Planet Python
Real Python: The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13
Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, "Python 3.13: Cool New Features for You to Try." Christopher's video course "What's New in Python 3.13" covers the topics from the article and shows the new features in action.
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ListenData: How to Automate WordPress using Python
This tutorial explains how to use Python to automate tasks in WordPress. It includes various functions to perform tasks such as creating, extracting, updating and deleting WordPress posts, pages and media items (images) directly from Python.
Table of Contents To read this article in full, please click hereThis post appeared first on ListenDataReal Python: Quiz: Structural Pattern Matching
In this quiz, you’ll test your understanding of Structural Pattern Matching in Python.
You’ll revisit the syntax of the match statement and case clauses, explore various types of patterns supported by Python, and learn about guards, unions, aliases, and name binding.
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PyCharm: How I do Django APIs in PyCharm
I learn so much from watching conference talks, especially live, when I’m vibing with the crowd. But sometimes I watch and think: “Wow, I wish I could show you how awesome that would be in PyCharm.”
That just happened. Here’s the explainer, with a little special something at the end.
Hello, DjangoConfI recently attended DjangoConf 2024 which kicked off this year’s DSF-PyCharm fundraiser. I attended Felipe’s DRF tutorial where he showed off using PyCharm and even a little bit about endpoints.
Afterwards, I ran into a PyCharm fan who told me what he really likes when using PyCharm for Django. It matched what I really like. Hence, a blog post.
The end is the pointMy superfan friend was an early adopter of endpoints, our feature for rethinking the API developer experience (DX) in Django, FastAPI, and Flask. Me too. It’s cool to have a listing of endpoints, jumping to the definition, and most of all – issuing an HTTP request right there in the IDE. No going out to Postman.
I covered endpoints and the HTTP client in my previous blog post. One extra point: he said Postman pricing is going up. I guess I should talk more about the HTTP Client.
Always be debuggingMost folks know that I’m a debugger stan, probably because I just won’t shut up about it. It turns out that he also uses the debugger first, meaning he runs the Django server, under the debugger, all the time, even when he isn’t debugging.
Why? First, it’s so fast, you don’t notice the speed hit. As he also knew, Python 3.12 lowers the impact of debugging and PyCharm uses this automatically. The bigger point though: when you want to poke around, you don’t need to stop the regular “run”, launch under “debug”, then return to “run.” That’s disruptive, so people just do print. Which makes me a sad panda.
If you’re always debugging, then poking around is already RIGHT THERE. Even if you don’t have a bug and just want to investigate. Even if you are in a template.
This is great with endpoints, as you can click a breakpoint in your code and issue a request without leaving the tool.
He made one last point – PyCharm’s Django support and debugger is more mature and polished. We’ve been doing this for a while!
I didn’t know there would be a testThere’s one more step to the higher-zen of using PyCharm to the fullest with Django. Why use the browser or an HTTP client at all? Why not just sit in a test module and let PyCharm + pytest bring joy to your world? In fact – don’t even run Django. No server process, less hassle.
Django makes it really easy to issue fake requests in a test, get the results back, and make sure things are cool. I like having my code on the left, my test on the right, and the test output on the bottom. In fact, I also like combining Always Be Testing with Always Be Debugging, which makes it crazy-easy to stop in the middle of a view and see what’s going on.
I like it so much, here’s a little video:
This works great for how code works. You can skip going to the browser, reloading, and poking around. You stay in the IDE, the flow. But there’s a catch.
Seeing is believingSometimes you need to see how the page looks. In the browser. With your eyeballs. Any chance PyCharm can improve the DX for this?
As it turns out, in 2023.3 we shipped Django Preview, a feature-rich browser in the IDE that keeps up as you type.
A love letter to DjangoThis concludes my speaking from the heart about my way of doing Django API development in PyCharm: endpoints, debugger, testing, and preview.
