Planet Python

Pydantic is a powerful data validation and settings management library for Python, engineered to enhance the robustness and reliability of your codebase. From basic tasks, such as checking whether a variable is an integer, to more complex tasks, like ensuring highly-nested dictionary keys and values have the correct data types, Pydantic can handle just about any data validation scenario with minimal boilerplate code.

In this tutorial, you’ll learn how to:

• Work with data schemas with Pydantic’s BaseModel
• Write custom validators for complex use cases
• Validate function arguments with Pydantic’s @validate_call
• Manage settings and configure applications with pydantic-settings

Throughout this tutorial, you’ll get hands-on examples of Pydantic’s functionalities, and by the end you’ll have a solid foundation for your own validation use cases. Before starting this tutorial, you’ll benefit from having an intermediate understanding of Python and object-oriented programming.

Get Your Code: Click here to download the free sample code that you’ll use to help you learn how Pydantic can help you simplify data validation in Python.

Python’s Pydantic Library

One of Python’s main attractions is that it’s a dynamically typed language. Dynamic typing means that variable types are determined at runtime, unlike statically typed languages where they are explicitly declared at compile time. While dynamic typing is great for rapid development and ease of use, you often need more robust type checking and data validation for real-world applications. This is where Python’s Pydantic library has you covered.

Pydantic has quickly gained popularity, and it’s now the most widely used data validation library for Python. In this first section, you’ll get an overview of Pydantic and a preview of the library’s powerful features. You’ll also learn how to install Pydantic along with the additional dependencies you’ll need for this tutorial.

Getting Familiar With Pydantic

Pydantic is a powerful Python library that leverages type hints to help you easily validate and serialize your data schemas. This makes your code more robust, readable, concise, and easier to debug. Pydantic also integrates well with many popular static typing tools and IDEs, which allows you to catch schema issues before running your code.

Some of Pydantic’s distinguishing features include:

• Customization: There’s almost no limit to the kinds of data you can validate with Pydantic. From primitive Python types to highly nested data structures, Pydantic lets you validate and serialize nearly any Python object.

• Flexibility: Pydantic gives you control over how strict or lax you want to be when validating your data. In some cases, you might want to coerce incoming data to the correct type. For example, you could accept data that’s intended to be a float but is received as an integer. In other cases, you might want to strictly enforce the data types you’re receiving. Pydantic enables you to do either.

• Serialization: You can serialize and deserialize Pydantic objects as dictionaries and JSON strings. This means that you can seamlessly convert your Pydantic objects to and from JSON. This capability has led to self-documenting APIs and integration with just about any tool that supports JSON schemas.

• Performance: Thanks to its core validation logic written in Rust, Pydantic is exceptionally fast. This performance advantage gives you swift and reliable data processing, especially in high-throughput applications such as REST APIs that need to scale to a large number of requests.

• Ecosystem and Industry Adoption: Pydantic is a dependency of many popular Python libraries such as FastAPI, LangChain, and Polars. It’s also used by most of the largest tech companies and throughout many other industries. This is a testament to Pydantic’s community support, reliability, and resilience.

These are a few key features that make Pydantic an attractive data validation library, and you’ll get to see these in action throughout this tutorial. Up next, you’ll get an overview of how to install Pydantic along with its various dependencies.

Installing Pydantic

Pydantic is available on PyPI, and you can install it with pip. Open a terminal or command prompt, create a new virtual environment, and then run the following command to install Pydantic:

Shell (venv) $ python -m pip install pydantic Copied!

This command will install the latest version of Pydantic from PyPI onto your machine. To verify that the installation was successful, start a Python REPL and import Pydantic:

Python >>> import pydantic Copied!

If the import runs without error, then you’ve successfully installed Pydantic, and you now have the core of Pydantic installed on your system.

Adding Optional Dependencies

You can install optional dependencies with Pydantic as well. For example, you’ll be working with email validation in this tutorial, and you can include these dependencies in your install:

Shell (venv) $ python -m pip install "pydantic[email]" Copied!

Pydantic has a separate package for settings management, which you’ll also cover in this tutorial. To install this, run the following command:

Shell (venv) $ python -m pip install pydantic-settings Copied!

With that, you’ve installed all the dependencies you’ll need for this tutorial, and you’re ready to start exploring Pydantic. You’ll start by covering models—Pydantic’s primary way of defining data schemas.

Read the full article at https://realpython.com/python-pydantic/ »

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This tutorial explains the steps to open Google Chrome using Selenium in Python.

To read this article in full, please click hereThis post appeared first on ListenData

CPython release automation, more Windows SBOMs About • Blog • Newsletter • Links CPython release automation, more Windows SBOMs

Published 2024-04-10 by Seth Larson
Reading time: minutes

This critical role would not be possible without funding from the Alpha-Omega project. Massive thank-you to Alpha-Omega for investing in the security of the Python ecosystem! CPython source and docs builds are automated in GitHub Actions

While I was away on vacation, the CPython Developer-in-Residence Łukasz was able to dry-run, review, and merge my pull request to automate source and docs builds in GitHub Actions on the python/release-tools repository.

The automation was successfully used for the CPython 3.10.14, 3.11.9, 3.12.3, and 3.13.0a6.

This work being merged is exciting because it isolates the CPython source and docs builds from individual release manager machines preventing those source tarballs from being unintentionally modified. Having builds automated is a pre-requisite for future improvements to the CPython release process, like adding automated uploads to python.org or machine/workflow identities for Sigstore. I also expect the macOS installer release process to be automated on GitHub Actions. Windows artifact builds are already using Azure Pipelines.

Release Managers have requested the process be further automated which is definitely possible, especially for gathering the built release artifacts and running the test suite locally.

Windows Software Bill-of-Materials coming for next CPython releases

I've been collaborating with Steve Dower to get SBOM documents generated for Windows Python artifacts. Challenges of developing new CI workflows aside we now have SBOM artifacts generating as expected. Next step is to automate the upload of the SBOM documents to python.org so they've automatically available to users.

During the development of Windows SBOMs I also noticed our CPE identifier for xz was incorrect partially due to difficulties using the CPE Dictionary search (they only allow 3+ character queries!) This issue doesn't impact any existing published SBOM since xz-utils is only bundled with Windows artifacts, not source artifacts.

Thoughts on xz-utils

I've been a part of many discussions about xz-utils, both active and passive, there are many thoughts percolating around our community right now. To capture where I'm at with many of these discussions I wanted to write down my own thoughts:

• "Insider threats" and other attacks on trust are not unique to open source software. We don't get to talk about other incidents as often because the attack isn't done in the open.
• Insider threats are notoriously difficult to defend against even with ample resources. Volunteer maintainers shouldn't be expected to defend against patient and well-resourced attackers alone.
• Multiple ecosystems took action immediately, including Python, to either remove compromised versions of xz or confirm they were not using affected versions. This sort of immediate response should only be expected with full-time staffing (thanks Alpha-Omega!), but I know that volunteers were involved in the broader response to xz.
• Many folks, myself included, have remarked that this could have just as easily been them. Reviewing the "Jia Tan" commits I can't say with certainty that I would have caught them in code review, especially coming from a long-time co-maintainer.

How has the nature of open source affected the response to this event?

• Security experts were able to review source code, commits, conversations, and the accounts involved immediately. We went from the public disclosure and alerts to having a timeline of how the malicious account was able to gain trust within a few hours.
• We get to learn how the attack transpired in detail to hopefully improve in the future.

Things to keep in mind when working on solutions:

• Blaming open source software or maintainers is rarely the answer, and it definitely isn't the answer here.
• There isn't a single solution to this problem. We need both social and technical approaches, not exclusively one or the other. Instead of pointing out how certain solutions or ways of supporting OSS "wouldn't have thwarted this exact attack", let's focus on how the sum of contributions and support are making attacks on open source more difficult in general.
• We need better visibility into critical languishing projects. xz likely wasn't on anyone's list of critical projects before this event (when it should have been!) It isn't sustainable to figure out which projects are on critical and need help by waiting for the next newsworthy attack or vulnerability.

I also reflected on how my own work is contributing to one of many solutions to problems like this. I've been focusing on reproducible builds, hardening of the CPython release process, and have been working closely with Release Managers to improve their processes.

