# Part 6: BROADCASTING

We have studied vectorized operations in the earlier section. The two main methods to perform vectorized operations are:

• to use Numpy’s universal functions (ufunc), as we have covered earlier, and,
• to use NumPy’s broadcasting feature, we will discuss here

Broadcasting is a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, division, etc.) on arrays of different sizes

We will go through some basic examples to revise the concept of broadcasting, covered in earlier sections.

First, Let suppose, we want to add two arrays having identical shape and none of the arrays need to be stretched to do the operation

import numpy as np

# creating arrays
ar1 = np.arange(1,4)
ar2 = np.arange(4,7)
print(f"ar1: {ar1}");print(f"ar2: {ar2}")
# adding two arrays using "+" operations
print(f"ar1 + ar2: {ar1+ar2}")
ar1: [1 2 3]
ar2: [4 5 6]
ar1 + ar2: [5 7 9]

Article Contents

## 1. RULES OF BROADCASTING

When the arrays don’t have an identical shape, broadcasting rules will be applied to figure out how the shape of arrays are adjusted:

1. Rule 1: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is adjusted by adding 1 to the left side of its shape
2. Rule 2: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the shape of other array

If even after applying the above two rules, the shapes of array can’t be adjusted, ValueError will be raised

Let’s study few cases, where these rules will be applied

### 1.1. Case A

Let suppose, we want to add a scalar 10 to the array ar1.

To broadcast, scalar of size 1 will be stretched to be size of ar1.

It is mental equivalent to add [10,10,10] to [1,2,3] in the example below.

print(f"ar1: {ar1}")
print(f"ar1+ 10: {ar1 + 10}")
ar1: [1 2 3]
ar1+ 10: [11 12 13]

### 1.2. Case B

In this case, we will add 1D array ar1 of size 3 to 2D array ar3 of size 9.

To broadcast, array ar1 will be stretched to be size of ar3.

See the code below to understand the concept:

ar3 = np.zeros((3,3))
print(f"ar1: \n{ar1}\nar1 Shape:\n{ar1.shape}")
print(f"\nar3: \n{ar3}\nar3 Shape:\n{ar3.shape}")

# adding two arrays
print(f"\nar1 + ar3: \n{ar1+ar3}")
ar1:
[1 2 3]
ar1 Shape:
(3,)

ar3:
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
ar3 Shape:
(3, 3)

ar1 + ar3:
[[1. 2. 3.]
[1. 2. 3.]
[1. 2. 3.]]

How broadcasting rules are applied:

• According to rule 1, shape of ar1 will be adjusted by adding 1 on left.
• ar1 shape: (3,)(1,3)
• According to rule 2, shape of ar1 will be adjusted to match the shape of ar2, along the axis where ar1 has value of 1.
• ar1 shape: (1,3)(3,3) to match with ar2 shape of (3,3)

### 1.3. Case C

In this case, we will add 1D array ar1 of size 3 to 2D array ar4 of size 3.

To broadcast, both arrays ar1 and ar4 will be stretched.

See the code below to understand the concept:

ar4 = np.arange(1,4).reshape(3,1)
print(f"ar1: \n{ar1}\nar1 Shape:\n{ar1.shape}")
print(f"\nar4: \n{ar4}\nar4 Shape:\n{ar4.shape}")

print(f"\nar1 + ar4: \n{ar1 + ar4}")
ar1:
[1 2 3]
ar1 Shape:
(3,)

ar4:
[[1]
[2]
[3]]
ar4 Shape:
(3, 1)

ar1 + ar4:
[[2 3 4]
[3 4 5]
[4 5 6]]

How broadcasting rules are applied:

• According to rule 1, shape of ar1 will be adjusted by adding 1 on left.
• ar1 shape: (3,) –> (1,3)
• According to rule 2, shape of both ar1 and ar4 will be adjusted along the axis where they have value of 1.
• ar1 shape: (1,3) –> (3,3) to match with ar4 shape of (3,1)
• ar4 shape: (3,1) –> (3,3) to match with ar1 shape of (1,3)

### 1.4. Case D

In this example, we will discuss the case that leads to ValueError because even after applying both rules of broadcasting, the arrays shape doesn’t match

ar5 = np.arange(6).reshape(3,2)
print(f"ar1: \n{ar1}\nar1 Shape:\n{ar1.shape}")
print(f"\nar5: \n{ar5}\nar5 Shape:\n{ar5.shape}")

# add ar1 and ar5
print(ar1+ar5)
ar1:
[1 2 3]
ar1 Shape:
(3,)

ar5:
[[0 1]
[2 3]
[4 5]]
ar5 Shape:
(3, 2)

ValueError: operands could not be broadcast together with shapes (3,) (3,2)

How broadcasting rules are applied:

• According to rule 1, shape of ar1 will be adjusted by adding 1 on left.
• ar1 shape: (3,) –> (1,3)
• According to rule 2, shape of ar1 will be adjusted to match the shape of ar5, along the axis where ar1 has value of 1.
• ar1 shape: (1,3) –> (3,3) to match with ar5 shape of (3,2)
• However, shapes of ar1, (3,3) still doesn’t match with shape of ar5, (3,2). This will raise an error