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

Wishlist Share
Share Course
Page Link
Share On Social Media

About Course

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!

Show More

What Will You 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

Course Content

Comparing Performance Python, Java, C, C++, Rust and Go

  • Implementing Prime Number Generator in Python
    20:16
  • Implementing a prime number generator in Java
    17:22
  • Implementing a prime number generator in C and C++
    21:54
  • Implementing a prime number generator in Rust
    16:21
  • Implementing a prime number generator in Go
    11:10
  • Comparing prime number generators written using Python, Java, C, Rust and Go
    19:27

Improving Performance of Python Code
In this section, I will cover the tricks and techniques to improve performance of Python code using alternative Python language runtimes and using other tools and frameworks to compile Python to native code.

Improving Performance of threads in Python
In this section, I will teach you how to improve performance of threads (provided by the threading module) in Python. You will understand about the implications of Global Interpreter Lock (GIL) and how to overcome the same to implement parallel processing in Python

Student Ratings & Reviews

No Review Yet
No Review Yet

Want to receive push notifications for all major on-site activities?