As I mentioned above, being full-time staff means I can respond quickly to events in the community. The "all-clear" message for CPython, PyPI, and all Python packages was given a few hours after xz-utils backdoor was disclosed to the Python Security Response Team.

I've also been working on Software Bill-of-Materials documents. These documents would not have done anything to stop an attack similar to this, but would have helped users of CPython detect if they were using a vulnerable component if the attack affected CPython.

Other items

• I'm attending SOSS Community Day and OSS Summit NA in Seattle April 15th to 19th. If you're there and want to chat reach out to me! I spent time this week preparing to speak at SOSS Community Day.
• Added support for Python 3.13 to Truststore.
• Triaged reports to the Python Security Response Team.

That's all for this week! 👋 If you're interested in more you can read last week's report.

Thanks for reading! ♡ Did you find this article helpful and want more content like it? Get notified of new posts by subscribing to the RSS feed or the email newsletter.

This work is licensed under CC BY-SA 4.0

#624 – APRIL 9, 2024
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Install and Execute Python Applications Using pipx

In this tutorial, you’ll learn about a tool called pipx, which lets you conveniently install and run Python packages as standalone command-line applications in isolated environments. In a way, pipx turns the Python Package Index (PyPI) into an app marketplace for Python programmers.
REAL PYTHON

Why Do Python Lists Multiply Oddly?

In Python you can use the multiplication operator on sequences to return a repeated version of the value. When you do this with a list containing an empty list you get what might be unexpected behavior. This article explains what happens and why.
ABHINAV UPADHYAY

Saga Pattern Made Easy

The Saga pattern lets you manage state across distributed transactions. But it’s difficult to build and maintain. Download this free technical guide to learn how to Automate Sagas Pattern with Temporal, the open source durable execution platform →
TEMPORAL TECHNOLOGIES sponsor

Inline Run Dependencies in pipx 1.4.2

PEP 723 adds the ability to specify dependencies within a Python script itself. The folks who write pipx have added an experimental feature that takes advantage of this future language change. This article shows you how the new feature looks and what pipx does with it.
HENRY SCHREINER

PEP 738 Accepted: Adding Android as a Supported Platform

PYTHON ENHANCEMENT PROPOSALS (PEPS)

PEP 742 Accepted: Narrowing Types With TypeIs

PYTHON ENHANCEMENT PROPOSALS (PEPS)

Django Bugfix Release Issued: 5.0.4

DJANGO SOFTWARE FOUNDATION

Discussions What Is the Most Useless Project You Have Worked On?

HACKER NEWS

Articles & Tutorials Enforcing Conventions in Django Projects With Introspection

This post talks about the importance of naming conventions in your code, but takes it to the next level: use scripts to validate that conventions get followed. By using introspection you can write rules for detecting code that doesn’t follow your conventions. Examples are for Django fields but the concept works for any Python code.
LUKE PLANT

Leveraging Docs and Data to Create a Custom LLM Chatbot

How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.
REAL PYTHON podcast

“Real” Anonymous Functions for Python

The topic of multi-line lambdas, or anonymous functions akin to languages like JavaScript, comes up with some frequency in the Python community. It popped up again recently. This article talks about the history of the topic and the current reasoning against it.
JAKE EDGE

How to Set Up Pre-Commit Hooks

Maintaining code quality can be challenging no matter the size of your project or the number of contributors. Pre-commit hooks make it a little easier. This article provides a step-by-step guide to installing and configuring pre-commit hooks on your project.
STEFANIE MOLIN • Shared by Stefanie Molin

Fix Python Code Smells With These Best Practices

A code smell isn’t something that is necessarily broken, but could be a sign of deeper problems. This post teaches you how to identify and eliminate seven Python code smells with practical examples.
ARJAN

New Open Initiative for Cybersecurity Standards

The PSF has joined with the Apache Software Foundation, the Eclipse Foundation, and other open source groups to form a group dedicated to cybersecurity initiatives in the open source community.
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10 Reasons I Stick to Django Rather Than FastAPI

FastAPI is an excellent library and is quite popular in the Python community. Regardless of his respect for it, David still sticks with Django. This post discusses his ten reasons why.
DAVID DAHAN

My Accessibility Review Checklist

Ensuring accessibility in your software is important, removing boundaries that limit some people from participating. This checklist is valuable for helping you determine whether your web code meets the accepted Web Content Accessibility Guidelines.
SARAH ABEREMANE

Python Deep Learning: PyTorch vs Tensorflow

PyTorch vs Tensorflow: Which one should you use? Learn about these two popular deep learning libraries and how to choose the best one for your project.
REAL PYTHON course

Python Project-Local Virtualenv Management Redux

Hynek talks about his Python tooling choices and how they’ve changed over the years, with a focus on environment management tools like uv and direnv.
HYNEK SCHLAWACK

Trying Out Rye

Hamuko decided to try out rye. This post goes into detail about what worked and what didn’t for them.
HAMUKO

Projects & Code drawpyo: Programmatically Generate Draw.io Charts

GITHUB.COM/MERRIMANIND

best-python-cheat-sheet: The Best* Python Cheat Sheet

GITHUB.COM/KIERANHOLLAND

rebound: Multi-Purpose N-Body Code

GITHUB.COM/HANNOREIN

Compatibility Layer Between Polars, Pandas, cuDF, and More!

GITHUB.COM/MARCOGORELLI • Shared by Marco Gorelli

Reduce the Size of GeoJSON Files

GITHUB.COM/BEN-N93 • Shared by Ben Nour

Events Weekly Real Python Office Hours Q&A (Virtual)

April 10, 2024
REALPYTHON.COM

Python Atlanta

April 11 to April 12, 2024
MEETUP.COM

DFW Pythoneers 2nd Saturday Teaching Meeting

April 13, 2024
MEETUP.COM

Inland Empire Python Users Group Monthly Meeting

April 17, 2024
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Data Ethics

April 17, 2024
MEETUP.COM

Python Meeting Düsseldorf

April 17, 2024
PYDDF.DE

Happy Pythoning!
This was PyCoder’s Weekly Issue #624.
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The PSF is pleased to announce its fourth batch of PSF Fellows for 2023! Let us welcome the new PSF Fellows for Q4! The following people continue to do amazing things for the Python community:

Jelle Zijlstra
Github, Quora

Thank you for your continued contributions. We have added you to our Fellow roster online.

The above members help support the Python ecosystem by being phenomenal leaders, sustaining the growth of the Python scientific community, maintaining virtual Python communities, maintaining Python libraries, creating educational material, organizing Python events and conferences, starting Python communities in local regions, and overall being great mentors in our community. Each of them continues to help make Python more accessible around the world. To learn more about the new Fellow members, check out their links above.

Let's continue recognizing Pythonistas all over the world for their impact on our community. The criteria for Fellow members is available online: https://www.python.org/psf/fellows/. If you would like to nominate someone to be a PSF Fellow, please send a description of their Python accomplishments and their email address to psf-fellow at python.org. Quarter 1 nominations are currently in review. We are accepting nominations for Quarter 2 2024 through May 20, 2024.

Are you a PSF Fellow and want to help the Work Group review nominations? Contact us at psf-fellow at python.org.

It’s time to eclipse the Python 3.11.9 release with two releases, one of which is the very last alpha release of Python 3.13:

 

Python 3.12.3

300+ of the finest commits went into this latest maintenance release of the latest Python version, the most stablest, securest, bugfreeest we could make it.

https://www.python.org/downloads/release/python-3123/

 

Python 3.13.0a6

What’s that? The last alpha release? Just one more month until feature freeze! Get your features done, get your bugs fixed, let’s get 3.13.0 ready for people to actually use! Until then, let’s test with alpha 6. The highlights of 3.13 you ask? Well:

• In the interactive interpreter, exception tracebacks are now colorized by default.
• A preliminary, experimental JIT was added, providing the ground work for significant performance improvements.
• The (cyclic) garbage collector is now incremental, which should mean shorter pauses for collection in programs with a lot of objects.
• Docstrings now have their leading indentation stripped, reducing memory use and the size of .pyc files. (Most tools handling docstrings already strip leading indentation.)
• The dbm module has a new dbm.sqlite3 backend that is used by default when creating new files.
• PEP 594 (Removing dead batteries from the standard library) scheduled removals of many deprecated modules: aifc, audioop, chunk, cgi, cgitb, crypt, imghdr, mailcap, msilib, nis, nntplib, ossaudiodev, pipes, sndhdr, spwd, sunau, telnetlib, uu, xdrlib, lib2to3.
• Many other removals of deprecated classes, functions and methods in various standard library modules.
• New deprecations, most of which are scheduled for removal from Python 3.15 or 3.16.
• C API removals and deprecations. (Some removals present in alpha 1 were reverted in alpha 2, as the removals were deemed too disruptive at this time.)

