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NumPy part 9: np.where()
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np.where() returns the indices where the condition is met (not the elements themselves) 👌
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👨‍💻#NumPy

Part 10 🎉 of Intro to NumPy, what a journey guys, thanks for sticking around for these posts!! On to part 100 shall we??
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Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning!
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👨‍💻#NumPy

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np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! 🎉👌
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Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match!
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👨‍💻#NumPy

np.cumsum() is a useful function when it comes to doing big data cumulative sums. See it, learn it, and use it 💪
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👨‍💻#NumPy

This post is inspired by a great question on the last post! So keep asking great questions and motivate future posts 💪
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👨‍💻#NumPy

Hi Data Science enthusiasts 👋
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Today, we are gonna talk about broadcasting in NumPy 🔢
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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 👌
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However, if it’s used by mistake it can create bugs and a lot of headaches 🤕
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That’s because in NumPy, you can easily do operations between matrices even if they don’t have the same shape 👌
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NumPy “broadcasts” the smaller matrix (if valid for the operation) and repeats the operation per element, row, column, etc 🤘
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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!
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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.
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Wanna know more? Check out the slides!

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👨‍💻#NumPy

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

👨‍💻#NumPy

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