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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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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
Hey everyone ๐Ÿ‘‹
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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 ๐Ÿ‘Œ
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How? Check out the slides!!

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๐Ÿ‘จโ€๐Ÿ’ป#Pandas
Howdy everyone ๐Ÿ‘‹๐Ÿ‘‹
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How about continuing our discussion on how to use Pandas to get valuable insights from our data? Shall we? ๐Ÿ‘Œ

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๐Ÿ‘จโ€๐Ÿ’ป#Pandas
Hi Data Science enthusiasts ๐Ÿ‘‹
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Today, we are gonna talk about broadcasting in NumPy ๐Ÿ”ข
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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 ๐Ÿ‘Œ
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However, if itโ€™s used by mistake it can create bugs and a lot of headaches ๐Ÿค•
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Thatโ€™s because in NumPy, you can easily do operations between matrices even if they donโ€™t have the same shape ๐Ÿ‘Œ
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NumPy โ€œbroadcastsโ€ the smaller matrix (if valid for the operation) and repeats the operation per element, row, column, etc ๐Ÿค˜
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In todayโ€™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!

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