Why are NumPy arrays so fast?

Numpy arrays are densely packed arrays of homogeneous type. Python lists, by contrast, are arrays of pointers to objects, even when all of them are of the same type. So, you get the benefits of locality of reference.

Also, many Numpy operations are implemented in C, avoiding the general cost of loops in Python, pointer indirection and per-element dynamic type checking. The speed boost depends on which operations you're performing, but a few orders of magnitude isn't uncommon in number crunching programs.

numpy arrays are specialized data structures. This means you don't only get the benefits of an efficient in-memory representation, but efficient specialized implementations as well.

E.g. if you are summing up two arrays the addition will be performed with the specialized CPU vector operations, instead of calling the python implementation of int addition in a loop.

Consider the following code:

import numpy as np
import time

a = np.random.rand(1000000)
b = np.random.rand(1000000)

tic = time.time()
c = np.dot(a, b)
toc = time.time()

print("Vectorised version: " + str(1000*(toc-tic)) + "ms")

c = 0
tic = time.time()
for i in range(1000000):
    c += a[i] * b[i]
toc = time.time()

print("For loop: " + str(1000*(toc-tic)) + "ms")

Output:

Vectorised version: 2.011537551879883ms
For loop: 539.8685932159424ms

Here Numpy is much faster because it takes advantage of parallelism (which is the case of Single Instruction Multiple Data (SIMD)), while traditional for loop can't make use of it.