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Last update: July 13, 2025 07:42 AM UTC

July 11, 2025

Python Engineering at Microsoft

Python in Visual Studio Code – July 2025 Release

We’re excited to announce the July 2025 release of the Python, Pylance and Jupyter extensions for Visual Studio Code!

This release includes the following announcements:

If you’re interested, you can check the full list of improvements in our changelogs for the Python, Jupyter and Pylance extensions.

Python Environments included as part of the Python extension

We’ve begun the roll-out of the Python Environments extension as an optional dependency with the Python extension. What this means is that you might now begin to see the Python Environments extension automatically installed alongside the Python extension, similar to the Python Debugger and Pylance extensions. You can find its features by clicking the Python icon that appears in the Activity Bar. This controlled roll-out allows us to gather early feedback and ensure reliability before general availability.

The Python Environments extension includes all the core capabilities we’ve introduced so far including: one-click environment setup using Quick Create, automatic terminal activation (via "python-envs.terminal.autoActivationType" setting), and all supported UI for environment and package management.

To use the Python Environments extension during the roll-out, make sure the extension is installed and add the following to your VS Code settings.json file:

"python.useEnvironmentsExtension": true

Disabled PyREPL for Python 3.13

We have disabled PyREPL for Python 3.13 and above to address indentation and cursor issues in the interactive terminal. For more details, see Disable PyREPL for >= 3.13.

Other Changes and Enhancements

We have also added small enhancements and fixed issues requested by users that should improve your experience working with Python and Jupyter Notebooks in Visual Studio Code. Some notable changes include:

We would also like to extend special thanks to this month’s contributors:

Try out these new improvements by downloading the Python extension and the Jupyter extension from the Marketplace, or install them directly from the extensions view in Visual Studio Code (Ctrl + Shift + X or ⌘ + ⇧ + X). You can learn more about Python support in Visual Studio Code in the documentation. If you run into any problems or have suggestions, please file an issue on the Python VS Code GitHub page.

The post Python in Visual Studio Code – July 2025 Release appeared first on Microsoft for Python Developers Blog.

July 11, 2025 02:50 PM UTC

PyBites

From SQL to SQLModel: A Cleaner Way to Work with Databases in Python

SQLModel is a library that lets you interact with databases through Python code with Python objects and type annotations instead of writing direct SQL queries.

Created by the same author of the extremely popular framework FastAPI, it aims to make interacting with SQL DBs in Python easier and elegant, with data validation and IDE support, without the need to learn SQL.

It’s an ORM (Object-Relational Mapper), meaning: it translates between classes/objects and SQL.

In this article, I will cover why you would use SQLModel over plain SQL queries, what benefits it brings to the table and the basics of using it in Python projects.

I’ll assume that you’re comfortable with Python (functions, classes, attributes). I’m not assuming any prior knowledge of SQL though. The article should be approachable to anyone with basic Python experience.

To keep the article manageable, I’ve intentionally left out things like grouping operations in functions, error handling, code execution output and performance optimization. Otherwise, this would span multiple parts.

Why use SQLModel?

The main reason you would want to use SQLModel is to avoid writing SQL in your Python code. Mixing code from another language (especially SQL queries) with your Python code can get messy. It take away from your code clarity and readability, it adds a maintenance cost and it’s not always secure.

Although there are other solutions that allow you to avoid writing SQL in Python, SQLModel comes with additional, very important features for free, most notably: data validation.

Here are the main features included in SQLModel:

SQLModel creator says that there was a lot of research and effort dedicated to make SQLModel model both a Pydantic & an SQLAlchemy model.

Another reason might be that you don’t (or don’t want to) know SQL. Which is fine although it will greatly help if you learn the very basics even if you choose to use SQLModel.

How to use SQLModel?