But I’d like to close by speaking from the heart about Django, leading with an odd little twist of fate about Django killing my project.
Real Python: Build a Contact Book App With Python, Textual, and SQLite
Building projects is a great way to learn programming and have fun at the same time. When you work on a project, you apply different coding skills simultaneously, which is good practice for what you’ll do in a real-life project. In this tutorial, you’ll create a contact book application with a text-based interface (TUI) based on Python and Textual. To store the contact data, your app will use an SQLite database.
In this tutorial, you’ll learn how to:
- Create the contact book app’s TUI using Textual
- Handle the database operations using SQLite
- Connect the app’s TUI with the database code and make it functional
At the end of this project, you’ll have a functional contact book application that will allow you to store and manage your contact information.
To get the complete source code for the application and the code for every step in this tutorial, click the link below:
Get Your Code: Click here to download the free sample code you’ll use to build a contact book app with Python, Textual, and SQLite.
Demo: A Contact Book Built With Python and TextualContact or address books are a widely used type of application. They can be found on phones and computers, allowing users to store and manage contact information for family, friends, coworkers, and so on.
In this tutorial, you’ll code a contact book TUI app with Python, Textual, and SQLite. Here’s a demo of how your contact book will look once you’ve followed all the steps:
Your contact book will provide a basic set of features for this type of application, and you’ll be able to display, add, and remove the information in your contacts list.
Project OverviewTo build your contact book app, you’ll organize the code in a few modules under a package. In this tutorial, you’ll use the following directory structure:
rpcontacts_project/ │ ├── rpcontacts/ │ ├── __init__.py │ ├── __main__.py │ ├── database.py │ ├── rpcontacts.tcss │ └── tui.py │ ├── README.md └── requirements.txtThe root directory of your project is rpcontacts_project/. Inside, there’s an rpcontacts/ subdirectory that holds the application’s main package.
You’ll cover the content of each file in this tutorial. The name of each file will give you an idea of its role in the application.
For example, __main__.py will host the application, and database.py will provide database-related code. Similarly, rpcontacts.tcss is a CSS file that will allow you to tweak the visual style of your Textual app. Finally, tui.py will contain the code to generate the app’s TUI, including the main screen and a couple of auxiliary screens or dialogs.
PrerequisitesTo get the most out of this project, you should have some previous knowledge of how to lay out a Python project and work with SQLite databases. You should also know the basics of working with Python classes. Some knowledge about writing CSS code would also be a plus.
To satisfy these knowledge requirements, you can take a look at the following resources:
- Python Application Layouts: A Reference
- Introduction to Python SQL Libraries
- Data Management With Python, SQLite, and SQLAlchemy
- HTML and CSS for Python Developers
Don’t worry if you don’t have all of the prerequisite knowledge before starting this tutorial—that’s completely okay! You’ll learn through the process of getting your hands dirty as you build the project. If you get stuck, then take some time to review the resources linked above. Then, get back to the code.
The contact book application you’ll build in this tutorial has a single external dependency, which is Textual. This library provides a rapid application development framework that allows you to create apps you can run in your terminal and browser.
To follow best practices in your development process, you can start by creating a virtual environment and then install Textual using pip:
Read the full article at https://realpython.com/contact-book-python-textual/ »[ 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 ]
Stefanie Molin: Mind Your Image Metadata
PyCharm: Where To Get Data for Your Data Science Projects
Whether you’re starting a new project or expanding an existing one, as a data scientist, you’re always on the lookout for new material to explore. Knowing where to get data for data science projects can be challenging, and finding “good data” can be even more difficult. In this article, we’ll look at what makes “good data”, what format that data might be in, where to find it, and what the next steps are.
What is “good data” for data science projects?Firstly, we should consider how relevant the dataset is to our work. You can stumble upon lots of datasets that overlap with your work in some way, but it can be difficult to decide which is the best one for you to put your effort into. In this scenario, we’ll briefly explore some of the attributes of the data.
