Planet Python

I’ve been working on lots of projects this year. Here are the ones I highlighted in this episode:

• Python Logging Book

• Python Logging Video Course

• ObjectListView3

• JupyterLab 101 Book on Kickstarter

• A book on Textual

• Updates for my ReportLab and wxPython books

• Python Papers Newsletter

The Python Show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Both last week at London tech leaders and this week at the Open Source Summit in Vienna I engaged in various discussions about pledging money to Open Source. At Sentry we have been funding our Open Source dependencies for a few years now and we're trying to encourage others to do the same.

It’s not an easy ask, of course. One quite memorable point raised was what I would call “accidental spending”. The story goes like this: an engineering team spins up a bunch of Kubernetes machines. As the fleet grows in scale some inefficiencies creep in. To troubleshoot or optimize, additional services such as load balancers, firewalls, cloud provider log services, etc. are provisioned with minimal discussion. Initially none of that was part of the plan, but ever so slightly for every computing resource, some extra stuff is paid on top creating largely hidden costs. Ideally all of that pays off (after all, hopefully by debugging quicker you reduce that downtime, by having that load balancer you can auto scale and save on unused computing resources etc.). But often, the payoff feels abstract and are hard to quantify.

I call those purchases “accidental” because they are proportional to the deployed infrastructure and largely acting like a tax on top of everything. Only after a while does the scale of that line item become apparent. On the other hand intentionally purchasing a third party system is a very intentional act. It's very deliberate, requiring conversations and more scrutiny is placed for putting a credit card into a new service. Companies providing services understand this and are positioning themselves accordingly. Their play could be to make the case that that their third party solution is better, cheaper etc.

Open Source funding could be seen through both of these lenses. Today, in many ways, pledging money to Open Source is a very intentional decision. It requires discussions, persuasion and justification. The purpose and the pay-off is not entirely clear. Companies are not used to the idea of funding Open Source and they don't have a strong model to reason about these investments. Likewise many Open Source projects themselves also don't have a good way of dealing with money and might lack the governance to handle funds effectively. After all many of these projects are run by individuals and not formal organizations.

Companies are unlikely to fund something without understanding the return on investment. One better understood idea is to turn that one “random person in Nebraska” maintaining a critical dependency into a well-organized team with good op-sec. But for that to happen, funding needs to scale from pennies to dollars, making it really worthwhile.

My colleague Chad Whitacre floated an idea: what if platforms like AWS or GitHub started splitting the check? By adding a line-item to the invoices of their customers to support Open Source finding. It would turn giving to Open Source into more of a tax like thing. That might leverage the general willingness to just pile up on things to do good things. If we all pay 3% on top of our Cloud or SaaS bills to give to Open Source this would quickly add up.

While I’m intrigued by the idea, I also have my doubts that this would work. It goes back to the problem mentioned earlier that some Open Source projects just have no governance or are not even ready to receive money. How much value you put on a dependency is also very individual. Just because an NPM package has a lot of downloads does not necessarily mean it's critical to the mission of the company. rrweb is a good example for us at Sentry. It sits at the core of our session replay product but since we we vendor a pinned fork, you would not see rrweb in your dependency tree. We also value that package more than some algorithm would be able to determine about how important that package is to us.

So the challenge with the tax — as appealing as it is — is that it might make the “purchase decision” of funding Open Source easier, but it would probably make the distribution problem much worse. Deliberate, intentional funding is key. At least for the moment.

Still, it’s worth considering. The “what if” is a powerful idea. Using a restaurant analogy, the “open-source tax” is like the mandatory VAT or health surcharge on your bill: no choice is involved. Another model could be more like the tip suggestions on a receipt offering a choice but also guidance on what’s appropriate to contribute.

The current model we propose with our upcoming Open Source Pledge is to suggest like a tip what you should give in relation to your developer work force. Take the average number of full time engineers you have over a year, multiply this by 2000. That is the amount in US dollars you should give to your Open Source dependencies.

That sounds like a significant amount! But let's put this in relation for a typical developer you employ: that's less than a fifth of what you would pay for FICA (Federal Insurance Contributions Act in the US) in the US. That's less than the communal tax you would pay in Austria. I'm sure you can think of similar payroll taxes in your country.

I believe that after step one of recognizing there is a funding problem follows an obvious step two: having a baseline funding amount that stands in relation to your business (you own or are a part of) of what the amount should be. Using the size of the development team as a metric offers an objective and quantifiable starting point. The beauty in my mind of the developer count in particular is that it's somewhat independently observable from both the outside and inside [1]. The latter is important! It creates a baseline for people within a company to start a conversation about Open Source funding.

If you have feedback on this, particular the pledge I invite you mail me or to leave a comment on the Pledge's issue tracker.

[1]There is an analogy to historical taxation here. For instance the Window Tax was taxation based on the number of Windows in a building. That made enforcement easy because you could count them from street level. The downside of taht was obviously the unintended consequences that this caused. Something to always keep in mind!

We’re thrilled to announce the launch of the new Python Data Science Extension Pack for Visual Studio Code! This powerful pack brings together some of the most popular and essential VS Code extensions, making it your one-stop shop for all things data science in Python.

What’s Inside?

Our extension pack is designed to streamline your data science journey from start to finish. Whether you’re preparing data, conducting analysis, visualizing results, or building and training machine learning models, we’ve got you covered.

This Data Science extension pack currently includes four extensions:

• Python – Provides rich support for the Python language such as IntelliSense, debugging, formatting, linting, code navigation, refactoring, variable explorer, test explorer, and more.
• Jupyter – Used to create and edit Jupyter Notebooks, add and run code/markdown cells, render plots, create presentation-friendly versions of your notebook by exporting to HTML or PDF and more.
• GitHub Copilot – An AI pair programmer tool that helps you write code faster and smarter.
• Data Wrangler – A code-centric data viewing and cleaning tool to explore, visualize, and clean tabular data.

Get started today

Dive into the world of data science by installing the Python Data Science Extension Pack for VS Code from the VS Code extension marketplace.

We encourage you to provide feedback and file issues. Additionally, if there are other VS Code extensions that you feel are essential to the data science workflow, please let us know by creating a ticket in our GitHub repo.

The post Announcing the new Python Data Science Extension Pack for VS Code appeared first on Python.

Although the final release of Python 3.13 is scheduled for October 2024, you can download and install a preview version today to explore the new features. Notably, the introduction of free threading and a just-in-time (JIT) compiler are among the most exciting enhancements, both designed to give your code a significant performance boost.

In this tutorial, you’ll:

• Compile a custom Python build from source using Docker
• Disable the Global Interpreter Lock (GIL) in Python
• Enable the Just-In-Time (JIT) compiler for Python code
• Determine the availability of new features at runtime
• Assess the performance improvements in Python 3.13
• Make a C extension module targeting Python’s new ABI

Check out what’s new in the Python changelog for a complete list of the upcoming features and improvements. This document contains a quick summary of the release highlights as well as a detailed breakdown of the planned changes.

To download the sample code and other resources accompanying this tutorial, click the link below:

Get Your Code: Click here to download the free sample code that shows you how to work with the experimental free threading and JIT compiler in Python 3.13.

Take the Quiz: Test your knowledge with our interactive “Python 3.13: Free-Threading and a JIT Compiler” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.13: Free-Threading and a JIT Compiler

In this quiz, you'll test your understanding of the new features in Python 3.13. You'll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, and more.