(Hey, fellow core developer, if a feature you find important is missing from this list, let Thomas know. It’s getting to be really important now!)

https://www.python.org/downloads/release/python-3130a6/  
We hope you enjoy the new releases!

Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself, or through contributions to the Python Software Foundation or CPython itself.

Thomas “can you tell I haven’t had coffee today” Wouters
on behalf of your release team,

Ned Deily
Steve Dower
Pablo Galindo Salgado
Łukasz Langa

Anaconda has announced a new partnership with Teradata to bring Python and R packages to Teradata VantageCloud through the Anaconda Repository.

But what does that mean? This new partnership allows engineers to:

• Rapidly deploy and operationalize AI/ML developed using open-source Python and R packages.
• Unlock innovation and the full potential of data at scale with a wide variety of Python and R functionality on VantageCloud Lake.
• Flexibly use packages and versions of their choice for large-scale data science, AI/ML and generative AI use-cases.
• Securely work with Python/R models into VantageCloud Lake with no intellectual property (IP) leakage.

Teradata VantageCloud Lake customers can download Python and R packages from the Anaconda Repository at no additional cost. Python packages are available immediately, and R packages will be released before the end of the year.

For more information about Teradata ClearScape Analytics, please visit Teradata.com.

Learn more about partnering with Anaconda here.

The post Anaconda Partners with Teradata for AI with Python packages in the Cloud appeared first on Mouse Vs Python.

From restaurant e-menus to airline boarding passes, QR codes have numerous applications that impact your day-to-day life and enrich the user’s experience. Wouldn’t it be great to make them look good, too? With the help of this video course, you’ll learn how to use Python to generate beautiful QR codes for your personal use case.

In its most basic format, a QR code contains black squares and dots on a white background, with information that any smartphone or device with a dedicated QR scanner can decode. Unlike a traditional bar code, which holds information horizontally, a QR code holds the data in two dimensions, and it can hold over a hundred times more information.

In this video course, you’ll learn how to:

• Generate a basic black-and-white QR code
• Change the size and margins of the QR code
• Create colorful QR codes
• Rotate the QR code
• Replace the static background with an animated GIF

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

<strong>Topics covered in this episode:</strong><br> <ul> <li><a href="https://github.com/brohrer/pacemaker"><strong>pacemaker</strong></a> - For controlling time per iteration loop in Python.</li> <li><a href="https://www.bleepingcomputer.com/news/security/pypi-suspends-new-user-registration-to-block-malware-campaign/">PyPI suspends new user registration to block malware campaign</a></li> <li><a href="https://hynek.me/articles/python-virtualenv-redux/"><strong>Python Project-Local Virtualenv Management Redux</strong></a></li> <li><a href="https://blog.cloudflare.com/python-workers">Python Edge Workers at Cloudflare</a></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=4oALfE-zDf8' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="378">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by us! Support our work through:</p> <ul> <li>Our <a href="https://training.talkpython.fm/"><strong>courses at Talk Python Training</strong></a></li> <li><a href="https://courses.pythontest.com/p/the-complete-pytest-course"><strong>The Complete pytest Course</strong></a></li> <li><a href="https://www.patreon.com/pythonbytes"><strong>Patreon Supporters</strong></a></li> </ul> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href="https://fosstodon.org/@mkennedy"><strong>@mkennedy@fosstodon.org</strong></a></li> <li>Brian: <a href="https://fosstodon.org/@brianokken"><strong>@brianokken@fosstodon.org</strong></a></li> <li>Show: <a href="https://fosstodon.org/@pythonbytes"><strong>@pythonbytes@fosstodon.org</strong></a></li> </ul> <p>Join us on YouTube at <a href="https://pythonbytes.fm/stream/live"><strong>pythonbytes.fm/live</strong></a> to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too.</p> <p>Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to <a href="https://pythonbytes.fm/friends-of-the-show">our friends of the show list</a>, we'll never share it.</p> <p><strong>Brian #1:</strong> <a href="https://github.com/brohrer/pacemaker"><strong>pacemaker</strong></a> - For controlling time per iteration loop in Python.</p> <ul> <li>Brandon Rohrer</li> <li>Good example of a small bit of code made into a small package.</li> <li>With speedups to dependencies, like with uv, for example, I think we’ll see more small projects.</li> <li>Cool stuff <ul> <li>Great README, including quirks that need to be understood by users. <ul> <li>“If the pacemaker experiences a delay, it will allow faster iterations to try to catch up. Heads up: because of this, any individual iteration might end up being much shorter than suggested by the pacemaker's target rate.”</li> </ul></li> <li>Nice use of <a href="https://docs.python.org/3/library/time.html#time.monotonic">time.monotonic()</a> <ul> <li>deltas are guaranteed to never go back in time regardless of what adjustments are made to the system clock.</li> </ul></li> </ul></li> <li>Watch out for <ul> <li>pip install pacemaker-lite <ul> <li>NOT pacemaker</li> <li>pacemaker is taken by a package named PaceMaker with a repo named pace-maker, that hasn’t been updated in 3 years. Not sure if it’s alive. </li> </ul></li> <li>No tests (yet). I’m sure they’re coming. ;) <ul> <li>Seriously though, Brandon says this is “a glorified snippet”. And I love the use of packaging to encapsulate shared code. Realistically, small snippet like packages have functionality that’s probably going to be tested by end user code.</li> <li>And even if there are tests, users should test the functionality they are depending on.</li> </ul></li> </ul></li> </ul> <p><strong>Michael #2:</strong> <a href="https://www.bleepingcomputer.com/news/security/pypi-suspends-new-user-registration-to-block-malware-campaign/">PyPI suspends new user registration to block malware campaign</a></p> <ul> <li><a href="https://status.python.org/incidents/dc9zsqzrs0bv">Incident Report for Python Infrastructure</a></li> <li><a href="https://medium.com/checkmarx-security/pypi-is-under-attack-project-creation-and-user-registration-suspended-heres-the-details-c3b6291d4579">PyPi Is Under Attack: Project Creation and User Registration Suspended — Here’s the details</a> <ul> <li>I hate medium, but it’s the best details I’ve found so far</li> </ul></li> </ul> <p><strong>Brian #3:</strong> <a href="https://hynek.me/articles/python-virtualenv-redux/"><strong>Python Project-Local Virtualenv Management Redux</strong></a></p> <ul> <li>Hynek</li> <li>Concise writeup of how Hynek uses various tools for dealing with environments</li> <li>Covers (paren notes are from Brian) <ul> <li>In project .venv directories</li> <li>direnv for handling .envrc files per project (time for me to try this again)</li> <li>uv for pip and pip-compile functionality</li> <li>Installing Python via python.org</li> <li>Using a .python-version-default file (I’ll need to play with this a bit) <ul> <li>Works with GH Action setup-python. (ok. that’s cool)</li> </ul></li> <li>Some fish shell scripting</li> <li>Bonus tip on using requires-python in .pyproject.toml and extracting it in GH actions to be able to get the python exe name, and then be able to pass it to Docker and reference it in a Dockerfile. (very cool)</li> </ul></li> </ul> <p><strong>Michael #4:</strong> <a href="https://blog.cloudflare.com/python-workers">Python Edge Workers at Cloudflare</a></p> <ul> <li>What are <a href="https://developers.cloudflare.com/workers/">edge workers</a>?</li> <li>Based on workers using Pyodide and WebAssembly</li> <li>This new support for Python is different from how Workers have historically supported languages beyond JavaScript — in this case, we have directly integrated a Python implementation into <a href="https://github.com/cloudflare/workerd">workerd</a>, the open-source Workers runtime.</li> <li>Python Workers can import a subset of popular Python <a href="https://developers.cloudflare.com/workers/languages/python/packages/">packages</a> including <a href="https://fastapi.tiangolo.com/">FastAPI</a>, <a href="https://python.langchain.com/docs/get_started/introduction">Langchain</a>, <a href="https://numpy.org/">numpy</a></li> <li>Check out the <a href="https://github.com/cloudflare/python-workers-examples">examples repo</a>.</li> </ul> <p><strong>Extras</strong> </p> <p>Michael:</p> <ul> <li><a href="https://fosstodon.org/@btskinn/112226004327304352">LPython follow up</a> from Brian Skinn</li> <li><a href="https://github.com/epogrebnyak/justpath/issues/26">Featured on Python Bytes badge</a></li> <li><a href="https://twitter.com/TalkPython/status/1777505296807850101">A little downtime</a>, thanks for the understanding <ul> <li>We were rocking a <a href="https://python-bytes-static.nyc3.digitaloceanspaces.com/python-bytes-health.png">99.98% uptime</a> until then. :)</li> </ul></li> </ul> <p><strong>Joke:</strong> </p> <ul> <li><a href="https://devhumor.com/media/gemini-says-that-c-is-not-safe-for-people-under-18">C++ is not safe for people under 18</a></li> <li>Baseball joke</li> </ul>

