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Mike Driscoll: JupyterLab 101 Kickstarter Stretch Goal
My Kickstarter for my latest Python book is still going on for another eight days. Now is a great time to pre-order the book as well as get my other Python books.
The project is fully funded, and I added a stretch goal.
Stretch GoalThe stretch goal is $5000 or 350 backers. If we reach either of those, I will create a video course version of the book and release it to all backers. I think being able to show JupyterLab in a video format may be an even better medium for teaching this type of topic, although the book may be better for quick browsing.
What You’ll LearnIn this book, you will learn how about the following:
- Installation and setup of JupyterLab
- The JupyterLab user interface
- Creating a Notebook
- Markdown in Notebooks
- Menus in JupyterLab
- Launching Other Applications (console, terminal, text files, etc)
- Distributing and Exporting Notebooks
- Debugging in JupyterLab
- Testing your notebooks
As a backer of this Kickstarter, you have some choices to make. You can receive one or more of the following, depending on which level you choose when backing the project:
- An early copy of JupyterLab 101 + all updates including the final version (ALL BACKERS)
- A signed paperback copy (If you choose the appropriate perk)
- Get all by Python courses hosted on Teach Me Python or another site (If you choose the appropriate perk)
- T-shirt with the book cover (If you choose the appropriate perk)
Get the book on Kickstarter today!
The post JupyterLab 101 Kickstarter Stretch Goal appeared first on Mouse Vs Python.
1xINTERNET blog: Community survey analysis and revised implementation of search in Drupal CMS (formerly Starshot)
As track leads for search in Drupal CMS (formerly Starshot), we developed a concept and aligned it with the other leaders. To refine our approach, we conducted a survey to gather feedback and identify potential improvements. This article presents the survey results and the planned implementation.
The Dot is closed. Long live KDE Planet!
As KDE grows, so does the interest in each of its projects. Gathering all KDE news in one place no longer works. The volume of updates coming from the KDE community as a whole has become too large to cover in its entirety on the Dot. With this in mind, we are archiving the Dot, but keeping its content accessible for historical reasons.
The news coming out of the community was curated and edited for the Dot. The current rate of news items being published today would've not only made that impractical, but would have also led to things being unjustly left out, giving only a partial view of what was going on.
But we are not leaving you without your source of KDE news! We have figured out something better: we have worked with KDE webmasters to set up a blogging system for contributors. You can now access Announcements, Akademy, the Association news, and the news from your favorite projects directly, unfiltered, unedited, straight from the source.
Or... If you want to keep up with what is going on in ALL KDE projects and news on a daily (often hourly) basis, use the Planet! Access it on the web or add an RSS feed to your reader. You can also follow KDE news as it happens in our Discuss forums and talk about it live with the rest of the community. You can even follow @planet.kde.org@rss-parrot.net on Mastodon to stay up to date.
If you just want the highlights, check out our social media:
Talk Python to Me: #478: When and how to start coding with kids
Metadrop: Improving headers in a Drupal site using Dries' HTTP Header Analyzer
In a previous article I wrote about the importance of the HTTP headers and web security avoiding the technical stuff. In this article I want to get into all the dirty technical details.
Some time ago we came across the HTTP Header Analyzer by Dries Buytaert. This tool, as the name suggests, analyses the headers of the HTTP response from a website. There are other header analysers out there, but this one, published by the creator of Drupal, also takes into account Drupal-specific headers. The tool displays a report of all headers found, along with an explanation of the purpose of the header and notes on the values of the header, sometimes including recommendations for better values. It also displays information about missing headers that should be present.
With all this information, the tool gives a score based on the missing headers, warnings and notices detected during the analysis. On the first runs on our website, I have to admit that the score was not bad, but not good: 6/10. Now I am happy to say that we get a score of 10/10 on the home page. Unfortunately, not all pages can get the highest score, as I explain below.
…The Drop Times: DrupalCon Barcelona 2024 Day 1: Awards, Driesnote, and Community
Specbee: Why You Can’t Ignore Google Crawlability, Indexability & Mobile-First Indexing in SEO
Krita 5.2.5 Released!
