Use ** instead of pow in Python
In Python, x**y
is much faster than:
Julia is more than 5 times faster than Python at scalar exponentiation, while Go was in-between Python and Julia in performance.
Python
Benchmarking was the same for integer or float base or exponent.
Python testing done with:
- Python 3.7.4
- Ipython 7.8.0
- Numpy 1.16.5
** operator
The ** operator in Python also has the advantage of returning int if inputs are int and arithmetic result is integer.
10**(-3)
8.22 ns ± 0.0182 ns per loop (mean ± std. dev. of 7 runs, 100000000 loops each)
pow(10, -3)
227 ns ± 0.313 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
math.pow(10, -3)
252 ns ± 1.56 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
numpy.power(10., -3)
1.5 µs ± 2.91 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)Numpy is known in general to be slower at scalar operations than Python-native operators and Python built-in math. But of course Numpy is generally a lot faster and easier for N-dimensional array operations.
Julia
Julia 1.2.0 was likewise benchmarked under power/ for reference on the same computer.
First we installed Julia BenchmarkTools:
import Pkg
Pkg.add("BenchmarkTools")The Julia wallclock time for
exponentiation
was the same for float and int as with Python.
3.399 nanoseconds
Go
Go 1.13.1 was benchmarked under power/:
go test -bench=PowerBenchmarkPower-12 33883672 31.8 ns/op