return t.count(None) == len(t)
and it is faster than using all
:
>>> setup = 't = [None, None]*100; t[1] = 1'
>>> timeit.timeit('all(item is None for item in t)', setup=setup)
0.8577961921691895
>>> timeit.timeit('t.count(None) == len(t)', setup=setup)
0.6855478286743164
and speed of all
decreases according to index of not None
element:
>>> setup = 't = [None, None]*100; t[100] = 1'
>>> timeit.timeit('all(item is None for item in t)', setup=setup)
8.18800687789917
>>> timeit.timeit('t.count(None) == len(t)', setup=setup)
0.698199987411499
BUT with big lists all is faster:
>>> setup = 't = [None, None]*10000; t[100] = 1'
>>> timeit.timeit('t.count(None) == len(t)', setup=setup)
47.24849891662598
>>> timeit.timeit('all(item is None for item in t)', setup=setup)
8.114514112472534
BUT not always though:
>>> setup = 't = [None, None]*10000; t[1000]=1'
>>> timeit.timeit('t.count(None) == len(t)', setup=setup)
47.475088119506836
>>> timeit.timeit('all(item is None for item in t)', setup=setup)
72.77452898025513
Conclusion that i make for myself about speed of all or count - very depends of data. If probability that you have all None in very big list - dont use all, it is very slow in that case.