Krita 5.2.5 is here, bringing over 50 bugfixes since 5.2.3 (5.2.4 was a Windows-specific hotfix). Major fixes have been done to audio playback, transform mask calculation and more!
In addition to the core team, special thanks to Maciej Jesionowski, Ralek Kolemios, Freya Lupen, Michael Genda, Rasyuqa A. H., Simon Ra and Sam James for a variety of fixes!
Changes since 5.2.3:- Correctly adjust audio playback when animation framerate is changed.
- Fix no layer being activated on opening a document (Bug 490375)
- [mlt] Fix incorrect usage of get_frame API (Bug 489146)
- Fix forbidden cursor blinking when switching tools with shortcuts (Bug 490255)
- Fix conflicts between mouse and touch actions requested concurrently (Bug 489537)
- Only check for the presence of bt2020PQColorSpace on Windows (Bug 490301)
- Run macdeployqt after searching for missing libs (Bug 490181)
- Fix crash when deleting composition
- Fix scaling down image with 1px grid spacing (Bug 490898)
- Fix layer activation issue when opening multiple documents (Bug 490843)
- Make clip-board pasting code a bit more robust (Bug 490636)
- Fix a number of issues with frame generation (Bug 486417)
- A number of changes related to qt6 port changes.
- Fix black canvas appearing when "Limit animation frame size" is active (Bug 486417)
- WebP: fix colorspace export issue when dithering is enabled (Bug 491231)
- WebP: preserve color profile on export if color model is RGB(A)
- Fix layer selection when a layer was removed while view was inactive
- Fix On-Canvas Brush Editor's decimal sliders (Bug 447800, Bug 457744)
- Make sure file layers are updated when image size or resolution changes (Bug 467257, Bug 470110)
- Fix Advanced Export of the image with filter masks or layer styles (Bug 476980)
- Avoid memory leak in the advanced export function
- Fix mipmaps not being regenerated after transformation was finished or cancelled (Bug 480973)
- [Gentoo] Don't use xsimd::default_arch in the pixel scaler code
- KisZug: Fix ODR violation for map_*
- Fix a crash in Filter Brush when changing the filter type (Bug 478419)
- PSD: Don't test reference layer for homogenous check (Bug 492236)
- Fix an assert that should have been a safe assert (Bug 491665)
- Set minimum freetype version to 2.11 (Bug 489377)
- Set Krita Default on restoring defaults (Bug 488478)
- Fix loading translated news (Bug 489477)
- Make sure that older files with simple transform masks load fine & Fix infinite loop with combination of clone + transform-mask-on-source (Bug 492320)
- Fix more cycling updates in clone/transform-masks combinations (Bug 443766)
- Fix incorrect threaded image access in multiple clone layers (Bug 449964)
- TIFF: Ignore resolution if set to 0 (Bug 473090)
- Specific Color Selector: Update labels fox HSX (Bug 475551)
- Specific Color Selector: Fix RGB sliders changing length (Bug 453649)
- Specific Color Selector: Fix float slider step 1 -> 0.01
- Specific Color Selector: Fix holding down spinbox arrows (Bug 453366)
- Fix clone layers resetting the animation cache (Bug 484353)
- Fix an assert when trying to activate an image snapshot (Bug 492114)
- Fix redo actions to appear when undoing juggler-compressed actions (Bug 491186)
- Update cache when cloning perspective assistants (Bug 493185)
- Fix a warning on undoing flattening a group (Bug 474122)
- Relink clones to the new layer when flattening (Bug 476514)
- Fix onion skins rendering on layers with a transform masks (Bug 457136)
- Fix perspective value for hovering pixel
- Fix Move and Transform tool to work with Pass-Through groups (Bug 457957)
- JPEG XL: Export: implement streaming encoding and progress reporting
- Deselect selection when pasting from the clipboard (Bug 459162)
If you're using the portable zip files, just open the zip file in Explorer and drag the folder somewhere convenient, then double-click on the Krita icon in the folder. This will not impact an installed version of Krita, though it will share your settings and custom resources with your regular installed version of Krita. For reporting crashes, also get the debug symbols folder.
