NumPy Operations - Ultimate Guide to Methods and Functions for Beginners!

Video Description

In this tutorial, we will dive much deeper into NumPy - focusing on important array methods, functions and of course - math! 🤓 We will begin with a quick recap of the previous tutorial and we will then move on with lots of detailed examples and handy tricks! You can find links to my previous tutorial (and other NumPy-related videos of mine) in the related videos section below. 📝 quick note: at 24:18 - the floating point numbers originated from (b/a) and not from np.floor() ⭐ CLONE MY CODE ⭐ ------------------------------------------------------- ** Sorry code is unavailable, RIP Wayscript 😭😭😭** 🎥 RELATED VIDEOS 🎥 ------------------------------------------------------- ⭐ Ultimate Guide to NumPy Arrays (PART 1 of this tutorial): https://youtu.be/lLRBYKwP8GQ ⭐ Python Learning Roadmap: https://youtu.be/wFEC7VbWBZo ⭐ Train Basic Neural Network with NumPy and Pandas: https://youtu.be/xpPX3fBM9dU ⭐ Basic Guide to Pandas: https://youtu.be/zN2Hua6oII0 ⏰ TIME STAMPS ⏰ ------------------------------------------------------- 00:00 - intro ---------------♻️ RECAP ♻️---------------- 00:30 - create 2D demo arrays 01:56 - dtype attribute ------------------------------------------------------- 02:52 - fill array with values 03:41 - assignment operators 04:12 - NumPy is Python or C? 06:11 - sum of array 06:52 - sum of columns or rows only 08:36 - product of array 09:15 - average of array (mean) 09:31 - minimum and maximum values 10:02 - index of minimum and maximum values 10:33 - peak to peak method (ptp) 11:10 - size attribute 11:50 - flatten vs ravel methods 13:03 - repeat function 14:10 - non-flat repeat function 14:50 - unique function 15:20 - diagonal function 16:05 - convert array to list 16:38 - save array to file 17:12 - swap axes of array 17:56 - transpose method 18:27 - T attribute 19:02 - NumPy documentation 20:07 - simple operations on 2 matrices 21:12 - modulo 22:31 - floor division 24:26 - matrix multiplication 27:41 - thanks for watching! :) 🔗 LINKS AND CREDITS 🔗 ------------------------------------------------------- ⭐ NumPy Documentation: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html ⭐ Emoji from: https://www.flaticon.com/ ⭐ Text Effects from: https://mixkit.co/free-premiere-pro-templates/titles/