NumPy - The Complete Guide | Python for Data Science
In this video, I provide a complete overview of the NumPy package.
I provided background information on how NumPy ndarrays are faster than the Python Lists.
I started with the basics of creating arrays and then gets into the more advanced stuff like Broadcasting, Pseudo-Random Numbers, Image Processing, etc.
Source Code: https://github.com/SaiBalaji202/NumPy...
For More Mathematical Functions - https://numpy.org/doc/stable/referenc...
This tutorial helps you to learn following topics:
1. NumPy Intro - 00:05
2. List vs NumPy - 00:26
3. Installing NumPy - 04:30
4. Different ways to create NumPy & ndarray - 05:52
4.1 Creating Arrays with Python Lists - 06:09
4.2 Array with Default Values - 07:58
4.3 Identity Matrix - 11:30
4.4 Array with Random Numbers - 11:54
4.5 Pseudo Random Numbers and seed() - 14:13
4.6 seed() - How it will work? - 15:03
4.6.1 seed() - Practical Example - 16:32
5. Important Attributes and Methods - 19:01
5.1 Attributes - 19:01
5.2 Methods - 20:23
6. Fetch Data - 24:04
7. Fancy Indexing - 27:40
8. Conditional Selection - 30:19
9. Arithmetic Operations - 31:28
9.1 Array with Array - 31:28
9.2 **Broadcasting - 33:52 (The Problem) & 34:25 (What is Broadcasting?)
9.2.1 Broadcasting Rules - 35:01
9.2.2 Practicing Broadcasting - 35:58
9.2.2 Will the stretching happens really? - 42:28
9.3 Arithmetic Operations on Scalar - 42:56
9.4 Zero Division - Error or Warning? - 43:17
1 0. Copy (Views vs Copy) - 44:07
10.1 Shallow Copy - 44:11
10.2 Python Shallow Copy - 45:19
10.2 Deep Copy in Python - 45:39
10.3 Deep Copy in NumPy - 46:00
11. Mathematical Functions - 47:35
12. Dot Product - 48:33
13. Sorting - 49:39
14. Converting Image to a NumPy Array - 50:38
15. Size of Array - 52:19
16. Conclusion- 52:41
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