To keep things simple, I will use a user info table as an example throughout the post.
Here is how the ‘user’ table in the DB may look like:

idnameemaildate_joinedis_admin

The SQL-in-my-Python way:

Lets start with the non-SQLModel way of interacting with DBs from Python.
When your Python app needs to save/retrieve/update/delete data from/to an SQL DB, we write SQL queries directly in Python code like this:

First, we connect to the DB (creating it if it doesn’t exist):

import sqlite3
from datetime import datetime
con = sqlite3.connect("user_db.db")

Then, we get a hold of a cursor to be able to execute SQL statements:

cur = con.cursor()

Then, we create the ‘user’ table with the necessary fields:

cur.execute("""CREATE TABLE IF NOT EXISTS user (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL,
            email TEXT NOT NULL,
            date_joined TEXT,
            is_admin INTEGER DEFAULT 0
            );
            """)

The string passed to .execute() is the SQL statement that we want to to execute on the DB.

Finally, we can perform CRUD operations (create, read, update, delete). Suppose we have this user data:

# id omitted. sqlite will auto create it and autoincrement it.
name = "John Doe"
date_joined = datetime.now()
email = 'john@example.com'
is_admin = False

Using same connection and cursor we’ve just created above, we can execute SQL queries like so:

cur.execute(
    "INSERT INTO user (name, email, date_joined, is_admin) VALUES (?, ?, ?, ?)",
    (name, email, date_joined, int(is_admin)),
)
con.commit()
user_id = 1
user1 = cur.execute("SELECT * FROM user WHERE id = ?", (user_id,)).fetchone()
print("User1: ", user1)
user_id = 1
new_name = "Jane Doe"
cur.execute("UPDATE user SET name=? WHERE id = ?", (new_name, user_id))
con.commit()
user_id = 1
cur.execute("DELETE FROM user WHERE id = ?", (user_id,))
con.commit()

You can already see how this is not the most efficient way and not the cleanest code. It’s a mix of Python and SQL with long strings and placeholders for values. In addition, you need to know at least basic SQL to write these queries.

I’m sure, some developers know and love SQL and prefer to use it directly in their Python code, however, in my opinion, there are some downsides to this approach:

Now let’s see what SQLModel has to offer to improve the situation.

The SQLModel way:

SQLModel offers a better, safer, and more elegant way to interact with SQL DBs from Python code. It allows us to work with DB records (rows) as regular Python objects.

Let’s see how we can use it instead.

1- Install SQLModel:

After creating and activating you virtual environment, use your favorite package manager to install SQLModel. Here, I’m using uv:

uv add sqlmodel

2- Create a model for your data:

Before using SQLModel to interact with the DB, we need first to create an SQLModel model. It’s exactly the same as a Pydantic model.
Using our ‘user’ example above, here is how our model would look:

from sqlmodel import SQLModel, Field
from datetime import datetime

class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str
    email: str
    date_joined: datetime = Field(default_factory=datetime.now)
    is_admin: bool = False

Here, in the class User, we’re sub-classing SQLModel from the sqlmodel library and telling it to create a corresponding SQL table in the database if it doesn’t exist, indicated by table=True.

Then we defined the fields of the class just like any other Python class with type annotations (it’s a Pydantic model).

We’re using the special Field function from SQLModel to set arguments for the id and date_joined fields.

The date_joined filed uses a default_factory to store the current timestamp.

The model itself represents a table in the database and each field represents a column in that table.

Now, we can create “user” instances from this SQLModel “User” model. Every instance we create will represent a row in the table.

3- Create DB and Table:

We’re now ready to create the DB, connect to it and create necessary tables.

For this task, we need to create an SQLAlchemy engine (remember, SQLModel uses SQLAlchemy under the hood).

The engine is an object that handles the communication with the DB.

We can create one using create_engine() from SQLModel. First we need to add it to our imports:

from sqlmodel import SQLModel, Field, create_engine

Then, we add the engine creation code below the model class as order here matters:

...
# below the code defining User model
sqlite_file_name = "users_db.db"
sqlite_url = f"sqlite:///{sqlite_file_name}"

engine = create_engine(sqlite_url, echo=True)

SQLModel.metadata.create_all(engine)

Here, we’re using SQLite and setting the DB URL. You can use any DB that is supported by SQLAlchemy.

