+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials)
+--- Thread: Python Performance Hacks - Part 1: Make Your Code Run Faster (/Thread-Python-Performance-Hacks-Part-1-Make-Your-Code-Run-Faster)
Python Performance Hacks - Part 1: Make Your Code Run Faster - AD-TEAM - 12-29-2024
![[Image: 38533975e19775ac63ddbc12aea3ae41.jpg]](https://i124.fastpic.org/big/2024/1229/41/38533975e19775ac63ddbc12aea3ae41.jpg)
Python Performance Hacks - Part 1: Make Your Code Run Faster
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.37 GB | Duration: 5h 30m
Master the craft of making Python code run faster comparable to code written in C/C++ and Rust programming languages
[b]What you'll learn[/b]
Compare performance of a simple compute intensive program in Python, C, Java, Rust and Go
Learn how to make your python code faster comparable to similar code written in C and Rust
Learn the tips and techniques to improve performance of Python code
Learn how to use PyPy, Pythran, Cython and Numba to improve performance of Python code
Learn about the limitations and best practices for using PyPy, Pythran, Cython and Numba
Learn how to improve performance of Threads in Python
[b]Requirements[/b]
Basic Knowledge of Python Programming
[b]Description[/b]
There's a famous quote that says "If you want to code faster, use Python; but if you want your code to run faster - just use C"!In this course - Python Performance Hacks - Part 1: Make Your Code Run Faster, I will teach how to make your python code run as fast as those written in C/C++/Rust. You will learn practical, hands-on techniques to enhance the speed and efficiency of your Python applications. This course is designed for Python developers who want to maximize performance without sacrificing code readability or maintainability. Whether you're developing web applications, data analysis scripts, or backend processes, you'll find the skills to optimize your code for high performance without sacrificing the great features, benefits and the essence of Python programming language.In this course, we'll dive into the essential strategies for improving Python performance, covering tools and alternative language runtimes that perform Just-In-Time compilation, Ahead-Of-Time optimization and much more. You will learn how to make your python code run fast comparable to code written in C/C++/Rust. You will also learn about the best practices and use-case scenarios for these tools in your python code. You will also learn tricks to parallelize threads (circumventing the limitations of Global-Interpreter-Lock or GIL).By the end of this course, you'll have a toolkit of performance-enhancing techniques to take your Python skills to the next level. Say goodbye to slow-running programs and hello to code that's lean, powerful, and optimized for speed. Enroll to this course to transform your Python skills and make your code run faster than ever!
Overview
Section 1: Comparing performance of Python, Java, C, Rust and Go
Lecture 1 Implementing a prime number generator in Python
Lecture 2 Implementing a prime number generator in Java
Lecture 3 Implementing a prime number generator in C and C++
Lecture 4 Implementing a prime number generator in Rust
Lecture 5 Implementing a prime number generator in Go
Lecture 6 Comparing prime number generators written using Python, Java, C, Rust and Go
Section 2: Improving Performance of Python Code
Lecture 7 PyPy Overview: Features, Limitations and Best Use-Case Scenarios
Lecture 8 Cython Overview: Features, Limitations and Best Use-Case Scenarios
Lecture 9 Numba Overview: Features, Limitations and Best Use-Case Scenarios
Lecture 10 Pythran and Codon: Overview
Section 3: Improving performance of Python threads
Lecture 11 Comparing thread performance among programs written in Python, Java, C and Rust
Lecture 12 Improving Thread Performance in Python
Beginner Python developers who want to improve performance of their Python code
![[Image: 1bgGxVRg_o.jpg]](https://images2.imgbox.com/0c/ba/1bgGxVRg_o.jpg)