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#4655
Enhancing Python Performance Training
Course 4655
- Duration: 1 day
- Sandbox: Yes
- Language: English
- Level: Advanced
Python is a slow language---but there are many ways to squeeze performance out of it. This hands-on course examines techniques and tools for speeding up your Python apps.
Train your whole team by bringing this course to your facility.
Team training is available online and in-person.
Enhancing Python Performance Training Information
In this Python course, you will learn how to:
- Identify bottlenecks in your apps.
- Use concurrent execution to make better use of your computer's resources.
- Speed up numerical apps using NumPy.
- Gain performance improvements using JIT compilation.
Prerequisites
You should first complete Learning Tree course 1905, Introduction to Python Training.
This is an advanced course that assumes familiarity with Python programming. However, it applies to all Python communities (e.g., web development, data science, automation).
Enhancing Python Performance Training Outline
- Measuring execution time
- cProfile
- py-spy
- Concurrency in Python
- threading
- asyncio
- multiprocessing
- Basic optimisations
- NumPy
- Numba
- JAX
- PyPy
- Cython
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Enhancing Python Performance Training FAQs
This course is for experienced Python programmers looking to expand their Python experience.
Yes. This is an advanced course that assumes familiarity with Python programming. However, it applies to all Python communities (e.g., web development, data science, automation).
The course focuses on gaining performance through using Python code. While languages such as C/C++ and Rust are essential in developing high-performance Python applications, they are beyond the scope of this course.
The course doesn't focus on any particular IDE. Instead, both Visual Studio Code and PyCharm are provided for use in exercises.
Yes. There are various opportunities to apply the ideas presented to sample Python apps.