Numpy Fundamentals | Array Operations

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In this video, you’ll learn how to perform efficient element-wise arithmetic with NumPy arrays and take advantage of its universal functions (ufuncs) for fast, vectorized computations. We’ll cover basic operators (+, –, *, /, **) and their ufunc equivalents (np.add, np.subtract, np.multiply, np.divide, np.power), then dive into trigonometric and special functions (np.sign, np.cos, np.tan, np.exp, np.log). Finally, you’ll see how to use comparison ufuncs (np.greater, np.less, etc.) to produce Boolean masks—essential tools for data analysis, machine learning, and scientific computing.

To follow along without installing anything locally, try this in Google Colab:
Introduction to Google Colab:    • Google Colab Basics  
Create a new Colab Notebook: https://colab.research.google.com/#cr...

Table of Contents:

0:00 Introduction to NumPy Arithmetic & UFuncs
0:18 Importing NumPy as mp
0:36 Element-wise Operators Overview
0:48 Creating Sample Arrays with np.arange
1:18 Addition: + vs. np.add
1:47 Subtraction: – vs. np.subtract
2:17 Division: / vs. np.divide
2:43 Exponentiation: ** vs. np.power
3:05 What Are Universal Functions?
3:25 Arithmetic UFuncs in Action
3:47 Trigonometric & Special UFuncs (sign, cos, tan, exp, log)
4:23 Comparison UFuncs & Boolean Arrays
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