Feeds

Small innovations can have a big impact. The paperclip was just a bent piece of wire that somebody repurposed in the late 19th century and we all now have in our desks. The Post-It note was just a low-grade adhesive applied to a piece of paper before it became a key office supply.

So this person, let's call them Jo, was hungry and had no money. Walking the streets, they come across a square where a group of people are working in a communal open kitchen, serving delicious hot meals free of charge.

Pantheon and Lytics announce a groundbreaking partnership at Google Cloud Next '24, offering businesses free access to Lytics' Personalization Engine, revolutionizing website personalization with cutting-edge Generative AI technology.

Let&aposs say that you&aposre working on a supervised Machine Learning problem, and you&aposre deciding how to encode the features in your training data.

With a categorical feature, you might consider using one-hot encoding or ordinal encoding. But with a continuous numeric feature, you would normally pass that feature directly to your model. (Makes sense, right?)

However, one alternative strategy that is sometimes used with continuous features is to "discretize" or "bin" them into categorical features before passing them to the model.

First, I&aposll show you how to do this in scikit-learn. Then, I&aposll explain whether I think it&aposs a good idea!

How to discretize in scikit-learn

In scikit-learn, we can discretize using the KBinsDiscretizer class:

When creating an instance of KBinsDiscretizer, you define the number of bins, the binning strategy, and the method used to encode the result:

As an example, here&aposs a numeric feature from the famous Titanic dataset:

And here&aposs the output when we use KBinsDiscretizer to transform that feature:

Because we specified 3 bins, every sample has been assigned to bin 0 or 1 or 2. The smallest values were assigned to bin 0, the largest values were assigned to bin 2, and the values in between were assigned to bin 1.

Thus, we&aposve taken a continuous numeric feature and encoded it as an ordinal feature (meaning an ordered categorical feature), and this ordinal feature could be passed to the model in place of the numeric feature.

Is discretization a good idea?

Now that you know how to discretize, the obvious follow-up question is: Should you discretize your continuous features?

Theoretically, discretization can benefit linear models by helping them to learn non-linear trends. However, my general recommendation is to not use discretization, for three main reasons:

1. Discretization removes all nuance from the data, which makes it harder for a model to learn the actual trends that are present in the data.
2. Discretization reduces the variation in the data, which makes it easier to find trends that don&apost actually exist.
3. Any possible benefits of discretization are highly dependent on the parameters used with KBinsDiscretizer. Making those decisions by hand creates a risk of overfitting the training data, and making those decisions during a tuning process adds both complexity and processing time. As such, neither option is attractive to me!

For all of those reasons, I don&apost recommend discretizing your continuous features unless you can demonstrate, through a proper model evaluation process, that it&aposs providing a meaningful benefit to your model.

Going further

🔗 Discretization in the scikit-learn User Guide

🔗 Discretize Predictors as a Last Resort from Feature Engineering and Selection (section 6.2.2)

This post is drawn directly from my upcoming course, Master Machine Learning with scikit-learn. If you&aposre interested in receiving more free lessons from the course, please join the waitlist below:

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/ »

[ 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 ]

Gain exclusive insights from renowned industry experts John Cloys, Penny Kronz, and Steve Persch as they unravel the intricacies of website evolution and digital innovation at EvolveDrupal Atlanta—Part 2. Discover what's shaping the future of web development in this must-read exploration.

Embarking on exploring the forefront of web development, EvolveDrupal Atlanta's industry experts offer insights into sessions covering inclusive design, Drupal 7's end-of-life, scalable software products, Drupal theming, and seamless migrations. Readers are invited to join for an exclusive peek into the future of web development.

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

Welcome back to “Skills Upgrade” a Talking Drupal mini-series following the journey of a D7 developer learning D10. This is episode 6.

