Numpy Fundamentals | Data Manipulation
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In this video, you’ll learn how to manipulate NumPy arrays for powerful data workflows—reshape them, flatten and ravel, transpose, concatenate and stack in any dimension, split into sub-arrays, insert/append/delete elements, manage copies vs views, broadcast operations across mismatched shapes, and sort with indices. These techniques are core to data analysis, machine learning pipelines, and scientific computing.
To try all these examples without installing Python locally, head over to Google Colab:
Introduction to Google Colab: • Google Colab Basics
Create a new Google Colab Notebook: https://colab.research.google.com/#cr...
Table of Contents:
0:00 Introduction to NumPy Array Manipulation
0:18 Importing NumPy as np
0:28 Inspecting Array Shape with .shape
1:23 Reshaping Arrays with .reshape
2:32 Flatten vs. Ravel (copy vs. view)
3:47 Transposing Arrays (.T and .transpose())
4:20 Concatenating Arrays (np.concatenate)
6:00 Vertical & Horizontal Stacking (vstack, hstack)
8:58 Depth Stacking & General np.stack
10:03 Splitting Arrays (split, hsplit, vsplit, dsplit)
12:46 Inserting, Appending, Deleting Elements
14:13 Copying vs. Viewing Arrays (.copy, .view, assignment)
16:37 Broadcasting Operations on Different Shapes
18:09 Sorting Arrays & Retrieving Sort Indices (argsort)
19:00 Wrap-Up & Further Resources
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