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Last update: September 26, 2024 09:43 PM UTC

September 26, 2024

PyCharm

Django Project Ideas

Django Project Ideas

Ready to get started on your Django project? Whether you want to build a personal project, improve your portfolio, or just experiment with a new technology, Django provides the perfect foundation.

We’ve compiled a list of Django project ideas that will help you expand your competence, whether you’re new to the framework or looking to advance your existing skills.

In this guide, we’ll explore a range of Django project ideas to help you get hands-on experience, ranging from simple apps to more advanced, full-stack applications.

When should I use Django?

Known as the “web framework for perfectionists with deadlines”, Django is a great choice for building robust, production-ready websites, apps, or services.

It’s an open-source and highly versatile Python web framework that can be used for everything from blogs and social media platforms to e-commerce sites, content management systems, and fitness trackers.

First released in 2005, Django continues to receive regular updates and enjoys a considerable following. Its “batteries included” design means you get what you need to build a full-stack web app right out of the box. You can also pair Django with a front-end JavaScript framework for projects that demand richly interactive user interfaces.

Where to find Django project ideas

Working on a real-world project is one of the best ways to get to grips with Django. You can start with something simple and extend it as you learn more about the framework. In the process, you’ll develop your skills and build a portfolio of work showcasing your abilities.

Stuck for ideas? Previous projects (both professional and personal), forums, dev meetups and hackathons, as well as platforms like GitHub, can all provide sources of inspiration.

GitHub is also useful for hosting and collaborating on open-source projects – this is where the source code or design documents are made publicly available for anyone to access, modify, and distribute. You can use and build upon various software, making them a great jumping-off point for a Django project.

Some GitHub open-source Django projects include: 

Are you unfamiliar with a particular area, such as routing or database design? You could choose to design a project that addresses this knowledge gap while learning Django simultaneously.

Django project ideas for beginners

If this is your first time building with Django, look no further. We’ve compiled a list of project ideas that are ideal for anyone using Django for the first time. These will help you find your feet as you explore what Django offers.

Before you start, reading this Django tutorial for beginners is useful. It walks through building a simple but non-trivial project by starting with a single file. The tutorial is an excellent starting point for anyone new to Django, as it introduces core concepts and helps you build a functional project from the ground up.

Once you’re familiar with Django’s principal elements and are comfortable building within the framework, you can get going with these more dynamic yet straightforward projects.

Air temperature app

This tutorial will walk you through creating a simple Django app using PyCharm that displays the current air temperature at your location. The app also allows users to explore weather conditions at random locations to add interactivity.

By following this guide, you’ll learn to:

This hands-on task will give you practical experience with core Django concepts and show how PyCharm can help you develop more efficiently while working with Django. It’s a great place to begin building web applications and sets you up for further starter projects.

Follow the step-by-step tutorial

Blog

Required tech stack:

Building your own blogging platform with Django is a great way to cover the basics of create, read, update, and delete (CRUD), setting up routes, and serving a site locally. To keep things simple, use the Django Admin site to add and update blog posts. To explore further, extend the basic blog functionality by implementing tags and filters, adding a search option, or enabling comments. 

We highly recommend the Django Girls Tutorial for beginners, which takes you through creating a small blog.

To-do list app

Required tech stack:

A simple task list with an option to check items off when complete is an excellent way to learn the basics of developing with Django. You’ll need a UI to add tasks and update their status and a simple database to record each entry. You can also add the ability to record due dates or set priorities.

Recipe organizer

Required tech stack:

Familiarize yourself with Django’s ORM (object-relational mapper) and database support by building an app to keep track of your favorite recipes. Add a web-based frontend with options to filter recipes by category, ingredients, and user ratings so you can easily browse for inspiration.

Quiz app

Required tech stack:

Create a quiz app or website to familiarize yourself with each layer of Django’s architecture. Build a question bank, give players a simple UI to select their answer, and calculate the score. Enhance the functionality by sorting questions into categories and adding options to choose topics or change the number of questions asked. You could also add a leaderboard to celebrate your highest scorers.

Authentication system

Required tech stack:

Login systems are a feature of most websites and apps. Fortunately, adding authentication and authorization to a web app is easier with Django’s built-in authentication system. Creating one as a practice project helps familiarize yourself with the functionality and provides a reusable pattern for future projects. Don’t forget to include key features such as email validation and a password reset option.

URL shortener

Required tech stack:

You can approach this project in a couple of ways, depending on your interests. One option is to build a simple app that sends requests to an existing URL-shortening service, such as Bitly’s API. Alternatively, create your own service by generating shortened URLs, storing the URLs in a database, and defining routing logic to match incoming requests to the appropriate URL. Add a simple frontend so you can generate new shortened URLs on demand.

Expense tracker

Required tech stack:

Expense management apps provide an opportunity to define different data models for expenses, such as invoices, payroll, and cash expenditures. As well as creating forms for users to enter the costs, you might have existing data to import. Consider creating graphs to show expense trends in each category.

Simple social network

Required tech stack:

Use Django to create your version of Instagram or Facebook and learn about user account management, security, and privacy along the way. Users will need to be able to manage connections, share images and videos, and view each other’s posts.

Personal portfolio

Required tech stack:

Sharing personal projects on your website enables you to showcase your skills as a developer. Create an engaging website for users to browse your work and view the source code. This project will also allow you to try deploying Django to a production web server. 

Learn how to build a personal portfolio using Django with this helpful guide.

Event calendar

Required tech stack:

An enjoyable beginner project with a frontend focus, building an event calendar gives you a reason to work with Django’s built-in templates or Jinja2. You can also combine Django with JavaScript libraries to make your calendar interactive.

Survey tool

Required tech stack:

An online survey tool requires UIs for building and responding to surveys, storage of the responses, and logic to aggregate answers. This project is an ideal opportunity to try out Django forms. If you want to extend it, add user authentication and/or visualizations for the survey data.

Advanced Django projects

If you’re familiar with Django or looking for more advanced project ideas, the following suggestions are for you. Get ideas to help you use more of Django’s rich capabilities and grow your developer skills.

Multi-user classifieds site

Build a site where multiple users can post classified ads. You’ll need to create pages for listing, creating, and editing ads. Since the site supports multiple users, you’ll also need to create and manage user accounts, manage permissions so only the ad owner can edit their ads, and create paginated ad listings to avoid overloading the browser. 

For the full experience, include a self-sign-up page and password reset management. For additional practice, consider adding an API to the site and/or using a frontend library like HTMX or Vue.js to make your pages more dynamic.

The book “Django In Action” guides you through building a musicians’ classified ad site called RiffMates. It also covers deeper topics such as testing, management commands, and Django power tools.

Chat app

Building a messaging app is an excellent way to explore real-time two-way communication using WebSockets and Django Channels. 

You’ll also need to create and manage user accounts, add contacts, and view message history. You can model the functionality on existing chat platforms like WhatsApp, X, or Telegram. Adding support for audio messages helps to extend your project further. 

This tutorial on Django Channels will help you create your chat app, going beyond the basics for a more comprehensive application. For an extra challenge, consider incorporating WebRTC and webcam video chat.

E-commerce store

Building an online retail site is a sizable project that demonstrates various skills. In addition to a database of products and a website that lets customers browse and search, you’ll need to handle user accounts, shopping carts, payments, and order updates. 

There is plenty to consider, from generating product image thumbnails to managing abandoned checkout processes. This project will help you understand advanced Django features like signals, middleware, and caching.

Medical records system

This project requires you to consider user authentication and authorization to ensure only those with the correct permissions can view a patient’s records. 

When defining your templates, ensure you present the data reverse chronologically (most recent first). You will also need to provide forms for authorized users to add new patients and update patients’ records. 

You may even connect to another system to import test results and associate them with the right patient, integrating third-party APIs for extended functionality.

Financial dashboard

Dashboards make fantastic projects for combining Django with a frontend JavaScript framework. You can pull in data from an API or add an option to import data manually from .csv files. For real-time data, use Django Channels. 

Leverage Django to manipulate the data in different ways and JavaScript to create visually appealing UIs. This project can also integrate with data visualization libraries such as D3.js or Chart.js for dynamic, interactive charts.

Property management system

Building a system to keep track of rented properties, record rent payments, and manage maintenance tasks and inspections is ideal for Django. 

This project gives you a chance to work with multiple data sources, implement complex business logic, and handle common functionality such as user authentication and notifications. Consider using the Django REST framework to create APIs that mobile apps or other systems can consume.