You would typically load this DB URL from the environment, see our article: How to handle environment variables in Python

Then we’re calling create_engine() passing it the URL and telling it to log what it’s doing to the terminal where we run the Python code with echo=True. This is very handy not only for debugging but also for learning.

Lastly, we call create_all() passing the engine to tell SQLModel to create everything: the DB and the table(s).

The create_all() takes an engine and creates a table for each model that inherits from SQLModel with table=True configured. Our model meets these conditions, so its table gets created.

One important thing to remember is that calling create_all() needs to happen after the code that defines the model class. Otherwise the table will not be created.

You can verify that the DB has indeed been created by simply checking for a new .db file with the same name as sqlite_file_name. If it’s there, you’re good to continue.

4- Create instances of the SQLModel to represent individual rows:

Now that we have our DB and table created, we can start creating instances of our User.

It’s straightforward. Just like instantiating any Python class, we do it like so:

user1 = User(name="ahmed", email="ahmed@example.com", is_admin=True)

NOTE: We don’t need to include the id field when creating or updating records in the DB (you can use it to find the record to update but not update id itself). It will be automatically created by SQLite and auto incremented for us. The same goes for the date_joined field as it uses a default_factory to store the current timestamp automatically when creating a new instance of User.

So far, our user (user1) lives only in memory, we need to persist it to the DB so that we can later retrieve/update/delete it.

Here is where SQLModel really shines. Forget all those messy SQL queries and value placeholders. SQLModel allows us to use Python code to cleanly and elegantly interact with the DB.

5- Create a session and perform DB operations:

The engine we created earlier handles the communication with the DB for the whole program. On top of the engine, we need a session.
A session uses the engine to perform operations on the DB. We need a new session for each group of operations that belong together.

Let’s create a session to add a row in the DB table for the user we just created.

First, we need to add Session to our imports:

from sqlmodel import SQLModel, Field, Session, create_engine

Next, we create the session and tell it to add the User instance to the DB. We’re using a with block to cleanly close the session after each operation even if there was an exception in the block:

...
# below the code that creates the DB & table above:
with Session(engine) as session:
    session.add(user1)

So far, the user instance is only added to the session (in memory) and still not sent to the DB.

This is where we see the benefit of using the session. It holds in memory the objects we need to save to the DB until we’re ready to commit, at which point, we call commit() which will use the engine to save all changes to the DB.

This allows for batch operations, instead of sending changes individually to the DB which may be expensive.

Now we can commit:

    session.commit()

You can use a DB browsing tool (like DB Browser for SQLite) to verify that a row has been created for the user instance.

And here is the complete creation code:

user1 = User(name="ahmed", email="ahmed@example.com", is_admin=True)

with Session(engine) as session:
    session.add(user1)
    session.commit()

To make things easier for us for the rest of the post and to have some data to work with in the DB, we’ll now create multiple users. Use the following code to create 3 more users and add them to the DB:

user2 = User(name="john", email="john@example.com", is_admin=False)
user3 = User(name="bob", email="bob@example.com", is_admin=True)
user4 = User(name="kate", email="kate@example.com", is_admin=False)

with Session(engine) as session:
    session.add(user2)
    session.add(user3)
    session.add(user4)

    session.commit()

How to do CRUD operations with SQLModel?

Now, that we know how to create a session to perform operations on the DB, it’s a matter of knowing how to do each CRUD operation using SQLModel.

Here is a simple table for each operation, SQL command and the corresponding SQLModel function or session method:

CRUD op.SQL cmnd.SQLModel fn./mthd.
createINSERT.add()
readSELECTselect()
updateUPDATEselect() & .add()
deleteDELETEdelete() or .delete()

We’ve already seen how to do the first one. We’ll explore the rest next.

Select (read/retrieve) multiple rows from DB:

“Where does SELECT come from?” you may ask. In SQL, we use SELECT to read rows from the DB. An SQL SELECT statement looks like this:

SELECT * FROM user

This means: select all columns (i.e. id, name, email, etc.) from table ‘user’. Not specifying any filtering conditions means: return all rows (records, user instances) you find in the DB.

