MP4 | Video: h264, 2560×1440 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 97 Lessons (4h 31m) | Size: 1.9 GB
Course Summary
Python memory management is often a black box to most developers. You probably know that Python uses reference counting, but how can you write code most efficiently to work with it? Did you know it also uses garbage collection? Do you know when that kicks it and its performance implications? With this course, you’ll learn all of these concepts and more. You will explore them with concrete code examples, not just theories. And you will learn to optimize your code to both use less memory and to run faster.
What students are saying
Can’t recommend this highly enough to anyone who wants to learn to code or learn Python in particular. One of Michael’s courses changed my career. Can’t say enough good things about Talk Python training.
— Chris
Source code and course GitHub repository
github.com/talkpython/python-memory-management-course
What’s this course about and how is it different?
This very unique course will teach not just how Python memory management works and how to create code that functions well within that world, it will provide many concrete techniques, tools, design patterns, and more to make your programs more memory efficient and computationally faster to boot.
If Python memory (allocations, clean up, and so on) has always felt like a weird black box that you have had to take for granted, join this course and open that box. There are many beautiful and interesting aspects of Python’s runtime behavior making your code run. You should understand what’s happening on your behalf.
What topics are covered
In this course, you will:
Learn how Python variables and data structures actually look in the CPython layer
See how the small object allocator treats most objects differently than your intuition
Understand Python’s memory allocation primitives: blocks, pools, and arenas
Locate the elements on C code responsible for Python memory behavior
See reference counting in action with live code explorations
Discover why reference counting alone is not enough for memory cleanup
Work with Python’s GC and see when it’s needed, and when it’s not
Compare different data structures to get a sense of their relative size
Use multiple clever but simple techniques to massively reduce memory during function calls
Lighten up your classes with properties
Leverage multiple memory profilers to investigate memory usage line by line and over time
And lots more
View the full course outline.
Who is this course for?
This course is for anyone who wants to understand how Python memory is managed and make their code more efficient and faster. If you’re tired of Python memory being a black box hiding its behavior, turn on the light with this course.
The student requirements are quite light for this course. You’ll need Basic Python language knowledge:
Classes
Functions
Properties
Variables
Loops
Iteration
Note: All software used during this course, including editors, Python language, etc., are 100% free and open source. You won’t have to buy anything to take the course.
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