“KeyError: 'GOOGLE_APPLICATION_CREDENTIALS‘”

It was way too early in the morning for this error. See if you can spot the problem. I hadn’t had my coffee before trying to debug the code I’d written the night before, so it will probably take you less time than it did me.

app.py:

from dotenv import load_dotenv from file_handling import initialize_constants load_dotenv() #...

file_handling.py:

import os from google.cloud import storage UPLOAD_FOLDER=None DOWNLOAD_FOLDER = None def initialize_cloud_storage(): """ Initializes the Google Cloud Storage client. """ os.environ["GOOGLE_APPLICATION_CREDENTIALS"] storage_client = storage.Client() bucket_name = #redacted return storage_client.bucket(bucket_name) def set_upload_folder(): """ Determines the environment and sets the path to the upload folder accordingly. """ if os.environ.get("FLASK_ENV") in ["production", "staging"]: UPLOAD_FOLDER = os.path.join("/tmp", "upload") os.makedirs(UPLOAD_FOLDER, exist_ok=True) else: UPLOAD_FOLDER = os.path.join("src", "upload_folder") return UPLOAD_FOLDER def initialize_constants(): """ Initializes the global constants for the application. """ UPLOAD_FOLDER = initialize_upload_folder() DOWNLOAD_FOLDER = initialize_cloud_storage() return UPLOAD_FOLDER, DOWNLOAD_FOLDER DOWNLOAD_FOLDER=initialize_cloud_storage() def write_to_gcs(content: str, file: str): "Writes a text file to a Google Cloud Storage file." blob = DOWNLOAD_FOLDER.blob(file) blob.upload_from_string(content, content_type="text/plain") def upload_file_to_gcs(file_path:str, gcs_file: str): "Uploads a file to a Google Cloud Storage bucket" blob = DOWNLOAD_FOLDER.blob(gcs_file) with open(file_path, "rb") as f: blob.upload_from_file(f, content_type="application/octet-stream")

See the problem?

This was just the discussion of a recent Pybites article.

When app.py imported initialize_constants from file_handling, the Python interpreter ran

DOWNLOAD_FOLDER = initialize_cloud_storage()

and looked for GOOGLE_APPLICATION_CREDENTIALS from the environment path, but load_dotenv hadn’t added them to the environment path from the .env file yet.

Typically, configuration variables, secret keys, and passwords are stored in a file called .env and then read as environment variables rather than as pure text using a package such as python-dotenv, which is what is being used here.

So, I had a few options.

I could call load_dotenv before importing from file_handling:

from dotenv import load_dotenv load_dotenv() from file_handling import initialize_constants

But that’s not very Pythonic.

I could call initialize_cloud_storage inside both upload_file_to_gcs and write_to_gcs

def write_to_gcs(content: str, file: str): "Writes a text file to a Google Cloud Storage file." DOWNLOAD_FOLDER = initialize_cloud_storage() blob = DOWNLOAD_FOLDER.blob(file) blob.upload_from_string(content, content_type="text/plain") def upload_file_to_gcs(file_path:str, gcs_file: str): "Uploads a file to a Google Cloud Storage bucket" DOWNLOAD_FOLDER = initialize_cloud_storage() blob = DOWNLOAD_FOLDER.blob(gcs_file) with open(file_path, "rb") as f: blob.upload_from_file(f, content_type="application/octet-stream")

But this violates the DRY principle. Plus we really shouldn’t be initializing the storage client multiple times. In fact, we already are initializing it twice in the way the code was originally written.

Going Global

So what about this?

DOWNLOAD_FOLDER = None def initialize_constants(): """ Initializes the global constants for the application. """ global DOWNLOAD_FOLDER UPLOAD_FOLDER = initialize_upload_folder() DOWNLOAD_FOLDER = initialize_cloud_storage() return UPLOAD_FOLDER, DOWNLOAD_FOLDER

Here, we are defining DOWNLOAD_FOLDER as having global scope.

This will work here.

This will work here, because upload_file_to_gcs and write_to_gcs are in the same module. But if they were in a different module, it would break.

Why does it matter?

Well, let’s go back to how Python handles imports. Remember that Python runs any code outside of a function or class at import. That applies to variable (or constant) assignment, as well. So if upload_file_to_gcs and write_to_gcs were in another module and importing DOWNLOAD_FOLDER from file_handling,p it would be importing it while assigned a value of None. It wouldn’t matter that by the time it was needed, it wouldn’t be assigned to None any longer. Inside this other module, it would still be None.

What would be necessary in this situation would be another function called get_download_folder.

def get_download_folder(): """ Returns the current value of the Google Cloud Storage bucket """ return DOWNLOAD_FOLDER

Then, in this other module containing the upload_file_to_gcs and write_to_gcs functions, I would import get_download_folder instead of DOWNLOAD_FOLDER. By importing get_download_folder, you can get the value of DOWNLOAD_FOLDER after it has been assigned to an actual value, because get_download_folder won’t run until you explicitly call it. Which, presumably wouldn’t be until after you’ve let initialize_cloud_storage do its thing.

I have another part of my codebase where I have done this. On my site, I have a tool that helps authors create finetunes of GPT 3.5 from their books. This Finetuner is BYOK, or ‘bring your own key’ meaning that users supply their own OpenAI API key to use the tool. I chose this route because charging authors to fine-tune a model and then charging them to use it, forever, is just not something that benefits either of us. This way, they can take their finetuned model and use it an any of the multiple other BYOK AI writing tools that are out there, and I don’t have to maintain writing software on top of everything else. So the webapp’s form accepts the user’s API key, and after a valid form submit, starts a thread of my Finetuner application.

This application starts in the training_management.py module, which imports set_client and get_client from openai_client.py and passes the user’s API key to set_client right away. I can’t import client directly, because client is None until set_client has been passed the API key, which happens after import.

from openai import OpenAI client = None def set_client(api_key:str): """ Initializes OpenAI API client with user API key """ global client client = OpenAI(api_key = api_key) def get_client(): """ Returns the initialized OpenAI client """ return client

When the function that starts a fine tuning job starts, it calls get_client to retrieve the initialized client. And by moving the API client initialization into another module, it becomes available to be used for an AI-powered chunking algorithm I’m working on. Nothing amazing. Basically, just generating scene beats from each chapter to use as the prompt, with the actual chapter as the response. It needs work still, but it’s available for authors who want to try it.

A Class Act

Now, we could go one step further from here. The code we’ve settled on so far relies on global names. Perhaps we can get away with this. DOWNLOAD_FOLDER is a constant. Well, sort of. Remember, it’s defined by initializing a connection to a cloud storage container. It’s actually a class. By rights, we should be encapsulating all of this logic inside of another class.

