Operations on Arrays

NumPy allows you to perform mathematical operations directly on entire arrays at once, without writing manual loops - a technique called vectorization that makes numerical computation both faster and more readable.

Element-Wise Arithmetic

import numpy as np

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

print(a + b)   # [11 22 33]
print(a * b)   # [10 40 90]
print(b / a)   # [10. 10. 10.]
print(a ** 2)  # [1 4 9]

Broadcasting

Broadcasting lets NumPy perform operations between arrays of different shapes by automatically expanding the smaller array, without actually copying data - saving both time and memory.

import numpy as np

arr = np.array([1, 2, 3])
print(arr + 10)   # [11 12 13] - 10 is "broadcast" across every element

Aggregate Functions

import numpy as np

data = np.array([4, 8, 15, 16, 23, 42])

print(data.sum())    # 108
print(data.mean())   # 18.0
print(data.max())    # 42
print(data.min())    # 4
print(data.std())    # standard deviation

Coming Up Next

Next, you'll learn how to access and work with specific parts of an array using indexing, slicing, and iteration.