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. 👍

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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 👍.

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Hello all and welcome to the 3rd episode of our Intro to Pandas series @bigdataguru 👋🙏.Our friends, the 4 founders, have been kind enough to show up once again to help us understand two important functions on Pandas 🎉.groupby()mean().Groupby() as the name suggests groups the rows of data frame based on the values of a column of columns..The result of the groupby is usually used for aggregation of data, in the case finding the mean number of employees employed in given states by these 4 companies.With those in our toolset, we can now do incredible things with data 🙌🏻🙌🏻.

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Hi data scientists 👋👋👋.A coincidence that the day we just finished was Valentine’s Day but I have been receiving a lot of love 💙 from you guys lately! Many of you have reached out and supported the content, just know that it’s appreciated and it will make this page better! 👌.With that, let’s get to today’s post, shall we?? .Of course when we are talking about Pandas, our good friends the founders are back to help us! 🎉.However our founders have been having a little argument lately. Even one was allegedly heard calling another one “hey boomer” ‼️ and the other responded back with “you millennial” 🤦🏻‍♂️ Even though arguments are not nice, this gives us the chance to use Pandas to settle who is in what generation!.pd.cut allows us to categorize a continuous spectrum into bins 👌 here our bins are the generations and the continuous spectrum is the year number 👍.After seeing exactly who’s in what generation, our founders realize that they should apologize to each other. They have promised to treat each other better in the next post so stay tuned 🗣.Correction: founders_df[“Birth”] should be founders_df[“BirthYear”] ‼️‼️

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Welcome to part 5 of Fun with Pandas featuring our founders 👌
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Fast and robust string operations are crucial to a large data framework. As shown, one can split columns based on unique patterns of their values .
Here, the separation of city from state/province and country is possible through finding the common pattern of “, “ between city and state and between state and country
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At the end, we are curious to know more about our South African born founder so we filter on South Africa 🇿🇦 .
No hard feelings Bill, Mark, and Jeff. We are curious about you too 😍

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Today, we are gonna talk about:
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assign()
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assign() lets do create a new column from a different column with some modification 💪
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Here we are subtracting our founders’ birth year from the current year to find their ages +/- 1 year 👍
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Later, we use the mean() function we covered in Part 3 of these series to find that together our favorite founders are 51.5 years young ‼️


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