Python Performance Hacks - Part 1: Make Your Code Run Faster

Master the craft of making Python code run faster comparable to code written in C/C++ and Rust programming languages

What you'll learn

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

Requirements

Basic Knowledge of Python Programming

Description

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