Similarly, in SQLModel we use its select()function and pass it the name of the model (which represent the DB table) we want to read from.

We can chain filtering and ordering preference to select() but we’re not going to do that just yet. Now, we want all rows from the table.

First we need to add select to our imports:

from sqlmodel import SQLModel, Field, Session, create_engine, select

Then, to read all rows from the ‘user’ table in the DB, we use the following pattern:

with Session(engine) as session:
    statement = select(User)
    results = session.exec(statement)
    users = results.all()
    for user in users:
        print(user)

NOTE: We’re still using the same engine from earlier but with a new session for each set of related DB operations.

Here, we’re storing the query statement in a variable then passing it to exec() to execute it. This will return a results object. We call its .all() method to return all User objects.

We can make the previous code more Pythonic:

with Session(engine) as session:
    users = session.exec(select(User)).all()
    for user in users:
        print(user)

However, for readability, especially if the statement is a long one (like the code block where we chain filtering, ordering and limiting), it may be better to store the statement in a variable first and then pass it to the exec() function.

Filtering, ordering and limiting the results:

As mentioned earlier, we can optionally chain filtering, ordering preference and/or limiting to select() using the methods .where(), .order_by() and .limit() respectively. We can use all of them, or mix and mach to suit our needs.

For example, we can select only the users that are admins:

with Session(engine) as session:
    statement = select(User).where(User.is_admin)
    users = session.exec(statement).all()
    for user in users:
        print(user)

One important thing to remember is that you want to pass a Python expression to .where() like:

select(User).where(User.name=="ahmed")

not a keyword argument like:

select(User).where(name="ahmed")

With the latter, you won’t get IDE auto-completion/suggestions nor inline errors. So, if you pass a non-existent attribute or miss-spell an existing one and it returned unexpected results, it would be hard to discover/debug.

We can also order the results by further chaining .order_by() passing it the field we want to order by, and calling either .asc() for ascending or .desc() for descending:

with Session(engine) as session:
    statement = (
        select(User)
        .where(User.is_admin)
        .order_by(User.date_joined.asc())
    )
    users = session.exec(statement).all()
    for user in users:
        print(user)

Here, we’re ordering by users by the date they joined. The first joined first.

And finally we can chain one more method to limit the results instead of returning the full list of rows which may be slow especially for large tables. We just pass the number of rows we want to .limit() the result to:

with Session(engine) as session:
    statement = (
        select(User)
        .where(User.is_admin)
        .order_by(User.date_joined.asc())
        .limit(2)
    )
    users = session.exec(statement).all()
    for user in users:
        print(user)

Select only one row from the DB:

So far, we have been selecting (reading) multiple rows from the DB. The select() statement unmodified returns an object that is an iterable. We’ve seen how we can use its method .all() to get a list of rows/records instead of the iterable object.

There might be times though where we may want only the first row. We can do that by calling .first() instead of .all(). This will return the first row if there was any:

...
    user = session.exec(statement).first()
    print(user)

If we’re absolutely sure that our query should return one (and only one) row, we can use the .one() method on the exec() result. For example, the id field is the primary key and it must be unique across the table. If we look a user up by id the query should return only one row, or None if no user was found with that id.

    with Session(engine) as session:
        statement = select(User).where(User.id==1)
        user = session.exec(statement).one()
        print(user)

This also helps in testing cases where we want to insure that only one row is returned. If multiple rows were returned, an exception is raised and the test fails.

NOTE: Knowing how to use select(), especially combined with .one() is very important as we will need them when updating and/or deleting to find the row(s) we want to update/delete.

Update rows

Updating rows using SQLModel is a three-step process:

Think of it like this: you pull the item out of the DB ‘box’, change some of its attributes and then put it back in the DB box.

(Optional): We can refresh the Python object/instance linked with that DB row so that it reflects the new changes.

