Vectorised operations

2 min read 5 runnable examples Quiz included

Element-wise arithmetic

Operators between arrays work element by element:

Python

import numpy as np

a = np.array([10, 20, 30, 40])
b = np.array([1, 2, 3, 4])

print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a ** 2)
print(a % 3)

Universal functions

NumPy has fast functions that apply to every element:

Python

import numpy as np

x = np.array([1, 4, 9, 16])
print(np.sqrt(x))
print(np.log10(np.array([1, 10, 100, 1000])))
print(np.round(np.array([1.234, 5.678]), 1))
print(np.maximum(np.array([1, 5, 3]), np.array([4, 2, 6])))

Indexing and slicing

1D arrays slice like lists; 2D arrays take [row, column]:

Python

import numpy as np

a = np.array([5, 10, 15, 20, 25])
print(a[0], a[-1], a[1:4])

m = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])
print(m[1, 2])        # row 1, column 2 → 6
print(m[0])           # first row
print(m[:, 1])        # second column
print(m[:2, 1:])      # top-right 2x2 block

Boolean masks

A comparison produces an array of True/False, which can filter another array. This is one of NumPy's most powerful ideas:

Python

import numpy as np

scores = np.array([72, 95, 58, 88, 41, 79])
passed = scores >= 60
print(passed)
print(scores[passed])                    # keep only passing scores
print(scores[(scores > 60) & (scores < 90)])   # & means "and"
print((scores >= 60).sum(), "students passed")

Use & (and), | (or) and ~ (not) between conditions, each in brackets.

Changing values with a mask

Python

import numpy as np

temps = np.array([22, -999, 25, 24, -999, 21])   # -999 means "sensor error"
temps[temps == -999] = 0
print(temps)

clipped = np.where(temps > 23, 23, temps)        # cap at 23
print(clipped)

np.where(condition, if_true, if_false) builds a new array choosing values element by element.

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