Note: We are no longer making 32-bit Windows builds.
- 64 bits Windows Installer: krita-5.2.5-setup.exe
- Portable 64 bits Windows: krita-5.2.5.zip
- Debug symbols. (Unpack in the Krita installation folder)
- 64 bits Linux: krita-5.2.5-x86_64.appimage
The separate gmic-qt AppImage is no longer needed.
(If, for some reason, Firefox thinks it needs to load this as text: right-click on the link to download.)
MacOSNote: We're not supporting MacOS 10.13 anymore, 10.14 is the minimum supported version.
- MacOS disk image: krita-5.2.5.dmg
We consider Krita on ChromeOS as ready for production. Krita on Android is still beta. Krita is not available for Android phones, only for tablets, because the user interface requires a large screen.
Source code md5sumFor all downloads, visit https://download.kde.org/stable/krita/5.2.5/ and click on "Details" to get the hashes.
KeyThe Linux AppImage and the source .tar.gz and .tar.xz tarballs are signed. You can retrieve the public key here. The signatures are here (filenames ending in .sig).
PyCoder’s Weekly: Issue #648 (Sept. 24, 2024)
#648 – SEPTEMBER 24, 2024
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Get a sneak peek at the upcoming features in Python 3.13 aimed at enhancing performance. In this tutorial, you’ll make a custom Python build with Docker to enable free threading and an experimental JIT compiler. Along the way, you’ll learn how these features affect the language’s ecosystem.
REAL PYTHON
Python code too slow? You can quickly create a Rust extension to speed it up. This post shows you how to re-implement some Python code as Rust, connect the Rust to Python, and optimize the Rust for even further performance improvements.
ITAMAR TURNER-TRAURING
Snyk is thrilled to announce DevSecCon 2024, Developing AI Trust Oct 8-9, a FREE virtual summit designed for DevOps, developer and security pros of all levels. Hear from industry pros John Hammond & Daniel Miessler for some practical tips on devsecops approach to secure development. Save our spot →
SNYK.IO sponsor
This post covers Python’s doctest which allows you to write tests within your code’s docstrings. This does two things: it gives you tests, but it also documents how your code can be used.
JUHA-MATTI SANTALA
A well-designed coding environment enhances your focus and productivity and 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.
REAL PYTHON course
What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast “ADHD For the Win!”
REAL PYTHON podcast
Intel makes it easy to develop AI applications on open frameworks and libraries. Seamlessly integrate into an open ecosystem and build AI solutions with more flexibility and choice. Explore the possibilities available with Intel’s OpenVINO toolkit →
INTEL CORPORATION sponsor
Simon has been on the Python Software Foundation Board for two years now and has recently returned from a board retreat. This post talks about what he has learned along the way, including just what does the PSF do, PyPI, PyCons, and more.
SIMON WILLISON
Goodhart’s law states: “When a measure becomes a target, it ceases to be a good measure.” Whether that’s test coverage, cyclomatic complexity, or code performance, all metrics are proxies and proxies can be gamed.
HILLEL WAYNE
This post covers how Mark did a deep dive on a large dataset covering building footprints in Asia. He uses a variety of tools including duckdb and the multiprocessing module in Python.
MARK LITWINTSCHIK
“Django comes with a form library, and yet we wrote a total replacement library… Django forms were fundamentally not usable for what we wanted to do.”
KODARE.NET
This article shows how you can create a case-insensitive string class using some basic meta programming with the dunder method __new__.
RODRIGO GIRÃO SERRÃO
Discover seven ways you can use Jupyter notebooks in PyCharm to explore and work with your data more quickly and effectively.
HELEN SCOTT
Talk Python interviews Charlie Marsh, the maintainer of ruff, and they talk about uv and other projects at Astral.
KENNEDY & MARSH podcast
Python 3.8 will stop getting security updates in November 2024. You really should upgrade!