It’s easier than it sounds, so let’s see how. Assume that we want to assign the user with id #4 administrator privileges.

with Session(engine) as session:
    statement = select(User).where(User.id == 4)
    results = session.exec(statement)
    user_to_update = results.one()

    user_to_update.is_admin = True
    session.add(user_to_update)
    session.commit()
    session.refresh(user_to_update)
    print("Updated user: ", user_to_update)

The code is almost self-explanatory. We find the user using select() filtering the rows with .where(). We then call the one() method of the results object to get only one user, and store that user’s instance in user_to_update.

We then change the is_admin attribute of the user instance to True, add it back to the session and commit the change to the DB.
Finally we refresh the user instance and print it to verify that the new is_admin value has indeed been updated and the user with id #4 is now an admin.

NOTE: Refreshing after updating is optional because SQLModel (via SQLAlchemy) will automatically refresh the object when you access one of its attributes, for example user.name. This lazy refresh ensures you’re seeing the latest data from the database.

However, if you don’t access any attributes after committing, the object might not refresh on its own. That’s why we explicitly call session.refresh(user) to make sure we’re working with the latest data even if we don’t immediately read from it.

That’s it. As you can probably see, it’s fairly easy to update a row with SQLModel.
Next, we’ll see how to perform the last CRUD operation: delete.

Delete specific rows:

Deleting rows from DB is a straightforward business. You select() the row(s) you want to delete, delete them and commit the session.

Here is how we would delete the user with id #3:

with Session(engine) as session:
    statement = select(User).where(User.id == 3)
    results = session.exec(statement)
    user_to_delete = results.one()

    session.delete(user_to_delete)
    session.commit()

We ‘select’ the user to be deleted, the one with the id #3, using select() and filter for the id with .where(). We then call .one() on the results to get the only row returned. After we’ve got the row, we tell the session to delete it and commit the deletion to the DB.

Delete all rows:

To delete all rows in the table, we use the delete() function from sqlmodel, not the .delete() method of the session. After importing the function, we execute it as a statement passing it the name of the model (‘User’ in our example).

WARNING: This would delete all user rows from the ‘user’ table in the DB.

from sqlmodel import SQLModel, Field, Session, create_engine, select, delete
with Session(engine) as session:
    statement = delete(User)
    session.exec(statement)
    session.commit()

That concludes our exploration of CRUD operations and, therefore, our discussion of SQLModel as a nice alternative to using SQL directly in Python code.

Conclusion

It’s been a journey! If you have made it so far, congrats on learning this cool library and adding a new tool to your toolbox.

I think SQLModel is one of the best SQL libraries for Python and is worth learning.

To continue learning, or if you need more technical details, I recommend the documentation. It’s well written, well presented and very approachable.

Thanks for your time. See you in another Pybites blog post.

July 11, 2025 01:51 PM UTC

Real Python

The Real Python Podcast – Episode #257: Comparing Real-World Python Performance Against Big O

How does the performance of an algorithm hold up when you put it into a realistic context? Where might Python code defy Big O notation expectations when using a profiler? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ 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 ]

July 11, 2025 12:00 PM UTC

Python Anywhere

Direct interaction of LLM chats with PythonAnywhere via the Model Context Protocol

tl;dr

Check out the instructions on GitHub and connect your Claude Desktop, GitHub Copilot, Cursor or any similar tool supporting MCP to PythonAnywhere directly.

July 11, 2025 10:00 AM UTC

EuroPython

July Newsletter: Can’t make it to Prague? Join Us Online!

Hello, Pythonistas! 🐍

EuroPython is coming fast, and we’re back with news about what you can yet do so you don’t miss any opportunities to learn and enjoy even more! 🎉

🧑‍💻 Can’t make it to Prague? Join us online!

Remote tickets for EuroPython 2025 are officially available! 👉https://europython.eu/remote  

Join the conference from anywhere in the world and take part in one of the biggest Python events of the year. With a remote ticket, you’ll be able to watch live sessions, keynotes, and selected panels, plus interact through chat and Q&As. 🌍

Whether you&aposre a developer, student, or enthusiast, the online experience brings the community to you. Don’t wait—secure your remote access and stay connected with Python’s vibrant ecosystem!