Interactive dashboards

Developing an interactive dashboard with Django is perfect for visualizing data from various sources. It also allows you to handle large datasets, perform real-time data updates, and integrate frontend visualization libraries with Django.

Use libraries like Plotly or Bokeh to create dynamic charts and graphs. Implement data filtering, time range selection, and report exporting. Add user authentication to provide personalized dashboards based on roles and permissions.

Stock exchange analysis

Build a stock market analysis platform using Django, a project that combines data science, machine learning, and the Django REST framework. It also offers more practical experience in handling lots of real-time data. 

Import historical stock prices, run technical analyses, and generate predictions using machine learning. Create RESTful APIs for data retrieval and analysis results, and integrate external APIs for real-time market data. Implement visualization tools to display trends, predictions, and alerts.

Processing large volumes of data

Use Django with Celery and Redis to process large datasets. This project will involve setting up task queues with Celery and managing them with Redis, helping you understand task scheduling and big data processing.

Implement features to handle server failures and ensure data integrity throughout the pipeline. Demonstrate how to scale the system by running Redis in cluster mode and balancing loads across multiple servers. Create dashboards to monitor task progress and performance metrics.

Generative AI RAG-based application

Django projects are a great way to gain practical experience working with language models. This project involves natural language processing and AI, helping to deepen your understanding.

Build a Django application for advanced AI-driven solutions using retrieval-augmented generation (RAG) techniques. Integrate pre-trained language models like GPT-3 or GPT-4 with a retrieval system to fetch relevant documents. Develop features for generating contextual responses, automated content creation, and personalized recommendations. Implement a feedback loop to continuously improve the system’s accuracy. 

AIOps for server scaling

Take on this assignment to help enhance your skills in AI, cloud computing, and automation.

Develop an AI-based system for predicting traffic and scaling servers automatically. Use machine learning models to analyze historical traffic data and forecast future loads. Integrate this system with cloud platforms like AWS or GCP to automatically provision and de-provision servers based on the predictions. Implement features for monitoring system performance, generating alerts, and providing dashboards for administrators. 

Customized dashboard for MLOps pipeline

Create a dashboard to manage and monitor machine learning pipelines, integrating tools like TensorFlow or PyTorch with Django. 

Allow users to track experiments, visualize training progress, and compare model performance. Implement features for managing datasets, configuring training parameters, and deploying models to production. Provide tools for monitoring model drift and triggering re-training when necessary. 

By the end of the project, you should understand the end-to-end machine learning lifecycle and how to operationalize ML models using Django.

Location-based food recommendation

Level up your skills in recommendation systems, geolocation services, and user personalization. This task involves using machine learning algorithms to recommend food options based on user location and preferences.

Integrate with mapping services like Google Maps or OpenStreetMap to provide location-based search and routing. Implement user profiles to store preferences, dietary restrictions, and past interactions. Use collaborative filtering or content-based filtering techniques to generate recommendations. Provide a user-friendly interface to browse and rate food options.

Langchain-based projects

Build applications utilizing Langchain for advanced natural language processing tasks. 

Create use cases such as automated summarization, question answering, or text generation. Integrate with Django to provide a web-based interface for users to interact with the system. Implement features for fine-tuning models on specific domains, evaluating model performance, and giving real-time predictions. 

Building these apps will help you explore the capabilities of state-of-the-art language models and how to integrate them with web applications.

Birthday message reminder

Developing a birthday message reminder is a fantastic project for helping to understand chatbot development and automating workflows with Django. You will implement a system using ChatGPT to generate unique birthday messages and send them to employees via Slack. 

Integrate with Slack’s API to send or direct messages to the appropriate channels. Use Django’s scheduling features to automate the message-sending process. Allow administrators to configure message templates, personalize messages based on employee data, and track message delivery. 

Cloud server management

Developing cloud servers is an ideal way to build your skills in cloud computing, infrastructure as code, and DevOps practices. You could begin by creating and managing cloud servers using Django and Pulumi. 

Allow users to provision and configure infrastructure as code, including virtual machines, databases, and networking resources. Implement features for monitoring resource usage, scaling infrastructure based on demand, and generating cost reports. Provide a web-based interface for administrators to manage cloud resources and view infrastructure status. 

Ready to get started?

We hope these project ideas have provided some inspiration to help you start building web apps with Django. Whatever your experience level, using an IDE optimized for your chosen language and framework makes software development more accessible and enjoyable. 

PyCharm Professional is a Python IDE with built-in support for Django, Flask, FastAPI, and more. Intelligent coding assistance, streamlined navigation, and powerful debugging are just some of the features that PyCharm offers to help you code faster and smarter.

Using PyCharm, you will also benefit from:

Use PyCharm’s full-stack development features and database tools to help you build powerful Django projects.

Get started with PyCharm now

More tutorials and guides 

September 26, 2024 09:04 AM UTC

Mike Driscoll

Textual – The New MaskedInput Widget

Textual v0.80.0 was released today, and it included the brand-new MaskedInput widget. If you have used other GUI toolkits, such as wxPython, you might already be familiar with a masked input widget. These widgets allow you to control the user’s input based on a mask string that the developer provides when instantiating the widget.

Let’s spend a few brief moments learning how this new widget works.

Getting the Latest Textual

Before you can use the MaskedInput widget, you must ensure you have version 0.80.0 or greater. If you have an older version, then you’ll need to upgrade by running the following command in your terminal:

python -m pip install textual --upgrade

Now that you have a 0.80.0 or greater, you can use this great widget!

Using the MaskedInput Widget

The Textual documentation has a credit card masked string for their demo application. Let’s start by looking at that example:

from textual.app import App, ComposeResult
from textual.widgets import Label, MaskedInput


class MaskedInputApp(App):
    # (1)!
    CSS = """
    MaskedInput.-valid {
        border: tall $success 60%;
    }
    MaskedInput.-valid:focus {
        border: tall $success;
    }
    MaskedInput {
        margin: 1 1;
    }
    Label {
        margin: 1 2;
    }
    """

    def compose(self) -> ComposeResult:
        yield Label("Enter a valid credit card number.")
        yield MaskedInput(
            template="9999-9999-9999-9999;0",  # (2)!
        )


if __name__ == "__main__":
    app = MaskedInputApp()
    app.run()

The template in this code says that you want four groups of four digits. The template automatically adds a dash after each four digits except the last one.

When you run this code, you will see something like this:

MaskedEdit Widget

Note that when the widget is empty or not completely filled out, there is a red outline around it. The widget will ignore all keys except for number keys. So if you press A-Z or any special characters like semi-colon or question mark, the MasketInput widget will ignore them and nothing appears in the widget.

When you have entered four groups of four integers though, you will see the red outline turn green. Here’s an example:

MaskedEdit widget with text

Note that this code does NOT actually validate that these are working credit card numbers. It only validates that there four groups of four integers.

A more common mask would be to create something to accept phone numbers. Let’s take the code above and rewrite the mask and the label text accordingly:

from textual.app import App, ComposeResult
from textual.widgets import Label, MaskedInput


class MaskedInputApp(App):
    # (1)!
    CSS = """
    MaskedInput.-valid {
        border: tall $success 60%;
    }
    MaskedInput.-valid:focus {
        border: tall $success;
    }
    MaskedInput {
        margin: 1 1;
    }
    Label {
        margin: 1 2;
    }
    """

    def compose(self) -> ComposeResult:
        yield Label("Enter a valid phone number.")
        yield MaskedInput(
            template="(999)-999-9999;0",
        )


if __name__ == "__main__":
    app = MaskedInputApp()
    app.run()

If you runt his code, the output it a little different, but the concept is the same:

MaskedEdit phone number

The MaskedInput widget is using regular expressions to mask characters. If you look at the documentation you can see how it works. Following are a few examples from the documentation:

A [A-Za-z] Yes
a [A-Za-z] No
N [A-Za-z0-9] Yes
n [A-Za-z0-9] No
X [^ ] Yes
x [^ ] No
9 [0-9] Yes

This widget may get additional updates to include more refined regular expressions, so it is definitely best to review the documentation for the latest information on the masked templates and how they work.

Wrapping Up

The MaskedInput widget is a great addition to the Textual TUI toolkit. There are lots of great widgets included with Textual now. While there may not be as many as wxPython or PyQt, those projects have been around for a couple of decades and have had much more time to mature. You can create really amazing and neat applications in your terminal with Python and Textual. You should try it out today!

