But there’s a whole menagerie of tools which, though helpful, never quite solve all the problems you encounter.

Enter `uv` and its magic: it bills itself as a replacement for:

pip, pip-tools, pipx, poetry, pyenv, twine, virtualenv, and more

source.

And I don’t think they are overselling themselves from my limited experience.

It usually takes awhile to convert me to becoming a tool user, but `uv` just worked out of the box, and was able to do some quick magic that turned me into a believer:

1. I could select the version of Python I wanted. This helped me experiment with pre-release libraries of lesser known machine learning packages such as PaddlePaddle’s 3.0rc1.

   > uv init --python 3.14 test
   Initialized project `test` at `/Users/wolf/Development/test`
   > cd test
   > uv add flask
   Using CPython 3.14.0a5
   Creating virtual environment at: .venv
   Resolved 9 packages in 431ms
         Built markupsafe==3.0.2
   Prepared 7 packages in 6.15s
   Installed 7 packages in 3ms
    + blinker==1.9.0
    + click==8.1.8
    + flask==3.1.0
    + itsdangerous==2.2.0
    + jinja2==3.1.6
    + markupsafe==3.0.2
    + werkzeug==3.1.3

2. I could run python scripts, snippets, or whole commands with throwaway virtual environments. With whatever version of Python I wanted—even the unreleased 3.14 to get me ready for some conversations at PyCon 2025.

   > uvx --python python3.14 pycowsay "Hello, from Python 3.14"
   
     -----------------------
   < Hello, from Python 3.14 >
     -----------------------
      \   ^__^
       \  (oo)\_______
          (__)\       )\/\
              ||----w |
              ||     ||

3. It doesn’t fight the system or make you learn many new things: it has a subcommand that replicates the `pip` interface.

   > uv pip install flask
   Resolved 9 packages in 135ms
   Installed 9 packages in 8ms
    + blinker==1.8.2
    + click==8.1.8
    + flask==3.0.3
    + importlib-metadata==8.5.0
    + itsdangerous==2.2.0
    + jinja2==3.1.6
    + markupsafe==2.1.5
    + werkzeug==3.0.6
    + zipp==3.20.2

4. It is, truly, blazingly fast. And, yes, they of course they use Rust. How else do you go fast these days? And stay cool?

So, give it a whirl yourself and let me know what you think. I guarantee you’ll be surprised, and a little (or a lot!) happier programming with Python!

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In this series, I explore the most exciting new features with you. I’ll include code examples so you can follow along. You can use either an early release candidate build, or wait until October for 3.12’s big production debut!

Improved Error Messages

This especially helps beginners. And, in general, all programmers.

Example of when you forget to import something:

>>> sys.version_info
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'sys' is not defined. Did you forget to import 'sys'?

Example of when you capitalize an import wrong:

from collections import chainmap
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name 'chainmap' from 'collections'. Did you mean: 'ChainMap'?

You can read more here.

Faster Comprehension Creation

Dictionary, list, and set comprehensions in previous versions of Python created a new anonymous function, and then called that function for every execution of the comprehension.

The optimization being implemented is called, “inlining,” but if you don’t know what that means no worries. Creating a comprehension will get up to 2x faster without any change from you!

Simple example with Python 3.11:

> python3.11 -m timeit '[i for i in [0]]'
5000000 loops, best of 5: 91.5 nsec per loop

Simple example with Python 3.12:

> python3.12 -m timeit '[i for i in [0]]'
5000000 loops, best of 5: 53 nsec per loop

I saw approximately a 91.5 / 53 = 1.73x speedup.

That’s on my laptop, with an Apple M1 CPU. While your results will vary, you should also see approximately a 2x speedup!

You can read more here.

Gotchas?

Well, the speedup you see also depends on what you were doing inside the comprehension.

The more stuff you do, the less you notice the speedup to the creation time of your comprehension. Sadly, it’s not magic (though Python often feels like magic!)