Free Threading and JIT in Python 3.13: What’s the Fuss?

Before going any further, it’s important to note that the majority of improvements in Python 3.13 will remain invisible to the average Joe. This includes free threading (PEP 703) and the JIT compiler (PEP 744), which have already sparked a lot of excitement in the Python community.

Keep in mind that they’re both experimental features aimed at power users, who must take extra steps to enable them at Python’s build time. None of the official channels will distribute Python 3.13 with these additional features enabled by default. This is to maintain backward compatibility and to prevent potential glitches, which should be expected.

Note: Don’t try to use Python 3.13 with the experimental features in a production environment! It may cause unexpected problems, and the Python Steering Council reserves the right to remove these features entirely from future Python releases if they prove to be unstable. Treat them as an experiment to gather real-world data.

In this section, you’ll get a birds-eye view of these experimental features so you can set the right expectations. You’ll find detailed explanations on how to enable them and evaluate their impact on Python’s performance in the remainder of this tutorial.

Free Threading Makes the GIL Optional

Free threading is an attempt to remove the Global Interpreter Lock (GIL) from CPython, which has traditionally been the biggest obstacle to achieving thread-based parallelism when performing CPU-bound tasks. In short, the GIL allows only one thread of execution to run at any given time, regardless of how many cores your CPU is equipped with. This prevents Python from leveraging the available computing power effectively.

There have been many attempts in the past to bypass the GIL in Python, each with varying levels of success. You can read about these attempts in the tutorial on bypassing the GIL. While previous attempts were made by third parties, this is the first time that the core Python development team has taken similar steps with the permission of the steering council, even if some reservations remain.

Note: Python 3.12 approached the GIL obstacle from a different angle by allowing the individual subinterpreters to have their independent GILs. This can improve Python’s concurrency by letting you run different tasks in parallel, but without the ability to share data cheaply between them due to isolated memory spaces. In Python 3.13, you’ll be able to combine subinterpreters with free threading.

The removal of the GIL would have significant implications for the Python interpreter itself and especially for the large body of third-party code that relies on it. Because free threading essentially breaks backward compatibility, the long-term plan for its implementation is as follows:

1. Experimental: Free threading is introduced as an experimental feature and isn’t a part of the official Python distribution. You must make a custom Python build to disable the GIL.
2. Enabled: The GIL becomes optional in the official Python distribution but remains enabled by default to allow for a transition period.
3. Disabled: The GIL is disabled by default, but you can still enable it if needed for compatibility reasons.

There are no plans to completely remove the GIL from the official Python distribution at the moment, as that would cause significant disruption to legacy codebases and libraries. Note that the steps outlined above are just a proposal subject to change. Also, free threading may not pan out at all if it makes single-threaded Python run slower than without it.

Until the GIL becomes optional in the official Python distribution, which may take a few more years, the Python development team will maintain two incompatible interpreter versions. The vanilla Python build won’t support free threading, while the special free-threaded flavor will have a slightly different Application Binary Interface (ABI) tagged with the letter “t” for threading.

This means that C extension modules built for stock Python won’t be compatible with the free-threaded version and the other way around. Maintainers of those external modules will be expected to distribute two packages with each release. If you’re one of them, and you use the Python/C API, then you’ll learn how to target CPython’s new ABI in the final section of this tutorial.

JIT Compiles Python to Machine Code

As an interpreted language, Python takes your high-level code and executes it on the fly without the need for prior compilation. This has both pros and cons. Some of the biggest advantages of interpreted languages include better portability across different hardware architectures and a quick development time due to the lack of a compilation step. At the same time, interpretation is much slower than directly executing code native to your machine.

Note: To be more precise, Python interprets bytecode instructions, an intermediate binary representation between pure Python and machine code. The Python interpreter compiles your code to bytecode when you import a module and stores the resulting bytecode in the __pycache__ folder. This doesn’t inherently make your Python scripts run faster, but loading a pre-processed bytecode can indeed speed up their startup time.

Languages like C and C++ leverage Ahead-of-Time (AOT) compilation to translate your high-level code into machine code before you ship your software. The benefit of this is faster execution since the code is already in the computer’s mother tongue. While you no longer need a separate program to interpret the code, you must compile it separately for all target platforms that you want supported. You should also handle platform-specific differences yourself.

Read the full article at https://realpython.com/python313-free-threading-jit/ »

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DjangoCon US starts next week in Durham, NC on September 22nd!

If you aren't able to join in person, please consider purchasing an online ticket: https://ti.to/defna/djangocon-us-2024

The conference is full of a variety of talks with excellent keynote speakers! It's shaping up to be an event you'll want to experience live.

If you'd like to learn more about DjangoCon US visit them at their website or reach out to them at hello@djangocon.us.

The Spyder team hosted a Birds of a Feather session at SciPy 2024, this time on the topic of users' experiences (good and bad) with the UI/UX of scientific interfaces and IDEs, and how their developers can better serve users. Here, we share what we learned from the session, as well as a link to the full detailed community notes.

#647 – SEPTEMBER 17, 2024
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How to Use Conditional Expressions With NumPy where()

This tutorial teaches you how to use the where() function to select elements from your NumPy arrays based on a condition. You’ll learn how to perform various operations on those elements and even replace them with elements from a separate array or arrays.
REAL PYTHON

PythonistR: A Match Made in Data Heaven

In data science you’ll sometimes hear a debate between R and Python. Cosima says ‘why not choose both?’ She outlines a data pipeline that uses the best tool for each job.
COSIMA MEYER

Transcribe Audio in 5 Lines of Code

Build AI apps that understand speech with insanely accurate speech-to-text models. Sign up for a free account and get $50 in credits to try AssemblyAI’s speech recognition models →
ASSEMBLY AI sponsor

Python HTTP Clients: Requests vs. HTTPX vs. AIOHTTP

Learn about the differences between Requests, HTTPX, and AIOHTTP, and when to use each library for your Python projects.
GEORGES HAIDAR

Quiz: Generate Images With DALL·E and the OpenAI API

In this quiz, you’ll test your understanding of generating images with DALL·E by OpenAI using Python. You’ll revisit concepts such as using the OpenAI Python library, making API calls for image generation, creating images from text prompts, and converting Base64 strings to PNG image files.
REAL PYTHON

Quiz: The Walrus Operator: Python’s Assignment Expressions

In this quiz, you’ll test your understanding of the Python Walrus Operator. This operator was introduced in Python 3.8, and understanding it can help you write more concise and efficient code.
REAL PYTHON

Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20

PYTHON.ORG

Python Release Python 3.13.0rc2

PYTHON.ORG

Articles & Tutorials Python Community Divided Over CoC Enforcement

Over the last few months there has been a lot of back and forth in the Python community, especially on the forums, around changes to bylaws and how the Code of Conduct is enforced. This article covers the history and context of the events.
JAKE EDGE

Django: Rotate Your Secret Key, Fast or Slow

Django’s SECRET_KEY setting is used for cryptographic signing in various places, such as for session storage and password reset tokens. If you need to rotate it you can allow read-only use of the old key to smooth the transition.
ADAM JOHNSON

Posit Connect - Help Your Analytics Team Share and Collaborate

Tired of tediously send files and trying to use general-purpose collaboration tools? Posit Connect makes it easy to share, collaborate, and get feedback on your data science work including Jupyter notebooks, Plotly dashboards, Streamlit, Quarto, Shiny or other interactive analytics applications →
POSIT sponsor