ITAMAR TURNER-TRAURING
GITHUB.COM/FPGMAAS • Shared by Florian Maas
Django Content Settings: Advanced Admin Editable SettingsDJANGO-CONTENT-SETTINGS.READTHEDOCS.IO • Shared by oduvan
Events Weekly Real Python Office Hours Q&A (Virtual) September 25, 2024
REALPYTHON.COM
September 25 to September 27, 2024
PYDATA.ORG
September 26 to September 27, 2024
PITERPY.COM
September 26 to September 27, 2024
DURIANPY.ORG
September 27 to September 30, 2024
PYCON.JP
September 28 to September 30, 2024
PYCON.ORG
October 3 to October 5, 2024
PYCON.ORG
October 4 to October 6, 2024
PYCON.ORG
Happy Pythoning!
This was PyCoder’s Weekly Issue #648.
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ImageX: Iconic Touch: Benefits of Icons and 10 Drupal Modules for Adding Icons to Your Website’s Pages
Authored by Nadiia Nykolaichuk.
Dirk Eddelbuettel: RcppFastAD 0.0.4 on CRAN: Updated Again
A new release 0.0.4 of the RcppFastAD package by James Yang and myself is now on CRAN.
RcppFastAD wraps the FastAD header-only C++ library by James which provides a C++ implementation of both forward and reverse mode of automatic differentiation. It offers an easy-to-use header library (which we wrapped here) that is both lightweight and performant. With a little of bit of Rcpp glue, it is also easy to use from R in simple C++ applications. This release updates the quick fix in release 0.0.3 from a good week ago. James took a good look and properly disambiguated the statement that lead clang to complain, so we are back to compiling as C++17 under all compilers which makes for a slightly wider reach.
The NEWS file for this release follows.
Changes in version 0.0.4 (2024-09-24)- The package now properly addresses a clang warning on empty variadic macros arguments and is back to C++17 (James in #10)
Courtesy of my CRANberries, there is also a diffstat report for the most recent release. More information is available at the repository or the package page.
If you like this or other open-source work I do, you can sponsor me at GitHub.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
The Drop Times: Lupus Decoupled Drupal: Bridging Drupal’s Backend Strength with Frontend Freedom
Drupal life hack's: Invoked Controllers as a Service
Drupal life hack's: Practical Use Cases of Tagged Services in Drupal
Talking Drupal: Talking Drupal #468 - Drupal AI
Today we are talking about Artificial Intelligence (AI), How to integrate it with Drupal, and What the future might look like with guest Jamie Abrahams. We’ll also cover AI SEO Analyzer as our module of the week.
For show notes visit: www.talkingDrupal.com/468
Topics- What is AI
- What is Drupal AI
- How is it different from other AI modules
- How do people use AI in Drupal
- How does Drupal AI make AI easier to integrate in Drupal
- What is RAG
- How has Drupal AI evolved from AI Interpolator
- What does the future of AI look like
Jamie Abrahams - freelygive.io yautja_cetanu
HostsNic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Martin Anderson-Clutz - mandclu.com mandclu
MOTW CorrespondentMartin Anderson-Clutz - mandclu.com mandclu
- Brief description:
- Have you ever wanted an AI-based tool to give your Drupal site’s editors feedback on the SEO readiness of their content? There’s a module for that.
- Module name/project name:
- Brief history
- How old: created in Aug 2024 by Juhani Väätäjä (j-vee)
- Versions available: 1.0.0-beta1, which supports Drupal 10.3 and 11
- Maintainership
- Actively maintained
- Number of open issues: none
- Usage stats:
- 2 sites
- Module features and usage
- Once you enable this module along with the AI module, you can select the default provider, and optionally modify the default prompt that will be used to generate the report
- With that done, editors (or anyone with the new “view seo reports” permission) will see an “Analyze SEO” tab on nodes throughout the site.