The post Textual – The New MaskedInput Widget appeared first on Mouse Vs Python.

September 26, 2024 02:11 AM UTC

September 25, 2024

Django Weblog

2025 DSF Board Nominations

Nominations are open for the 2025 Django Software Foundation Board of Directors.

In 2023 we introduced a staggered term for directors. Of our 7 directors, there are 4 positions currently open, with each position serving for two years.

Decisions around the 2025 officer roles will be made during the meeting of the new board. You don’t need to specify which position you are nominating for.

As you know, the Board guides the direction of the marketing, governance and outreach activities of the Django community. We provide funding, resources, and guidance to Django events on a global level. Further we provide support to the Django community with an established Code of Conduct and make decisions and enforcement recommendations for violations. We work with our corporate and individual members to raise funds to help support our great community.

In order for our community to continue to grow and advance the Django Web framework, we need your help. The Board of Directors consists of seven volunteers who are elected to two year terms. This is an excellent opportunity to help advance Django. We can’t do it without volunteers, such as yourself. Anyone including current Board members, DSF Members, or the public at large can apply to the Board. It is open to all.

2025 DSF Board Nomination Form

If you are interested in helping to support the development of Django we’d enjoy receiving your application for the Board of Directors. Please fill out the 2025 DSF Board Nomination form by October 25, 2024 Anywhere on Earth to be considered.

If you have any questions about applying, the work, or the process in general please don’t hesitate to reach out via email to foundation@djangoproject.com.

Thank you for your time and we look forward to working with you in 2025.

The 2024 DSF Board of Directors.

September 25, 2024 04:03 PM UTC

Real Python

Python 3.13 Preview: A Modern REPL

One of Python’s strong points is its interactive capabilities. By running python you start the interactive interpreter, or REPL, which allows you to perform quick calculations or explore and experiment with your code. In Python 3.13, the interactive interpreter has been completely redesigned with new modern features.

Python’s REPL has remained largely unchanged for decades. Instead, alternative interpreters like IPython, bpython, and ptpython have addressed some of the built-in REPL’s shortcomings, providing more convenient interactive workflows for developers. As you’re about to learn, Python 3.13 brings many significant improvements to the interactive interpreter.

In this tutorial, you’ll:

  • Run Python 3.13 and explore the new REPL
  • Browse through the help system
  • Work with multiline statements
  • Paste code into your REPL session
  • Navigate through your interpreter history

The upgraded REPL is just one of the new features coming in Python 3.13. You can read about all the changes in the what’s new section of Python’s changelog. Additionally, you can dig deeper into the work done on free threading and a Just-In-Time compiler.

Get Your Code: Click here to download the free sample code that shows you how to use some of the new features in Python 3.13.

A New Interactive Interpreter (REPL) in Python 3.13

To try out the new REPL for yourself, you need to get your hands on a version of Python 3.13. Before the official release in October 2024, you can install a pre-release version. After October 2024, you should be able to install Python 3.13 through any of the regular channels.

A REPL, or a Read-Eval-Print Loop, is a program that allows you to work with code interactively. The REPL reads your input, evaluates it, and prints the result before looping back and doing the same thing again.

Note: To learn about the built-in REPL in Python and how it works in general, check out The Python Standard REPL: Try Out Code and Ideas Quickly.

In any version of Python, you can start this interactive shell by typing the name of your Python executable in your terminal. Typically, this will be python, but depending on your operating system and your setup, you may have to use something like py or python3 instead.

Once you start the REPL in Python 3.13, you’ll see a small but noticeable difference. The familiar Python interactive shell prompt, consisting of three right angle brackets (>>>), is now colored differently from the rest of the text:

The new REPL in Python 3.13 shows a colored prompt

The color difference indicates that the shell now supports color. In the new shell, color is mainly used to highlight output in tracebacks. If your terminal doesn’t display color, then the new REPL will automatically detect this and fall back to its plain, colorless display.

If you prefer to keep your interpreter free of color even when it’s supported, you can disable this new feature. One option is to set the new environment variable PYTHON_COLORS to 0:

Shell 
$ PYTHON_COLORS=0 python
Python 3.13.0rc2 (main, Sep 13 2024, 17:09:27) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
Copied!

Setting PYTHON_COLORS=0 disables color in the REPL. If you set the environment variable to 1, you’ll get the colored prompt and output. However, since it’s the default, this is rarely necessary.

Before going any further, you’ll exit the REPL. As you may know, the old REPL, that you’ve used on Python 3.12 and earlier, has been widely commented on for the following idiosyncrasy:

Python 
>>> exit
Use exit() or Ctrl-D (i.e. EOF) to exit
Copied!

The shell clearly understands your intention to end the session. Still, it makes you jump through hoops and add parentheses to your command. In Python 3.13, the REPL now understands special commands that you can write without any parentheses:

  • exit or quit: Exit the interpreter
  • help or F1: Access the help system
  • clear: Clear the screen

Having these commands more easily available is a small thing, but it removes some friction when using the interactive interpreter. You can still use parentheses and type something like exit() if you prefer. These commands are not reserved though. That means that you could shadow them with variable assignments:

Python 
>>> exit = True
>>> exit
True
>>> exit()
Traceback (most recent call last):
  ...
TypeError: 'bool' object is not callable

>>> del exit
>>> exit
Copied!

Here you create a variable named exit, effectively disabling the exit command. To end your REPL session, you can either delete the exit variable or use one of the alternative ways to exit the interpreter.

Note: Even with the new improvements, the built-in REPL is not as powerful as some of the third-party alternatives. If you’re already using any of these, there’s no immediate reason to revert back to the standard interactive interpreter.

The main advantage of the new REPL is that it’s installed together with Python. In other words, it’ll always be available for you. This is great if you find yourself in a situation where you can’t or don’t want to install a third-party library.

You can learn more about the alternative interactive interpreters in the following tutorials:

You can also read more about alternative REPLs in the guide to the standard REPL.

Read the full article at https://realpython.com/python313-repl/ »

[ 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 ]

September 25, 2024 02:00 PM UTC

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.

JupyterLab 101 Mockup

The project is fully funded, and I added a stretch goal.

Stretch Goal

The 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 Learn

In this book, you will learn how about the following:

Rewards to Choose From

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:

Get the book on Kickstarter today!

The post JupyterLab 101 Kickstarter Stretch Goal appeared first on Mouse Vs Python.