Multiversion Python Thoughts

Armin has played around with enabling multiple versions of a library to be installed for the same instance of Python in the past, and recent feature additions to uv are making it come closer to fruition.
ARMIN RONACHER

How We Made Notebooks Load 10 Times Faster

“When we received feedback our Notebooks UI was taking too long too load, our engineers dove into ways to improve the developer experience — bringing some load times from 30 seconds down to less than one.”
LUIS NEVES

When to Use .__repr__() vs .__str__() in Python

In this video course, you’ll learn the difference between the string representations returned by .__repr__() vs .__str__() and understand how to use them effectively in classes that you define.
REAL PYTHON course

Python Bytes Episode #400

The Python Bytes podcast just delivered show #400. This is a huge accomplishment. This episode celebrates the achievement, and also covers: Python 3.13RC, Docker with uv, the humanize project, and more.
PYTHON BYTES podcast

Improved print Readability With pprint

The pretty print module (pprint) provides more readable output for complex data structures and this post shows you how to use the library and what you can get out of it.
JUHA-MATTI SANTALA

“Next Level Python” Humble Bundle for Charity

Make mastering Python your mission: This mix of online courses, books, exercises, and productivity tools is here to help you succeed—whether you’re a beginner or a skilled Python pro. Support Girls Who Code and get Python books, software, and video courses collectively valued at $1,882 for a pay-what-you-want price →
HUMBLEBUNDLE.COM sponsor

Switching From pyenv to uv

Will has recently switched from using a variety of packaging tools to just using uv. This post is a summary of what needed to change when going from pyenv to uv.
WILL KAHN-GREENE

uv Under Discussion on Mastodon

There is a deep conversation going on about the longevity of uv on Mastodon and for those not on the platform, Simon has summarized it.
SIMON WILLISON

Why Not Comments

This post talks about why you might want to include information in your code comments about why you didn’t take a particular approach.
HILLEL WAYNE

How to Build a Perfect Docker Image for a Poetry Project

This article describes how to build a secure, fast to build, and lightweight Docker image for your Poetry-based project
CODEMAGEDDON • Shared by Sergey

Python macOS Framework Builds

Glyph explains just what a Framework is on macOS and why CPython on macOS should be built that way.
GLYPH LEFKOWITZ

Projects & Code PSP (Python Scaffolding Projects)

GITHUB.COM/MATTEOGUADRINI • Shared by Matteo Guadrini

pocketpy: Portable Python 3.x Interpreter in Modern C

GITHUB.COM/POCKETPY

graphiti: Build Dynamic, Temporally-Aware Knowledge Graphs

GITHUB.COM/GETZEP

picows: Ultra-Fast Websocket Client and Server for Asyncio

GITHUB.COM/TARASKO

django-cotton: Component Based Design to Django Templates

GITHUB.COM/WRABIT

Events PyData Amsterdam 2024

September 18 to September 21, 2024
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Weekly Real Python Office Hours Q&A (Virtual)

September 18, 2024
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PyCon India 2024

September 20 to September 24, 2024
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PyCon TW 2024

September 21 to September 23, 2024
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DjangoCon US 2024

September 22 to September 27, 2024
DJANGOCON.US

PyBay 2024

September 23 to September 24, 2024
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PyCon Africa 2024

September 24 to September 29, 2024
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PyData Paris 2024

September 25 to September 27, 2024
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PyCon JP 2024

September 27 to September 30, 2024
PYCON.JP

Python Norte 2024

September 27 to September 29, 2024
PYTHONNORTE.ORG

PyCon Niger 2024

September 28 to September 30, 2024
PYCON.ORG

Happy Pythoning!
This was PyCoder’s Weekly Issue #647.
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When using Python's help function, have you ever wondered what the various symbols (/, *, [, and ]) mean? Understanding those symbols will help you better understand how to use the functions and classes you're working with.

Table of contents

1. What do all the symbols mean in help output?
2. Multiple function signatures
3. Pay attention to the nouns
4. The symbols of help
5. Default values: the = symbol
6. Unlimited arguments: the * symbol before an argument name
7. Keyword-only arguments: a lone * symbol
8. Positional-only arguments: a lone / symbol
9. Arbitrary keyword arguments: the ** symbol
10. Square brackets: optional arguments
11. Ellipsis (...) and other weird things
12. The conventions of Python's help function

What do all the symbols mean in help output?

We'll cover what the * and / symbols below mean:

>>> help(sorted) Help on built-in function sorted in module builtins: sorted(iterable, /, *, key=None, reverse=False) Return a new list containing all items from the iterable in ascending order. A custom key function can be supplied to customize the sort order, and the reverse flag can be set to request the result in descending order.

We'll also talk about the different formats that help output comes in. For example, note the square brackets in [x] below and note that there are two different styles noted for calling int:

>>> help(int) Help on class int in module builtins: class int(object) | int([x]) -> integer | int(x, base=10) -> integer

We'll start by giving a name to that line which indicates how a function, method, or class is called.

Multiple function signatures

A function signature notes the …

Read the full article: https://www.pythonmorsels.com/understanding-help/

A well-designed coding environment not only enhances your focus and productivity but also makes coding sessions more enjoyable. In this Code Conversation, your instructor Philipp Ascany will guide you step-by-step through the process of finding, installing, and adjusting color themes in VS Code. You’ll explore the various options available in VS Code and learn how to make fine adjustments to create a setup that suits your personal preferences.

In this video course, you’ll:

• Learn about Themes in VS Code
• Find a VS Code Color Theme
• Select a Theme
• Install Your Theme
• Make Additional Adjustments

By the end of the course, you’ll have a coding environment that not only looks great but also enhances your overall coding experience.

[ 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 ]

In this quiz, you’ll test your understanding of Python’s standard package manager, pip. You’ll revisit the concepts behind pip, important commands, and how to install packages.

