Hi Data Science enthusiasts 👋
.
Today, we are gonna talk about broadcasting in NumPy 🔢
.
Broadcasting is a powerful, useful yet tricky feature in NumPy. If you know it well and use it intentionally, you can simplify a lot of code 👌
.
However, if it’s used by mistake it can create bugs and a lot of headaches 🤕
.
That’s because in NumPy, you can easily do operations between matrices even if they don’t have the same shape 👌
.
NumPy “broadcasts” the smaller matrix (if valid for the operation) and repeats the operation per element, row, column, etc 🤘
.
In today’s code snippet, a scalar broadcasts into the same size of a matrix to be subtracted. Similarly, a row and column vector broadcasts into the right shape before getting subtracted!
.
Wanna know how? Check out the post!

.👨‍💻#NumPy

Partitioning is an important technique when you have a large amount of data and like to partition it based on a pivot value. NumPy can do this very efficiently and it leads to some cool applications.
.
Wanna know more? Check out the slides!

.

.

👨‍💻#NumPy

Being fluent in NumPy goes a long way in becoming a data scientist 🏃 Today we are taking an important step in that direction! 🚀
.
Wanna know more? Check out the slides!
.

👨‍💻#NumPy