To start with, how consistent is the dataset? Specifically, are there any missing values? Data might be missing for a variety of acceptable reasons, but it can also be a sign of selection bias or other factors that might skew your results. Often, we can choose to either accept missing data or delete the records that contain it before we do our analysis, but knowing about missing data early in the process can help you make an informed decision to use that dataset or not.
Along with missing data, it’s worth checking to see if any of the data is duplicated. Duplicated data might be fine, but it might also signify a lack of consistency that could skew your results. Duplicated data might also reduce your confidence in the dataset as a whole, so it’s important to consider when choosing your dataset.
Another aspect to consider for good data is timeliness. The time over which the data was gathered is usually pertinent to the questions you want to answer when you start analyzing it. Checking if the data was collected in the timespan that you’re interested in and considering the continuity of that timespan is helpful.
When you’re starting your journey into data science and picking your first few datasets to play with, you don’t need to worry about picking the perfect dataset – focus on the process and exploring instead. When you’re ready to learn more about datasets and how to avoid common pitfalls, I recommend you watch this talk from Dr. Jodie Burchell – Garbage data in, garbage models out.
Do you want structured or unstructured data?Structured data is what you’ll find in a table where each row is an observation, and each column is a variable or field. By contrast, unstructured data usually needs to be pre-processed before you can work with it in a data science project, or it can be used by specialist models that can process it internally. Examples of unstructured data include text, images, and sound.
As you might have guessed, unstructured data is used more in advanced and specialized subfields in data science, like natural language processing and computer vision. Most data scientists start with, and continue working with, structured data for many of their projects. I recommend that this is where you start, too.
I recommend you keep the notion of structured and unstructured data in mind as we explore standard data formats.
What are standard data formats?In addition to the quality of the data, we also have to choose between available data formats. You’ll come across two broad types of data formats as a data scientist: downloadable data (often CSV) and databases.
Downloadable data is nearly always structured data and often takes the form of comma-separated value (CSV) files. These downloads are available from various online repositories. They are among some of the most prolific and most accessible sources of data. If you’re new to data exploration, this is the best place to get started, as they’re easy to find, human-readable, and easy to work with without any extra steps.
If you’re ready to enter the world of databases, it’s worth understanding that they are further subdivided into relational (SQL) and non-relational (non-SQL) databases. As a broad rule, relational databases contain structured data and non-relational databases contain non-structured data, but determining whether data is structured is not an exact science. Instead, think of non-relational databases as being adaptable to the shape of the data they are storing.
Databases are commonly used in the following cases: when you have large datasets, when multiple people need to access and modify the data simultaneously, when datasets need to be able to scale, and when data is unstructured (non-SQL only). In addition, if you’re commissioned to do data analysis for your company, you may find that you’re given a database to work with as it’s already in-house.
PyCharm Professional has excellent support for SQL and non-SQL databases. If your work involves using various databases and writing SQL queries, you can check out our webinar on Visual SQL Development with PyCharm to get more information about the functionality. Alternatively, you can learn how to explore tables without writing a single line of SQL with PyCharm and import your dataset into PyCharm and explore it.
Try PyCharm Professional for free
Where can I find datasets for my data science projects?Once you’re ready to find out how to get data, there are plenty of resources you can download to use for your data science project. This is not an endless list, but it’s a good place to start and a natural progression for your data science journey.
UCI Machine Learning RepositoryThe UCI Machine Learning Repository has over 600 datasets covering a host of exciting topics for you to explore, such as biology, health, physics, and climate. UCI datasets also have a diverse set of data types, including images, sequential, and time series. I recommend looking at a few different datasets and types of data if you’re new to data science, as it will help you expand your understanding of what data often looks like.
KaggleAnother well-known website for datasets is Kaggle. Not only can you sign up to Kaggle to download datasets for data science projects, but it also has a large community of like-minded people who run company-sponsored competitions designed to help you develop your data science skills. If you’re looking for a famous dataset that you’ve seen used in numerous examples, you’ll almost certainly find it hosted on Kaggle.