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<strong>Topics covered in this episode:</strong><br> <ul> <li><strong>“<a href="https://github.com/overhangio/tutor/issues/937?featured_on=pythonbytes">We must replace uwsgi by something else</a>”</strong></li> <li><strong><a href="https://pythonspeed.com/articles/intro-rust-python-extensions?utm_source=pocket_shared&featured_on=pythonbytes">Let’s build and optimize a Rust extension for Python</a></strong></li> <li><strong><a href="https://www.reversinglabs.com/blog/fake-recruiter-coding-tests-target-devs-with-malicious-python-packages?featured_on=pythonbytes">Fake recruiter coding tests target devs with malicious Python packages</a></strong></li> <li><a href="https://pyfound.blogspot.com/2024/08/ask-questions-or-tell-us-what-you-think.html?utm_source=pocket_shared&featured_on=pythonbytes"><strong>Monthly PSF Board Office Hours</strong></a></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=XKI5gtnKMus' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="401">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by ScoutAPM: <a href="https://pythonbytes.fm/scout"><strong>pythonbytes.fm/scout</strong></a></p> <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 <strong>Monday</strong> at 10am 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>Michael #1:</strong> <strong>“<a href="https://github.com/overhangio/tutor/issues/937?featured_on=pythonbytes">We must replace uwsgi by something else</a>”</strong></p> <ul> <li>uWSGI is now in maintenance mode: https://uwsgi-docs.readthedocs.io/en/latest/ <ul> <li><em>The project is in maintenance mode</em> <em>(only</em> <em>bugfixes and updates for new languages apis). Do not expect quick answers on github issues and/or pull requests</em> <em>(sorry</em> <em>for that) A big thanks to all of the users and contributors since 2009.</em></li> </ul></li> <li>Reasonable options look like: <ul> <li><a href="https://github.com/emmett-framework/granian?featured_on=pythonbytes">granian</a></li> <li><a href="https://www.uvicorn.org?featured_on=pythonbytes">uvicorn</a></li> <li><a href="https://hypercorn.readthedocs.io/en/latest/index.html?featured_on=pythonbytes">hypercorn</a></li> <li><a href="https://gunicorn.org?featured_on=pythonbytes">gunicorn</a> (potentially with uvicorn workers for async)</li> </ul></li> </ul> <p><strong>Brian #2:</strong> <a href="https://pythonspeed.com/articles/intro-rust-python-extensions?utm_source=pocket_shared&featured_on=pythonbytes">Let’s build and optimize a Rust extension for Python</a></p> <ul> <li>Itamar Turner-Trauring</li> <li>Example: algorithm for approximating the number of unique values in a list</li> <li>Comparison to non-approximation <ul> <li>non-approx is faster but uses way more memory</li> </ul></li> <li>Rust version <ul> <li>Use Maturin and PyO3</li> <li>Pull in Rust dependencies (rand for random numbers)</li> </ul></li> <li>Optimization <ul> <li>link-time optimization</li> <li>faster random</li> <li>store hashes only</li> </ul></li> <li>Future optimizations <ul> <li>change algorithm maybe</li> <li>pass numpy array instead of Python list (I’d like to see that spedup)</li> </ul></li> </ul> <p><strong>Michael #3:</strong> <a href="https://www.reversinglabs.com/blog/fake-recruiter-coding-tests-target-devs-with-malicious-python-packages?featured_on=pythonbytes">Fake recruiter coding tests target devs with malicious Python packages</a></p> <ul> <li>via python weekly</li> <li>GitHub projects that have been linked to previous, targeted attacks in which developers are lured using fake job interviews.</li> <li>Attackers posing as employees of major financial services firms.</li> <li>This previously happened via other means such as NPM</li> <li>This analysis revealed that the direct parent of the detected, malicious files is a PythonPYC file, meaning that once again the team encountered malware hidden in a compiled Python file.</li> <li>“The README files tell would-be candidates to make sure the project is running successfully on their system before making modifications.”</li> <li>What can you do (according to Michael)? <ul> <li>Try out new packages in a docker container</li> <li>Work on code and projects using a VM which has snapshotting (to roll back completely after you’re done)</li> <li>Fire up <a href="https://learn.microsoft.com/en-us/azure/virtual-desktop/users/connect-windows?pivots=remote-desktop-msi&featured_on=pythonbytes">a Windows desktop in the cloud</a> for the project then destroy it</li> </ul></li> </ul> <p><strong>Brian #4:</strong> <a href="https://pyfound.blogspot.com/2024/08/ask-questions-or-tell-us-what-you-think.html?utm_source=pocket_shared&featured_on=pythonbytes"><strong>Monthly PSF Board Office Hours</strong></a></p> <ul> <li>“The Office Hours will be sessions where you can share with us how we can help your community, express your perspectives, and provide feedback for the PSF.”</li> <li>“Unless we have a dedicated topic for a session, you are not limited to talking with us about the above topics, although the discussions should be focused on Python, the PSF, and our community. If you think there’s something we can help with or we should know, we welcome you to come and talk to us!”</li> <li>Upcoming office hours <ul> <li>October 8th, 2024: 9pm UTC</li> <li>November 12th, 2024: 2pm UTC</li> <li>December 10th, 2024: 9pm UTC</li> <li>January 14th, 2025: 2pm UTC</li> <li>February 11th, 2025: 9pm UTC</li> <li>March 11th, 2025: 1pm UTC</li> <li>April 8th, 2025: 9pm UTC</li> <li>May 13th, 2025: 1pm UTC (Live from PyCon US!)</li> <li>June 10th, 2025: 9pm UTC</li> <li>July 9th, 2025: 1pm UTC</li> <li>August 12th, 2025: 9pm UTC</li> </ul></li> </ul> <p><strong>Extras</strong> </p> <p>Brian:</p> <ul> <li><a href="https://2025.pycascades.com?featured_on=pythonbytes">PyCascades CFP closes Friday, Sept 20</a> <ul> <li>PyCascades is in Portland in 2025 (Feb 8 &amp; 9)</li> </ul></li> <li><p>uv <a href="https://github.com/astral-sh/uv/pull/7263?featured_on=pythonbytes">now supports Python 3.13.0rc2</a></p> <pre><code>uv self update uv venv -p 3.13 </code></pre></li> <li><p><a href="https://github.com/astral-sh/uv/issues/7193?featured_on=pythonbytes">Free threaded is still an open issue</a></p></li> </ul> <p>Michael:</p> <ul> <li><a href="https://www.humblebundle.com/software/next-level-python-from-talk-python-and-friends-software?featured_on=pythonbytes">Big Python Humble Bundle with both of our products</a> <ul> <li>Get $1,800 worth of Python content and tools for $30 and contribute to charity</li> <li>Includes 5 <a href="https://training.talkpython.fm/courses/all?featured_on=pythonbytes">Talk Python courses</a></li> <li>Several of Brian’s and his book</li> </ul></li> <li><a href="https://djangonaut.space/comms/2024-opening-session-3/?featured_on=pythonbytes">Djangonaut Space Session 3 Applications Open!</a> <ul> <li>I interviewed <a href="https://talkpython.fm/episodes/show/451/djangonauts-ready-for-blast-off?featured_on=pythonbytes">Sarah and Tushar on Talk Python</a></li> </ul></li> <li><a href="https://alt-tab-macos.netlify.app?featured_on=pythonbytes">AltTab: Windows alt-tab on macOS</a></li> </ul> <p><strong>Joke:</strong> <a href="https://devhumor.com/media/elections-403-for-bidden?featured_on=pythonbytes">Election joke</a></p>

Albert Cervera has found that trytond allows to execute reports for records that user has no read access and also for reports limited to a set of group that the user is not.

Impact

CVSS v3.0 Base Score: 4.3

• Attack Vector: Network
• Attack Complexity: Low
• Privileges Required: Low
• User Interaction: None
• Scope: Unchanged
• Confidentiality: Low
• Integrity: None
• Availability: None

Workaround

There is no known workaround.

Resolution

All affected users should upgrade trytond to the latest version.

Affected versions per series:

• trytond:
  • 7.2: <= 7.2.8
  • 7.0: <= 7.0.17
  • 6.0: <= 6.0.51

Non affected versions per series:

• trytond:
  • 7.2: >= 7.2.9
  • 7.0: >= 7.0.18
  • 6.0: >= 6.0.52

Reference

• https://bugs.tryton.org/13505
• https://bugs.tryton.org/13506

Concerns?

Any security concerns should be reported on the bug-tracker at https://bugs.tryton.org/ with the confidential checkbox checked.

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The standard package manager for Python is pip. It allows you to install and manage packages that aren’t part of the Python standard library. If you’re looking for an introduction to pip, then you’ve come to the right place!

In this tutorial, you’ll learn how to:

• Set up pip in your working environment
• Fix common errors related to working with pip
• Install and uninstall packages with pip
• Manage projects’ dependencies using requirements files

You can do a lot with pip, but the Python community is very active and has created some neat alternatives to pip. You’ll learn about those later in this tutorial.

