Measuring Performance in Python
Contents
Measuring Performance in Python¶
In this section, we’ll cover how to measure performance using tools from Python and IPython
IPython¶
If you are using IPython (such as in a Jupyter notebook), there are two “magics” that are very useful: %time and %timeit
def fib(n):
"""
A bad and inefficient function to compute fibonacci numbers
"""
if n == 0 or n == 1:
return 1
return fib(n-1) + fib(n-2)
print(list(fib(i) for i in range(5)))
[1, 1, 2, 3, 5]
%time just displays the wall time to compute
%time fib(20)
CPU times: user 8.86 ms, sys: 0 ns, total: 8.86 ms
Wall time: 8.84 ms
10946
%timeit will run a function multiple times and displays statistics
%timeit fib(20)
2.18 ms ± 56.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
This takes longer to compute, but will be more accuratate
Python¶
If you’re not using IPython, or want more control over your timing operations, you’ll need to write your own timing code.
The simplest way to do this is to use the time module
import time
start = time.time()
x = fib(20)
end = time.time()
print("{:.2g} sec.".format(end - start))
0.0023 sec.
Memory Use¶
See this article
Tracking memory use is not as simple as it can be in some languages because Python uses