Remote attendees can now apply for Financial Aid for EuroPython 2025! We aim to make the conference accessible to everyone, regardless of location or financial situation. 

👉https://europython.eu/remote 

alt

👾 Sign up for Discord

We’ve created an official Discord server to help everyone across the conference connect!

This server will be the main way in which last-minute information will be communicated both before and during the conference, so make sure to sign up! Importantly, some sessions may have special instructions, such as things you need to prepare beforehand, and these will be communicated by the speakers on Discord.

Please check your email and follow the instructions. You will need a valid ticket to register on the server.

🎯 Register for a summit!

Join us at the EuroPython 2025 Summits during the tutorial days (July 14–15) in Prague! 🤝

Whether it’s the C-API Summit, WASM Summit, or others, these gatherings bring together passionate developers for focused collaboration and discussion. You can find the full list of summits on the EuroPython website under the Events tab.

To attend, you’ll need an in-person ticket (Tutorial, Conference-only, or Combined). Spots are limited, so be sure to register your interest on each summit’s page and secure your place in shaping Python’s future. See you there! 

🎉 Buy social event tickets!

Tickets for the EuroPython 2025 social event on Thursday, July 17th at 19:30 CEST on Střelecký Ostrov, Prague, are now available! 

Immerse yourself in a relaxed riverside evening featuring live music and jam sessions (feel free to bring instruments), as well as board games, picnic blankets, and sports like volleyball, croquet, pétanque and tennis. Light snacks and drinks (including vegetarian, vegan, gluten free options) will be provided. 

You can find your voucher in your email box.

There are only 14 tickets left in the first batch, but there will be more available during the conference. These tickets are limited and sold separately. 

👉Check out more info about the social event. 

👉Grab yours in the ticket shop now!

🏰 Enjoy Prague!

Make the most of your stay by exploring beautiful Prague! Stroll through the historic Old Town, cross the iconic Charles Bridge, or enjoy a quiet moment at Prague Castle.

Don’t miss out on local cafés, river views, and vibrant summer culture. Whether you love architecture, art, or just great food and beer, there’s something here for everyone. 

Check out our Prague guide for tips and inspiration. See you in the heart of Europe!

🌱 Take your spot at the Beginners’ Day!

Are you at the beginning of your tech career and are looking for guidance? We still have spots at our Beginners’ Day on Saturday 19th July!

The Unconference (€5) is a series of panels and discussions designed to help people just getting into tech to start or grow their career. It’s perfect for those hunting for, or just starting, their first job in tech.

For those looking to learn Python programming in a supportive and fun environment, we also have two workshops. Django Girls (FREE) is a hands-on workshop teaching the basics of web development, and Humble Data (€5) is a hands-on workshop teaching the basics of data science.

Beginners’ Day is open to everyone, and you don’t need a EuroPython ticket to attend. From students to those exploring a career change, we warmly invite anyone curious about starting their programming journey. Expect a friendly, fun, and supportive environment that will leave you feeling more confident and inspired to continue learning.

Please see this page for more details and to apply. Places are limited and will be given on a first come, first serve basis.

📝 Make Your Resume Shine thanks to the CV Feedback Session!

Looking for your first tech job or a career switch? Join our CV Feedback Session on Wednesday, 16th July (14:00–17:00) at the Open Space.

Get personalized, hands-on feedback from hiring professionals in small group sessions (12 people per 30 min).

Bring a printed copy of your CV.

🎯 Register here 

Spots are limited – sign up early!

🤖 Join 3D Printing Workshop featuring Prusa Research! 

Curious about 3D printing? Already a fan? Drop by the Prusa Research 3D printing workshop at EuroPython on Wednesday 16th July — no registration needed!

You&aposll get hands-on experience with real 3D printers, slicers, and filaments. Whether you&aposre just starting out or already printing like a pro, Prusas team will be there to guide you. Chat with experts, ask questions, and learn advanced tips and tricks.

It’s a great opportunity to explore 3D printing at your own pace — come and go as you like. Everyone’s welcome!