Get Your Cheat Sheet: Click here to download a free pip cheat sheet that summarizes the most important pip commands.

Getting Started With pip

So, what exactly does pip do? pip is a package manager for Python. That means it’s a tool that allows you to install and manage libraries and dependencies that aren’t distributed as part of the standard library. The name pip was introduced by Ian Bicking in 2008:

I’ve finished renaming pyinstall to its new name: pip. The name pip is [an] acronym and declaration: pip installs packages. (Source)

Package management is so important that Python’s installers have included pip since versions 3.4 and 2.7.9, for Python 3 and Python 2, respectively. Many Python projects use pip, which makes it an essential tool for every Pythonista.

The concept of a package manager might be familiar to you if you’re coming from another programming language. JavaScript uses npm for package management, Ruby uses gem, and the .NET platform uses NuGet. In Python, pip has become the standard package manager.

Finding pip on Your System

The Python installer gives you the option to install pip when installing Python on your system. In fact, the option to install pip with Python is checked by default, so pip should be ready for you to use after installing Python.

Note: On some Linux (Unix) systems like Ubuntu, pip comes in a separate package called python3-pip, which you need to install with sudo apt install python3-pip. It’s not installed by default with the interpreter.

You can verify that pip is available by looking for the pip3 executable on your system. Select your operating system below and use your platform-specific command accordingly:

• Windows
• Linux + macOS

Windows PowerShell PS> where pip3 Copied!

The where command on Windows will show you where you can find the executable of pip3. If Windows can’t find an executable named pip3, then you can also try looking for pip without the three (3) at the end.

Shell $ which pip3 Copied!

The which command on Linux systems and macOS shows you where the pip3 binary file is located.

On Windows and Unix systems, pip3 may be found in more than one location. This can happen when you have multiple Python versions installed. If you can’t find pip in any location on your system, then you may consider reinstalling pip.

Instead of running your system pip directly, you can also run it as a Python module. In the next section, you’ll learn how.

Running pip as a Module

When you run your system pip directly, the command itself doesn’t reveal which Python version pip belongs to. This unfortunately means that you could use pip to install a package into the site-packages of an old Python version without noticing. To prevent this from happening, you should run pip as a Python module:

Shell $ python -m pip Copied!

Notice that you use python -m to run pip. The -m switch tells Python to run a module as an executable of the python interpreter. This way, you can ensure that your system default Python version runs the pip command. If you want to learn more about this way of running pip, then you can read Brett Cannon’s insightful article about the advantages of using python -m pip.

Note: Depending on how you installed Python, your Python executable may have a different name than python. You’ll see python used in this tutorial, but you may have to adapt the commands to use something like py or python3 instead.

Sometimes you may want to be more explicit and limit packages to a specific project. In situations like this, you should run pip inside a virtual environment.

Using pip in a Python Virtual Environment Read the full article at https://realpython.com/what-is-pip/ »

[ 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 ]

This tutorial guides you on how to create dynamic QR codes in Python. It involves a bit more than just generating the QR code itself. Before reading this, you must know how a QR code generator works. Steps to Create Dynamic QR Codes Dynamic QR codes require the ability to track and update the information […]

The post How to Create Dynamic QR Code in Python appeared first on TechBeamers.

Jupyter notebooks allow you to tell stories by creating and sharing data, equations, and visualizations sequentially, with a supporting narrative as you go through the notebook.

Jupyter notebooks in PyCharm Professional provide functionality above and beyond that of browser-based Jupyter notebooks, such as code completion, dynamic plots, and quick statistics, to help you explore and work with your data quickly and effectively.  

Let’s take a look at 7 ways you can use Jupyter notebooks in PyCharm to achieve your goals. They are:

• Creating or connecting to an existing notebook
• Importing your data
• Getting acquainted with your data
• Using JetBrains AI Assistant 
• Exploring your code with PyCharm
• Getting insights from your code
• Sharing your insights and charts

The Jupyter notebook that we used in this demo is available on GitHub.

1. Creating or connecting to an existing notebook

You can create and work on your Jupyter notebooks locally or connect to one remotely with PyCharm. Let’s take a look at both options so you can decide for yourself.

Creating a new Jupyter notebook

To work with a Jupyter notebook locally, you need to go to the Project tool window inside PyCharm, navigate to the location where you want to add the notebook, and invoke a new file. You can do this by using either your keyboard shortcuts ⌘N (macOS) / Alt+Ins (Windows/Linux) or by right-clicking and selecting New | Jupyter Notebook.

Give your new notebook a name, and PyCharm will open it ready for you to start work. You can also drag local Jupyter notebooks into PyCharm, and the IDE will automatically recognise them for you. 

Connecting to a remote Jupyter notebook

Alternatively, you can connect to a remote Jupyter notebook by selecting Tools | Add Jupyter Connection. You can then choose to start a local Jupyter server, connect to an existing running local Jupyter server, or connect to a Jupyter server using a URL – all of these options are supported.

Now you have your Jupyter notebook, you need some data!

2. Importing your data

Data generally comes in two formats, CSV or database. Let’s look at importing data from a CSV file first.

Importing from a CSV file

Polars and pandas are the two most commonly used libraries for importing data into Jupyter notebooks. I’ll give you code for both in this section, and you can check out the documentation for both Polars and pandas and learn how Polars is different to pandas. 

You need to ensure your CSV is somewhere in your PyCharm project, perhaps in a folder called `data`. Then, you can invoke import pandas and subsequently use it to read the code in:

import pandas as pd df = pd.read_csv("../data/airlines.csv")

In this example, airlines.csv is the file containing the data we want to manipulate. To run this and any code cell in PyCharm, use ⇧⏎ (macOS) / Shift+Enter (Windows/Linux). You can also use the green run arrows on the toolbar at the top.

If you prefer to use Polars, you can use this code:

import polars as pl df = pl.read_csv("../data/airlines.csv") Importing from a database

If your data is in a database, as is often the case for internal projects, importing it into a Jupyter notebook will require just a few more lines of code. First, you need to set up your database connection. In this example, we’re using postgreSQL. 

For pandas, you need to use this code to read the data in:

import pandas as pd engine = create_engine("postgresql://jetbrains:jetbrains@localhost/demo") df = pd.read_sql(sql=text("SELECT * FROM airlines"), con=engine.connect())

And for Polars, it’s this code:

import polars as pl engine = create_engine("postgresql://jetbrains:jetbrains@localhost/demo") connection = engine.connect() query = "SELECT * FROM airlines" df = pl.read_database(query, connection) 3. Getting acquainted with your data

Now we’ve read our data in, we can take a look at the DataFrame or `df` as we will refer to it in our code. To print out the DataFrame, you only need a single line of code, regardless of which method you used to read the data in:

df DataFrames

PyCharm displays your DataFrame as a table firstly so you can explore it. You can scroll horizontally through the DataFrame and click on any column header to order the data by that column. You can click on the Show Column Statistics icon on the right-hand side and select Compact or Detailed to get some helpful statistics on each column of data.   

Dynamic charts

You can use PyCharm to get a dynamic chart of your DataFrame by clicking on the Chart View icon on the left-hand side. We’re using pandas in this example, but Polars DataFrames also have the same option. 

Click on the Show Series Settings icon (a cog) on the right-hand side to configure your plot to meet your needs:

In this view, you can hover your mouse over your data to learn more about it and easily spot outliers:

You can do all of this with Polars, too. 