Hugging FaceHugging Face is another resource that is rich in datasets. You can filter the results by modalities, including audio, geospatial, and video, and provide a range for the size of your dataset, which can be particularly helpful when you want to start small. Hugging Face has many natural language and computer vision datasets, so you might want to head over there once you’re past the basics and interested in more specialized fields.
Many moreThere are many more places that you can go on your data science journey to find fun datasets to explore. You can check out GitHub for curated open source datasets, FiveThirtyEight for datasets relating to American politics and sports, and lastly, one of my favorites, the UK government, to get datasets relating to public services and the economy in the UK.
What are the next steps?Congratulations! You’ve gained a better understanding of what “good data” is, and you know where to look to find datasets for data science projects. Once you’ve chosen a dataset, you’re ready to start preparing and analyzing your data.
Remember, you can use Jupyter notebooks inside PyCharm to explore both file format and database datasets.
You can read or watch a video showing just some of the ways you can use Jupyter notebooks inside PyCharm to boost your productivity on your data science journey with your chosen dataset.
Talk Python to Me: #480: Ahoy, Narwhals are bridging the data science APIs
Django Weblog: Why Django supports the Open Source Pledge
We at the Django Software Foundation are pleased to share that Sentry, alongside other partners, has launched the Open Source Pledge — an initiative designed to address sustainability challenges in open source.
The Open Source Pledge is a commitment for member companies to pay OSS maintainers meaningfully for their work. When maintainers are adequately supported, they can better sustain their projects, ensuring the growth, stability, and security of the broader ecosystem.
The sustainability challenge in the Django communityIn our community and OSS at large, the challenge is real and significant. Django packages are often maintained by small teams or even individuals, often unpaid. As the demands on these projects grow, so too does the pressure on the maintainers. And without financial support, maintainers often move on without a clear succession plan. The potential failure of these projects not only impacts the developers involved but also the thousands of companies and millions of users who rely on these critical pieces of infrastructure.
Here are a few assorted examples from Django packages in the top 10 by download counts:
- Is DRF still considered alive?, Moving REST framework forward
- Lots of open PRs with no feedback or action
- Recruiting maintainers
- We need more roadies in jazzband
The Open Source Pledge is simple but impactful: member companies commit a minimum of $2,000 per year, per developer on staff, to support open source maintainers. Additionally, companies are encouraged to publish an annual report detailing their payments, creating transparency and accountability within the community.
We encourage companies of all sizes to join the Pledge and contribute to the sustainability of the software we all depend on. By making a financial commitment, you are not just supporting maintainers—you are investing in the stability, security, and growth of the entire tech ecosystem.
If you're interested in joining the Open Source Pledge or learning more about the sustainability issues facing OSS, please visit the initiative’s page. Together, we can build a stronger, more sustainable open source future. And if you believe in this cause, we encourage you to share this post to help broaden awareness and inspire further commitments from peers and partners.
PyCoder’s Weekly: Issue #650 (Oct. 8, 2024)
#650 – OCTOBER 8, 2024
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In this video course, you’ll learn how Python mutable and immutable data types work internally and how you can take advantage of mutability or immutability to power your code.
REAL PYTHON course
Learn how to run DuckDB in an in-browser Python environment to enable simple querying on remote files, interactive documentation, and easy to use training materials.
ALEX MONAHAN
Looking to add new functionality to your Django app? Learn how to integrate Speech-to-Text and build a working app that transcribes audio files—with 100+ free hours to get started →
ASSEMBLY AI sponsor
This post talks about combining the new experimental free threading feature of Python 3.13 with Asyncio.
CHANGS.CO.UK • Shared by Jamie Chang
Earlier this week Trey considered whether to switch from virtualenvwrapper to using local .venv managed by direnv. He then also started experimenting with uv and Starship. This post explains why and his new configuration.
