‼️ To ensure every new post is visible to you, please turn on post notification at the top right of the post ‼️.Welcome to part 7 of our journey through NumPy 👋.When it comes to data manipulation, being able to filter data points of various ranges is a must 👍.NumPy makes it really easy to filter data points and reset their values 👌.I use this technique daily in my job to get the data in the form I need before feeding it to my machine learning model 🙏.How are you planning to use NumPy in your projects ⁉️❓
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👨💻#NumPy
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👨💻#NumPy
Welcome back to another NumPy lesson! 👋.Basketball players make a great excuse to learn about arg functions in NumPy 🏀.argmax() returns the index of the element with the maximum value.argmin() returns the index of the element with the minimum value.argsort() returns the indices of the array in ascending order 👌.Basically what adding arg to the name of the function does is to make return the *index* of the element and not the element itself.This is handy for this example because we then use the index to retrieve the name of the player from the other array 🤯😏.
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👨💻#NumPy
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👨💻#NumPy
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NumPy part 9: np.where()
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np.where() returns the indices where the condition is met (not the elements themselves) 👌
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👨💻#NumPy
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NumPy part 9: np.where()
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np.where() returns the indices where the condition is met (not the elements themselves) 👌
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👨💻#NumPy
Part 10 🎉 of Intro to NumPy, what a journey guys, thanks for sticking around for these posts!! On to part 100 shall we??
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Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning!
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👨💻#NumPy
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Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning!
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👨💻#NumPy
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np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! 🎉👌
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Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match!
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👨💻#NumPy
np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! 🎉👌
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Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match!
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👨💻#NumPy
np.cumsum() is a useful function when it comes to doing big data cumulative sums. See it, learn it, and use it 💪
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👨💻#NumPy
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👨💻#NumPy