4. Using JetBrains AI Assistant

JetBrains AI Assistant has several offerings that can make you more productive when you’re working with Jupyter notebooks inside PyCharm. Let’s take a closer look at how you can use JetBrains AI Assistant to explain a DataFrame, write code, and even explain errors. 

Explaining DataFrames

If you’ve got a DataFrame but are unsure where to start, you can click the purple AI icon on the right-hand side of the DataFrame and select Explain DataFrame. JetBrains AI Assistant will use its context to give you an overview of the DataFrame:

You can use the generated explanation to aid your understanding.

Writing Code 

You can also get JetBrains AI Assistant to help you write code. Perhaps you know what kind of plot you want, but you’re not 100% sure what the code should look like. Well, now you can use JetBrains AI Assistant to help you. Let’s say you want to use ‘matplotlib’ to create a chart that finds the relationship between ‘TimeMonthName’ and ‘MinutesDelayedWeather’. By specifying the column names, we’re giving more context to the request which improves the reliability of the generated code. Try it with the following prompt:

Give me code using matplotlib to create a chart which finds the relationship between ‘TimeMonthName’ and ‘MinutesDelayedWeather’ for my dataframe df

If you like the resulting code, you can use the Insert Snippet at Caret button to insert the code and then run it:

import matplotlib.pyplot as plt # Assuming your data is in a DataFrame named 'df' # Replace 'df' with the actual name of your DataFrame if different # Plotting plt.figure(figsize=(10, 6)) plt.bar(df['TimeMonthName'], df['MinutesDelayedWeather'], color='skyblue') plt.xlabel('Month') plt.ylabel('Minutes Delayed due to Weather') plt.title('Relationship between TimeMonthName and MinutesDelayedWeather') plt.xticks(rotation=45) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.tight_layout() plt.show()

If you don’t want to open the AI Assistant tool window, you can use the AI cell prompt to ask your questions. For example, we can ask the same question here and get the code we need:

Explaining errors

You can also get JetBrains AI Assistant to explain errors for you. When you get an error, click Explain with AI: 

You can use the resulting output to further your understanding of the problem and perhaps even get some code to fix it!

5. Exploring your code

PyCharm can help you get an overview of your Jupyter notebook, complete parts of your code to save your fingers, refactor it as required, debug it, and even add integrations to help you take it to the next level.

Tips for navigating and optimizing your code

Our Jupyter notebooks can grow large quite quickly, but thankfully you can use PyCharm’s Structure view to see all your notebook’s headings by clicking ⌘7 (macOS) / Alt+7 (Windows/Linux).

Code completion

Another helpful feature that you can take advantage of when using Jupyter notebooks inside PyCharm is code completion. You get both basic and type-based code completion out of the box with PyCharm, but you can also enable Full Line Code Completion in PyCharm Professional, which uses a local AI model to provide suggestions. Lastly, JetBrains AI Assistant can also help you write code and discover new libraries and frameworks. 

Refactoring

Sometimes you need to refactor your code, and in that case, you only need to know one keyboard shortcut ⌃T (macOS) / Shift+Ctrl+Alt+T (Windows/Linux) then you can choose the refactoring you want to invoke. Pick from popular options such as Rename, Change Signature, and Introduce Variable, or lesser-known options such as Extract Method, to change your code without changing the semantics: 

As your Jupyter notebook grows, it’s likely that your import statements will also grow. Sometimes you might import a package such as polars and numpy, but forget that numpy is a transitive dependency of the Polars library and as such, we don’t need to import it separately.  

To catch these cases and keep your code tidy, you can invoke Optimize Imports ⌃⌥O (macOS) / Ctrl+Alt+O (Windows/Linux) and PyCharm will remove the ones you don’t need. 

Debugging your code

You might not have used the debugger in PyCharm yet, and that’s okay. Just know that it’s there and ready to support you when you need to better understand some behavior in your Jupyter notebook. 

Place a breakpoint on the line you’re interested in by clicking in the gutter or by using ⌘F8 (macOS) / Ctrl+F8 (Windows/Linux), and then run your code with the debugger attached with the debug icon on the top toolbar:

You can also invoke PyCharm’s debugger in your Jupyter notebook with ⌥⇧⏎ (macOS) / Shift+Alt+Enter (Windows/Linux). There are some restrictions when it comes to debugging your code in a Jupyter notebook, but please try this out for yourself and share your feedback with us. 

Adding integrations into PyCharm 

IDEs wouldn’t be complete without the integrations you need. PyCharm Professional 2024.2 brings two new integrations to your workflow: DataBricks and HuggingFace.

You can enable the integrations with both Databricks and HuggingFace by going to your Settings <kbd>⌘</kbd> (macOS) / <kbd>Ctrl+Alt+S</kbd> (Windows/Linux), selecting Plugins and searching for the plugin with the corresponding name on the Marketplace tab.

6. Getting insights from your code

When analyzing your data, there’s a difference between categorical and continuous variables. Categorical data has a finite number of discrete groups or categories, whereas continuous data is one continuous measurement. Let’s look at how we can extract different insights from both the categorical and continuous variables in our airlines dataset.

Continuous variables

We can get a sense of how continuous data is distributed by looking at measures of the average value in that data and the spread of the data around the average. In normally distributed data, we can use the mean to measure the average and the standard deviation to measure the spread. However, when data is not distributed normally, we can get more accurate information using the median and the interquartile range (this is the difference between the seventy-fifth and twenty-fifth percentiles). Let’s look at one of our continuous variables to understand the difference between these measurements.

In our dataset, we have lots of continuous variables, but we’ll work with `NumDelaysLateAircraft` to see what we can learn. Let’s use the following code to get some summary statistics for just that column:

df['NumDelaysLateAircraft'].describe()

Looking at this data, we can see that there is a big difference between the `mean` of ~789 and the ‘median’ (our fiftieth percentile, indicated by “50%” in the table below) of ~618.

This indicates a skew in our variable’s distribution, so let’s use PyCharm to explore it further. Click on the Chart View icon at the top left. Once the chart has been rendered, we’ll change the series settings represented by the cog on the right-hand side of the screen. Change your x-axis to `NumDelaysLateAircraft` and your y-axis to `NumDelaysLateAircraft`. 

Now drop down the y-axis using the little arrow and select `count`. The final step is to change the chart type to Histogram using the icons in the top-right corner:

Now that we can see the skew laid out visually, we can see that most of the time, the delays are not too excessive. However, we have a number of more extreme delays – one aircraft is an outlier on the right and it was delayed by 4,509 minutes, which is just over three days!

In statistics, the mean is very sensitive to outliers because it’s a geometric average, unlike the median, which, if you ordered all observations in your variable, would sit exactly in the middle of these values. When the mean is higher than the median, it’s because you have outliers on the right-hand side of the data, the higher side, as we had here. In such cases, the median is a better indicator of the true average delay, as you can see if you look at the histogram.

Categorical variables

Let’s take a look at how we can use code to get some insights from our categorical variables. In order to get something that’s a little more interesting than just `AirportCode`, we’ll analyze how many aircraft were delayed by weather, `NumDelaysWeather`, in the different months of the year, `TimeMonthName`.