TREY HUNNER
Some template blocks are meant to be overloaded and forgetting to do so results in rendering bugs. This post talks about creating a new tag that throws an exception which alerts your tests if you forget to overload.
TOM CARRICK
Simplify workloads and elevate customer service. Build customized AI assistants that respond to voice prompts with powerful language and comprehension capabilities - all based on your unique needs with Intel’s OpenVINO toolkit.
INTEL CORPORATION sponsor
Learn what the Arrange, Act, and Assert (AAA) pattern is, how it works, the benefits it offers, and its role in unit test automation. Note: sample code is not in Python, but the concepts apply to all unit testing.
ANTONELLO ZANINI
This article outlines the system that Rodrigo uses to prepare his Python talks. Steal his ideas and suggestions so that you, too, can start giving talks at your local meetups and at PyCons all over the world.
MATHSPP.COM • Shared by Rodrigo
A singleton pattern is one where only one instance of an object type is allowed at a time. One way to implement this concept is through the use of a decorator. This post teaches you how.
PIETER CLAERHOUT
This Python Enhancement Proposal specifies a mechanism by which projects hosted on pypi.org can safely host wheel artifacts on external sites other than PyPI.
PYTHON.ORG
The use of containers can mean a lot of calls to PyPI. This post talks about caching properly to reduce the load on our shared community servers.
MICHAEL KENNEDY
If you need to cycle through values, one way to do that is with deque. This post shows you through an example service for a game engine.
JUHA-MATTI SANTALA
All you need to know about the latest Python release’s changes to the Global Interpreter Lock and Just-in-Time compilation.
DREW SILCOCK
“Not a real dinosaur and not real poetry.” This post is about Paul changing how what tools he uses for his Python setup.
PAUL COCHRANE
GITHUB.COM/TESORIO • Shared by Caio Ariede
foc: A Collection of Python Functions for Somebody’s Sanity spiderweb: A Small Web Framework pipefunc: DAGs for Scientific WorkflowsGITHUB.COM/PIPEFUNC • Shared by Bas Nijholt
django-unique-user-email: Emial Logins With User Model Events Weekly Real Python Office Hours Q&A (Virtual) October 9, 2024
REALPYTHON.COM
October 9 to October 14, 2024
PYCON.ORG
October 10 to October 11, 2024
PYCON.ORG
October 10 to October 11, 2024
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October 12, 2024
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October 14 to October 16, 2024
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October 16 to October 21, 2024
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October 16 to October 19, 2024
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October 17 to October 19, 2024
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Happy Pythoning!
This was PyCoder’s Weekly Issue #650.
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Real Python: What's New in Python 3.13
Python 3.13 was published on October 7, 2024. This new version is a major step forward for the language, although several of the biggest changes are happening under the hood and won’t be immediately visible to you.
In a sense, Python 3.13 is laying the groundwork for some future improvements, especially to the language’s performance. As you watch the course, you’ll learn more about the background for this and dive into some new features that are fully available now.
In this video course, you’ll learn about some of the improvements in the new version, including:
- Improvements made to the interactive interpreter (REPL)
- Clearer error messages that can help you fix common mistakes
- Advancements done in removing the global interpreter lock (GIL) and making Python free-threaded
- The implementation of an experimental Just-In-Time (JIT) compiler
- A host of minor upgrades to Python’s static type system
In this video course, you’ll explore these changes and see how this new version of Python can work for you.
If you want to try any of the examples in this video course, then you’ll need to use Python 3.13. The Python 3 Installation & Setup Guide and How Can You Install a Pre-Release Version of Python? walk you through several options for adding a new version of Python to your 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 ]
Real Python: Quiz: Python Closures: Common Use Cases and Examples
In this quiz, you’ll test your understanding of Python closures. Closures are a common feature in functional programming languages and are particularly popular in Python because they allow you to create function-based decorators.
