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# Utilizing NumPy and Linear Algebra for Quicker Python Code – The Actual Python Podcast

## Feb 24, 2023 1h 8m

Are you continue to utilizing loops and lists to course of your information in Python? Have you ever heard of a Python library with optimized information buildings and built-in operations that may pace up your information science code? This week on the present, Jodie Burchell, developer advocate for information science at JetBrains, returns to share secrets and techniques for harnessing linear algebra and NumPy in your tasks.

Jodie particulars how most individuals start their information science journey utilizing loops to iterate over values and apply operations sequentially. We discuss how loops are pleasant for newbies, being clear to learn and straightforward to debug, however sadly don’t scale nicely, particularly with giant quantities of information.

Jodie shares among the fundamentals of linear algebra and learn how to arrange information into vectors. We discuss how the NumPy library leverages these ideas to enhance information processing. We talk about how the library contains operations for vector and matrix addition and subtraction, and why these operations are extra environment friendly than loops. We additionally cowl how NumPy shops arrays in reminiscence and when working with them is quicker vs when it’s not.

Matters:

• 00:00:00 – Introduction
• 00:02:35 – Vectorize all of the issues! – PyCon UK 2022 Discuss
• 00:06:39 – Changing into acquainted with linear algebra
• 00:09:05 – Rookies begin with loops
• 00:11:25 – Beginning with fundamental linear algebra
• 00:12:25 – The fundamental unit of a vector
• 00:18:06 – NumPy representing vectors in Python
• 00:23:25 – Sponsor: InfluxDB
• 00:24:13 – Block administration
• 00:25:54 – Changing a loop with vector-based operations
• 00:34:06 – NumPy broadcasting
• 00:38:52 – Approximating nearest neighbors
• 00:43:49 – Video Course Highlight
• 00:45:15 – Fixing the issue
• 00:46:44 – Eliminating nested loops
• 00:48:54 – A peek beneath the hood
• 00:53:28 – How arrays vs lists are saved in reminiscence
• 01:00:24 – Contemplating a GPU
• 01:03:37 – Actual Python sources on the topic
• 01:04:08 – Upcoming talks and conferences
• 01:07:31 – Thanks and goodbye

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