So what could that look like? Well, it should initialize the upload and download folders, and expose them as properties, and then use the functions write_to_gcs and upload_file_to_gcs as methods like this:

class FileStorageHandler: def __init__(self): self._upload_folder = self._set_upload_folder() self._download_folder = self._initialize_cloud_storage() @property def upload_folder(self): return self._upload_folder @property def download_folder(self): return self._download_folder def _initialize_cloud_storage(self): """ Initializes the Google Cloud Storage client. """ os.environ["GOOGLE_APPLICATION_CREDENTIALS"] storage_client = storage.Client() bucket_name = #redacted return storage_client.bucket(bucket_name) def _set_upload_folder(self): """ Determines the environment and sets the path to the upload folder accordingly. """ if os.environ.get("FLASK_ENV") in ["production", "staging"]: upload_folder = os.path.join("/tmp", "upload") os.makedirs(upload_folder, exist_ok=True) else: upload_folder = os.path.join("src", "upload_folder") return upload_folder def write_to_gcs(self, content: str, file_name: str): """ Writes a text file to a Google Cloud Storage file. """ blob = self._download_folder.blob(file_name) blob.upload_from_string(content, content_type="text/plain") def upload_file_to_gcs(self, file_path: str, gcs_file_name: str): """ Uploads a file to a Google Cloud Storage bucket. """ blob = self._download_folder.blob(gcs_file_name) with open(file_path, "rb") as file_obj: blob.upload_from_file(file_obj)

Now, we can initialize an instance of FileStorageHandler in app.py and assign UPLOAD_FOLDER and DOWNLOAD_FOLDER to the properties of the class.

from dotenv import load_dotenv from file_handling import FileStorageHandler load_dotenv() folders = FileStorageHandler() UPLOAD_FOLDER = folders.upload_folder DOWNLOAD_FOLDER = folders.download_folder Key take away

In the example, the error arose because initialize_cloud_storage was called at the top level in file_handling.py. This resulted in Python attempting to access environment variables before load_dotenv had a chance to set them.

I had been thinking of module level imports as “everything at the top runs at import.” But that’s not true. Or rather, it is true, but not accurate. Python executes code based on indentation, and functions are indented within the module. So, it’s fair to say that every line that isn’t indented is at the top of the module. In fact, it’s even called that: top-level code, which is defined as basically anything that is not part of a function, class or other code block.

And top-level code runs runs when the module is imported. It’s not enough to bury an expression below some functions, it will still run immediately when the module is imported, whether you are ready for it to run or not. Which is really what the argument against global variables and state is all about, managing when and how your code runs.

Understanding top-level code execution at import helped solved the initial error and design a more robust pattern.

Next steps

The downside with using a class is that if it gets called again, a new instance is created, with a new connection to the cloud storage. To get around this, something to look into would be to implement something called a Singleton Pattern, which is outside of the scope of this article.

Also, the code currently doesn’t handle exceptions that might arise during initialization (e.g., issues with credentials or network connectivity). Adding robust error handling mechanisms will make the code more resilient.

Speaking of robustness, I would be remiss if I didn’t point out that a properly abstracted initialization method should retrieve the bucket name from a configuration or .env file instead of leaving it hardcoded in the method itself.

Execution Environments serves us the benefits of containerization by solving the issues such as software dependencies, portability. Ansible Execution Environment are Ansible control nodes packaged as container images. There are two kinds of Ansible execution environments

• Base, includes the following

  • fedora base image
  • ansible core
  • ansible collections : The following set of collections
    ansible.posix
    ansible.utils
    ansible.windows

• Minimal, includes the following

  • fedora base image
  • ansible core

I have been the release manager for Ansible Execution Environments. After building the images I perform certain steps of tests to check if the versions of different components of the newly built correct or not. So I wrote eercheck to ease the steps of tests.

What is eercheck?

eercheck is a command line tool to test Ansible community execution environment before release. It uses podman py to connect and work with the podman container image, and Python unittest for testing the containers.

eercheck is a command line tool to test Ansible Community Execution Environment before release. It uses podman-py to connect and work with the podman container image, and Python unittest for testing the containers. The project is licensed under GPL-3.0-or-later.

How to use eercheck?

Activate the virtual environment in the working directory.

python3 -m venv .venv source .venv/bin/activate python -m pip install -r requirements.txt

Activate the podman socket.

systemctl start podman.socket --user

Update vars.json with correct version numbers.Pick the correct versions of the Ansible Collections from the .deps file of the corresponding Ansible community package release. For example for 9.4.0 the Collection versions can be found in here. You can find the appropriate version of Ansible Community Package here. The check needs to be carried out each time before the release of the Ansible Community Execution Environment.

Execute the program by giving the correct container image id.

./containertest.py image_id

Happy automating.

While many people went hunting for Easter eggs, the Python community stayed active through March 2024. The free-threaded Python project reached a new milestone, and you can now experiment with disabling the GIL in your interpreter.

The Python Software Foundation does a great job supporting the language with limited resources. They’ve now announced a new position that will support users of PyPI. NumPy is an old workhorse in the data science space. The library is getting a big facelift, and the first release candidate of NumPy 2 is now available.

Dive in to learn more about last month’s most important Python news.

Free-Threaded Python Reaches an Important Milestone

Python’s global interpreter lock (GIL) has been part of the CPython implementation since the early days. The lock simplifies a lot of the code under the hood of the language, but also causes some issues with parallel processing.

Over the years, there have been many attempts to remove the GIL. However, until PEP 703 was accepted by the steering council last year, none had been successful.

The PEP describes how the GIL can be removed based on experimental work done by Sam Gross. It suggests that what’s now called free-threaded Python is activated through a build option. In time, this free-threaded Python is expected to become the default version of CPython, but for now, it’s only meant for testing and experiments.

When free-threaded Python is ready for bigger audiences, the GIL will still be enabled by default. You can then set an environment variable or add a command-line option to try out free-threaded Python:

Read the full article at https://realpython.com/python-news-march-2024/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Integrating with Jira APIs 2024-04-08, by Dariusz Suchojad Overview

Continuing in the series of articles about newest cloud connections in Zato 3.2, this episode covers Atlassian Jira from the perspective of invoking its APIs to build integrations between Jira and other systems.

There are essentially two use modes of integrations with Jira:

1. Jira reacts to events taking place in your projects and invokes your endpoints accordingly via WebHooks. In this case, it is Jira that explicitly establishes connections with and sends requests to your APIs.
2. Jira projects are queried periodically or as a consequence of events triggered by Jira using means other than WebHooks.

The first case is usually more straightforward to conceptualize - you create a WebHook in Jira, point it to your endpoint and Jira invokes it when a situation of interest arises, e.g. a new ticket is opened or updated. I will talk about this variant of integrations with Jira in a future instalment as the current one is about the other situation, when it is your systems that establish connections with Jira.

The reason why it is more practical to first speak about the second form is that, even if WebHooks are somewhat easier to reason about, they do come with their own ramifications.

To start off, assuming that you use the cloud-based version of Jira (e.g. https://example.atlassian.net), you need to have a publicly available endpoint for Jira to invoke through WebHooks. Very often, this is undesirable because the systems that you need to integrate with may be internal ones, never meant to be exposed to public networks.

Secondly, your endpoints need to have a TLS certificate signed by a public Certificate Authority and they need to be accessible on port 443. Again, both of these are something that most enterprise systems will not allow at all or it may take months or years to process such a change internally across the various corporate departments involved.

Lastly, even if a WebHook can be used, it is not always a given that the initial information that you receive in the request from a WebHook will already contain everything that you need in your particular integration service. Thus, you will still need a way to issue requests to Jira to look up details of a particular object, such as tickets, in this way reducing WebHooks to the role of initial triggers of an interaction with Jira, e.g. a WebHook invokes your endpoint, you have a ticket ID on input and then you invoke Jira back anyway to obtain all the details that you actually need in your business integration.

The end situation is that, although WebHooks are a useful concept that I will write about in a future article, they may very well not be sufficient for many integration use cases. That is why I start with integration methods that are alternative to WebHooks.

Alternatives to WebHooks

If, in our case, we cannot use WebHooks then what next? Two good approaches are:

1. Scheduled jobs
2. Reacting to emails (via IMAP)

Scheduled jobs will let you periodically inquire with Jira about the changes that you have not processed yet. For instance, with a job definition as below:

Now, the service configured for this job will be invoked once per minute to carry out any integration works required. For instance, it can get a list of tickets since the last time it ran, process each of them as required in your business context and update a database with information about what has been just done - the database can be based on Redis, MongoDB, SQL or anything else.