- Generated reports are stored in the database, for ongoing reference
- The reports are also revision-specific, so you could run reports on both a published node and a draft revision
- There’s a separate “create seo reports” permission needed to generate reports. Within the form an editor can modify the default prompt, for example to get suggestions on optimizing for a specific topic, or to add or remove areas from the generated report.
- By default the report will include areas like topic authority and depth, detailed content analysis, and even technical considerations like mobile responsiveness and accessibility. It’s able to do the latter by generating the full HTML markup of the node, and passing that to the AI provider for analysis
- It feels like it was just yesterday that the AI module had its first release, so I think it’s great to see that there are community-created additions like this one already evolving as part of Drupal’s AI ecosystem
Real Python: Advanced Python import Techniques
In Python, you use the import keyword to make code in one module available in another. Imports in Python are important for structuring your code effectively. Using imports properly will make you more productive, allowing you to reuse code while keeping your projects maintainable.
This video course provides a comprehensive overview of Python’s import statement and how it works. The import system is powerful, and this course will teach you how to harness this power. While you’ll cover many of the concepts behind Python’s import system, this video course is mostly example driven, so you’ll learn from the numerous code examples shared throughout.
In this video course, you’ll learn how to:
- Use modules, packages, and namespace packages
- Manage namespaces and avoid shadowing
- Avoid circular imports
- Import modules dynamically at runtime
- Customize Python’s import system
[ 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 ]
PyCharm: PyCharm vs. Jupyter Notebook
Jupyter notebooks are an important tool for data scientists, providing an easy option for conducting experiments and presenting results. According to our Developer Ecosystem Survey 2023, at least 35% of data professionals use Jupyter notebooks. Furthermore, over 40% of these users spend more than 20% of their working time using these resources.
There are several implementations of notebook technology available to data professionals. At first we’ll look at the well-known Jupyter Notebook platforms by Project Jupyter. For the purposes of this article, we’ll refer to the Project Jupyter implementations of notebooks as “vanilla Jupyter” in order to avoid confusion, since there are several other implementations of the tool.
While vanilla Jupyter notebooks can be sufficient for some tasks, there are other cases where it would be better to rely on another tool for working with data. In this article, we’ll outline the key differences between PyCharm Professional and vanilla Jupyter when it comes to data science applications.
What is Jupyter Notebook?Jupyter Notebook is an open-source platform that allows users to create and share code, visualizations, and text. It’s primarily used for data analysis and scientific research. Although JupyterLab offers some plugins and tools, its capabilities and user experience are significantly more limited than PyCharm’s.
What is PyCharm Professional?PyCharm is a comprehensive integrated development environment (IDE) that supports a wide range of technologies out of the box, offering deep integration between them. In addition to enhanced support for Jupyter notebooks, PyCharm Professional also provides superior database support, Python script editing, and GitHub integration, as well as support for AI Assistant, Hugging Face, dbt-Core, and much more.
Feature comparison: PyCharm Pro vs. Jupyter Language supportWhile Jupyter notebooks claim to support over 40 programming languages, their usage is limited to the .ipynb format, which makes working with traditional file extensions like .py, .sql, and others less convenient. On the other hand, while PyCharm offers support for fewer languages – Python, JavaScript and TypeScript, SQL, and R (via plugin), along with several markup languages like HTML and CSS – the support is much more comprehensive.
Often, Jupyter notebooks and Python scripts serve different purposes. Notebooks are typically used for prototyping and experimentation, while Python scripts are more suitable for production. In PyCharm Professional, you can work with both of these formats and it’s easy to convert .ipynb files into .py files. See the video below for more information.
The smartest code completionIf you’ve ever written code in PyCharm Professional, you’ll have definitely noticed its code completion capabilities. In fact, the IDE offers several different types of code completion. In addition to the standard JetBrains completion, which provides suggestions based on an extensive understanding of your project and libraries, there’s also runtime completion, which can suggest names of data objects like columns in a pandas or Polars DataFrame, and ML-powered, full-line completion that suggests entire lines of code based on the current file. Additionally, you can enhance these capabilities with LLM-powered tools such as JetBrains AI Assistant, GitHub Copilot, Amazon Whisper, and others.
