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 👍
.
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
Lesson of the day: apply() in Pandas 💪🎉 with the help of our favorite founders who are back yet again!!
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apply() allows you to “apply” 😮 any user defined function to column(s) of data 👌
.
Swipe to see what we were curious to find out about our founders using apply() 🔍 .
.
👨💻#Pandas
.
apply() allows you to “apply” 😮 any user defined function to column(s) of data 👌
.
Swipe to see what we were curious to find out about our founders using apply() 🔍 .
.
👨💻#Pandas
Hey everyone 👋
.
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 👌
.
How? Check out the slides!!
.
👨💻#Pandas
.
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 👌
.
How? Check out the slides!!
.
👨💻#Pandas
Howdy everyone 👋👋
.
How about continuing our discussion on how to use Pandas to get valuable insights from our data? Shall we? 👌
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👨💻#Pandas
.
How about continuing our discussion on how to use Pandas to get valuable insights from our data? Shall we? 👌
.
👨💻#Pandas
Hi Data Science enthusiasts 👋
.
Today, we are gonna talk about broadcasting in NumPy 🔢
.
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 👌
.
However, if it’s used by mistake it can create bugs and a lot of headaches 🤕
.
That’s because in NumPy, you can easily do operations between matrices even if they don’t have the same shape 👌
.
NumPy “broadcasts” the smaller matrix (if valid for the operation) and repeats the operation per element, row, column, etc 🤘
.
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!
.
Wanna know how? Check out the post!
.👨💻#NumPy
.
Today, we are gonna talk about broadcasting in NumPy 🔢
.
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 👌
.
However, if it’s used by mistake it can create bugs and a lot of headaches 🤕
.
That’s because in NumPy, you can easily do operations between matrices even if they don’t have the same shape 👌
.
NumPy “broadcasts” the smaller matrix (if valid for the operation) and repeats the operation per element, row, column, etc 🤘
.
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!
.
Wanna know how? Check out the post!
.👨💻#NumPy