NumPy Axis Explained | Aggregate Functions in NumPy | Python for Data Science| Part8
In this video, we break down NumPy’s powerful aggregate functions like sum(), mean(), min(), max() and show how they work across different axes using real-world examples. Whether you're a Python beginner, a data science student, or someone preparing for machine learning and AI, this video will help you master axis=0 vs axis=1 vs axis=2 for 1D, 2D, and 3D arrays.
👩🏫 What you'll learn:
What are aggregate functions in NumPy?
Difference between axis=0, axis=1, and axis=2
Easy-to-understand real-life examples (students and marks)
How to use np.sum(), np.mean(), np.min(), np.max() on different shaped arrays
Best practices when applying aggregation across dimensions
🔧 Tools Covered:
Python
NumPy
🧠 Perfect for:
Python Learners | Data Analysts | ML & AI Beginners | Students learning NumPy | Coding Interviews Prep
📌 Watch previous videos in this NumPy tutorial playlist to follow along!
📁 Code and resources: https://github.com/NikitasGithub/nump...
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