In contrast, code completion in Jupyter notebooks is limited. Vanilla Jupyter notebooks lack awareness of your project’s context, there’s no local ML-based code completion, and there’s no runtime code completion for database objects.
Code quality features and debuggerPyCharm offers several tools to enhance your code quality, including smart refactorings, quick-fixes, and AI Assistant – none of which are available in vanilla Jupyter notebooks.
If you’ve made a mistake in your code, PyCharm Professional will suggest several actions to fix it. These become visible when you click on the lightbulb icon.
PyCharm Professional also inspects code on the file and project level. To see all of the issues you have in your current file, you can click on the image in the top right-hand corner.
While vanilla Jupyter notebooks can highlight any issues after a code cell has been executed (as seen below), it doesn’t have features that allow you to analyze your entire file or project.
PyCharm provides a comprehensive and advanced debugging environment for both Python scripts and Jupyter notebooks. This debugger allows you to step into your code, running through the execution steps line by line, and pinpointing exactly where an error was made. If you’ve never used the debugger in PyCharm, you can learn how to debug a Jupyter notebook in PyCharm with the help of this blog by Dr. Jodie Burchell. In contrast, vanilla Jupyter offers basic debugging tools such as cell-by-cell execution and interactive %debug commands.
RefactoringsWeb-based Jupyter notebooks lack refactoring capabilities. If you need to rename a variable, introduce a constant, or perform any other operation, you have to do it manually, cell by cell. In PyCharm Professional, you can access the Refactoring menu via Control + T and use it to make changes in your file faster. More information about refactorings in PyCharm you can find in the video.
Other code-related featuresIf you forget how to work with a library in vanilla Jupyter notebooks, you need to open another tab in a browser to look up the documentation, taking you out of your development environment and programming flow.
In PyCharm Professional, you can get information about a function or library you’re currently using right in the IDE by hovering over the code.
If you have a subscription to AI Assistant you can also use it for troubleshooting, such as asking it to explain code and runtime errors, as well as finding potential problems with your code before you run it.
Working with tablesDataFrames are one of the most important types of data formats for the majority of data professionals. In vanilla Jupyter notebooks, if you print a pandas or Polars DataFrame, you’ll see a static, output with a limited number of columns and rows shown. Since the DataFrame outputs in Jupyter notebooks are static, this makes it difficult to explore your data without writing additional code.
In PyCharm Professional, you can use interactive tables that allow you to easily view, navigate, sort, and filter data. You can create charts and access essential data insights, including descriptive statistics and missing values – all without writing a single line of code.
What’s more, the interactive tables are designed to give you a lot of information about your data, including details of:
- Data type symbols in the column headers
- The size of your DataFrame (in our case it is 2390 rows and 82 columns).
- Descriptive statistics and missing values and many more.
If you want to get more information about how interactive tables work in PyCharm, check out the documentation.
Versioning and GitHub integrationIn PyCharm Professional, you have several version control options, including Git.
With PyCharm’s GitHub integration, you can see and revert your changes with the help of the IDE’s built-in visual diff tool. This enables you to compare changes between different commits of your notebooks. You can find an in-depth overview of the functionality in this tutorial.
Another incredibly useful feature is the local history, which automatically saves a version history of your changes. This means that if you haven’t committed something, and you need to roll back to an earlier version, you can do so with the click of a button.
In vanilla Jupyter notebooks, you have to rely on the CLI git tool. In addition, Git is the only way of versioning your work, meaning there is no way to revert changes if you haven’t committed them.
NavigationWhen you work on your project in Jupyter Notebook, you always need to navigate either within a given file or the whole project. On the other hand, the navigation functionality in PyCharm is significantly richer.
Beyond the Structure view that is also present in JupyterLab, you can also find some additional features to navigate your project in our IDEs. For example, double pressing Shift will help you find anything in your project or settings.
In addition to that, you can find the specific source of code in your project using PyCharm Professional’s Find Usages, Go to Implementation, Go to Declaration, and other useful features.
