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Data science, Machine learning, and Artificial Intelligence. We post daily contents related to machine learning focusing on Numpy, Pandas, and ML effectively.
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Neural networks are at the center of attention for machine learning ๐Ÿ™.So itโ€™s important to get introduced early on our journey ๐Ÿ‘..๐Ÿ—ฃ
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๐Ÿ‘จโ€๐Ÿ’ป #Machine_Learning
Part 8 of Intro to Machine Learning Series
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๐Ÿ‘จโ€๐Ÿ’ป #Machine_Learning
Part 9 of Intro to Machine Learning Series
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๐Ÿ‘จโ€๐Ÿ’ป #Machine_Learning
Part 10 of Intro to Machine Learning Series
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๐Ÿ‘จโ€๐Ÿ’ป #Machine_Learning
Welcome to part 5 of Fun with Pandas featuring our founders ๐Ÿ‘Œ
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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 โ€œ, โ€œ 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 ๐Ÿ‡ฟ๐Ÿ‡ฆ .
No hard feelings Bill, Mark, and Jeff. We are curious about you too ๐Ÿ˜

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๐Ÿ‘จโ€๐Ÿ’ป#Pandas
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 ๐Ÿ’ช
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Here we are subtracting our foundersโ€™ birth year from the current year to find their ages +/- 1 year ๐Ÿ‘
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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 โ€ผ๏ธ


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๐Ÿ‘จโ€๐Ÿ’ป#Pandas
Lesson of the day: apply() in Pandas ๐Ÿ’ช๐ŸŽ‰ with the help of our favorite founders who are back yet again!!
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apply() allows you to โ€œapplyโ€ ๐Ÿ˜ฎ any user defined function to column(s) of data ๐Ÿ‘Œ
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Swipe to see what we were curious to find out about our founders using apply() ๐Ÿ” .

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๐Ÿ‘จโ€๐Ÿ’ป#Pandas