September 25, 2024 01:26 PM UTC

Talk Python to Me

#478: When and how to start coding with kids

Do you have kids? Maybe nieces and nephews? Or maybe you work in a school environment? Maybe it's just friend's who know you're a programmer and ask about how they should go about introducing programming concepts with them. Anna-Lena Popkes is back on the show to share her research on when and how to teach kids programming. We spend the second half of the episode talking about concrete apps and toys you might consider for each age group. Plus, some of these things are fun for adults too. ;)<br/> <br/> <strong>Episode sponsors</strong><br/> <br/> <a href='https://talkpython.fm/workos'>WorkOS</a><br> <a href='https://talkpython.fm/training'>Talk Python Courses</a><br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Anna-Lena</b>: <a href="https://alpopkes.com?featured_on=talkpython" target="_blank" >alpopkes.com</a><br/> <br/> <b>Magical universe repo</b>: <a href="https://github.com/zotroneneis/magical_universe?featured_on=talkpython" target="_blank" >github.com</a><br/> <b>Machine learning basics repo</b>: <a href="https://github.com/zotroneneis/machine_learning_basics?featured_on=talkpython" target="_blank" >github.com</a><br/> <br/> <b>PyData recording "when and how to start coding with kids"</b>: <a href="https://www.youtube.com/embed/aTum4tV4geY" target="_blank" >youtube.com</a><br/> <br/> <b>Robots and devices</b><br/> <b>Bee Bot</b>: <a href="https://www.terrapinlogo.com/beebot-ss.html?featured_on=talkpython" target="_blank" >terrapinlogo.com</a><br/> <b>Cubelets</b>: <a href="https://modrobotics.com?featured_on=talkpython" target="_blank" >modrobotics.com</a><br/> <b>BBC Microbit</b>: <a href="https://microbit.org?featured_on=talkpython" target="_blank" >microbit.org</a><br/> <b>RaspberryPi</b>: <a href="https://www.raspberrypi.com?featured_on=talkpython" target="_blank" >raspberrypi.com</a><br/> <b>Adafruit Qualia ESP32 for CircuitPython</b>: <a href="https://www.adafruit.com/product/5800?featured_on=talkpython" target="_blank" >adafruit.com</a><br/> <b>Zumi</b>: <a href="https://www.robolink.com/products/zumi?featured_on=talkpython" target="_blank" >robolink.com</a><br/> <br/> <b>Board games</b><br/> <b>Think Fun Robot Turtles Board Game</b>: <a href="https://www.amazon.com/Think-Fun-Turtles-Coding-Preschoolers/dp/B00HN2BXUY/?featured_on=talkpython" target="_blank" >amazon.com</a><br/> <br/> <b>Visual programming:</b><br/> <b>Scratch Jr.</b>: <a href="https://www.scratchjr.org?featured_on=talkpython" target="_blank" >scratchjr.org</a><br/> <b>Scratch</b>: <a href="https://www.scratch.org?featured_on=talkpython" target="_blank" >scratch.org</a><br/> <b>Blocky</b>: <a href="https://developers.google.com/blockly?featured_on=talkpython" target="_blank" >google.com</a><br/> <b>Microbit's Make Code</b>: <a href="https://makecode.microbit.org?featured_on=talkpython" target="_blank" >microbit.org</a><br/> <b>Code Club</b>: <a href="https://codeclubworld.org?featured_on=talkpython" target="_blank" >codeclubworld.org</a><br/> <br/> <b>Textual programming</b><br/> <b>Code Combat</b>: <a href="https://codecombat.com?featured_on=talkpython" target="_blank" >codecombat.com</a><br/> <b>Hedy</b>: <a href="https://www.hedycode.com?featured_on=talkpython" target="_blank" >hedycode.com</a><br/> <b>Anvil</b>: <a href="https://anvil.works?featured_on=talkpython" target="_blank" >anvil.works</a><br/> <br/> <b>Coding classes / summer camps (US)</b><br/> <b>Portland Community College Summer Teen Program</b>: <a href="https://www.pcc.edu/community/special-classes/teen/?featured_on=talkpython" target="_blank" >pcc.edu</a><br/> <b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=bPaseFf1Hio" target="_blank" >youtube.com</a><br/> <b>Episode transcripts</b>: <a href="https://talkpython.fm/episodes/transcript/478/when-and-how-to-start-coding-with-kids" target="_blank" >talkpython.fm</a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe to us on YouTube</b>: <a href="https://talkpython.fm/youtube" target="_blank" >youtube.com</a><br/> <b>Follow Talk Python on Mastodon</b>: <a href="https://fosstodon.org/web/@talkpython" target="_blank" ><i class="fa-brands fa-mastodon"></i>talkpython</a><br/> <b>Follow Michael on Mastodon</b>: <a href="https://fosstodon.org/web/@mkennedy" target="_blank" ><i class="fa-brands fa-mastodon"></i>mkennedy</a><br/></div>

September 25, 2024 08:00 AM UTC

September 24, 2024

PyCoder’s Weekly

Issue #648 (Sept. 24, 2024)

#648 – SEPTEMBER 24, 2024
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The PyCoder’s Weekly Logo

Python 3.13 Preview: Free Threading and a JIT Compiler

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

Let’s Build and Optimize a Rust Extension for 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

Join John Hammond & Daniel Miessler at DevSecCon 2024

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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

Document Intended Usage Through Tests With doctest

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

Quiz: Python 3.13: Free-Threading and a JIT Compiler

REAL PYTHON

Quiz: Lists vs Tuples in Python

REAL PYTHON

Articles & Tutorials

Customizing VS Code Through Color Themes

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

Thriving as a Developer With ADHD

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

Build AI Apps with More Flexibility for the Edge

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

Things I’ve Learned Serving on the Board of the PSF

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 in Software Engineering

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

AI-Extracted Asian Building Footprints

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

Why We Wrote a New Form Library for Django

“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

Case-Insensitive String Class

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

7 Ways to Use Jupyter Notebooks Inside PyCharm

Discover seven ways you can use Jupyter notebooks in PyCharm to explore and work with your data more quickly and effectively.
HELEN SCOTT

Unified Python Packaging With uv

Talk Python interviews Charlie Marsh, the maintainer of ruff, and they talk about uv and other projects at Astral.
KENNEDY & MARSH podcast

It’s Time to Stop Using Python 3.8

Python 3.8 will stop getting security updates in November 2024. You really should upgrade!
ITAMAR TURNER-TRAURING

Projects & Code

LightAPI: Lightweight API Framework

GITHUB.COM/IKLOBATO

peepdb: CLI Tool to View Database Tables

GITHUB.COM/EVANGELOSMEKLIS

dante: Zero-Setup Document Store for Python

GITHUB.COM/SENKO

cookiecutter-uv: A Modern Template for Python Projects

GITHUB.COM/FPGMAAS • Shared by Florian Maas

Django Content Settings: Advanced Admin Editable Settings

DJANGO-CONTENT-SETTINGS.READTHEDOCS.IO • Shared by oduvan

Events

Weekly Real Python Office Hours Q&A (Virtual)

September 25, 2024
REALPYTHON.COM

PyData Paris 2024

September 25 to September 27, 2024
PYDATA.ORG

PiterPy 2024

September 26 to September 27, 2024
PITERPY.COM

PyConf Mini Davao 2024

September 26 to September 27, 2024
DURIANPY.ORG

PyCon JP 2024

September 27 to September 30, 2024
PYCON.JP

PyCon Niger 2024

September 28 to September 30, 2024
PYCON.ORG

PyConZA 2024

October 3 to October 5, 2024
PYCON.ORG

PyCon ES 2024

October 4 to October 6, 2024
PYCON.ORG

Happy Pythoning!
This was PyCoder’s Weekly Issue #648.
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September 24, 2024 07:30 PM UTC

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:

[ 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 ]

September 24, 2024 02:00 PM UTC

PyCharm

PyCharm vs. Jupyter Notebook

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 support

While 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 completion

If 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 debugger

PyCharm 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.

Refactorings

Web-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 features

If 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 tables

DataFrames 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: 

If you want to get more information about how interactive tables work in PyCharm, check out the documentation.

Versioning and GitHub integration

In 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.

Navigation

When 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.

Visualizations

In 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 notebooks

Vanilla 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 Professional

PyCharm 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 trial

Below are other blog posts that you might find useful for boosting your productivity with PyCharm Professional.

September 24, 2024 12:52 PM UTC

Hynek Schlawack

Production-ready Python Docker Containers with uv

Starting with 0.3.0, Astral’s uv brought many great features, including support for cross-platform lock files uv.lock. Together with subsequent fixes, it has become Python’s finest workflow tool for my (non-scientific) use cases. Here’s how I build production-ready containers, as fast as possible.

September 24, 2024 12:00 AM UTC

September 23, 2024

Real Python

Python Virtual Environments: A Primer

In this tutorial, you’ll learn how to work with Python’s venv module to create and manage separate virtual environments for your Python projects. Each environment can use different versions of package dependencies and different versions of Python.

Once you’ve learned to work with virtual environments, you’ll be able to help other programmers reproduce your development setup and make sure that your projects never create dependency conflicts.

By the end of this tutorial, you’ll know how to:

  • Create and activate a Python virtual environment
  • Explain why you want to isolate external dependencies
  • Visualize what Python does when you create a virtual environment
  • Customize your virtual environments using optional arguments to venv
  • Deactivate and remove virtual environments
  • Choose additional tools for managing your Python versions and virtual environments

Working with virtual environments is a common and effective technique used in Python development. Gaining a better understanding of how they work, why you need them, and what you can do with them will help you master your Python programming workflow.

Throughout the tutorial, you can select code examples for either Windows, Ubuntu Linux, or macOS. Pick your platform at the top right of the relevant code blocks to get the commands that you need, and feel free to switch between them if you want to learn how to work with virtual environments on other operating systems.

Free Bonus: Click here to download a free cheat sheet that summarizes the main venv commands you’ll learn about in this tutorial.

Take the Quiz: Test your knowledge with our interactive “Python Virtual Environments: A Primer” quiz. You’ll receive a score upon completion to help you track your learning progress:

Python Virtual Environments (venv)

Interactive Quiz

Python Virtual Environments: A Primer

In this quiz, you'll test your understanding of Python virtual environments. With this knowledge, you'll be able to avoid dependency conflicts and help other developers reproduce your development environment.

How Can You Work With a Python Virtual Environment?

If you just need to get a virtual environment up and running to continue working on your favorite project, then this section is for you.

