Length of each string in a NumPy array

You can use vectorize of numpy. It is much faster.

mylen = np.vectorize(len)
print mylen(arr)

UPDATE 06/20: Cater for u+0000 character and non-contiguous inputs - thanks @ M1L0U

Here is a comparison of a couple of methods.

Observations:

• For input size >1000 lines, viewcasting + argmax is consistently and by a large margin fastest.
• Python solutions profit from converting the array to a list first.
• map beats list comprehension
• np.frompyfunc and to a lesser degree np.vectorize fare better than their reputation

.

 contiguous
method ↓↓                  size →→  |     10|    100|   1000|  10000| 100000|1000000
------------------------------------+-------+-------+-------+-------+-------+-------
np.char.str_len                     |  0.006|  0.037|  0.350|  3.566| 34.781|345.803
list comprehension                  |  0.005|  0.036|  0.312|  2.970| 28.783|293.715
list comprehension after .tolist()  |  0.002|  0.011|  0.117|  1.119| 12.863|133.886
map                                 |  0.002|  0.008|  0.080|  0.745|  9.374|103.749
np.frompyfunc                       |  0.004|  0.011|  0.089|  0.861|  8.824| 88.739
np.vectorize                        |  0.025|  0.032|  0.132|  1.046| 12.112|133.863
safe argmax                         |  0.026|  0.026|  0.056|  0.290|  2.827| 32.583

 non-contiguous
method ↓↓                  size →→  |     10|    100|   1000|  10000| 100000|1000000
------------------------------------+-------+-------+-------+-------+-------+-------
np.char.str_len                     |  0.006|  0.037|  0.349|  3.575| 34.525|344.859
list comprehension                  |  0.005|  0.032|  0.306|  2.963| 29.445|292.527
list comprehension after .tolist()  |  0.002|  0.011|  0.117|  1.043| 11.081|130.644
map                                 |  0.002|  0.008|  0.081|  0.731|  7.967| 99.848
np.frompyfunc                       |  0.005|  0.012|  0.099|  0.885|  9.221| 92.700
np.vectorize                        |  0.025|  0.033|  0.146|  1.063| 11.844|134.505
safe argmax                         |  0.026|  0.026|  0.057|  0.291|  2.997| 31.161

Code:

import numpy as np

flist = []
def timeme(name):
    def wrap_gen(f):
        flist.append((name, f))
        return(f)
    return wrap_gen

@timeme("np.char.str_len")
def np_char():
    return np.char.str_len(A)

@timeme("list comprehension")
def lst_cmp():
    return [len(a) for a in A]

@timeme("list comprehension after .tolist()")
def lst_cmp_opt():
    return [len(a) for a in A.tolist()]

@timeme("map")
def map_():
    return list(map(len, A.tolist()))

@timeme("np.frompyfunc")
def np_fpf():
    return np.frompyfunc(len, 1, 1)(A)

@timeme("np.vectorize")
def np_vect():
    return np.vectorize(len)(A)
    
@timeme("safe argmax")
def np_safe():
    assert A.dtype.kind=="U"
    # work around numpy's refusal to viewcast non contiguous arrays
    v = np.lib.stride_tricks.as_strided(
        A[0,None].view("u4"),(A.size,A.itemsize>>2),(A.strides[0],4))
    v = v[:,::-1].astype(bool)
    l = v.argmax(1)
    empty = (~(v[:,0]|l.astype(bool))).nonzero()
    l = v.shape[1]-l
    l[empty] = 0
    return l
    
A = np.random.choice(
    "Blind\x00text do not use the quick brown fox jumps over the lazy dog "
    .split(" "),1000000)[::2]

for _, f in flist[:-1]:
    assert (f()==flist[-1][1]()).all()

from timeit import timeit

for j,tag in [(1,"contiguous"),(2,"non-contiguous")]:
    print('\n',tag)
    L = ['|+' + len(flist)*'|',
         [f"{'method ↓↓                  size →→':36s}", 36*'-']
         + [f"{name:36s}" for name, f in flist]]
    for N in (10, 100, 1000, 10000, 100000, 1000000):
        A = np.random.choice("Blind\x00text do not use the quick brown fox"
                             " jumps over the lazy dog ".split(" "),j*N)[::j]
        L.append([f"{N:>7d}", 7*'-']
                 + [f"{timeit(f, number=10)*100:7.3f}" for name, f in flist])
    for sep, *line in zip(*L):
        print(*line, sep=sep)