Use this code to group `NumDelaysWeather` with `TimeMonthName`:

result = df[['TimeMonthName', 'NumDelaysWeather']].groupby('TimeMonthName').sum() result

This gives us the DataFrame again in table format, but click the Chart View icon on the left-hand side of the  PyCharm UI to see what we can learn:

This is okay, but it would be helpful to have the months ordered according to the Gregorian calendar. Let’s first create a variable for the months that we expect:

month_order = [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ]

Now we can ask PyCharm to use the order that we’ve just defined in `month_order`:

# Convert the 'TimeMonthName' column to a categorical type with the specified order df["TimeMonthName"] = pd.Categorical(df["TimeMonthName"], categories=month_order, ordered=True) # Now you can group by 'TimeMonthName' and perform sum operation, specifying observed=False result = df[['TimeMonthName', 'NumDelaysWeather']].groupby('TimeMonthName', observed=False).sum() result

We then click on the Chart View icon once more, but something’s wrong!

Are we really saying that there were no flights delayed in February? That can’t be right. Let’s check our assumption with some more code:

df['TimeMonthName'].value_counts()

Aha! Now we can see that `Febuary` has been misspelt in our data set, so the correct spelling in our variable name does not match. Let’s update the spelling in our dataset with this code:

df["TimeMonthName"] = df["TimeMonthName"].replace("Febuary", "February") df['TimeMonthName'].value_counts()

Great, that looks right. Now we should be able to re-run our earlier code and get a chart view that we can interpret:

From this view, we can see that there is a higher number of delays during the months of December, January, and February, and then again in June, July, and August. However, we have not standardized this data against the total number of flights, so there may just be more flights in those months, which would cause these results along with an increased number of delays in those summer and winter months.

7. Sharing your insights and charts

When your masterpiece is complete, you’ll probably want to export data, and you can do that in various ways with Jupyter notebooks in PyCharm. 

Exporting a DataFrame

You can export a DataFrame by clicking on the down arrow on the right-hand side:

You have lots of helpful formats to choose from, including SQL, CSV, and JSON:

Exporting charts

If you prefer to export the interactive plot, you can do that too by clicking on the Export to PNG icon on the right-hand side:

Viewing your notebook as a browser

You can view your whole Jupyter notebook at any time in a browser by clicking the icon in the top-right corner of your notebook:

Finally, if you want to export your Jupyter notebook to a Python file, 2024.2 lets you do that too! Right-click on your Jupyter notebook in the Project tool window and select Convert to Python File. Follow the instructions, and you’re done!

Summary

Using Jupyter notebooks inside PyCharm Professional provides extensive functionality, enabling you to create code faster, explore data easily, and export your projects in the formats that matter to you. 

Download PyCharm Professional to try it out for yourself! Get an extended trial today and experience the difference PyCharm Professional can make in your data science endeavors.

Use the promo code “PyCharmNotebooks” at checkout to activate your free 60-day subscription to PyCharm Professional. The free subscription is available for individual users only.

Activate your 60-day trial

Smart IoT integrations with Akenza and Python 2024-09-16, by Dariusz Suchojad Overview

The Akenza IoT platform, on its own, excels in collecting and managing data from a myriad of IoT devices. However, it is integrations with other systems, such as enterprise resource planning (ERP), customer relationship management (CRM) platforms, workflow management or environmental monitoring tools that enable a complete view of the entire organizational landscape.

Complementing Akenza's capabilities, and enabling the smooth integrations, is the versatility of Python programming. Given how flexible Python is, the language is a natural choice when looking for a bridge between Akenza and the unique requirements of an organization looking to connect its intelligent infrastructure.

This article is about combining the two, Akenza and Python. At the end of it, you will have:

• A bi-directional connection to Akenza using Python and WebSockets
• A Python service subscribed to and receiving events from IoT devices through Akenza
• A Python service that will be sending data to IoT devices through Akenza

Since WebSocket connections are persistent, their usage enhances the responsiveness of IoT applications which in turn helps to exchange occurs in real-time, thus fostering a dynamic and agile integrated ecosystem.

Python and Akenza WebSocket connections

First, let's have a look at full Python code - to be discussed later.

# -*- coding: utf-8 -*- # Zato from zato.server.service import WSXAdapter # ############################################################################################### # ############################################################################################### if 0: from zato.server.generic.api.outconn.wsx.common import OnClosed, \ OnConnected, OnMessageReceived # ############################################################################################### # ############################################################################################### class DemoAkenza(WSXAdapter): # Our name name = 'demo.akenza' def on_connected(self, ctx:'OnConnected') -> 'None': self.logger.info('Akenza OnConnected -> %s', ctx) # ############################################################################################### def on_message_received(self, ctx:'OnMessageReceived') -> 'None': # Confirm what we received self.logger.info('Akenza OnMessageReceived -> %s', ctx.data) # This is an indication that we are connected .. if ctx.data['type'] == 'connected': # .. for testing purposes, use a fixed asset ID .. asset_id:'str' = 'abc123' # .. build our subscription message .. data = {'type': 'subscribe', 'subscriptions': [{'assetId': asset_id, 'topic': '*'}]} ctx.conn.send(data) else: # .. if we are here, it means that we received a message other than type "connected". self.logger.info('Akenza message (other than "connected") -> %s', ctx.data) # ############################################################################################## def on_closed(self, ctx:'OnClosed') -> 'None': self.logger.info('Akenza OnClosed -> %s', ctx) # ############################################################################################## # ##############################################################################################

Now, deploy the code to Zato and create a new outgoing WebSocket connection. Replace the API key with your own and make sure to set the data format to JSON.

Receiving messages from WebSockets

The WebSocket Python services that you author have three methods of interest, each reacting to specific events:

• on_connected - Invoked as soon as a WebSocket connection has been opened. Note that this is a low-level event and, in the case of Akenza, it does not mean yet that you are able to send or receive messages from it.

• on_message_received - The main method that you will be spending most time with. Invoked each time a remote WebSocket sends, or pushes, an event to your service. With Akenza, this method will be invoked each time Akenza has something to inform you about, e.g. that you subscribed to messages, that

• on_closed - Invoked when a WebSocket has been closed. It is no longer possible to use a WebSocket once it has been closed.

Let's focus on on_message_received, which is where the majority of action takes place. It receives a single parameter of type OnMessageReceived which describes the context of the received message. That is, it is in the "ctx" that you will both the current request as well as a handle to the WebSocket connection through which you can reply to the message.

The two important attributes of the context object are:

• ctx.data - A dictionary of data that Akenza sent to you

• ctx.conn - The underlying WebSocket connection through which the data was sent and through you can send a response

Now, the logic from lines 30-40 is clear:

• First, we check if Akenza confirmed that we are connected (type=='connected'). You need to check the type of a message each time Akenza sends something to you and react to it accordingly.

• Next, because we know that we are already connected (e.g. our API key was valid) we can subscribe to events from a given IoT asset. For testing purposes, the asset ID is given directly in the source code but, in practice, this information would be read from a configuration file or database.

• Finally, for messages of any other type we simply log their details. Naturally, a full integration would handle them per what is required in given circumstances, e.g. by transforming and pushing them to other applications or management systems.

A sample message from Akenza will look like this:

INFO - WebSocketClient - Akenza message (other than "connected") -> {'type': 'subscribed', 'replyTo': None, 'timeStamp': '2023-11-20T13:32:50.028Z', 'subscriptions': [{'assetId': 'abc123', 'topic': '*', 'tagId': None, 'valid': True}], 'message': None} How to send messages to WebSockets

An aspect not to be overlooked is communication in the other direction, that is, sending of messages to WebSockets. For instance, you may have services invoked through REST APIs, or perhaps from a scheduler, and their job will be to transform such calls into configuration commands for IoT devices.

