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Hi everyone 👋👋.My friends are here again for part 2 of our intro to Pandas👌🎉👏.In Pandas, you can easily extract more useful data points from existing data in the table, and because Pandas has been optimized to work on large amounts of data, column operations are super fast 💨..Here I divide the founders’ net worth by their age, to get a sense of their average wealth accumulation rate.Then I am interested to see who’s accumulated wealth the fastest, so I sort the column in the descending order 🙌🏻.Super fast, in a few lines, I have answered a couple of my questions about my favorite founders 👍. . 👨‍💻#Pandas

Hi everyone 👋👋.I wanted to introduce Pandas to you in case it’s new to you. We will be working a lot with it in the future so a nice introduction will go a long way 🙌.I have asked a few of my friends ‼️ to help me introduce Pandas to you by showing up on the post 😂😂.Jokes aside, Pandas is a really powerful data analytics library in Python that I use almost everyday. It’s robust, fast, and great for prototyping data science problems 🧠..It quickly feels like you’re working with a database, so if you know SQL this won’t feel too different..Let me know who your favorite founder is from the 4 on the picture. I’ll keep mine a secret for now. 👍 . 👨‍💻#Pandas

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

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