Topics

• Review Chad's goals for the previous week

• Review Chad's questions

  • Array structures
  • accordion.html.twig
  • D7 to D10 migrations

• Tasks for the upcoming week

  • [testing_example](https://git.drupalcode.org/project/examples/-/tree/4.0.x/modules/testing_example?
  • Be sure to install drupal/core-dev dependencies using composer require –dev drupal/core-devref_type=heads) from Examples module.
  • Set up phpunit.xml file in project root - using this file to start
  • Run existing tests using command line from the project root. Something like: phpunit web/modules/contrib/examples/modules/testing_example/tests
  • Review test code in module.
  • Start with FrontPageLinkTest.php, then FrontPageLinkDependenciesTest.php, then TestingExampleMenuTest.php

Resources

Understand Drupal - Migrations Chad's Drupal 10 Learning Curriclum & Journal Chad's Drupal 10 Learning Notes

The Linux Foundation is offering a discount of 30% off e-learning courses, certifications and bundles with the code, all uppercase DRUPAL24 and that is good until June 5th https://training.linuxfoundation.org/certification-catalog/

Hosts

AmyJune Hineline - @volkswagenchick

Guests

Chad Hester - chadkhester.com @chadkhest Mike Anello - DrupalEasy.com @ultimike

Akademy 2024 will be a hybrid event held simultaneously in Würzburg, Germany, and online. The Call for Participation is open! Send us your talk ideas and abstracts.

Why talk at #Akademy2024

Akademy attracts artists, designers, developers, translators, users, writers, companies, public institutions and many other KDE friends and contributors. We celebrate the achievements and help determine the direction for the next year. We all meet together to discuss and plan the future of the Community and the technology we build. You will meet people who are receptive to your ideas and can help you with their skills and experience. You will get an opportunity to present your application, share ideas and best practices, or gain new contributors. These sessions offer the opportunity for gaining support, and making your plans for your project become a reality.

How to get started

Do not worry about details or slides right now. Just think of an idea and submit some basic details about your talk. You can edit your abstract after the initial submission. All topics relevant to the KDE Community are welcome. Here are a few ideas to get you started on your proposal:

• New people and organisations that are discovering KDE,
• Work towards KDE's goals: KDE For All, Sustainable Software, and Automate And Systematize Internal Processes,
• Giving people more digital freedom and autonomy with KDE,
• New technological developments,
• Guides on how to participate for new users, intermediates and experts,
• What's new after porting KDE Frameworks, Plasma and applications to Qt6
• Anything else that might interest the audience?

To get an idea of talks that were accepted, check out the program from previous years: 2023, 2022, 2021, 2020, 2019, 2018, and 2017.

For more details and information, visit our Call for Participation.

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

ceb and I are members of the Derril Water Solar Park cooperative.

We were recently invited to vote on whether the coop should bid for a Contract for Difference, in a government green electricity auction.

We’ve voted No.

• “Green electricity” from your mainstream supplier is a lie
• Ripple
• Contracts for Difference
• Ripple and CfD
• Voting No

“Green electricity” from your mainstream supplier is a lie

For a while ceb and I have wanted to contribute directly to green energy provision. This isn’t really possible in the mainstream consumer electricy market.

Mainstream electricity suppliers’ “100% green energy” tariffs are pure greenwashing. In a capitalist boondoogle, they basically “divvy up” the electricity so that customers on the (typically more expensive) “green” tariff “get” the green electricity, and the other customers “get” whatever is left. (Of course the electricity is actually all mixed up by the National Grid.) There are fewer people signed up for these tariffs than there is green power generated, so this basically means signing up for a “green” tariff has no effect whatsoever, other than giving evil people more money.

Ripple

About a year ago we heard about Ripple. The structure is a little complicated, but the basic upshot is:

Ripple promote and manage renewable energy schemes. The schemes themselves are each an individual company; the company is largely owned by a co-operative. The co-op is owned by consumers of electricity in the UK., To stop the co-operative being an purely financial investment scheme, shares ownership is limited according to your electricity usage. The electricity is be sold on the open market, and the profits are used to offset members’ electricity bills. (One gotcha from all of this is that for this to work your electricity billing provider has to be signed up with Ripple, but ours, Octopus, is.)

It seemed to us that this was a way for us to directly cause (and pay for!) the actual generation of green electricity.