Here is the core part of such a service, reusing the same Akenza WebSocket connection:

# -*- coding: utf-8 -*- # Zato from zato.server.service import Service # ############################################################################################## # ############################################################################################## class DemoAkenzaSend(Service): # Our name name = 'demo.akenza.send' def handle(self) -> 'None': # The connection to use conn_name = 'Akenza' # Get a connection .. with self.out.wsx[conn_name].conn.client() as client: # .. and send data through it. client.send('Hello') # ############################################################################################## # ##############################################################################################

Note that responses to the messages sent to Akenza will be received using your first service's on_message_received method - WebSockets-based messaging is inherently asynchronous and the channels are independent.

Now, we have a complete picture of real-time, IoT connectivity with Akenza and WebSockets. We are able to establish persistent, responsive connections to assets, we can subscribe to and send messages to devices, and that lets us build intelligent automation and integration architectures that make use of powerful, emerging technologies.

More resources

➤ Python API integration tutorial
➤ What is an integration platform?
➤ Python Integration platform as a Service (iPaaS)
➤ What is an Enterprise Service Bus (ESB)? What is SOA?

More blog posts➤

Hello Everyone 👋 It is that time of year again when we recognize someone from our community in memory of our friend Malcolm.

Malcolm was an early core contributor to Django and had both a huge influence and impact on Django as we know it today. Besides being knowledgeable he was also especially friendly to new users and contributors. He exemplified what it means to be an amazing Open Source contributor. We still miss him to this day.

The prize

The Django Software Foundation Prizes page summarizes it nicely:

The Malcolm Tredinnick Memorial Prize is a monetary prize, awarded annually, to the person who best exemplifies the spirit of Malcolm’s work - someone who welcomes, supports, and nurtures newcomers; freely gives feedback and assistance to others, and helps to grow the community. The hope is that the recipient of the award will use the award stipend as a contribution to travel to a community event -- a DjangoCon, a PyCon, a sprint -- and continue in Malcolm’s footsteps.

Please make your nominations using our form: 2024 Malcolm Tredinnick Memorial Prize.

We will take nominations until Monday, September 30th, 2024, Anywhere on Earth, and will announce the winner(s) soon after the next DSF Board meeting in October. If you have any questions please reach out to the DSF Board at foundation@djangoproject.com.

Submit a nomination

If your Python code isn’t fast enough, you have many options for compiled languages to write a faster extension. In this article we’ll focus on Rust, which benefits from:

• Modern tooling, including a package repository called crates.io, and built-in build tool (cargo).
• Excellent Python integration and tooling. The Rust package (they’re known as “crates”) for Python support is PyO3. For packaging you can use setuptools-rust, for integration with existing setuptools projects, or for standalone extensions you can use Maturin.
• Memory- and thread-safe, so it’s much less prone to crashes or memory corruption compared to C and C++.

In particular, we’ll:

• Implement a small algorithm in Python.
• Re-implement it as a Rust extension.
• Optimize the Rust version so it runs faster.

Read more...

Python's Boolean operators are used for combining Boolean expressions and negating Boolean expressions.

Table of contents

1. Combining two if statements using and
2. Combining expressions with Boolean operators
3. Using or instead of and
4. Negating expressions
5. Embrace and, or, and not in your Boolean expressions

Combining two if statements using and

Here we have a program called word_count.py:

words_written_today = int(input("How many words did you write today? ")) if words_written_today < 50_000/30: print("Yay! But you need to write more still.") else: print("Congratulations!")

This program has an if statement that checks whether we've written enough words each day, with the assumption that we need to write 50,000 words every 30 days.

If our word count is under 1,666 words (50,000 / 30) it will say we need to write more:

$ python3 word_count.py How many words did you write today? 500 Yay! But you need to write more still.

We'd like to modify our if condition to also make sure that we only require this if today's date is in the month of November.

We could do that using Python's datetime module:

>>> from datetime import date >>> is_november = date.today().month == 11

That is_november variable will be True if it's November and False otherwise:

>>> is_november False

If we combine this with the code we had before, we could use two if statements:

from datetime import date words_written_today = int(input("How many words did you write today? ")) is_november = date.today().month == 11 if words_written_today < 50_000/30: if is_november: print("Yay! But you need to write more still.") else: print("Congratulations!") else: print("Congratulations!")

One of our if statements checks whether we're under our word limit. The other if statement checks whether it's the month of November. If both are true then we end up printing out that we still need to write more words. Otherwise we print a success message:

$ python3 word_count.py How many words did you write today? 500 Congratulations!

This works, but there is a better way to write this code.

We could instead use Python's and operator to combine these two conditions into one:

from datetime import date words_written_today = int(input("How many words did you write today? ")) is_november = date.today().month == 11 if is_november and words_written_today < 50_000/30: print("Yay! But you need to write more still.") else: print("Congratulations!")

We're using a single if statement to asking whether it's November and whether our word count is less than we expect.

Combining expressions with Boolean operators

Python's and operator is a …

Read the full article: https://www.pythonmorsels.com/boolean-operators/

Cog is my tool for using bits of Python to generate content inside an otherwise static file. I used it in extreme ways to generate my GitHub profile page.

If you haven’t seen it before, you can customize your GitHub profile by creating a README.md in a repo named the same as your username. So my profile is rendered from nedbat/nedbat/README.md.

My profile has a bit of static text, but much of it is badges, blog posts, links to PyPI projects, and so on. The README.md is literally a Markdown file that can be displayed by GitHub, but it’s full HTML comments containing Python code that generates the content. The generation happens once a day in a GitHub action.

There are three kinds of lines in a file run through cog: static content, code that will generate content, and generated content. My README.md is lop-sided: it has 225 lines of code, 38 of static content, and 43 of generated content.

The badges are made with shields.io image URLs. To make this easier, there are Python functions for Markdown image syntax, for building shields.io badge URLs, and so on.

I can’t walk through all of the code, but I can show a few simplified versions to convey the idea. Read the file itself if you are interested in the full details.

This makes a shields.io URL:

def shields_url(
    label=None,
    message=None,
    color=None,
    label_color=None,
    logo=None,
):
    params = {"style": "flat"}
    url = "".join([
        "/badge/",
        quote(label or ""),
        "-",
        quote(message),
        "-",
        color,
        ])
    url = "https://img.shields.io" + url
    if label_color:
        params["labelColor"] = label_color
    if logo:
        params["logo"] = logo
    return url + "?" + urlencode(params)

This makes a Markdown image:

def md_image(image_url, text, link):
    return f'[]({link})'

Now we can make a Markdown badge:

def badge(text=None, link=None, **kwargs):
    return md_image(image_url=shields_url(**kwargs), text=text, link=link)

Anything print’ed will become part of the generated portions of the file. We can add a badge to the page with:

print(badge(
    logo="discord", logo_color="white", label_color="7289da",
    message="Discord", color="ffe97c",
    text="Python Discord", link="https://discord.gg/python",
))

There are other functions built on top of these to make Mastodon badges, Stack Overflow badges, a row of badges for a PyPI project, and so on.

Building the page ends up pulling data from 10 URLs, including a JSON summary of my blog for including blog posts. It’s satisfying to be able to have this update automatically instead of having to copy data around.

The result is a convenient mix of static and generated, and it was a fun exercise in light-touch automation.