This tutorial uses Python’s venv module to create virtual environments. This module is part of Python’s standard library, and it’s been the officially recommended way to create virtual environments since Python 3.5.

Note: There are other great third-party tools for creating virtual environments, such as conda and virtualenv, that you’ll learn more about later in this tutorial. Either of these tools can help you set up a virtual environment and also go beyond just that.

For basic usage, venv is an excellent choice because it already comes packaged with your Python installation. With that in mind, you’re ready to create your first virtual environment.

Create It

Any time you’re working on a Python project that uses external dependencies you’re installing with pip, it’s best to first create a virtual environment:

Windows PowerShell 
PS> py -m venv venv\
Copied!

This command allows the Python launcher for Windows to select an appropriate version of Python to execute. It comes bundled with the official installation and is the most convenient way to execute Python on Windows.

You can bypass the launcher and run the Python executable directly using the python command, but if you haven’t configured the PATH and PATHEXT variables, then you might need to provide the full path:

Windows PowerShell 
PS> C:\Users\Name\AppData\Local\Programs\Python\Python312\python -m venv venv\
Copied!

The system path shown above assumes that you installed Python 3.12 using the Windows installer provided by the Python downloads page. The path to the Python executable on your system might be different. Working with PowerShell, you can find the path using the where.exe python command.

Note: You don’t need to include the backslash (\) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

Shell 
$ python3 -m venv venv/
Copied!

Many Linux operating systems ship with a version of Python 3. If python3 doesn’t work, then you’ll have to first install Python and you may need to use the specific name of the executable version that you installed, for example, python3.12 for Python 3.12.x. If that’s the case for you, remember to replace mentions of python3 in the code blocks with your specific version number.

Note: You don’t need to include the slash (/) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

Shell 
$ python3 -m venv venv/
Copied!

Older versions of macOS come with a system installation of Python 2.7.x that you should never use to run your scripts. If you’re working on macOS < 12.3 and invoke the Python interpreter with python instead of python3, then you might accidentally start up the outdated system Python interpreter.

If running python3 doesn’t work, then you’ll have to first install a modern version of Python.

Note: You don’t need to include the slash (/) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

This command creates a new virtual environment named venv using Python’s built-in venv module. The first venv that you use in the command specifies the module, and the second venv/ sets the name for your virtual environment. You could name it differently, but calling it venv is a good practice for consistency.

Activate It

Great! Your project now has its own virtual environment. Generally, before you start to use it, you’ll activate the environment by executing a script that comes with the installation:

Windows PowerShell 
PS> venv\Scripts\activate
(venv) PS>
Copied!

If your attempt to run this command produces an error, then you’ll first have to loosen the execution policy.

Shell 
$ source venv/bin/activate
(venv) $
Copied!

Before you run this command, make sure that you’re in the folder containing the virtual environment you just created. If you’ve named your virtual environment something other than venv, then you’ll have to use that name in the path instead of venv when you source the activation script.

Note: You can also work with your virtual environment without activating it. To do this, you provide the full path to its Python interpreter when executing a command. However, you’ll likely want to activate the virtual environment after you create it to save yourself the effort of having to repeatedly type long pathnames.

Once you can see the name of your virtual environment in your command prompt—in this case (venv)—then you’ll know that your virtual environment is active. Now you’re all set and ready to install your external packages!

Read the full article at https://realpython.com/python-virtual-environments-a-primer/ »

[ 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 ]

September 23, 2024 02:00 PM UTC

Django Weblog

PyCharm &amp; Django Campaign 2024 - encore

The Django Software Foundation's biggest fundraising event of the year is here!

Get 30% off PyCharm, Support Django

Each year, our friends at JetBrains, the creators of PyCharm, run an incredible deal. You get a 30% discounted year of PyCharm, AND the DSF gets 100% of the money. Yes, 100%! It's making a donation and directly getting a great product in return! This is available for new users, and those who had used PyCharm in the past, stopped, and want to try again.

The fundraiser

The fundraiser started during DjangoCon Europe in June, and is now back on from September 22nd to October 6th. Buy PyCharm and support Django!

In the past, JetBrains through the PyCharm fundraiser has provided approximately one quarter of the Django Software Foundation's budget! 

Donations like this fundraiser allow the DSF to function. Our two wonderful Fellows, Natalia Bidart and Sarah Boyce keep Django running smoothly, picking up pieces that would otherwise not happen.

The other side of the DSF is our support for Django groups across the globe. We supported every DjangoCon, particularly with donating funding towards opportunity grants for more people to be able to attend these conferences. The DSF also supports smaller events around the world, including DjangoGirls events.

PyCharm

Finally, I want to tell you about PyCharm itself.

PyCharm is an integrated development environment (IDE) that helps professional Python web developers be more productive, be more confident, and write better code. It supports the full Python web workflow out of the box, including popular Python web frameworks, such as Django, frontend technologies, and databases.

Here are the main benefits of using PyCharm in your Django development:

Get Django work done with PyCharm, a powerful IDE tailored for Django web development!

Consider this the easiest charitable donation you will ever make, when you get such a great product in return!

Get 30% off PyCharm, Support Django

Other ways to donate

If you would like to donate in another way, especially if you are already a PyCharm customer, here are other ways to donate to the DSF:

September 23, 2024 11:43 AM UTC

Python Bytes

#402 How to monetize your blog

<strong>Topics covered in this episode:</strong><br> <ul> <li><strong>Architecture Decision Records</strong> <strong>(ADRs)</strong></li> <li><strong><a href="https://narwhals-dev.github.io/narwhals/?featured_on=pythonbytes">narwhals</a>: extremely lightweight compatibility layer between dataframes</strong></li> <li><strong><a href="https://www.theverge.com/2024/9/20/24249770/microsoft-three-mile-island-nuclear-power-plant-deal-ai-data-centers?featured_on=pythonbytes">Microsoft wants Three Mile Island to fuel its AI power needs</a></strong></li> <li><a href="https://github.com/deluan/zsh-in-docker?featured_on=pythonbytes"><strong>zsh-in-docker</strong></a></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=N7w_ESVW40I' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="402">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by ScoutAPM: <a href="https://pythonbytes.fm/scout"><strong>pythonbytes.fm/scout</strong></a></p> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href="https://fosstodon.org/@mkennedy"><strong>@mkennedy@fosstodon.org</strong></a></li> <li>Brian: <a href="https://fosstodon.org/@brianokken"><strong>@brianokken@fosstodon.org</strong></a></li> <li>Show: <a href="https://fosstodon.org/@pythonbytes"><strong>@pythonbytes@fosstodon.org</strong></a></li> </ul> <p>Join us on YouTube at <a href="https://pythonbytes.fm/stream/live"><strong>pythonbytes.fm/live</strong></a> to be part of the audience. Usually <strong>Monday</strong> at 10am PT. Older video versions available there too.</p> <p>Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to <a href="https://pythonbytes.fm/friends-of-the-show">our friends of the show list</a>, we'll never share it. </p> <p><strong>Brian #1:</strong> <strong>Architecture Decision Records</strong> <strong>(ADRs)</strong></p> <ul> <li>Suggested by Christian Gesell</li> <li><a href="https://cognitect.com/blog/2011/11/15/documenting-architecture-decisions?featured_on=pythonbytes">Documenting Architecture Decisions</a> <ul> <li>Mychael Nygard</li> <li>Original article from 2011</li> </ul></li> <li><a href="https://www.redhat.com/architect/architecture-decision-records?featured_on=pythonbytes">Why you should be using architecture decision records to document your project</a> <ul> <li>Red Hat</li> <li>Includes a quick overview and links to some templates</li> </ul></li> <li>Notes so far <ul> <li>Writing this out helps me solidify my thinking about a problem.</li> <li>I’m doing this both before starting, and while implementing a first draft</li> <li>GitHub and GitLab render markdown so well that generating a docs site is unnecessary, just throwing these files in something like docs/adr is enough.</li> <li>The lightweight process is enough but not too much.</li> <li>I’ve already filled out None for lots of sections, like “options considered”</li> <li>I’m still playing with what level of decision should have an ADR.</li> </ul></li> <li>My template that I’ve been using so far <ul> <li>Saved in <a href="https://github.com/okken/ok/blob/main/docs/adr/000-adr-template.md?plain=1&featured_on=pythonbytes">000-adr-template.md</a></li> <li>For easy copy/paste/modify for new records.</li> </ul></li> <li>File name is something like 001-some-change.md</li> </ul> <p><strong>Michael #2:</strong> <a href="https://narwhals-dev.github.io/narwhals/?featured_on=pythonbytes">narwhals</a>: extremely lightweight compatibility layer between dataframes</p> <ul> <li>Recently had Marco <a href="https://www.youtube.com/watch?v=FSH7BZ0tuE0">on Talk Python to discuss</a></li> <li>Primarily for library creators who want to support interacting with multiple data frame libraries (.e.g. Pandas &amp; Polars)</li> <li>Just use a subset of the Polars API</li> </ul> <p><strong>Brian #3:</strong> <a href="https://www.theverge.com/2024/9/20/24249770/microsoft-three-mile-island-nuclear-power-plant-deal-ai-data-centers?featured_on=pythonbytes">Microsoft wants Three Mile Island to fuel its AI power needs</a></p> <ul> <li>“Microsoft just signed a deal to revive the shuttered Three Mile Island nuclear power plant. If approved by regulators, the software maker would have exclusive rights to 100 percent of the output for its AI data center needs.”</li> <li>Also ran on CNN and other sources: <ul> <li><a href="https://www.cnn.com/2024/09/20/energy/three-mile-island-microsoft-ai/index.html?featured_on=pythonbytes">Three Mile Island is reopening and selling its power to Microsoft</a></li> </ul></li> <li>Three Mile Island was the site of the worst nuclear disaster in the US, when one of two reactors experienced a partial meltdown, in 1979. </li> <li>It was still operating up until 2019, and now expected to re-open in 2028</li> <li>Will be renamed “Crane Clean Energy Center”</li> <li>related <ul> <li><a href="https://www.mcsweeneys.net/articles/the-department-of-energy-wants-you-to-know-your-conservation-efforts-are-making-a-difference?featured_on=pythonbytes">The Department of Energy Wants You to Know Your Conservation Efforts Are Making a Difference</a> <ul> <li>“By switching all the lightbulbs in your house to LED, you saved enough energy for a self-driving car to make an unprotected lefthand turn across three lanes of traffic.”</li> <li>“We know you adopted energy-saving practices to help conserve our planet’s resources and bring down our collective carbon footprint, but what you ultimately accomplished is just as important: helping AI do something menial and stupid.”</li> </ul></li> </ul></li> </ul> <p><strong>Michael #4:</strong> <a href="https://github.com/deluan/zsh-in-docker?featured_on=pythonbytes"><strong>zsh-in-docker</strong></a></p> <ul> <li>Install Zsh, Oh My Zsh and plugins inside a Docker container with one line!</li> <li>Yes docker containers should be light, but also, think of how painful it can be when you run into trouble.</li> <li>With Oh My ZSH, you get a nice experience when you have to result to docker exec -it CONTAINER zsh</li> <li>Just enter a single command in your docker file: <pre><code>RUN sh -c "$(wget -O- https://github.com/deluan/zsh-in-docker/releases/download/v1.2.0/zsh-in-docker.sh)" -- \ -t robbyrussell </code></pre></li> </ul> <p><strong>Extras</strong> </p> <p>Michael:</p> <ul> <li>self-hosting <a href="https://mkennedy.codes?featured_on=pythonbytes">mkennedy.codes</a></li> <li><a href="https://levelup.gitconnected.com/from-concerts-to-code-my-journey-from-live-sound-engineering-to-software-development-f164f0fa6f2e?featured_on=pythonbytes">Loren's journey</a> to developer</li> <li><a href="https://pythonspeed.com/articles/stop-using-python-3.8/?featured_on=pythonbytes">It’s time to stop using Python 3.8</a></li> <li>Sonoma → Sequoia → Sonoma (yikes!)</li> <li><a href="https://passkeys.directory?featured_on=pythonbytes">Passkeys</a>, maybe they will work out <a href="https://python-bytes-static.nyc3.digitaloceanspaces.com/passkeys-2.jpg?featured_on=pythonbytes">if we don’t let them become lock-in</a> (<a href="https://bitwarden.com/passwordless-passkeys/?featured_on=pythonbytes">bitwarden’s</a><a href="https://bitwarden.com/passwordless-passkeys/?featured_on=pythonbytes"> support</a>)</li> </ul> <p><strong>Joke:</strong> </p> <ul> <li><a href="https://modem.io/blog/blog-monetization/?featured_on=pythonbytes">How to Monetize a Blog</a> <ul> <li>Don’t forget to click on the bottom link: <a href="https://modem.io/blog/blog-monetization-making-of?featured_on=pythonbytes">Credits / how this was made</a></li> </ul></li> </ul>

September 23, 2024 08:00 AM UTC

Hynek Schlawack

Python Project-Local Virtualenv Management Redux

One of my first TIL entries was about how you can imitate Node’s node_modules semantics in Python on UNIX-like operating systems. A lot has happened since then (to the better!) and it’s time for an update. direnv still rocks, though.

September 23, 2024 12:00 AM UTC

Armin Ronacher

FSL: A Better Business/Open Source Balance Than AGPL

subtext: in my opinion, and for companies (and their users) that want a good balance between protecting their core business with Open Source ideals.

Following up to my thoughts on the case for funding Open Source, there is a second topic I want to discuss in more detail: Open Source and commercialization. As our founder likes to say: Open Source is not a business model. And indeed it really isn't. However, this does not mean that Open Source and Open Source licenses aren't a critical consideration for a technology company and a fascinating interconnection between the business model and license texts.

As some of you might know I'm a strong proponent of the concept now branded as “Fair Source” which we support at Sentry. Fair Source is defined by a family of springing licenses that give you the right to read and modify code, while also providing an exclusivity period for the original creator to protect their core business. After a designated time frame, the code transitions into Open Source via a process called DOSP: Delayed Open Source Publication. This is not an entirely new idea, and I have been writing about it a few times before [1] [2].

A recurring conversation I have in this context is the AGPL (Affero General Public License) as an alternative vehicle for balancing business goals and Open Source ideals. This topic also has resurfaced recently because of Elasticsearch'es Open Source, Again post where they announced that they will license Elasticsearch under the AGPL.

In my view, while AGPL is a true Open Source license, it is an inferior choice compared to the FSL (the Functional Source License, a Fair Source license) for many projects. Let me explain my reasoning.

The Single Vendor Model

When you take a project like Sentry, which started as an Open Source project and later turned into a VC funded company, its model revolves around a commercial entity being in charge. That model is often referred to as “single vendor.” This is also the case with companies like Clickhouse Inc. or Elastic and their respective projects.

Sentry today is no longer Open Source, it's Fair Source (FSL licensed). Elastic on the other hand is indeed unquestionable Open Source (AGPL among others). What both projects have in common is that they value brand (including trademarks), that they have strong opinions on how that project should be run, and they use a CLA to give themselves the right to re-licenses it under other terms.

In a "single vendor" setup, the company behind the project holds significant power (for ~150 years give or take).

The Illusion of Equality

When you look at the AGPL as a license it's easy to imagine that everybody is equal. Every contributor to a project agrees with the underlying license assumptions of the AGPL and acts accordingly. However, in practice, things are more complicated — especially when it comes to commercial usage. Many legal departments are wary of the AGPL and the broader GPL family of licenses. Some challenges are also inherent to the licenses such as not being able to publish *GPL code to the app store.

You can see this also with Elasticsearch. The code is not just AGPL licensed, you can also retrieve it under the ELv2 and SSPL licensing terms. Something that Elastic can do due to the CLAs in place.

Compare this to Linux, which is licensed under GPLv2 with a syscall exception. This very specific license was chosen by Linus Torvalds to ensure the project's continued success while keeping it truly open. In Linux' case, no single entity has more rights than anyone else. There is not even a realistic option to relicense to a newer version of the GPL.

The FSL explicitly recognizes the reality that the single vendor holds significant power but balances it by ensuring that this power diminishes over time. This idea can also be found in copyright law, where a creator's work eventually enters the public domain. A key difference with software though is that it continuously evolves, making it hard to pinpoint when it might eventually become public domain as thousands of people contribute to it.

The FSL is much more aggressive in that aspect. If we run Sentry into the ground and the business fails, within two years, anyone can pick up the pieces and revive it like a Phoenix from the ashes. This isn't just hypothetical. Bryan Cantrill recently mentioned the desire of Oxide forking CockroachDB once its BUSL change date kicks in. While that day hasn't come yet, it's a real possibility.