So, we bought shares in one these co-operatives: we are co-owners of the Derril Water Solar Farm. We signed up for the maximum: funding generating capacity corresponding to 120% of our current electricity usage. We paid a little over £5000 for our shares.

Contracts for Difference

The UK has a renewable energy subsidy scheme, which goes by the name of Contracts for Difference. The idea is that a renewable energy generation company bids in advance, saying that they’ll sell their electricity at Y price, for the duration of the contract (15 years in the current round). The lowest bids win. All the electricity from the participating infrastructure is sold on the open market, but if the market price is low the government makes up the difference, and if the price is high, the government takes the winnings.

This is supposedly good for giving a stable investment environment, since the price the developer is going to get now doesn’t depends on the electricity market over the next 15 years. The CfD system is supposed to encourage development, so you can only apply before you’ve commissioned your generation infrastructure.

Ripple and CfD

Ripple recently invited us to agree that the Derril Water co-operative should bid in the current round of CfDs.

If this goes ahead, and we are one of the auction’s winners, the result would be that, instead of selling our electricity at the market price, we’ll sell it at the fixed CfD price.

This would mean that our return on our investment (which show up as savings on our electricity bills) would be decoupled from market electricity prices, and be much more predictable.

They can’t tell us the price they’d want to bid at, and future electricity prices are rather hard to predict, but it’s clear from the accompanying projections that they think we’d be better off on average with a CfD.

The documentation is very full of financial projections and graphs; other factors aren’t really discussed in any detail.

The rules of the co-op didn’t require them to hold a vote, but very sensibly, for such a fundamental change in the model, they decided to treat it roughly the same way as for a rules change: they’re hoping to get 75% Yes votes.

Voting No

The reason we’re in this co-op at all is because we want to directly fund renewable electricity.

Participating in the CfD auction would involve us competing with capitalist energy companies for government subsidies. Subsidies which are supposed to encourage the provision of green electricity.

It seems to us that participating in this auction would remove most of the difference between what we hoped to do by investing in Derril Water, and just participating in the normal consumer electricity market.

In particular, if we do win in the auction, that’s probably directly removing the funding and investment support model for other, market-investor-funded, projects.

In other words, our buying into Derril Water ceases to be an additional green energy project, changing (in its minor way) the UK’s electricity mix. It becomes a financial transaction much more tenously connected (if connected at all) to helping mitigate the climate emergency.

So our conclusion was that we must vote against.

comments

We are thrilled to announce the winners of the Kubuntu Brand Graphic Design contest and the Wallpaper Contest! These competitions brought out the best in creativity, innovation, and passion from the Kubuntu community, and we couldn’t be more pleased with the results.

Kubuntu Brand Graphic Design Contest Winners

The Kubuntu Council is excited to reveal that after much deliberation and awe at the sheer talent on display, the winner
of the Kubuntu Brand Graphic Design contest is Fabio Maricato! Fabio’s entry captivated us with its innovative
approach and deep understanding of the Kubuntu brand essence. Coming in a close second is Desodi, whose creative flair and original design impressed us all. In third place, we have John Tolorunlojo, whose submission showcased exceptional creativity and skill.

Wallpaper Contest Honours

For the Wallpaper Contest, we had the pleasure of selecting three outstanding entries that will grace the screens of Kubuntu 24.04 LTS users worldwide. Congratulations to Gregorio, Dilip, and Jack Sharp for their stunning wallpaper contributions. Each design brings a unique flavor to the Kubuntu desktop experience, embodying the spirit of our community.

A Heartfelt Thank You

We extend our deepest gratitude to every participant who shared their artistry and vision with us. The number and quality of the submissions were truly beyond our expectations, reflecting the vibrant and creative spirit of the Kubuntu community. It’s your passion and engagement that make Kubuntu not just a powerful operating system, but a canvas for creativity.

Looking Ahead

The Kubuntu Council is thrilled with the success of these contests, and we are already looking forward to future opportunities to showcase the talents within our community. We believe that these winning designs not only celebrate the individuals behind them but also symbolise the collective creativity and innovation that Kubuntu stands for.

