Neural networks are at the center of attention for machine learning 🙏.So it’s important to get introduced early on our journey 👍..🗣
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👨💻 #Machine_Learning
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👨💻 #Machine_Learning
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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👨💻#Pandas
.
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
.
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 😍
.
👨💻#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 💪
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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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👨💻#Pandas
.
assign()
.
assign() lets do create a new column from a different column with some modification 💪
.
Here we are subtracting our founders’ birth year from the current year to find their ages +/- 1 year 👍
.
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 ‼️
.
👨💻#Pandas