Integrations built around scheduled jobs make most sense when you need to make periodic sweeps across a large swaths of business data, these are the "Give me everything that changed in the last period" kind of interactions when you do not know precisely how much data you are going to receive.

In the specific case of Jira tickets, though, an interesting alternative may be to combine scheduled jobs with IMAP connections:

The idea here is that when new tickets are opened, or when updates are made to existing ones, Jira will send out notifications to specific email addresses and we can take advantage of it.

For instance, you can tell Jira to CC or BCC an address such as zato@example.com. Now, Zato will still run a scheduled job but instead of connecting with Jira directly, that job will look up unread emails for it inbox ("UNSEEN" per the relevant RFC).

Anything that is unread must be new since the last iteration which means that we can process each such email from the inbox, in this way guaranteeing that we process only the latest updates, dispensing with the need for our own database of tickets already processed. We can extract the ticket ID or other details from the email, look up its details in Jira and the continue as needed.

All the details of how to work with IMAP emails are provided in the documentation but it would boil down to this:

# -*- coding: utf-8 -*- # Zato from zato.server.service import Service class MyService(Service): def handle(self): conn = self.email.imap.get('My Jira Inbox').conn for msg_id, msg in conn.get(): # Process the message here .. process_message(msg.data) # .. and mark it as seen in IMAP. msg.mark_seen()

The natural question is - how would the "process_message" function extract details of a ticket from an email?

There are several ways:

1. Each email has a subject of a fixed form - "[JIRA] (ABC-123) Here goes description". In this case, ABC-123 is the ticket ID.
2. Each email will contain a summary, such as the one below, which can also be parsed:

Summary: Here goes description Key: ABC-123 URL: https://example.atlassian.net/browse/ABC-123 Project: My Project Issue Type: Improvement Affects Versions: 1.3.17 Environment: Production Reporter: Reporter Name Assignee: Assignee Name

1. Finally, each email will have an "X-Atl-Mail-Meta" header with interesting metadata that can also be parsed and extracted:

X-Atl-Mail-Meta: user_id="123456:12d80508-dcd0-42a2-a2cd-c07f230030e5", event_type="Issue Created", tenant="https://example.atlassian.net"

The first option is the most straightforward and likely the most convenient one - simply parse out the ticket ID and call Jira with that ID on input for all the other information about the ticket. How to do it exactly is presented in the next chapter.

Regardless of how we parse the emails, the important part is that we know that we invoke Jira only when there are new or updated tickets - otherwise there would not have been any new emails to process. Moreover, because it is our side that invokes Jira, we do not expose our internal system to the public network directly.

However, from the perspective of the overall security architecture, email is still part of the attack surface so we need to make sure that we read and parse emails with that in view. In other words, regardless of whether it is Jira invoking us or our reading emails from Jira, all the usual security precautions regarding API integrations and accepting input from external resources, all that still holds and needs to be part of the design of the integration workflow.

Creating Jira connections

The above presented the ways in which we can arrive at the step of when we invoke Jira and now we are ready to actually do it.

As with other types of connections, Jira connections are created in Zato Dashboard, as below. Note that you use the email address of a user on whose behalf you connect to Jira but the only other credential is that user's API token previously generated in Jira, not the user's password.

Invoking Jira

With a Jira connection in place, we can now create a Python API service. In this case, we accept a ticket ID on input (called "a key" in Jira) and we return a few details about the ticket to our caller.

This is the kind of a service that could be invoked from a service that is triggered by a scheduled job. That is, we would separate the tasks, one service would be responsible for opening IMAP inboxes and parsing emails and the one below would be responsible for communication with Jira.

Thanks to this loose coupling, we make everything much more reusable - that the services can be changed independently is but one part and the more important side is that, with such separation, both of them can be reused by future services as well, without tying them rigidly to this one integration alone.

# -*- coding: utf-8 -*- # stdlib from dataclasses import dataclass # Zato from zato.common.typing_ import cast_, dictnone from zato.server.service import Model, Service # ########################################################################### if 0: from zato.server.connection.jira_ import JiraClient # ########################################################################### @dataclass(init=False) class GetTicketDetailsRequest(Model): key: str @dataclass(init=False) class GetTicketDetailsResponse(Model): assigned_to: str = '' progress_info: dictnone = None # ########################################################################### class GetTicketDetails(Service): class SimpleIO: input = GetTicketDetailsRequest output = GetTicketDetailsResponse def handle(self): # This is our input data input = self.request.input # type: GetTicketDetailsRequest # .. create a reference to our connection definition .. jira = self.cloud.jira['My Jira Connection'] # .. obtain a client to Jira .. with jira.conn.client() as client: # Cast to enable code completion client = cast_('JiraClient', client) # Get details of a ticket (issue) from Jira ticket = client.get_issue(input.key) # Observe that ticket may be None (e.g. invalid key), hence this 'if' guard .. if ticket: # .. build a shortcut reference to all the fields in the ticket .. fields = ticket['fields'] # .. build our response object .. response = GetTicketDetailsResponse() response.assigned_to = fields['assignee']['emailAddress'] response.progress_info = fields['progress'] # .. and return the response to our caller. self.response.payload = response # ########################################################################### Creating a REST channel and testing it

The last remaining part is a REST channel to invoke our service through. We will provide the ticket ID (key) on input and the service will reply with what was found in Jira for that ticket.

We are now ready for the final step - we invoke the channel, which invokes the service which communicates with Jira, transforming the response from Jira to the output that we need:

$ curl localhost:17010/jira1 -d '{"key":"ABC-123"}' { "assigned_to":"zato@example.com", "progress_info": { "progress": 10, "total": 30 } } $

And this is everything for today - just remember that this is just one way of integrating with Jira. The other one, using WebHooks, is something that I will go into in one of the future articles.

More blog posts➤

Hello, Python enthusiasts! &#x1F44B;

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Only 91 days left until EuroPython 2024!

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Interested in open source and happen to be near Brno/Czech Republic in April? Join the Czech Open Source Policy Forum and have the chance to celebrate the launch of the Czech Republic&aposs first Open Source Policy Office (OSPO). More info at: https://pretix.eu/om/czospf2024/

OSSCi Prague Meetup: May 16, 2024 (In-Person)

Join the forefront of innovation at OSSci Prague Meetup, where open source meets science. Call for Speakers is open!  https://pydata.cz/ossci-cfs.html

PyCon DE & PyData Berlin: April 22-24 2024

Dive into three days of Python and PyData excellence at Pycon DE! Visit https://2024.pycon.de/ for details.

PyCon Italy: May 22-25 2024

PyCon Italia 2024 will happen in Florence. The schedule is online and you can check it out at their nice website: https://2024.pycon.it/

GeoPython 2024: May 27-29, 2024

GeoPython 2024 will happen in Basel, Switzerland. For more information visit their website: https://2024.geopython.net/

&#x1F92D; Py.Jokes

Can you imagine our newsletter without joy and laughter? We can’t. &#x1F63E;&#x1F645;‍♀️❌ Here’s this month&aposs PyJoke:

pip install pyjokesimport pyjokesprint(pyjokes.get_joke()) How many programmers does it take to change a lightbulb? None, they just make darkness a standard! &#x1F423; See You All Next Month

Before saying goodbye, thank you so much for reading this far.

We can’t wait to reunite with all you amazing people in beautiful Prague again.

Let me remind you how pretty Prague is during summer. &#x1F33A;&#x1F33C;&#x1F33A;

Rozkvetlá jarní Praha, b&#x159;ezen 2024 by Radoslav Vnen&#x10D;ák

Remember to take good care of yourselves, stay hydrated and mind your posture!

Oh, and don’t forget to force encourage your friends to join us at EuroPython 2024! &#x1F60C;

It’s time again to make new Python memories together!

Looking forward to meeting you all here next month!

