Broadcasting in NumPy is widely used, yet poorly understood❗️

Today we see:
- What is broadcasting
- Rules to apply broadcasting
- Examples of broadcasting
- Why it's Useful

A Thread 🧵👇

Broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations.

The smaller array is “broadcast” across the larger array, such that the 2 have compatible shapes.

Check this out👇

In the image below, scalar "b" is being stretched into an array with the same shape as "a".

But how do we generalise these things?

continue reading ... 📖

💫 General Rules:

1) Broadcasting starts with the trailing (i.e. rightmost) dimensions and works its way left .

2) Two dimensions are compatible, either when they are equal or one of them is 1.

Check out the examples 👇

Another example to make our understanding concrete. 👊

Try yourself! 🚀 👇

Why use broadcasting❓

Broadcasting provides a means of vectorising array operations so that looping occurs in C instead of Python.

It does this without making needless copies of data and usually leads to efficient algorithm implementations.

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