Hey everyone 👋
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As data scientists, we are data hungry!! Good news is data is available everywhere on the internet, and Pandas has the feature to import all of that goodness easily into a DataFrame 👌
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How? Check out the slides!!

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👨‍💻#Pandas

Howdy everyone 👋👋
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How about continuing our discussion on how to use Pandas to get valuable insights from our data? Shall we? 👌

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👨‍💻#Pandas

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!
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👨‍💻#NumPy