Data Science

Being fluent in NumPy goes a long way in becoming a data scientist 🏃 Today we are taking an important step in that direction! 🚀 . Wanna know more? Check out the slides! . 👨‍💻#NumPy

Level up your Python skills with our Telegram channel! 🐍🔥 Join now for valuable Python insights, tutorials, and community discussions. Let's learn and code together! 💻🌟 https://t.me/+gumUMX-TjOdiOGY0

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. . Wanna know more? Check out the slides! . . 👨‍💻#NumPy

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

This post is inspired by a great question on the last post! So keep asking great questions and motivate future posts 💪 . . 👨‍💻#NumPy

[🗣 Instagram always experiments with what posts show up on your feed, turn on Post Notifications to see u0040bigdataguru’s posts as soon as they’re available 👌] . np.cumsum() is a useful function when it comes to doing big data cumulative sums. See it, learn it, and use it 💪 . . . 👨‍💻#NumPy

[ Check out the quiz posted in stories to check your understanding 👌] . np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! 🎉👌 . Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match! . 👨‍💻#NumPy

Part 10 🎉 of Intro to NumPy, what a journey guys, thanks for sticking around for these posts!! On to part 100 shall we?? . Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning! . . 👨‍💻#NumPy

🗣 Turn on post notifications to be always in the know . NumPy part 9: np.where() . np.where() returns the indices where the condition is met (not the elements themselves) 👌 . . . 👨‍💻#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 🤯😏. . 👨‍💻#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 ⁉️❓ . 👨‍💻#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 😂👌 . 👨‍💻#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. . 👨‍💻#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 . 👨‍💻#NumPy

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. . 👨‍💻#NumPy

Telegram Is Banning All Channels Now Join Our Back Up Channel Now 🔥 Channel Link: https://t.me/+RBQ3r1I5bKMzMzU0 Deleting link soon Join Now ☝️☝️☝️ Please Join our back up channel now before this one is banned 🙌 JOIN NOW

Let’s continue our NumPy discussion, shall we? 👍 . I found NumPy’s matmul and multiply confusing when I was starting out, so why not compare them in a post? . matmul is the equivalent of the dot product between two matrices, where the last dimension of the first matrix has to be equal to the first dimension of the second matrix. Easy, right? 💪 . On the other hand, multiply is the convenient operation of taking two same size matrices and multiplying them element wise. This will come in handy when we talk about building deep neural nets 🔜 . Don’t forget to swipe to see the code examples ‼️‼️ . Question: NumPy has another operation called “dot” 🤯 what do you think that does (hint is in this post) . 👨‍💻#NumPy

NumPy is one of my favorite packages. It’s one of the most powerful numeric packages available on any programming languages..Tied with Python, it makes for a quick script fiddling with a lot of numbers..Did you know that you could develop your machine learning algorithms in NumPy? That’s how I initially learned to implement my AI algorithms..Btw, does anyone have the answer to guru’s question in the last line of code? 🤔 . 👨‍💻#NumPy

🗣️ SWIPE FOR DEMO . Last Part of COVID-19 Data Visualization Project with Bokeh 😍 . In this part, you’re going to add a slider to browse through the dates and make it an interactive demo

🗣️ Presenting COVID-19 Data Visualization Project with Bokeh (Part 2) . In this part, you’re going to use the world diagram from the previous part and color code it based on COVID-19 total cases.