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In this tutorial, you'll learn how to profile your Python programs using numerous tools available in the standard library, third-party libraries, as well as a powerful tool foreign to Python. Along the way, you'll learn what profiling is and cover a few related concepts.
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Jul 2, 2024 · Python's performance is deeply influenced by its interpreter architecture. Unlike compiled languages that convert code into machine language beforehand, Python processes code line-by-line. This line-by-line execution offers advantages in rapid prototyping and development flexibility but comes with trade-offs in speed, especially in tasks requiring intensive computation.
- Use list comprehensions. When you’re working in Python, loops are common. You’ve probably come across list comprehensions before. They’re a concise and speedy way to create new lists.
- Remember the built-In functions. Python comes with a lot of batteries included. You can write high-quality, efficient code, but it’s hard to beat the underlying libraries.
- Use xrange() instead of range(). Python 2 used the functions range() and xrange() to iterate over loops. The first of these functions stored all the numbers in the range in memory and got linearly large as the range did.
- Consider writing your own generator. The previous tip hints at a general pattern for optimization—namely, that it’s better to use generators where possible.
Feb 24, 2024 · In the realm of programming, Python stands out for its readability and ease of use, but it is often criticized for its performance. However, with the right techniques, Python’s performance can be…
Apr 26, 2024 · Tools and Techniques for Performance Testing in Python. Popular Python Profiling Tools. Developers can choose from a variety of tools that facilitate detailed performance analyses: Built-in Modules: Python’s own cProfile and profile modules provide essential profiling data that can help identify which functions are slow and why.
Yappi is a python profiler that allows you to easily identify performance bottlenecks. It is written in C, making it one of the most efficient profilers available. It has a customizable API that lets you profile only the specific parts of your code that you need to focus on, giving you more control over the profiling process.
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Mar 31, 2024 · Python’s built-in functions and libraries are designed for high performance. For data-heavy tasks, leveraging libraries like NumPy and Pandas can lead to significant speed improvements. Built-in ...