Check out this blog post for more information about navigation in Jupyter notebooks in PyCharm.
VisualizationsIn addition to libraries that are available in vanilla Jupyter notebooks, PyCharm also provides further visualization possibilities with the help of interactive tables. This means you don’t have to remember or type boilerplate code to create graphs.
How to choose between PyCharm and vanilla Jupyter notebooksVanilla Jupyter notebooks are a lightweight tool. If you need to do some fast experiments, it makes sense to use this implementation.
On the other hand, PyCharm Professional is a feature-rich IDE that simplifies the process of working with Jupyter notebooks. If you need to work with complex projects with a medium or large codebase, or you want to add a significant boost to productivity, PyCharm Professional is likely to be more suitable, allowing you to complete your data project more smoothly and quickly .
Get started with PyCharm ProfessionalPyCharm Professional is a data science IDE that supports Python, rich databases, Jupyter, Git, Conda, and other technologies right out of the box. Work on projects located in local or remote development environments. Whether you’re developing data pipelines, prototyping machine learning models, or analyzing data, PyCharm equips you with all the tools you need.
The best way to understand the difference between tools is to try them for yourself. We strongly recommend downloading PyCharm and testing it in your real-life projects.
Download PyCharm Professional and get an extended 60-day trial by using the promo code “PyCharmNotebooks”. The free subscription is available for individual users only.
Activate your 60-day trialBelow are other blog posts that you might find useful for boosting your productivity with PyCharm Professional.
Plasma Browser Integration 2.0
I’m pleased to announce the immediate availability of Plasma Browser Integration version 2.0 on the Chrome Web Store and Microsoft Edge Add-ons page. This release updates the extension to Manifest Version 3 which will be required by Chrome soon. The major version bump reflects the amount of work it has taken to achieve this port.
Konqi surfing the world wide webPlasma Browser Integration bridges the gap between your browser and the Plasma desktop. It lets you share links, find browser tabs and visited websites in KRunner, monitor download progress in the notification center, and control music and video playback anytime from within Plasma, or even from your phone using KDE Connect!
Despite the version number, there aren’t many user-facing changes. This release comes with the usual translation updates, however. Since this release doesn’t bring any advantage to Firefox users over the previous 1.9.1, it will not be provided on the Mozilla add-ons store.
We have taken the opportunity of reworking the extension manifest to make the “history” permission mandatory. When the browser history KRunner module was originally added, the permission was optional as we feared it might scare users away when presented with a scary permission warning when updating the extension.
(also see the Changelog Page on our Community Wiki)
The Drop Times: Drupal CMS Expected by 15 Jan, XB Further Away in 2025: A Quick and Dirty Summary of Driesnote
Data Transparency in Open Source AI: Protecting Sensitive Datasets
The Open Source Initiative (OSI) is running a blog series to introduce some of the people who have been actively involved in the Open Source AI Definition (OSAID) co-design process. The co-design methodology allows for the integration of diverging perspectives into one just, cohesive and feasible standard. Support and contribution from a significant and broad group of stakeholders is imperative to the Open Source process and is proven to bring diverse issues to light, deliver swift outputs and garner community buy-in.
This series features the voices of the volunteers who have helped shape and are shaping the Definition.
Meet Tarunima PrabhakarI am the research lead and co-founder at Tattle, a civic tech organization that builds citizen centric tools and datasets to respond to inaccurate and harmful content. My broad research interests are in the intersection of technology, policy and global development. Prior to starting Tattle, I worked as a research fellow at the Center for Long-Term Cybersecurity at UC, Berkeley studying the deployment of behavioral credit scoring algorithms towards financial inclusion goals in the global majority. I’ve also been fortunate to work on award-winning ICTD and data driven development projects with stellar non-profits. My career working in low-resource environments has turned me into an ardent advocate for Open Source development and citizen science movements.
Protecting Sensitive DatasetsI recently gave a lightning talk at IndiaFOSS where I shared about Uli, a project to co-design solutions to online gendered abuse in Indian languages. As a part of this project, we’re building and maintaining datasets that are useful for machine learning models that detect abuse. The talk exhibited the importance of and the care that must be given when choosing a license for sensitive data, and why open datasets in Open Source AI should be carefully considered.