With much joy and excitement,

EuroPython 2024 Team &#x1F917;

  

 This is the last bug fix release of Python 3.11 

This is the ninth maintenance release of Python 3.11

Python 3.11.9 is the newest major release of the Python programming language, and it contains many new features and optimizations. Get it here:

https://www.python.org/downloads/release/python-3119/

Major new features of the 3.11 series, compared to 3.10

Among the new major new features and changes so far:

• PEP 657 – Include Fine-Grained Error Locations in Tracebacks
• PEP 654 – Exception Groups and except*
• PEP 673 – Self Type
• PEP 646 – Variadic Generics
• PEP 680 – tomllib: Support for Parsing TOML in the Standard Library
• PEP 675 – Arbitrary Literal String Type
• PEP 655 – Marking individual TypedDict items as required or potentially-missing
• bpo-46752 – Introduce task groups to asyncio
• PEP 681 – Data Class Transforms
• bpo-433030– Atomic grouping ((?>…)) and possessive quantifiers (*+, ++, ?+, {m,n}+) are now supported in regular expressions.
• The Faster Cpython Project is already yielding some exciting results. Python 3.11 is up to 10-60% faster than Python 3.10. On average, we measured a 1.22x speedup on the standard benchmark suite. See Faster CPython for details.

More resources

• Online Documentation

• PEP 664, 3.11 Release Schedule
• Report bugs at https://bugs.python.org.

• Help fund Python and its community.

And now for something completely different

A kugelblitz is a theoretical astrophysical object predicted by general relativity. It is a concentration of heat, light or radiation so intense that its energy forms an event horizon and becomes self-trapped. In other words, if enough radiation is aimed into a region of space, the concentration of energy can warp spacetime so much that it creates a black hole. This would be a black hole whose original mass–energy was in the form of radiant energy rather than matter, however as soon as it forms, it is indistinguishable from an ordinary black hole.

We hope you enjoy the new releases!Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation.

https://www.python.org/psf/

Your friendly release team,
Ned Deily @nad 
Steve Dower @steve.dower 
Pablo Galindo Salgado @pablogsal

Use Python's http.server module to serve up a static website on your own machine.

Table of contents

1. A directory trees of index.html files
2. Serving up HTML files with http.server
3. Customizing http.server with CLI arguments
4. Using http.server as a module
5. Use python -m http.server for a local HTTP server

A directory trees of index.html files

We have a directory here that represents a static website:

~/comprehensions/_build/dirhtml $ ls index.html index.html

We not only have an index.html file, but also a bunch of sub-directories, each with their own index.html file:

~/comprehensions/_build/dirhtml $ ls generator-expressions index.html

The only way to really navigate this website locally is to serve up these files using some sort of HTTP server that is aware of these index files.

Python comes bundled with an HTTP server that we can use. It's called http.server.

Serving up HTML files with http.server

If we run this module …

Read the full article: https://www.pythonmorsels.com/http-server/

A few examples that will save the day probably* 95% of time.

*I don’t have the actual data but seriously, I bet you’ll find those tips useful more often than not!

Introduction

This article was originally posted on Medium.

I use f-strings every day. The irony is I also every day end up searching the Web to find the correct format to use. Until one day I thought a better use of my time would be to create a cheat sheet of the most common formatting cases — AKA this article. It covers the following:

• integers, floats, numbers and scientific notation
• percentages %
• dates
• padding
• adding +/- sign in front of a number

It’s important to note that the f-strings were first introduced in Python 3.6 (PEP 498 if you REALLY must know) so make sure to check the Python version first, if things don’t work for you.

The format

The most basic f-string format goes like this:

You can use this format to print numbers, texts, or even evaluate expressions.

author = "pawjast" year = 2022 print(f"Example 1: {author}") # string print(f"Example 2: {year}") # number print(f"Example 3: {2 + 2}") # expression

The output:

Example 1: pawjast Example 2: 2022 Example 3: 4

You might have noticed that the 2+2 expression in Example 3 got evaluated and 4 was printed.

You’ll get both, the name and the value if you add = sign to a variable.

text = "Data Science Blog" print(f"{text=}")

The output:

text='Data Science Blog'

Template below generalizes how you can add a specific format to a variable:

Next paragraphs shows examples of how to use it.

Recap on numbers

Last stop before going any further. I’ll be using different ways of writing numbers later in the article so let’s review the most common ones.

int_1 = 1 int_with_separator = 1_000 # `int` with 1,000 separator float_1 = 1.125 float_2 = 3.50 scientific_1 = 1.23e2 # 1.23 * 10^2 print(f"Example 1 - int: {int_1}") print(f"Example 2 - int with _ as thousands separator: {int_with_separator}") print(f"Example 3 - float: {float_1}") print(f"Example 4 - float with trailing zero: {float_2}") print(f"Example 5 - float in scientific notation: {scientific_1}")

The output:

Example 1 - int: 1 Example 2 - int with _ as thousands separator: 1000 Example 3 - float: 1.125 Example 4 - float with trailing zero: 3.5 Example 5 - float in scientific notation: 123.0

You can observe that:

• floats had the trailing zeros truncated (Example 4)
• scientific notation was printed as a regular float
• the rest of the variables were printed “as is”

Floats

The float type is enforced by using code f. What you want to control in floats is the number of decimal places.

pi_val = 3.141592 print(f"Example 1: {pi_val:f}") print(f"Example 2: {pi_val:.0f}") print(f"Example 3: {pi_val:.1f}") print(f"Example 4: {pi_val:.3f}")

The output:

Example 1: 3.141592 Example 2: 3 Example 3: 3.1 Example 4: 3.142

Side note: f-strings are flexible enough to allow nesting.

float_val = 1.5 precision = 3 print(f"{float_val:.{precision}f}")

The output:

1.500 Percentage

Use code % to enforce percentage type. Percentage is still a float so you can still use .<whole_number> to control the precision.

val = 0.5 print(f"Example 1: {val:%}") print(f"Example 2: {val:.0%}")

The output:

Example 1: 50.000000% Example 2: 50%

More examples of controlling precision in %:

val = 1.255 print(f"Example 1: {val:.0%}") print(f"Example 2: {val:.1%}")

The output:

Example 1: 125% Example 2: 125.5% Scientific notation

If you want scientific notation to be printed as such (and not as a regular float) it can be enforced with e or E code.

val = 1.23e3 # 1.23 * 10^3 print(f"Example 1: {val:e}") print(f"Example 2: {val:E}")

The output:

1: 1.230000e+03 2: 1.230000E+03

No surprise the precision can be controlled in this case too.

val = 1.2345e3 print(f"{val:.2e}")

The output:

1.23e+03

You can even print a regular number in scientific notation.

val = 2022 print(f"{val:.3e}")

The output:

2.022e+03 Integers

Integers are enforced using code d.

val = 1 print(f"{val:d}")

The output:

1

Adding , to the code will print the thousands separator.

int_1 = 1000 int_2 = 1000_000_000 print(f"{int_1:,d}") print(f"{int_2:,d}")

The output:

1,000 1,000,000,000 Numbers

If the aim is to just print a number, you can use generic code — n.

val_int = 1 val_float = 1.234 val_scient = 4.567e2 print(f"{val_int =: n}") print(f"{val_float =: n}") print(f"{val_scient =: n}")

The output:

val_int = 1 val_float = 1.234 val_scient = 456.7

You can still use .<whole_number> format to control the precision.