💰Sponsors Highlight

We would like to thank our sponsors for supporting the conference, especially to platinum ones: 

alt

If you’re interested in job opportunities by EuroPython sponsors, please check out all available jobs on our blog: https://blog.europython.eu/europython-2025-job-ads/ 

💞 Upcoming Events in the Python Community

👋 See You All Next Month 

Enjoyed this update? Help us spread the word! Like, share, and subscribe — and don’t forget to tell your friends about us.

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July 11, 2025 06:00 AM UTC

HoloViz

Panel Material UI Announcement

July 11, 2025 12:00 AM UTC

July 09, 2025

Real Python

What Is Python's __init__.py For?

Python’s special __init__.py file marks a directory as a regular Python package and allows you to import its modules. This file runs automatically the first time you import its containing package. You can use it to initialize package-level variables, define functions or classes, and structure the package’s namespace clearly for users.

By the end of this tutorial, you’ll understand that:

  • A directory without an __init__.py file becomes a namespace package, which behaves differently from a regular package and may cause slower imports.
  • You can use __init__.py to explicitly define a package’s public API by importing specific modules or functions into the package namespace.
  • The Python convention of using leading underscores helps indicate to users which objects are intended as non-public, although this convention can still be bypassed.
  • Code inside __init__.py runs only once during the first import, even if you run the import statement multiple times.

Understanding how to effectively use __init__.py helps you structure your Python packages in a clear, maintainable way, improving usability and namespace management.

Note: The filename __init__.py is hard to pronounce. Most people find it easier to call it dunder-init.

You may have seen files named __init__.py scattered throughout large Python projects and wondered exactly what they do. Or you may have used __init__.py files yourself without a clear idea of why they’re necessary or how to exploit their features. You might also have noticed that your Python code sometimes works even if you forget to add __init__.py to your packages.

So, what is __init__.py for?

Get Your Code: Click here to download the free sample code that shows you how to use Python’s __init__.py.

Take the Quiz: Test your knowledge with our interactive “What Is Python's __init__.py For?” quiz. You’ll receive a score upon completion to help you track your learning progress:

What Is Python's __init__.py For?

Interactive Quiz

What Is Python's __init__.py For?

Test your understanding of Python's __init__.py files to master how they shape your packages, enhance project structure, and keep your code clean.

In Short: __init__.py Declares a Folder as a Regular Python Package

The special file __init__.py serves as a marker to indicate that its containing directory is a regular package. When people talk about a package in Python, they generally mean a regular package. The __init__.py file is a Python source file, which means that it’s also a module.

If you want to review the terms module and package and how they’re used in Python, then expand the collapsible section below:

In Python, the terms module and package both describe units containing code:

Module Package
Definition A single Python file containing code A directory containing one or more Python modules
Naming The filename without the .py extension The directory name
Contents Functions, classes, and variables Modules and optionally subpackages
Purpose Organizing small projects, simple code reuse Organizing large projects, code reuse
Importing Import directly (import module) Import package or its modules (import package.module)

As you can see, modules and packages both help organize Python code, with modules representing single files and packages representing directories that can group multiple modules.

The existence of __init__.py makes the Python loader identify its containing directory as a regular package. This means you can import the whole directory, specific modules within it, or even individual functions, variables, and classes from those modules using the package name.

You can also import those code objects into other modules, which allows your code to be reused in many flexible ways. You’ll see several examples of this shortly.

As mentioned, __init__.py may be an empty file, in which case it serves only to identify a package.

What Happens When I Add Code to __init__.py?

If __init__.py does contain code, then that code will be executed when the package is first imported.

The code in __init__.py can perform a variety of useful tasks. For example, it can import other modules or packages and define its own functions or data. When a package is imported, all of its own code, along with everything it has itself imported, becomes accessible to the importing module.

You’ll start with a simple example based on a single package. Here’s the setup:

tools_project/
│
└── tools/
    └── __init__.py

So, your project is named tools_project. It has a package named tools. The tools/__init__.py file could have been completely empty, but here it contains a few lines of code:

Python tools_project/tools/__init__.py 
__version__ = "1.0.0"
magic_number = 42
Copied!

