Without using lambda:
from operator import mul
# from functools import reduce # python3 compatibility
reduce(mul, list, 1)
it is better and faster. With python 2.7.5
from operator import mul
import numpy as np
import numexpr as ne
# from functools import reduce # python3 compatibility
a = range(1, 101)
%timeit reduce(lambda x, y: x * y, a) # (1)
%timeit reduce(mul, a) # (2)
%timeit np.prod(a) # (3)
%timeit ne.evaluate("prod(a)") # (4)
In the following configuration:
a = range(1, 101) # A
a = np.array(a) # B
a = np.arange(1, 1e4, dtype=int) #C
a = np.arange(1, 1e5, dtype=float) #D
Results with python 2.7.5
| 1 | 2 | 3 | 4 |
-------+-----------+-----------+-----------+-----------+
A 20.8 µs 13.3 µs 22.6 µs 39.6 µs
B 106 µs 95.3 µs 5.92 µs 26.1 µs
C 4.34 ms 3.51 ms 16.7 µs 38.9 µs
D 46.6 ms 38.5 ms 180 µs 216 µs
Result: np.prod is the fastest one, if you use np.array as data structure (18x for small array, 250x for large array)
with python 3.3.2:
| 1 | 2 | 3 | 4 |
-------+-----------+-----------+-----------+-----------+
A 23.6 µs 12.3 µs 68.6 µs 84.9 µs
B 133 µs 107 µs 7.42 µs 27.5 µs
C 4.79 ms 3.74 ms 18.6 µs 40.9 µs
D 48.4 ms 36.8 ms 187 µs 214 µs
Is python 3 slower?