Speed comparison. numpy vs python standard

Question

I made a few experiment and found a number of cases where python's standard random and math library is faster than numpy counterpart.

I think there is a tendency that python's standard library is about 10x faster for small scale operation, while numpy is much faster for large scale (vector) operations. My guess is that numpy has some overhead which becomes dominant for small cases.

My question is: Is my intuition correct? And will it be in general advisable to use the standard library rather than numpy for small (typically scalar) operations?

Examples are below.

import math import random import numpy as np 

Log and exponential

%timeit math.log(10) # 158 ns ± 6.16 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)  %timeit np.log(10) # 1.64 µs ± 93.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)  %timeit math.exp(3) # 146 ns ± 8.57 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)  %timeit np.exp(3) # 1.72 µs ± 78.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) 

Generate normal distribution

%timeit random.gauss(0, 1) # 809 ns ± 12.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)  %timeit np.random.normal() # 2.57 µs ± 14.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) 

Choosing a random element

%timeit random.choices([1,2,3], k=1) # 1.56 µs ± 55.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)  %timeit np.random.choice([1,2,3], size=1) # 23.1 µs ± 1.04 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) 

Same with numpy array

arr = np.array([1,2,3])  %timeit random.choices(arr, k=1) # 1.72 µs ± 33.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)  %timeit np.random.choice(arr, size=1) # 18.4 µs ± 502 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) 

With big array

arr = np.arange(10000)  %timeit random.choices(arr, k=1000) # 401 µs ± 6.16 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)  %timeit np.random.choice(arr, size=1000) # 41.7 µs ± 1.39 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) 

Answer 1

numpy is only really a performance improvement for large blocks of data. The overhead of making sure the memory blocks line up correctly before pouring an ndarray into a c-compiled numpy function will generally overwhelm any time benefit if the array isn't relatively large. This is why so many numpy questions are basically "How do I take this loopy code and make it fast," and why it is considered a valid question in this tag where nearly any other tag will toss you to Code review before they get past the title.

So, yes, your observation is generalizable. Vectorizing is the whole point of numpy. numpy code that isn't vectorized is always slower than bare python code, and is arguably just as "wrong" as cracking a single walnut with a jackhammer. Either find the right tool or get more nuts.

Answer 2

NumPy is used primarily for performance with arrays. This relies on the use of contiguous memory blocks and more efficient lower-level iteration. Applying a NumPy mathematical function on a scalar or calculating a random number are not vectorisable operations. This explains the behaviour you are seeing.

See also What are the advantages of NumPy over regular Python lists?

And will it be in general advisable to use the standard library rather than NumPy for small (typically scalar) operations?

It's rare that the bottleneck for a program is caused by operations on scalars. In practice, the differences are negligible. So either way is fine. If you are already using NumPy there's no harm in continuing to use NumPy operations on scalars.

It's worth making a special case of calculating random numbers. As you might expect, the random number selected via random vs NumPy may not be the same:

assert random.gauss(0, 1) == np.random.normal()  # AssertionError assert random.choices(arr, k=1)[0] == np.random.choice(arr, size=1)[0]  # AssertionError 

You have additional functionality in NumPy to make random numbers "predictable". For example, running the below script repeatedly will only ever generate the same result:

np.random.seed(0) np.random.normal() 

The same applies to np.random.choice. So there are differences in how the random number is derived and the functionality available. For testing, or other, purposes you may wish to be able to produce consistent "random" numbers.