Stay tuned for the official inclusion of the winning wallpaper designs in Kubuntu 24.04 LTS, and keep an eye on our website for future contests and community events.

Once again, congratulations to our winners and a massive thank you to all who participated. Your contributions continue to shape and enrich the Kubuntu experience for users around the globe.

Celebrate with Us!

Check out our special banner commemorating the announcement and join us in celebrating the creativity and dedication of our winners and participants alike. Your efforts have truly made this contest a memorable one.

Here’s to many more years of innovation, creativity, and community in the world of Kubuntu.

The results of our contest, our proudly displayed in our Github Repository

Cheers!

Authored by: Nadiia Nykolaichuk.

#624 – APRIL 9, 2024
View in Browser »

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.
PYTHON SOFTWARE FOUNDATION

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
MEETUP.COM

Data Ethics

April 17, 2024
MEETUP.COM

Python Meeting Düsseldorf

April 17, 2024
PYDDF.DE

Happy Pythoning!
This was PyCoder’s Weekly Issue #624.
View in Browser »

[ Subscribe to 🐍 PyCoder’s Weekly 💌 – Get the best Python news, articles, and tutorials delivered to your inbox once a week >> Click here to learn more ]

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.

Now that we are back from our six month period in Argentina, we decided to adopt a kitten, to bring more diversity into our lives. Perhaps this little girl will teach us to think outside the box!

Yesterday we witnessed a solar eclipse — Mexico City was not in the totality range (we reached ~80%), but it was a great experience to go with the kids. A couple dozen thousand people gathered for a massive picnic in las islas, the main area inside our university campus.

Afterwards, we went briefly back home, then crossed the city to fetch the little kitten. Of course, the kids were unanimous: Her name is Eclipse.

This blog post was written by DrupalCon Portland Higher Education Summit Committee members Megan Bygness Bradley and Michael Miles.

As a part of the landscape of higher education web technology, many of us are navigating the digital realm somewhat disconnected from one another. We’re solving similar problems, but do not often have the opportunity to talk to others about the whys, hows, and the gotchas of implementing within the sphere of higher ed. DrupalCon Portland's Higher Education Summit is tailor-made for you! It's not just another conference; it's an amazing opportunity to connect, collaborate, and elevate your expertise in Drupal with your peers in the higher education sector.

Why Attend?

The Higher Education Summit at DrupalCon isn't just about listening to speakers; it's about engaging in meaningful discussions, sharing experiences, and building valuable connections within the higher education community. 

Dive Deep into Drupal Best Practices

Whether you're a seasoned Drupal user or just getting started, this summit offers a wealth of knowledge and expertise tailored to the higher education sector. Learn about the latest Drupal developments, strategies for site management, effective documentation and training techniques, and more.

Connect with Peers

Connect with fellow web developers, content creators, designers, strategists, and managers from universities and colleges around the world. Share insights, learn from each other's experiences, and build a network of like-minded professionals who understand the unique challenges and opportunities within higher education.

Gain Insights from Drupal Experts in Higher Ed 

From lightning talks to sponsor presentations, the summit features a lineup of industry experts sharing their insights and experiences. Learn from speakers who have successfully navigated the intersection of Drupal and higher education. See the Summit schedule here.

Participate in Interactive Discussions

The summit format is designed to be relaxed and informal, fostering open discussions and collaboration. Engage in small group discussions after every talk and Birds of a Feather sessions focused on topics such as site management, documentation and training, design and UX, AI, and more.

Collaborate and Network

In a rapidly evolving digital landscape, staying ahead of the curve is essential. Discover how Drupal can empower your institution by collaborating and networking with people just like you from across the world.

Don't miss out on this unique opportunity to expand your knowledge, network with peers, and gain insights from industry experts at DrupalCon's Higher Education Summit. Whether you're looking to optimize your Drupal workflow, enhance the user experience, or navigate the challenges of higher education, this summit has something for you. Register now and elevate your expertise in Drupal within the higher education sector!

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.