m = np.r_[False,arr.astype(bool),False]
    idx = np.flatnonzero(m[:-1] != m[1:])
    l = idx[1::2]-idx[::2]
    out = np.full(len(arr),np.nan,dtype=float)
    out[arr!=0] = np.repeat(np.arange(len(l)),l)
    return out

Timings (tiling given input to 1Mx) -

In [153]: arr_big = np.tile(arr,1000000)

In [154]: %timeit np.where(arr_big==0,np.nan,label(arr_big)[0]-1)
     ...: %timeit rank_chunks(arr_big)
     ...: %timeit rank_chunks_v2(arr_big)
1 loop, best of 3: 312 ms per loop
1 loop, best of 3: 263 ms per loop
1 loop, best of 3: 229 ms per loop