Take this quiz after reading our Python Closures: Common Use Cases and Examples tutorial.
[ 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 ]
Django Weblog: Django bugfix release issued: 5.1.2
Today we've issued the 5.1.2 bugfix release.
The release package and checksums are available from our downloads page, as well as from the Python Package Index. The PGP key ID used for this release is Natalia Bidart: 2EE82A8D9470983E.
Python Software Foundation: Join the Python Developers Survey 2024: Share your experience!
This year we are conducting the eighth iteration of the official Python Developers Survey. The goal is to capture the current state of the language and the ecosystem around it. By comparing the results with last year’s, we can identify and share with everyone the hottest trends in the Python community and the key insights into it.
We encourage you to contribute to our community’s knowledge by sharing your experience and perspective. Your participation is valued! The survey should only take you about 10-15 minutes to complete.
Contribute to the Python Developers Survey 2024!
This year we aim to reach even more of our community and ensure accurate global representation by highlighting our localization efforts:
- The survey is translated into Spanish, Portuguese, Chinese, Korean, Japanese, German, French and Russian. It has been translated in years past, as well, but we plan to be louder about the translations available this year!
- To assist individuals in promoting the survey and encouraging their local communities and professional networks we have created a Promotion Kit with images and social media posts translated into a variety of languages. We hope this promotion kit empowers folks to spread the invitation to respond to the survey within their local communities.
- We’d love it if you’d share one or more of the posts below to your social media or any community accounts you manage, as well as share the information in discords, mailing lists, or chats you participate in.
- If you would like to help out with translations you see are missing, please request edit access to the doc and share what language you will be translating to. Translation into languages the survey may not be translated to is also welcome.
- If you have ideas about what else we can do to get the word out and encourage a diversity of responses, please comment on the corresponding Discuss thread.
The survey is organized in partnership between the Python Software Foundation and JetBrains. After the survey is over, we will publish the aggregated results and randomly choose 20 winners (among those who complete the survey in its entirety), who will each receive a $100 Amazon Gift Card or a local equivalent.
Mariatta: Perks of Being a Python Core Developer
I’ve been a Python core developer since January 27, 2017.
Being a Python core developer comes with perks, privileges, and also responsibilities.
Sometimes I can’t tell whether something is a perk, or a privilege, or a responsibility. I think depends on who you’re talking to, they might see it as an optional nice thing they could get/do, but the same thing might be seen as burden responsibility to others.
Python Morsels: Python 3.13's best new features
Python 3.13 comes with a brand new REPL and improvements to virtual environments and the Python debugger.
Table of contents
- Important but not my favorite
- The New Python REPL
- Git-friendly virtual environments
- Python Debugger improvements
- Try out Python 3.13
First, I'd like to note that I'm not going to talk about the experimental free-threaded mode, the experimental just-in-time compilation option, or many other features that don't affect most Python developers today.
Instead, let's focus on some of the more fun things.
The New Python REPLMy favorite feature by far …
Read the full article: https://www.pythonmorsels.com/python-313-whats-new/Julien Tayon: Writing an interactive tcl/tk interpreter proxy to wish in python
As a convinced tkinter/FreeSimpleGUI user, I see this as an extreme claim that requires solid evidences.
When all is said and done, wish interpreter is not interactive, and for testing simple stuff it can get annoying very fast. Thus, it would be nice to add readline to the interface.
So here is a less than 100 line of code exercice of soing exactly so while having fun with : readline and multiprocessing (I would have taken multithreading if threads were easy to terminate).
Readline I quote The readline module defines a number of functions to facilitate completion and reading/writing of history files from the Python interpreter. Basically, it adds arrow navigation in history, back search with Ctrl+R, Ctrl+K for cuting on the right, Ctrl+Y for yanking ... all the facilities of interaction you have in bash or ipython for instance.
