Python
wiki.python.org › moin › PythonSpeed › PerformanceTips
PythonSpeed/PerformanceTips - Python Wiki
The Python interpreter performs some periodic checks. In particular, it decides whether or not to let another thread run and whether or not to run a pending call (typically a call established by a signal handler). Most of the time there's nothing to do, so performing these checks each pass around the interpreter loop can slow things down.
Miguel Grinberg
blog.miguelgrinberg.com › post › is-python-really-that-slow
Is Python Really That Slow? - miguelgrinberg.com
My standard response when someone asks me how I deal with Python being such a slow language is that Python is by far the fastest to write, cleanest, more maintainable programming language I know, and that a bit of a runtime performance penalty is a small price to pay when I'm rewarded with significant productivity gains.
News faster CPython, JIT and 3.14
I worked a bit on studying the effect of the new CPython JIT and more generally of performance for simple CPU bounded pure Python code. I focused on a very simple benchmark involving only very simple pure Python: def short_calcul(n): result = 0 for i in range(1, n + 1): result += i return result ... More on discuss.python.org
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March 20, 2025I’m starting a series on Python performance optimizations, Looking for real-world use cases!
For API development utilizing async has to be at the top for me. Switching from synchronous to asynchronous has been a game changer for me, when I am choosing libraries I look for ones that have this capability if it’s not available then I look for ways to integrate it. One example is using taskiq over celery for sending longer tasks to worker nodes asynchronously. More on reddit.com
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August 23, 2025Why Python is slow and how to make it faster
Seeing lots of comments being dismissive of the efforts of improving Python is a bit disheartening. Of course you should strive to write proper code, it's doesn't mean that having a good, fast implementation of the language isn't a large improvement. I'm thankful for people working on this, just getting glue code, scripts or not easily JIT'able code to run faster is very cool! More on reddit.com
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January 8, 2024How much faster will python be in 10 years?
To my best approximation, it'll be about 69% faster than 2023 levels.
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October 10, 2023Videos
27:57
Performant Python Patterns Small changes to speed up your Python ...
01:38:32
High Performance Python; Improving Code Efficiency and Performance ...
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Your Code Can Be FASTER | Measure and Fix Your Python's Performance ...
03:49
Python Performance Optimization Explained (2026 Guide) - YouTube
01:54
These 10 Tricks Make Python Code Way Faster - YouTube
00:52
THIS makes your Python faster - YouTube
Python
speed.python.org
Python Speed Center
Analyze performance over time · Compare different executables and revisions · Powered by Codespeed, Django and Python
GitHub
github.com › python › pyperformance
GitHub - python/pyperformance: Python Performance Benchmark Suite · GitHub
The pyperformance project is intended to be an authoritative source of benchmarks for all Python implementations. The focus is on real-world benchmarks, rather than synthetic benchmarks, using whole applications when possible.
Starred by 1K users
Forked by 202 users
Languages Python 84.0% | HTML 14.4% | Shell 1.6%
Medium
medium.com › @hieutrantrung.it › pythons-performance-revolution-how-3-11-made-speed-a-priority-4cdeee59c349
Python’s Performance Revolution: How 3.11+ Made Speed a Priority | by Trung Hiếu Trần | Medium
January 5, 2026 - This article explores how Python transformed from a language often dismissed for performance-critical work into one actively competing on speed, examining the technical innovations introduced in Python 3.11 through 3.14 (released October 2025[³]) that are making Python faster than ever before.
Medium
klaviyo.tech › how-to-improve-python-performance-6a43210f7359
How to Improve Python Performance | Klaviyo Engineering
December 19, 2022 - All timings in this post are in seconds, and unless otherwise noted, all timings were run using Python 3.11.0 on a 2.8 GHz Quad-Core Intel Core i7 MacBook with 16GB of RAM. Python 3.11.0 speed was nearly identical to Python 3.9.16 for almost all runs. ... As expected, hash table-based data structures like Counter, dict, frozenset, OrderedDict, and set performed the best.
TheServerSide
theserverside.com › tip › Tips-to-improve-Python-performance
9 tips to improve Python performance | TheServerSide
To select the best type to use for a collection, you must first understand the data with which you are working and the operations you want to perform. Using timeit we can see that testing membership with a set can be significantly faster than with a list: > python testtimeit_data_type.py Execution time(with list): 7.966896300087683 seconds Execution time(with set): 4.913181399926543 seconds
Lost
lost.co.nz › articles › sixteen-years-of-python-performance
Sixteen Years of Python Performance - Lost
September 9, 2024 - I build and benchmarked 17 versions of Python against each other so that you don't have to. Let's compare the performance of every Python version from 2.6 all the way to the current 3.14 alpha release. Spoiler, it's going great!
SOFTFORMANCE
softformance.com › home › blog › 25 tips for optimizing python performance
Optimizing Python Code for Performance: Tips & Tricks | SoftFormance
January 10, 2024 - While writing Python code, the developers should review it regularly. The point is to remove unnecessary code parts and save memory. There are multiple ways for removing dead code. These include multiprocessing, using content managers, and relying on preload managers. Don’t forget to monitor the performance of your Python apps because this allows you to properly evaluate the efficiency of your work.
