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Array Arithmetic and Broadcasting

In NumPy, you can do math directly on arrays without using loops.
This is called element-wise operations — and it’s fast and clean.


Array Arithmetic

If two arrays are the same size, NumPy will apply operations element by element.

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

print(a + b)  # [11 22 33]
print(a * b)  # [10 40 90]

You can also subtract, divide, or raise to powers: a - b, a / b, a ** 2


Broadcasting

If arrays are not the same shape, NumPy tries to match them using broadcasting.

This means smaller arrays are “stretched” so the operation can still work.

Broadcasting Example
a = np.array([1, 2, 3])
b = 10

print(a + b)  # [11 12 13]

NumPy applied + 10 to every element in a.

It can also work between 2D and 1D arrays in some cases (you’ll see this in practice).


Summary

  • Use +, -, *, /, ** directly on arrays
  • Operations happen element-by-element
  • Broadcasting allows arrays of different shapes to work together

What’s Next

You’ll now practice array arithmetic and see how broadcasting works in action.

Quiz
0 / 1

What is the purpose of broadcasting in NumPy?

To perform operations only on arrays of the same shape.

To convert arrays into a different data type.

To allow operations between arrays of different shapes by stretching smaller arrays.

To perform operations on single elements of an array.

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