Python
wiki.python.org › moin › PythonSpeed › PerformanceTips
PythonSpeed/PerformanceTips - Python Wiki
There is a function in the sys module, setcheckinterval, which you can call to tell the interpreter how often to perform these periodic checks. Prior to the release of Python 2.3 it defaulted to 10. In 2.3 this was raised to 100. If you aren't running with threads and you don't expect to be catching many signals, setting this to a larger value can improve the interpreter's performance, sometimes substantially.
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 - By initializing a list or array with a fixed size, you avoid repeated resizing and allow Python (or libraries like NumPy) to manage memory more efficiently. This technique is particularly valuable in numerical computations, simulations, and large-scale data processing, where even small optimizations can add up. Pre-allocation helps reduce fragmentation, improves cache locality, and ensures more predictable performance.
I’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
60
69
August 23, 2025News 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
0
1
March 20, 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
186
305
January 8, 2024How does Python 3.13 perform vs 3.11 in single-threaded mode?
There are lots of different ways to measure performance. If you care about a specific workload, you'll have to benchmark it yourself. There are various resources that compare performance between different Python versions or describe optimization work: https://lost.co.nz/articles/sixteen-years-of-python-performance/ https://speed.python.org/ https://github.com/faster-cpython ← includes archives of benchmark results The TL;DR is that CPython tends to make performance improvements with every release, though individual benchmarks might see regressions. Historically, there was a large regression when switching to Python 3, but that is irrelevant now. Python 3.11 saw significant work on performance (reported as 25% average uplift in the release notes ). While you can expect Python 3.13 to be a bit faster, it focused on laying the groundwork for larger optimizations in the future (JIT, free-threaded mode). Those features are too experimental to be used in production, though. If you care about the last 5% of performance, I'd recommend compiling Python yourself with optimizations for your specific CPU architecture. Pre-built binaries tend to sacrifice a bit of performance for broader compatibility. More on reddit.com
40
104
April 27, 2025Videos
27:57
Performant Python Patterns Small changes to speed up your Python ...
01:38:32
High Performance Python; Improving Code Efficiency and Performance ...
14:12
Make Your Python Code 10x Faster (7 Simple Fixes) - YouTube
16:06
Using Python? Start here! | Maximize Your Python's Speed Part 1 ...
10:00
How Much FASTER Is Python 3.13 Without the GIL? - YouTube
00:58
Top 5 API Performance Tips #javascript #python #web #coding ...
Miguel Grinberg
blog.miguelgrinberg.com › post › python-3-14-is-here-how-fast-is-it
Python 3.14 Is Here. How Fast Is It? - miguelgrinberg.com
In Python 3.13 the free-threading interpreter ran about 2.2x faster than the standard interpreter. In 3.14 the performance improvement is about 3.1x.
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.
TheServerSide
theserverside.com › tip › Tips-to-improve-Python-performance
9 tips to improve Python performance | TheServerSide
The following nine tips specifically target Python performance, although several could apply to other languages as well: Select correct data types. Know standard functions, methods and libraries.
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:
-
Make Python code run faster
-
Optimize memory usage
-
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!
Top answer 1 of 5
44
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.
2 of 5
14
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.
Miguel Grinberg
blog.miguelgrinberg.com › post › is-python-really-that-slow
Is Python Really That Slow? - miguelgrinberg.com
A JIT compiler is a module that translates Python bytecode to machine code on the fly at runtime, which can result in significant performance improvements.
Upsun
upsun.com › blog › python-performance-improvements
How Python's updates make it faster and more efficient | Upsun
January 8, 2026 - From specialized, adaptive interpreters to more efficient memory management and the introduction of JIT compilation, Python’s recent versions are evolving rapidly, offering performance gains of ~10%-60% across various workloads.
Lost
lost.co.nz › articles › sixteen-years-of-python-performance
Sixteen Years of Python Performance - Lost
September 9, 2024 - For more than 70 years computer ... them faster and more memory efficient. Python recently replaced its legendary TimSort, and today Rust 1.81 released with two new adaptive sorting algorithms featuring serious performance improvements....
Python.org
discuss.python.org › python help
News faster CPython, JIT and 3.14 - Python Help - Discussions on Python.org
March 20, 2025 - 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 def long_calcul(num): result = 0 for i in range(num): result += short_calcul(i) - short_calcul(i) return result I’m not claiming that this code is representati...
Reddit
reddit.com › r/python › why python is slow and how to make it faster
r/Python on Reddit: Why Python is slow and how to make it faster
January 8, 2024 -
As there was a recent discussion on Python's speed, here is a collection of some good articles discussing about Python's speed and why it poses extra challenges to be fast as CPU instructions/executed code.