As an example, even just making a list of 1,000 integers looks almost the same in Python 3.11 and 3.12. Don’t worry that 3.12 is slightly slower here, this is a Release Candidate build (still under development):

> python3.11 -m timeit '[i for i in range(1000)]'
20000 loops, best of 5: 11.9 usec per loop

> python3.12 -m timeit '[i for i in range(1000)]'
20000 loops, best of 5: 14.6 usec per loop

With that said, if you use a lot of comprehensions, and their computation is relatively lightweight, you will notice significant speedups!

Stay Tuned for Part 2-4!

In Part 2, we’ll talk about new updates to format strings, and automated deletion of temp files on close.

In Part 3, we’ll talk about a handy new CLI for sqlite3, and a handy new CLI for UUIDs. Python is becoming the Swiss Army Knife of CLIs!

Finally, in Part 4, we will celebrate the launch of Python 3.12 on October 2, 2023.

No Starch Press is my absolute favorite publisher in the world. Their tech books are top notch, written by famous engineering authors, and with an approachable to read style.

Humble Bundle has a deal currently where you pay what you want for a set of great Python books. You can donate to charities, or just pay the minimum ($36 for 18 books, down to $1 for two of the books!).

There’s something in here for everyone, whether you’re continuing on your Python journey from a Weekend.Dev workshop, or introducing Python to children.

Acing Python Interviews

It’s all about practice, practice, practice. Try problems on your own. Try talking about them with your friends or colleagues.

Because you’ll need to both solve problems, and communicate about them clearly. Don’t underestimate how important communication is during an interview!

How can you practice solving problems?

1. Checkout Kaggle if you need Data Science practice with Python. It’ll jumpstart you with notebooks, datasets, and if you come out on top you can even win money! It’s really just like the data analysis projects we do in Weekend.Dev workshops.

2. Checkout LeetCode interview coding collections. They have classic questions, and also real interview questions that have leaked from all tech companies large and small. Yes, I’ve seen firsthand how interview technical questions leak within days of using them!

3. Checkout Advent of Code for something more fun—gamified practice! If you find Kaggle and LeetCode too dry, Advent of Code may be just the thing for you. You can attempt problems from all of the years, back to 2015. The puzzles have fun, story-based backgrounds and annual themes. Every year there are 25 challenges, released daily from December 1 to December 25. Gear up and you can even compete this December!

1. ⚡ Python is fast now! 3.11 and 3.12 include huge performance boosts. The core team is focused on speed, so expect more in the future.

2. 🌐 Python is coming to the web browser! Traditionally, it just ran on your computer or a server.

3. 📱 Python is coming to mobile! There was discussion of adding iOS and Android support in the future.

4. ❤️ Python Loves Rust! There are new projects using Rust under the hood to make things fast.

⚡ Python is Fast?

That's right! Python 3.11 runs programs on average 25% quicker.

• Specializing Adaptive Interpreter: the core team has built support for Python bytecode instructions that specialize based on data types they are processing (like integer and float).

• Working Around the GIL with asyncio: concurrency is limited by a Global Interpreter Lock (GIL) in Python. In this talk, Łukasz Langa discussed using asyncio and shared memory to easily work around this.

• 3.12+ Roadmap: the core team made it clear they would continue the work with speeding up Python and projects like the Specializing Adaptive Interpreter. This also includes optimizing usage of memory.

📱 Python: Coming to the Web and Mobile!

Web and mobile are new emerging themes for Python. And, well, as the most popular programming langauge in the world, it is odd that Python isn't on the most popular user platforms! That is all changing rapidly:

• PyScript: first announced in 2022, it is becoming a platform pushed by Anaconda and now its own community. You can use PyScript to build front end applications in Python, or for teaching easily with just a web browser.

• WASM Target: as of Python 3.11, Python released initial support for WASM—a way of running code natively in the browser. At PyCon, there was even a WASM working group!

• Beeware's Briefcase: has support for packaging Python for iOS and Android. The community discussed supporting this more widely, similar to WASM.

❤️ Python Loves Rust

The Python and Rust community are deepening collaborations with new tooling and frameworks combining both languages.

• Robyn: need a fast API framework? Robyn is for you. Flask-like Python syntax, with super fast Rust underneath. It's currently one of the quickest web frameworks in the world.

• Ruff: is a static analyzer for Python written in Rust. It's so quick, and has so much functionality, Al Swiegart wondered if we even needed any other tools!

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