Readthedocs
pyperformance.readthedocs.io
The Python Performance Benchmark Suite — Python Performance Benchmark Suite 1.14.0 documentation
The pyperformance project is intended to be an authoritative source of benchmarks for all Python implementations.
JetBrains
blog.jetbrains.com › pycharm › 2025 › 11 › 10-smart-performance-hacks-for-faster-python-code
10 Smart Performance Hacks For Faster Python Code | The PyCharm Blog
November 17, 2025 - Whenever possible, modify objects in place instead of creating duplicates. This reduces memory usage and improves performance by avoiding the overhead of allocating and populating new structures. Many built-in data structures in Python provide in-place methods (e.g.
Sentry
blog.sentry.io › python-performance-testing-a-comprehensive-guide
Python Performance Testing: A Comprehensive Guide | Sentry
September 30, 2022 - Performance testing verifies and documents a system's speed, reliability, and scalability. It's testing that, rather than focusing on features and functionality, tests how software responds to a load and how quickly it executes its tasks.
Stackify
stackify.com › 20-simple-python-performance-tuning-tips
Python Performance Tuning: 20 Simple Tips - Stackify
May 2, 2023 - Python optimizes developer productivity, but many solutions aren't always optimized for python performance. Here are 20 tips to improve performance.
Medium
effective-programmer.com › high-performance-python-70d73de83a75
High Performance Python | Effective Programmer | Effective Programmer
March 9, 2025 - Simple and Effective Techniques for Improving Python Performance.
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[image] Pierre Augier:
One can argue “not ready”, “just infrastructure”, “retaining correctness and making sure it doesn’t hurt performance rather than making everything faster”, but seeing that the project is still stuck in this state is not great, especially when one compares with the promises…
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The JIT is not finished yet and is classified as experimental. In its current stage, it does not yet attempt to improve performance. The first phase was getting it designed, developed, and released at all. Now that the framework is in place, performance can improve as work continues.
Reddit
reddit.com › r/python › i’m starting a series on python performance optimizations, looking for real-world use cases!
r/Python on Reddit: I’m starting a series on Python performance optimizations, Looking for real-world use cases!
August 23, 2025 -
Hey everyone,
I’m planning to start a series (not sure yet if it’ll be a blog, video, podcast, or something else) focused on Python performance. The idea is to explore concrete ways to:
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Make Python code run faster
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Optimize memory usage
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Reduce infrastructure costs (e.g., cloud bills)
I’d love to base this on real-world use cases instead of just micro-benchmarks or contrived examples.
If you’ve ever run into performance issues in Python whether it’s slow scripts, web backends costing too much to run, or anything else I’d really appreciate if you could share your story.
These will serve as case studies for me to propose optimizations, compare approaches, and hopefully make the series valuable for the community.
Thanks in advance for any examples you can provide!
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For API development utilizing async has to be at the top for me. Switching from synchronous to asynchronous has been a game changer for me, when I am choosing libraries I look for ones that have this capability if it’s not available then I look for ways to integrate it. One example is using taskiq over celery for sending longer tasks to worker nodes asynchronously.
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The 1 billion row challenge has been done in many languages, including Python and it is more realistic than people would assume. Anyone who has tried a implementing <20 sec pure Python solution has surely encountered the following issues; usually in this order: RAM exhaustion. Choosing our performance metrics (ie: time, memory, wattage?). What tools to use to measure chosen metrics (there is no one size fits all). Encoding/decoding overhead. Minimizing memory allocations. Arithmetic computation overhead for min/avg/max values + rounding (a lot of solutions didn't bother with this even though it was required). 'Indexing' a large file for multiprocessing solution. Rapid Python object creation (creates CPU bottlenecking). All of these could be subjects for your channel. And if you think it is not a real world scenario, I have worked at companies that had situations incredibly similar to this.
Real Python
realpython.com › python-profiling
Profiling in Python: How to Find Performance Bottlenecks – Real Python
May 3, 2024 - But, when you look closer at the documentation of random.uniform() or its implementation, then you’ll find that it’s a pure-Python function. Such functions can be orders of magnitude slower than built-in functions implemented in C. In this case, you can safely replace the call to uniform(0, 1) with random() because both functions are mathematically equivalent for these specific input values. When you do, you’ll observe an improvement in computation time—by as much as a whopping 40 percent! For the ultimate performance analysis experience, though, you’ll want to use the Linux perf tool.
DEV Community
dev.to › leapcell › python-performance-tips-you-must-know-24n5
Python Performance Tips You Must Know - DEV Community
January 29, 2025 - To improve performance, we should try to reduce unnecessary function calls and attempt to combine multiple operations into one, thereby reducing execution time and resource consumption.
O'Reilly
oreilly.com › library › view › high-performance-python › 9781492055013
High Performance Python, 2nd Edition [Book]
April 30, 2020 - Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.
Authors Micha GorelickIan Ozsvald
Published 2020
Pages 468
GeeksforGeeks
geeksforgeeks.org › python › tips-to-maximize-your-python-code-performance
10 Tips to Maximize Your Python Code Performance - GeeksforGeeks
July 23, 2025 - Use built-in functions and libraries- Python has a lot of built-in functions and libraries that are highly optimized and can save you a lot of time and resources. Avoid using global variables-Global variables can slow down your code, as they ...