With the Uli project, we created a dataset annotated by gender rights activists and researchers who speak Hindi, Tamil and Indian English. Then, we fine-tuned Twitter’s XLM-RoBERTa model to detect gender abuse, which we deployed as a browser plugin. When activated, the Uli plugin would redact abusive tweets from a person’s feed. Another dataset we created was of slur words in the three languages that might be used to target people. Such a list is not only useful for the Uli plugin- these words are redacted from web pages if the plugin is installed- but they are also useful for any platform needing to moderate conversations in these languages. At the time of the launch of the plugin, we chose to license the two datasets under an Open Data License (ODL). The model is hosted on Hugging Face and the code is available on GitHub.
As we have continued to maintain and grow Uli, we have reconsidered how we license the data. When thinking about how to license this data, several factors come into play. First, annotating a dataset on abuse is labor-intensive and mentally exhausting, and the expert annotators should be fairly compensated for their expertise. Second, when these datasets are used by platforms for abuse detection, it creates a potential loophole—if abusive users realize the list of flagged words is public, they can change their language to evade moderation.
These concerns have led us to think carefully about how to license the data. On one end of the spectrum, we could continue to make everything open, regardless of commercial use. On the other end, we could keep all the data closed. We’ve historically operated as an Open Source organization, and every decision we make about data access impacts how we license our machine learning models as well. We are trying to find a happy medium that lets us balance the numerous concerns- recognition of effort and effectiveness of the data on one hand, and transparency, adaptability and extensibility on the other.
As we’ve thought about different strategies for data licensing, we haven’t been sure what that would mean for the license of the machine learning models. And that’s partly because we don’t have a clear definition for what “Open Source AI” really means.
It is for this reason that we’ve closely followed the Open Source Initiative’s (OSI) process for converging on a definition for Open Source AI. OSI has been grappling with the definition of “Open Source AI” as it pertains to the four freedoms: the freedom to use, study, modify, and share. Over the past year, the OSI has been iterating on a definition for Open Source AI, and they’ve reached a point where they propose the following:
- Open weights: The model weights and parameters should be open.
- Open source code: The source code used to train the system should be open.
- Open data or transparent data: Either the dataset should be open, or there should be enough detailed information for someone to recreate the dataset.
It’s important to note that the dataset doesn’t necessarily have to be open. The departure from a stance of maximally open dataset accounts for the complexity in the collection and management of data driving real world ML applications. While frontier models need to deal with copyright and privacy concerns, many smaller projects like ours worry about the uneven power dynamics between those creating the data and the entities using it. In our specific case, opening data also reduces its efficacy.
But having struggled with papers that describe research or data without sharing the dataset itself, I also recognize that ‘enough detailed information’ might not be information enough to repeat, adapt or extend another group’s work. In the end, the question becomes: how much information about the dataset is enough to consider the model “open?” It’s a fine line, and not everyone is comfortable with OSI’s stance on this issue. For our project in particular, we are considering the option of staggered data release- older data is released under an open data license, while the newest data requires users to request access.
If you have strong opinions on this process, I encourage you to visit the OSI website and leave feedback. The OSI process is influential, and your input on open weights, open code, and their specifications around data openness could shape the future of Open Source AI.
You can learn more about the participatory process behind the Uli dataset here, and about Uli and Tattle on their respective websites.
How to get involvedThe OSAID co-design process is open to everyone interested in collaborating. There are many ways to get involved:
- Join the forum: share your comment on the drafts.
- Leave comment on the latest draft: provide precise feedback on the text of the latest draft.
- Follow the weekly recaps: subscribe to our monthly newsletter and blog to be kept up-to-date.
- Join the town hall meetings: we’re increasing the frequency to weekly meetings where you can learn more, ask questions and share your thoughts.
- Join the workshops and scheduled conferences: meet the OSI and other participants at in-person events around the world.