numpy vectorization

Learn vectorization with numpy.

What is numpy vectorization?

numpy processes large numeric arrays in C instead of Python loops. Vectorization applies an operation to all elements of an array at once.

For example np.array([1, 2, 3]) ** 2 squares all elements; no loop needed. This is tens of times faster than a Python for loop. numpy is the standard in scientific computing and data analysis. Vectorization both shortens code and speeds it up. The difference matters with large data.

Array operations

Arithmetic on an np.array applies to all elements. a + 1 adds 1 to each, a * 2 multiplies by two, a ** 2 squares.

Operations work between two arrays too: a + b adds element-wise. Since these run in C they are much faster than a Python for loop. Summary methods like .sum(), .mean(), .max() also run vectorized. The difference matters with large datasets.

Exercise Compute squares with numpy

Import the module with import numpy as np. Create an array with a = np.array([1, 2, 3]). Square all elements with b = a ** 2. Print b.

Show example solution

import numpy as np

a = np.array([1, 2, 3])
b = a ** 2
print(b)

Test yourself

Complete the numpy import

import numpy as ____

a = np.array([1, 2, 3])

Show answer

import numpy as np

a = np.array([1, 2, 3])

Match numpy concepts

np.array

Creates an array
** 2

Squares all elements
vectorization

Loop-free operation
numpy

Numeric computing
Why is vectorization faster?
- It uses Python loops
- It applies the operation in C to the whole array
- It uses less memory
- It writes shorter code
Show answer

It applies the operation in C to the whole array — numpy operations run in C; they apply to the whole array without Python interpreter overhead.

Next lesson: Summary - Performance

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