-
Pycon talk: Anthony Shaw - Why Python is slow
-
Pycon talk: Mark Shannon - How we are making CPython faster
-
Python 3.13 will ship with --enable-jit, --disable-gil
-
Python performance: it’s not just the interpreter
-
Cinder: Instagram's performance-oriented Python fork
Also remember, the raw CPU speed rarely matters, as many workloads are IO-bound, network-bound, or a performance question is irrelevant... or: Python trades some software development cost for increased hardware cost. In these cases, Python extensions and specialised libraries can do the heavy lifting outside the interpreter (PyArrow, Polards, Pandas, Numba, etc.).
Top answer 1 of 25
289
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!
2 of 25
73
I love python but i came to hate dynamic typing. Yeah you have the typing module but that's just a band-aid. And it boggles my mind that in 2024 such a mainstream language does not support proper multi-threading. Good thing they are working on this.
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 ...
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 - ... Result: Up to 10–20% improvement in function-heavy code. ... While not strictly a performance feature, Python 3.11’s improved error messages help developers debug faster — an indirect but real performance win.
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.
Medium
medium.com › @quanticascience › performance-optimization-in-python-e8a497cdaf11
Performance Optimization in Python | by QuanticaScience | Medium
February 24, 2024 - Garbage Collection: Python automatically identifies and recycles memory that is no longer in use by objects, using reference counting and generational cycles to improve efficiency. Memory Profiling: Tools like memory_profiler and psutil are available to monitor memory usage, helping developers to pinpoint and resolve memory-related issues. Efficiency in memory utilization not only enhances performance but is also pivotal for the scalability of applications.
SOFTFORMANCE
softformance.com › home › blog › 25 tips for optimizing python performance
Optimizing Python Code for Performance: Tips & Tricks | SoftFormance
January 10, 2024 - Some of the techniques for improving Python code performance include concatenating strings with join, applying multiple assignments, using generators as keys for sorting, interning strings, and using the built-in “timeit” module.
Reddit
reddit.com › r/python › how does python 3.13 perform vs 3.11 in single-threaded mode?
r/Python on Reddit: How does Python 3.13 perform vs 3.11 in single-threaded mode?
April 27, 2025 -
When Python 3.12 was released, I had held back from migrating my Python 3.11 applications as there were some mixed opinions back then about Python 3.12's performance vs 3.11. Then, 3.13 was released, and I decided to give it some time to mature before evaluating it.
Now, we're in Python 3.13.3 and the last bugfix release of 3.11 is out. When I Google'd, I only found performance studies on Python 3.13 in its experimental free-threaded mode, which is definitely slower than 3.11. However, I found nothing about 3.13 in regular GIL mode.
What are you guys' thoughts on this? Performance-wise, how is Python 3.13 compared to Python 3.11 when both are in GIL-enabled, single-threaded mode? Does the experimental JIT compiler in 3.13 help in this regard?
Top answer 1 of 10
167
This sounds like the sort of thing you could do some benchmarks on and share your results for the next person and to get technical feedback on the outcomes.
2 of 10
79
There are lots of different ways to measure performance. If you care about a specific workload, you'll have to benchmark it yourself. There are various resources that compare performance between different Python versions or describe optimization work: https://lost.co.nz/articles/sixteen-years-of-python-performance/ https://speed.python.org/ https://github.com/faster-cpython ← includes archives of benchmark results The TL;DR is that CPython tends to make performance improvements with every release, though individual benchmarks might see regressions. Historically, there was a large regression when switching to Python 3, but that is irrelevant now. Python 3.11 saw significant work on performance (reported as 25% average uplift in the release notes ). While you can expect Python 3.13 to be a bit faster, it focused on laying the groundwork for larger optimizations in the future (JIT, free-threaded mode). Those features are too experimental to be used in production, though. If you care about the last 5% of performance, I'd recommend compiling Python yourself with optimizations for your specific CPU architecture. Pre-built binaries tend to sacrifice a bit of performance for broader compatibility.
Medium
klaviyo.tech › how-to-improve-python-performance-6a43210f7359
How to Improve Python Performance | Klaviyo Engineering
December 19, 2022 - Tips, tricks, and techniques to optimize Python including NumPy, Scalene, Numba, Cython, and asyncio.
Scout APM
scoutapm.com › blog › python-performance-tips
15 Tips for Better Python Performance | Scout Monitoring
February 16, 2021 - The C code would outshine its Python equivalent in performance every single time. The reason for this is, that when the data type of a variable is explicitly declared to the runtime environments, it can apply optimizations specific to certain data types to improve performance quite easily.
Binmile
binmile.com › blog › python-performance-optimization
Performance Optimization in Python: Tools & Techniques [2025 Guide]
February 10, 2025 - To properly use Cython, one should find the critical parts of the application that are bottlenecks in performance. After discovering these areas, coders can rewrite them in Cython, making the most of C-level speed without compromising on the readability and simplicity of Python. This particular tool not only speeds up the execution but also offers smooth integration with C or C++ libraries, allowing programmers to gain access to an environment of improved, low-level code.
Address 2803 Philadelphia Pike, Suite B 191, 19703, Claymont