Hi Data Science enthusiasts 👋
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Today, we are gonna talk about broadcasting in NumPy 🔢
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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 👌
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However, if it’s used by mistake it can create bugs and a lot of headaches 🤕
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That’s because in NumPy, you can easily do operations between matrices even if they don’t have the same shape 👌
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NumPy “broadcasts” the smaller matrix (if valid for the operation) and repeats the operation per element, row, column, etc 🤘
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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!
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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
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.
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Wanna know more? Check out the slides!
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👨💻#NumPy
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Wanna know more? Check out the slides!
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👨💻#NumPy
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Being fluent in NumPy goes a long way in becoming a data scientist 🏃 Today we are taking an important step in that direction! 🚀
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Wanna know more? Check out the slides!
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👨💻#NumPy
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Wanna know more? Check out the slides!
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👨💻#NumPy
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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👨💻#Pandas
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👨💻#Pandas
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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👨💻#Pandas
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👨💻#Pandas
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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👨💻#Pandas
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👨💻#Pandas