We are gonna use multiprocessing because tcl/tl is event oriented, hence, asynchronuous hence, we may have string coming from the tcl stdout while we do nothing and we would like to print them.
We also introduce like in ipython some magic prefixed with # (comment in tcl) like #? for the help. A session should look like this : # pack [ button .c -text that -command { puts "hello" } ] # tcl output> hello # here we pressed the button "that" tcl output> hello # here we pressed the button "that" # set name 32 # puts $name tcl output> 32 # #? #l print current recorded session #? print current help #! calls python code like #!save(name="temp") which saves the current session in current dir in "temp" file bye exit quit quit the current session # #l pack [ button .c -text that -command { puts "hello" } ] set name 32 puts $name # #!save("my_test.tcl") # quit The code in itself is fairly easy to read the only catch is that wish accepts multiline input. I can't because I don't know how to parse tcl. As a result I « eval in tcl » every line to know if there is an error and ask politely tcl to do the job of signaling the error with a « catch/error » (the equivalent of python try + raise an exception). #!/usr/bin/env python3 # -*- coding: utf8 -*- from subprocess import Popen, PIPE, STDOUT from multiprocessing import Process import sys, os import atexit import os import readline from select import select from time import sleep ### interactive session with history with readline histfile = os.path.join(os.path.expanduser("~"), ".wish_history") try: readline.read_history_file(histfile) # default history len is -1 (infinite), which may grow unruly readline.set_history_length(-1) except FileNotFoundError: pass ### saving history at the end of the session atexit.register(readline.write_history_file, histfile) ### opening wish wish = Popen(['wish'], stdin=PIPE, stdout=PIPE, stderr=PIPE, bufsize=-1, ) os.set_blocking(wish.stdout.fileno(), False) os.set_blocking(wish.stderr.fileno(), False) os.set_blocking(wish.stdin.fileno(), False) def puts(s): out = f"""set code [ catch {{ {s} }} p ] if {{$code}} {{ error $p }} """ select([], [wish.stdin], []) wish.stdin.write(out.encode()) def gets(): while True: wish.stdout.flush() tin = wish.stdout.read() if tin: print("\ntcl output> " + tin.decode()) sleep(.1) def save(fn="temp"): with open(fn,"wt") as f: f.write(session) session=s="" def load(fn="temp"): global session with open(fn, "rt") as f: while l:= f.readline(): session+=l + "\n" puts(l) # async io in tcl requires a background process to read the input t =Process(target=gets, arwish=()) t.start() while True: s = input("# ") if s in { "bye", "quit", "exit" }: t.terminate() wish.stdin.write("destroy .".encode()) break elif s == "#l": print(session) elif s == "#?": print(""" #l print current recorded session #? print current help #! calls python code like #!save(name="temp") which saves the current session in current dir in "temp" file #!load(name="temp") which load the session stored in current dir in "temp" file bye exit quit quit the current session """ ) continue elif s.startswith("#!"): print(eval(s[2:])) continue else: puts(s) if err:=wish.stderr.readline(): sys.stderr.write(err.decode()) else: if s and not s.startswith("#"): session += s + "\n" This code is available on pypi as iwish (interactive wish) and the git link is in the README.
Python Insider: Python 3.13.0 (final) released
Python 3.13.0 is now available
https://www.python.org/downloads/release/python-3130/
This is the stable release of Python 3.13.0
Python 3.13.0 is the newest major release of the Python programming language, and it contains many new features and optimizations compared to Python 3.12. (Compared to the last release candidate, 3.13.0rc3, 3.13.0 contains two small bug fixes and some documentation and testing changes.)
Major new features of the 3.13 series, compared to 3.12Some of the new major new features and changes in Python 3.13 are:
New features- A new and improved interactive interpreter, based on PyPy’s, featuring multi-line editing and color support, as well as colorized exception tracebacks.