Side note: in this case whole_number determines the total number of digits printed, not the number of decimal points! On top of that, n code will decide the best output format for a number.

val_float_1 = 1.234 val_float_2 = 20.234 val_float_3 = 123.456 print(f"{val_float_1 =: .2n}") # prints as truncated float print(f"{val_float_2 =: .2n}") # prints as int print(f"{val_float_3 =: .2n}") # prints as scientific notation

The output:

val_float_1 = 1.2 val_float_2 = 20 val_float_3 = 1.2e+02 Dates from datetime import date, datetime

Printing a date “as is” works exactly like printing any other variable.

day = date( year=2022, month=9, day=1 ) print(f"{day}")

The output:

2022-09-01

To recreate the format you will use the following codes:

• %Y for year
• %m for month
• %d for day

With those codes you can e.g. create a new date format.

print(f"{day:%Y-%m-%d}") # default appearance print(f"{day:%Y/%m/%d}") # use `/` as separator

The output:

2022-09-01 2022/09/01

It’s also possible to print a month as a text:

• %b — short version
• %B — long version

print(f"{day:%Y %b %d}") print(f"{day:%Y %B %d}")

The output:

2022 Sep 01 2022 September 01

The same variable can be printed multiple times.

print(f"{day:%b or %B}?") print(f"{day:%Y %Y %Y}"

The output:

Sep or September? 2022 2022 2022

Reusing the same variable can be useful e.g. when you want to print the date and provide the day of the week as text (using code %A ).

print(f"{day:%Y %b %d (%A)}")

The outcome:

2022 Sep 01 (Thursday)

Last but not least, you can swap %Y for %y to get short version of the year.

print(f"{day:%y.%m.%d}")

The output:

22.09.01 Datetime

Let’s specify two datetime variables.

day_and_time = datetime( year=2022, month=9, day=1, hour=17, minute=30, second=45 ) now = datetime.now() print(f"{day_and_time}") print(f"{now}") # with mircoseconds

The output:

2022-09-01 17:30:45 2022-12-08 15:49:37.810347

Time format requires the following codes:

• %H — hour
• %M — minute
• %S — second
• %f — millisecond (1s = 1000 milliseconds)

Therefore, the default format would look like below.

print(f"{now:%Y-%m-%d %H:%M:%S.%f}")

The outcome:

2022-12-08 15:49:37.810347

f-string obviously creates a string (try this code to confirm it: type(f”now:%Y-%m-%d %H:%M%S.%f}”)). Therefore, if e.g. you are not happy with the number of decimal points for milliseconds, you can easily truncate them.

print(f"{now:%Y-%m-%d %H:%M:%S.%f}"[:22])

The output:

2022-12-08 15:49:37.81

In order to change the 24hr format to 12hr format, you will need to:

• swap hour code from %H to %I
• Optionally add %p at the end if you want to add AM/PM to the time

print(f"24hr: {day_and_time:%Y-%m-%d %H:%M:%S}") print(f"12hr: {day_and_time:%Y-%m-%d %I:%M:%S}") print(f"12hr with AM/PM: {day_and_time:%Y-%m-%d %I:%M:%S %p}")

The output:

24hr: 2022-09-01 17:30:45 12hr: 2022-09-01 05:30:45 12hr with AM/PM: 2022-09-01 05:30:45 PM

Few more useful date formats:

• %j for day of the year
• %W for week of the year (assuming Monday as first day of the week)
• %U for week of the year (assuming Sunday as first day of the week

day = date( year=2018, month=9, day=17 ) print(f"The date: {day}") print(f"Day of the year: {day: %j}") print(f"Week of the year (Mon): {day: %W}") print(f"Week of the year (Sun): {day: %U}")

The outcome:

The date: 2018-09-17 Day of the year: 260 Week of the year (Mon): 38 Week of the year (Sun): 37 Padding

Padding with empty spaces.

val = 1 print(f"1: {val:1d}") print(f"2: {val:2d}") print(f"3: {val:3d}")

The output:

1: 1 2: 1 3: 1

Padding with zeros.

val = 1 print(f"1: {val:01d}") print(f"2: {val:02d}") print(f"3: {val:03d}")

The output:

1: 1 2: 01 3: 001

I usually use zero — padding with for loop to keep the text nicely aligned in the terminal.

for i in range(11): print(f"{i:02d}")

The output:

01 02 ... 09 10 Positive/negative sign

In some cases you will require to show a +/- sign next to a number.

positive = 1.23 negative = -1.23 print(f"1: {positive:+.2f} {negative:+.2f}") print(f"2: {positive:-.2f} {negative:-.2f}") print(f"3: {positive: .2f} {negative: .2f}")

The output:

1: +1.23 -1.23 2: 1.23 -1.23 3: 1.23 -1.23 Combining all things together # print variable with name, limit precision and thousands separator val = 11500.23456 print(f"{val = :,.3f}")

The output:

val = 11,500.235

We’re done .

Full code

 GitHub notebook

Connect with me

 Medium  Twitter / X  GitHub  Substack  LinkedIn

Resources

1⃣ https://docs.python.org/3/reference/lexical_analysis.html#f-strings

2⃣ https://docs.python.org/3/library/string.html#format-specification-mini-language

3⃣ https://strftime.org/

How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

We’re excited to announce the April 2024 release of the Python and Jupyter extensions for Visual Studio Code!

This release includes the following announcements:

• Improved debug config flow for Flask and Django
• Module and import analysis on Jupyter’s Run Dependent Cells with Pylance
• Hatch environment discovery
• Automatic environment selection for pipenv, pyenv, and Poetry projects
• Report Issue command improvements

If you’re interested, you can check the full list of improvements in our changelogs for the Python, Jupyter and Pylance extensions.

Improved debug config flow for Flask and Django

Creating launch configurations for Flask and Django apps just got easier! Improvements have been made to detect possible startup files in your workspace when creating a launch.json for your web app. For Django, the Python Debugger extension looks for manage.py or app.py files in the root or a subdirectory one level lower in your workspace. For Flask, the extension looks for wsgi.py, app.py, or init.py files that contain the declaration of a Flask application (for example, app = Flask()). If none of those files are not found in the project, the dropdown shows a Default option for the corresponding project type, even though that file may not be present.

In the case your file was not detected, you can enter the file path directly or browse other files in your directory.

Module and import analysis on Jupyter’s Run Dependent Cells with Pylance

Dependency analysis for Jupyter cells has improved with the latest Pylance pre-release. With these changes, Pylance aids Jupyter in understanding module imports, which is especially useful when you have a cell that imports a module that was defined in a previous cell.

To enable this feature, install the latest Pylance pre-release in VS Code Insiders, and enable the jupyter.executionAnalysis.enabled and notebook.consolidatedRunButton settings.

Hatch environment discovery

Hatch environments are now discovered and activated, by default, similar to other common environments, such as Venv, Conda, and Poetry. Furthermore, in the case of Hatch where an explicit environment identifier is not registered, the extension is able to determine the environment type (Hatch) from the environment locator.

Automatic environment selection for pipenv, pyenv, and Poetry projects

If your workspace contains a pipenv, pyenv, or Poetry environment, the corresponding environment is now automatically selected for your workspace. Previously, the extension correctly discovered these environments, but selected the default global interpreter, requiring you to manually select the appropriate environment for your workspace. Now, the Python extension infers the default environment based on its presence and any corresponding configuration files. For example, in the case of pyenv, the extension looks at the .python-version file to automatically select the appropriate interpreter for the workspace.

Report Issue command improvements

The Python and Python Debugger extensions have adopted changes making it easier for you to report issues to our repos! Filing an issue with the Report Issue command (workbench.action.openIssueReporter) does most of the heavy lifting, prompting you for additional info so our team can efficiently triage the problem you are encountering.

To file an issue using the Report Issue command for @vscode-python or @vscode-python-debugger, choose Python or Python Debugger respectively from the extension dropdown.

Other Changes and Enhancements

We have also added small enhancements and fixed issues requested by users that should improve your experience working with Python and Jupyter Notebooks in Visual Studio Code. Some notable changes include:

• Better error messages when “Move to file” refactoring fails in @pylance-release#4345
• REPL Smart Send (python.REPL.enableREPLSmartSend) is now enabled by default

We would also like to extend special thanks to this month’s contributors:

• @bn-d Update minimum VS Code version in @vscode-black-formatter#445
• @flying-sheep Add support for Hatch environments in @vscode-python#22779

Call for Community Feedback

As we are planning and prioritizing future work, we value your feedback! Below are a few issues we would love feedback on:

• Design proposal for test coverage in (@vscode-python#22827)

Try out these new improvements by downloading the Python extension and the Jupyter extension from the Marketplace, or install them directly from the extensions view in Visual Studio Code (Ctrl + Shift + X or ⌘ + ⇧ + X). You can learn more about Python support in Visual Studio Code in the documentation. If you run into any problems or have suggestions, please file an issue on the Python VS Code GitHub page.

The post Python in Visual Studio Code – April 2024 Release appeared first on Python.

PyCharm 2024.1 is out with many key updates, including full line code completion for frontend frameworks, documentation preview for Hugging Face models and datasets, and multiple improvements to the user experience – just to name a few! You can download the latest version from our download page, or even easier, update your current version through […]