Dying Companies

Let's face it: companies fail. I have no intentions for Sentry to be one of them, but you never know. Companies also don't just die just once, they can do so repeatedly. Xapian is an example I like to quote here. It started out as a GPL v2+ licensed search project called Muscat which was built at Cambridge. After several commercial acquisitions and transitions, the project eventually became closed source (which was possible because the creators held the copyright). Some of the original creators together with the community forked the last GPLv2 version into a project that eventually became known as Xapian.

What's the catch? The catch is that the only people who could license it more liberally than GPLv2 are long gone from the project. Xapian refers to its current license “a historical accident”. The license choice causes some challenges specifically to how Xapian is embedded. There are three remaining entities that would need to agree to the relicensing. From my understanding none of those entities commercially use Xapian's original code today but also have no interest in actually supporting a potential relicensing.

Unlike trademark law which has a concept of abandonment, the copyright situation is stricter. It would take two lifetimes for Xapian to enter the public domain and at that point it will be probably be mostly for archival purposes.

Equal Grounds Now or Later

If Xapian's original code would have been FSL licensed, it would have been Apache 2.0 (or MIT with the alternative model) many times over. You don't need to hope that the original license holder still cares, by the time you get hold of the source code, you already have an irrevocable promise that it will eventually turn into Apache 2.0 (or MIT with the alternative license choice) which is about as non-strings attached as it can get.

So in some ways a comparison is “AGPL now and forever” vs “FSL now, Apache 2.0/MIT in two years”.

That's not to say that AGPL (or SSPL) don't have their merits. Xapian as much as it might suffer from their accidental license choice also is a successful Open Source project that helped a lot of developers out there. Maybe the license did in fact work out well for them, and because everybody is in the same boat it also has created a community of equals.

I do believe however it's important to recognize that “single-vendor AGPL with a CLA” is absolutely not the same as “community driven AGPL project without the CLA”.

The title claims that FSL balances Open Source better than AGPL, and it's fair to question how a license that isn't Open Source can achieve that. The key lies in understanding that Fair Source is built on the concept of delayed Open Source. Yes, there's a waiting period, but it’s a relatively short one: just two years. Count to two and the code transitions to full, unshackled openness. And that transition to Open Source is a promise that can't be taken from you.

[1]Originally about the BUSL license which introduced the idea (Open Source, SaaS and Monetization)
[2]Later about our own DOSP based license, the FSL (FSL: A License For the Bazaar, Not the Cathedral).

September 23, 2024 12:00 AM UTC

Quansight Labs Blog

Multi-dimensional Sparse Arrays in SciPy

My work focused on extending support for COOrdinate sparse arrays in SciPy to n-dimensions.

September 23, 2024 12:00 AM UTC

September 22, 2024

Seth Michael Larson

PyCon Taiwan 2024 Keynote

PyCon Taiwan 2024 Keynote

About • Blog • Newsletter • Links

PyCon Taiwan 2024 Keynote

Published 2024-09-22 by Seth Larson
Reading time: minutes

Here are my slides and overview of my PyCon Taiwan 2024 Keynote titled "Bytes, Pipes, and People". The video will be published to YouTube, subscribe to the PyCon Taiwan YouTube channel to be notified when available.

Software security has historically been treated as extra or "nice-to-have", not a core feature that users expect. This means we have accumulated plenty of tech debt. Now there are growing incentives and requirements for producing secure software to meet user expectations.

Luckily for us, many of the tools, data, and systems already exist to help us build a culture of security for Python. These tools help relay messages between software creators and users so we can collaborate on this shared goal.

By actively participating you are starting the positive feedback loop of software security, making users safer faster!

Below is a list of items that actions can implement to build a culture of security for Python:

Maintainers

  • Adopt Trusted Publishers if you use GitHub Actions, GitLab CI/CD, Google Cloud Build, or ActiveState to publish Python packages.
  • Use lock files for the build and publish workflow, such as pip-tools, Poetry, or PDM.
  • Adopt a lightweight security policy. Do not stress about CVEs: fix, release, publish a CVE.
  • Contribute new insecure code detections to Bandit.

Users

  • Update dependencies that have vulnerabilities. Prioritize projects that are connected to the internet.
  • Update software on a semi-regular basis to avoid out-of-date and end-of-life software. Staying up-to-date helps you being able to upgrade to fixed versions in the future.
  • Run tests with PYTHONWARNINGS with DeprecationWarning and PendingDeprecationWarning set to errors to avoid missing deprecated features.
  • Create a secure open source usage policy, using verified data to evaluate open source projects. Do not install new projects without checking your policy first.
  • If you need a Software Bill-of-Materials document there are tools available to generate one. Those tools will improve over time from new Python package SBOM standards.
  • Add a vulnerability scanner like pip-audit, Grype, or Trivy.

Tools and Links

References

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

September 22, 2024 12:00 AM UTC

September 21, 2024

Python Morsels

Prompting a user for input

We can prompt our users for input with Python's built-in input function.

Table of contents

  1. Prompting for user input
  2. Customizing the prompt text
  3. Prompt users with Python's built-in input function

Prompting for user input

Python has a built-in input function that we can use to prompt a user of our program to enter some text:

>>> color = input("Favorite color:")
Favorite color:▯

Our Python REPL is now hanging and waiting for us (the user) to input text. Let's type purple:

>>> color = input("Favorite color:")
Favorite color:purple▯

After the user has typed the text they'd like to enter, they can hit the Enter key to lock-in the value.

>>> color = input("Favorite color:")
Favorite color:purple
>>>

Now the color variable contains the string purple:

>>> color
'purple'

Customizing the prompt text

Note that the input function …

Read the full article: https://www.pythonmorsels.com/prompting-for-input/

September 21, 2024 10:38 PM UTC

Erik Marsja

Using Pandas to Read JSON from URL

The post Using Pandas to Read JSON from URL appeared first on Erik Marsja.

When working with data in Python, using Pandas to read JSON from URL is an excellent tool that lets you directly load JSON data from a web source into a Pandas dataframe. This tutorial will teach you the steps to accomplish this task, building upon our previous discussions on reading JSON with Python more generally.

Table of Contents

How to use Pandas to Read JSON from URL

First, let us look at a simple example of using Pandas to read JSON from a URL.

import pandas as pd
# URL containing JSON data
url = "http://api.open-notify.org/astros.json"
# Read JSON data from URL into a DataFrame
df = pd.read_json(url)
# Display the dataframe
print(df)

In the code chunk above, we start by importing the Pandas library. The URL variable contains the web address where the JSON data is hosted. The pd.read_json(url) function is then used to read the JSON data from the URL and load it into a Pandas DataFrame, which is a two-dimensional labeled data structure with columns of potentially different types. Finally, print(df) displays the DataFrame, allowing us to see the imported data in tabular format.

json read into pandas dataframe


Now that we have seen a basic example, let us learn more about the parameters of the pd.read_json() method to understand how we can customize the reading process.

Parameters of the read_json Method

The pd.read_json() method has several parameters that allow you to fine-tune how the JSON data is read and converted into a dataframe. Here is an overview of the most important parameters:

With these parameters allows you to better control how JSON data is read and processed, enabling you to tailor the DataFrame to your needs.

Summary

To summarize, we have learned how to use Pandas to read JSON data from a URL. We explored a practical example and detailed the parameters of the pd.read_json() method, enhancing our ability to customize the data reading process. Handling nested JSON data can be more challenging, but that will be covered in a future post.
I would appreciate it if you could share this post and leave your comments below. Your feedback is invaluable!

Resources

Here are some other reading data-related tutorials:

The post Using Pandas to Read JSON from URL appeared first on Erik Marsja.

September 21, 2024 09:01 AM UTC

September 20, 2024

Real Python

The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

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!"

[ 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 ]

September 20, 2024 12:00 PM UTC

PyCharm

What’s New in PyCharm 2024.2.2!

PyCharm 2024.2.2 is here with many key updates, including Python support improvements, new Django features, and enhancements to the Data View tool window! 

Visit our What’s New page for more details on all these features and to explore many others. You can download the latest version from our download page or update your current version through our free Toolbox App. 

pycharm 2024.2.2
Download PyCharm 2024.2.2

PyCharm 2024.2.2 highlights

Django enhancements  PRO

New code completion suggestions

When working with models, PyCharm now offers field completion suggestions in a variety of cases, such as Model.save(update_fields[…]), Model.refresh_from_db(fields=[…]), Model.clean_fields(exclude=[…]), and so on.

