Hello data scientists 👋 and welcome to the post of the day 🙏 .It’s time we talk about NumPy slicing. NumPy slicing is a very common technique to manipulate large data 📊.It can be used with any number of dimensions of data with the same syntax 🤔.Here I was able to extract my favorite fruits from a large array of food items. Neat, right? 💡.Keep in mind array[:, 1:3] will take all the rows (:) and columns 1 and 2 of the dataset.
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👨‍💻#NumPy

Hiii data scientists 👋👋.Hope your week is off to a great start.I know I couldn’t start mine properly without talking about some NumPy 🤦🏻‍♂️.3 new functions and a useful concept for today, shall we? 👍.zeros(), ones() and diag() are three super useful functions I use all the time using NumPy. They give us the three basic matrixes: matrix of zeros, ones, and a diagonal matrix with specific values, respectively 👌.Doing arithmetic with NumPy matrices is super easy, unless what you’re doing is a matrix operation, all other operations occur at element level such a scalar multiplier and scalar addition 🙌.Liked today’s lesson? Let me know what you would like to learn next
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👨‍💻#NumPy

Hi data science enthusiasts 👋 and welcome to part 5 of intro to NumPy.Today, we are gonna talk about broadcasting in NumPy 🔢.Broadcasting is a powerful, useful yet tricky feature in NumPy. If you know it by heart 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 the matrices 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 matrix can be added to a scalar element-wise through this feature, a column vector can get added to every column, and a row vector can get added to every row.Again, really powerful stuff 💪💪.Hope you’re enjoying the journey we are on so far as much as I am.
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👨‍💻#NumPy

Hello Data Science & NumPy enthusiasts 👋👋.Of course it’s time for yet another episode on NumPy 🙏.Stacking arrays horizontally and vertically is something I do almost everyday.When you train large networks, your data becomes very large arrays of features, and very often, it’s needed to stack them to be able to feed them to the next layer of the network.So it’s supeeeer helpful to know how to do that in NumPy and voila it’s not so bad with np.vstack and np.hstack, you can stack up your arrays as long as the sizes match in the direction you’re stacking 👍.Happy stacking 😂👌
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👨‍💻#NumPy

‼️ 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

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