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]
```

## 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:

**Rule 1:**If the two arrays, the shape of the one with fewer dimensions is adjusted by adding**differ in their number of dimensions**`1`

to the left side of its`shape`

**Rule 2:**If the, the array with**shape of the two arrays does not match in any dimension**`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