- An experimental free-threaded build mode, which disables the Global Interpreter Lock, allowing threads to run more concurrently. The build mode is available as an experimental feature in the Windows and macOS installers as well.
- A preliminary, experimental JIT, providing the ground work for significant performance improvements.
- The locals() builtin function (and its C equivalent) now has well-defined semantics when mutating the returned mapping, which allows debuggers to operate more consistently.
- A modified version of mimalloc is now included, optional but enabled by default if supported by the platform, and required for the free-threaded build mode.
- 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.
- The minimum supported macOS version was changed from 10.9 to 10.13 (High Sierra). Older macOS versions will not be supported going forward.
- WASI is now a Tier 2 supported platform. Emscripten is no longer an officially supported platform (but Pyodide continues to support Emscripten).
- iOS is now a Tier 3 supported platform.
- Android is now a Tier 3 supported platform.
- Support for type defaults in type parameters.
- A new type narrowing annotation, typing.TypeIs.
- A new annotation for read-only items in TypeDicts.
- A new annotation for marking deprecations in the type system.
- 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.
- 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.)
- New deprecations, most of which are scheduled for removal from Python 3.15 or 3.16.
For more details on the changes to Python 3.13, see What’s new in Python 3.13.
More resources- Online Documentation
- PEP 719, 3.13 Release Schedule
- Report bugs via GitHub Issues.
- Help fund Python directly (or via GitHub Sponsors), and support the Python community.
Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation.
Choo-choo from the release train,
Your release team,
Thomas Wouters
Ned Deily
Steve Dower
Łukasz Langa
Real Python: Python News Roundup: October 2024
October is always an important month for Python, as this is when a new major version is released. Python 3.13 is the new version this year, and it brings several new features that lay the groundwork for other changes in the future. As one version of Python comes to life, another is put to rest. Python 3.8 is already five years old, which means that this version won’t be supported any longer.
There are also exciting developments happening in the wider Python community. In this newsletter, you can read about Polars’ improved support for plotting, as well as how Django developers gathered for the annual DjangoCon US conference.
Time to jump in and read about what’s happening in the world of Python!
Python 3.13 Release Slightly DelayedThe release of Python 3.13, the newest version of Python, was originally scheduled for October 1, 2024. However, a few days before that date, release manager Thomas Wouters decided to postpone the release until October 7, 2024:
I’m a little concerned with the impact of the incremental GC change in 3.13, which recently showed up. It’s not clear that the incremental GC provides significant improvements (although the smaller pauses are probably desirable), it clearly has slightly more overhead in common cases, and we’re still discovering pathological cases.
I don’t think we should release 3.13.0 with the incremental GC. (Source)
The incremental garbage collector was a small improvement slated for Python 3.13. In many cases, the new garbage collection algorithm improves performance. Unfortunately, it was found to slow down Python significantly in some rare cases.
As a result, the core developers decided to revert the implementation and use the traditional garbage collector in Python 3.13. At the same time, the new implementation is being scrutinized and currently the goal is to include incremental garbage collection in Python 3.14.
Delaying a major Python release is never an easy choice. However, erring on the side of caution is a good approach, and it’s great to see that the Python 3.13 release is being handled responsibly.
Python 3.13 HighlightsAs always, a new Python release brings many improvements and new features. You can explore these in-depth in Python 3.13: Cool New Features for You to Try. In particular, the new release includes:
- A brand new interactive interpreter (REPL)
- Colored tracebacks and improved error messages
- A separate, free-threaded version of Python that runs without the global interpreter lock (GIL)
- An experimental just-in-time (JIT) compiler
- Several improvements to Python’s static type system
For free threading and the JIT compiler, you need to compile Python with special build flags. Read Python 3.13 Preview: Free Threading and a JIT Compiler to learn more about how to explore these two new features. Additionally, Python 3.13 Preview: A Modern REPL provides more detail on the new REPL.
Read the full article at https://realpython.com/python-news-october-2024/ »[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]
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