New code completion suggestions - Django

Quick-fix to create a method for an unresolved ViewSet

If a ViewSet has an unresolved reference, PyCharm suggests a quick-fix to introduce the missing method. Use Alt + Enter to call it.

Quick-fix to create a method for an unresolved ViewSet
What’s new!

Data View  PRO

You can now look at n-dimensional NumPy arrays in the Data View tool window. Define the array you would like to inspect, along with a specific dimension or slice, in a special field at the bottom of the tool window, and PyCharm will display a table with the results.

Data view n-dimensional arrays

Python support improvements

Support for default types for type parameters (PEP 696)

Improve typing with PyCharm’s support for the Python 3.13 ability to define the default types for type parameters. The IDE now incorporates default types for type parameters both for old-style and new-style generic classes, functions, and type aliases, and it takes them into account in type inference.

Support for default types for type parameters (PEP 696)

Pattern matching: Foldable match statements

To improve the readability of code with large pattern-matching statements, you can now use folding for entire match statements or for separate cases inside them. 

Pattern matching: Foldable match statements
Download PyCharm 2024.2.2

Visit our What’s New page to learn about other useful features included in this release, or read the release notes for the full breakdown, including more details on the features mentioned here. 

If you encounter any problems, please report them in our issue tracker so we can address them promptly. 

Connect with us on X (formerly Twitter) to share your thoughts on PyCharm 2024.2.2! 

September 20, 2024 11:33 AM UTC

Programiz

Getting Started with Python

In this tutorial, you will learn to write your first Python program.

September 20, 2024 08:07 AM UTC

Talk Python to Me

#477: Awesome Text Tricks with NLP and spaCy

Do you have text that you want to process automatically? Maybe you want to pull out key products or topics of conversation? Maybe you want to get the sentiment? The possibilities are many with this week's topic: NLP with spaCy and Python. Our guest, Vincent D. Warmerdam, has worked on spaCy and other tools at Explosion AI and he's here to give us his tips and tricks for working with text from Python.<br/> <br/> <strong>Episode sponsors</strong><br/> <br/> <a href='https://talkpython.fm/posit'>Posit</a><br> <a href='https://talkpython.fm/training'>Talk Python Courses</a><br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Course: Getting Started with NLP and spaCy</b>: <a href="https://training.talkpython.fm/courses/getting-started-with-spacy" target="_blank" >talkpython.fm</a><br/> <br/> <b>Vincent on X</b>: <a href="https://x.com/fishnets88?featured_on=talkpython" target="_blank" >@fishnets88</a><br/> <b>Vincent on Mastodon</b>: <a href="https://fosstodon.org/@koaning" target="_blank" >@koaning</a><br/> <br/> <b>Programmable Keyboards on CalmCode</b>: <a href="https://www.youtube.com/playlist?list=PLGj5nRqy15j93TD0iReqfLL9lU1lZFEs6" target="_blank" >youtube.com</a><br/> <b>Sample Space Podcast</b>: <a href="https://www.youtube.com/playlist?list=PLSIzlWDI17bRULf7X_55ab7THqA9TJPxd" target="_blank" >youtube.com</a><br/> <br/> <b>spaCy</b>: <a href="https://spacy.io?featured_on=talkpython" target="_blank" >spacy.io</a><br/> <b>Course: Build An Audio AI App</b>: <a href="https://training.talkpython.fm/courses/build-an-audio-ai-app-with-python-and-assemblyai" target="_blank" >talkpython.fm</a><br/> <b>Lemma example</b>: <a href="https://github.com/talkpython/audio-ai-with-assemblyai-course/blob/d5418ec26b4e8d9e524f2dedfc5c93c6edc817b8/code/02_feature_search/src/services/search_service.py#L80" target="_blank" >github.com</a><br/> <b>Code for spaCy course</b>: <a href="https://github.com/talkpython/nlp-with-python-and-spacy-course" target="_blank" >github.com</a><br/> <br/> <b>Python Bytes transcripts</b>: <a href="https://github.com/mikeckennedy/python_bytes_show_notes?featured_on=talkpython" target="_blank" >github.com</a><br/> <b>scikit-lego</b>: <a href="https://github.com/koaning/scikit-lego?featured_on=talkpython" target="_blank" >github.com</a><br/> <b>Projects that import "this"</b>: <a href="https://calmcode.io/til/python-import-this?featured_on=talkpython" target="_blank" >calmcode.io</a><br/> <b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=AE7FPjaVNN0" target="_blank" >youtube.com</a><br/> <b>Episode transcripts</b>: <a href="https://talkpython.fm/episodes/transcript/477/awesome-text-tricks-with-nlp-and-spacy" target="_blank" >talkpython.fm</a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe to us on YouTube</b>: <a href="https://talkpython.fm/youtube" target="_blank" >youtube.com</a><br/> <b>Follow Talk Python on Mastodon</b>: <a href="https://fosstodon.org/web/@talkpython" target="_blank" ><i class="fa-brands fa-mastodon"></i>talkpython</a><br/> <b>Follow Michael on Mastodon</b>: <a href="https://fosstodon.org/web/@mkennedy" target="_blank" ><i class="fa-brands fa-mastodon"></i>mkennedy</a><br/></div>

September 20, 2024 08:00 AM UTC

Wingware

Wing Python IDE Version 10.0.6 - September 20, 2024

Wing 10.0.6 adds support for Python 3.13 and fixes some issues with AI development, code refactoring, and unit testing with pytest.

See the change log for details.

Download Wing 10 Now: Wing Pro | Wing Personal | Wing 101 | Compare Products


What's New in Wing 10


Wing 10 Screen Shot

AI Assisted Development

Wing Pro 10 takes advantage of recent advances in the capabilities of generative AI to provide powerful AI assisted development, including AI code suggestion, AI driven code refactoring, description-driven development, and AI chat. You can ask Wing to use AI to (1) implement missing code at the current input position, (2) refactor, enhance, or extend existing code by describing the changes that you want to make, (3) write new code from a description of its functionality and design, or (4) chat in order to work through understanding and making changes to code.

Examples of requests you can make include:

"Add a docstring to this method"
"Create unit tests for class SearchEngine"
"Add a phone number field to the Person class"
"Clean up this code"
"Convert this into a Python generator"
"Create an RPC server that exposes all the public methods in class BuildingManager"
"Change this method to wait asynchronously for data and return the result with a callback"
"Rewrite this threaded code to instead run asynchronously"

Yes, really!

Your role changes to one of directing an intelligent assistant capable of completing a wide range of programming tasks in relatively short periods of time. Instead of typing out code by hand every step of the way, you are essentially directing someone else to work through the details of manageable steps in the software development process.

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Support for Python 3.12, 3.13, and ARM64 Linux

Wing 10 adds support for Python 3.12 and 3.13, including (1) faster debugging with PEP 669 low impact monitoring API, (2) PEP 695 parameterized classes, functions and methods, (3) PEP 695 type statements, and (4) PEP 701 style f-strings.

Wing 10 also adds support for running Wing on ARM64 Linux systems.

Poetry Package Management

Wing Pro 10 adds support for Poetry package management in the New Project dialog and the Packages tool in the Tools menu. Poetry is an easy-to-use cross-platform dependency and package manager for Python, similar to pipenv.

Ruff Code Warnings & Reformatting

Wing Pro 10 adds support for Ruff as an external code checker in the Code Warnings tool, accessed from the Tools menu. Ruff can also be used as a code reformatter in the Source > Reformatting menu group. Ruff is an incredibly fast Python code checker that can replace or supplement flake8, pylint, pep8, and mypy.


Try Wing 10 Now!


Wing 10 is a ground-breaking new release in Wingware's Python IDE product line. Find out how Wing 10 can turbocharge your Python development by trying it today.

Downloads: Wing Pro | Wing Personal | Wing 101 | Compare Products

See Upgrading for details on upgrading from Wing 9 and earlier, and Migrating from Older Versions for a list of compatibility notes.

September 20, 2024 01:00 AM UTC

Matt Layman

Docker Go, JS, Static Files - Building SaaS #203

In this episode, we continued on the cloud migration path. We need to build a Docker container with all the necessary static files. Some of these come from Go via Hugo for a content blog. Some comes from JavaScript via Tailwind for CSS. Some come from Python via Sphinx for documentation. All need to built into the same image. That’s what we covered on this stream.

September 20, 2024 12:00 AM UTC