With those files in place, start a REPL session in the tools_project directory, and watch what happens when you import and use the tools package:

Python 
 1>>> import tools
 2>>> tools.__version__
 3'1.0.0'
 4>>> tools.magic_number
 542
Copied!

Read the full article at https://realpython.com/python-init-py/ »

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July 09, 2025 02:00 PM UTC

Quiz: What Is Python's __init__.py For?

In this quiz, you’ll test your knowledge of Python’s __init__.py file.

Understanding this file’s role will help you create more organized and reusable Python code.

[ 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 ]

July 09, 2025 12:00 PM UTC

July 08, 2025

PyCoder’s Weekly

Issue #689: Design Patterns, Signals, TorchAudio, and More (July 8, 2025)

#689 – JULY 8, 2025
View in Browser »

The PyCoder’s Weekly Logo

Design Patterns You Should Unlearn in Python

The Gang of Four design patterns specify object oriented solutions to common issues in code, except Python doesn’t have many of the problems the solutions are aiming to solve. This article talks about some of the common patterns and the easier ways to solve the problems they intend to address in Python. See also Part 2.
RACEYCHAN

Signals: State Management for Python Developers

If you’ve ever debugged why your cache didn’t invalidate or notifications stopped firing after a “simple” state change, this guide is for you. Signals are becoming a JavaScript standard, but Python developers can use the same patterns to eliminate “forgot to update that thing” bugs.
TUAN ANH BUI • Shared by Tuan Anh Bui

Prevent Postgres Slowdowns on Python Apps with this Check List

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Avoid performance regressions in your Python app by staying on top of Postgres maintenance. This monthly check list outlines what to monitor, how to catch slow queries early, and ways to ensure indexes, autovacuum, and I/O are performing as expected →
PGANALYZE sponsor

Use TorchAudio to Prepare Audio Data for Deep Learning

Learn to prepare audio data for deep learning in Python using TorchAudio. Explore how to load, process, and convert speech to spectrograms with PyTorch tools.
REAL PYTHON

Quiz: Use TorchAudio to Prepare Audio Data for Deep Learning

REAL PYTHON

PyPy v7.3.20 Release

PYPY.ORG

Django Bugfix Release 5.2.4

DJANGO SOFTWARE FOUNDATION

Articles & Tutorials

Solving Problems and Saving Time in Chemistry With Python

What motivates someone to learn how to code as a scientist? How do you harness the excitement of solving problems quickly and make the connection to the benefits of coding in your scientific work? This week on the show, we speak with Ben Lear and Christopher Johnson about their book “Coding For Chemists.”
REAL PYTHON podcast

Django’s Ecosystem

The Django library’s website has added a new page called “Django’s Ecosystem”. It has two parts: a source of resources for more information and a detailed package listing. The package listing is broken down by type and includes debugging tools, static file management, API development, and much more.
DJANGO SOFTWARE FOUNDATION

Workshop: Unpack OWASP Top 10 LLMs with Snyk

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Join Snyk and OWASP Leader Vandana Verma Sehgal on Tuesday, July 15 at 11:00AM ET for a live session covering: ✓ The top LLM vulnerabilities ✓ Proven best practices for securing AI-generated code ✓ Snyk’s AI-powered tools automate and scale secure dev. See live demos plus earn 1 CPE credit! here →
SNYK.IO sponsor

Use Keyword-Only Arguments in Dataclasses

Python dataclasses are a really nice feature for constructing classes that primarily hold or work with data. This post describes a small tip: using the kw_only=True aspect to enforce keyword arguments. See also the associated HN discussion.
CHRISTIAN HAMMOND

Application Logging in Python: Recipes for Observability

The logging module is powerful, but it can be somewhat complex. This tutorial covers structured JSON output, centralizing logging configuration, using contextvars to automatically enrich your logs with request-specific data, and other useful patterns for your observability needs.
AYOOLUWA ISAIAH • Shared by Ayooluwa Isaiah

Escaping Contravariance Hell

Ever used a Python type checker and got a frustrating error message like “This violates the Liskov substitution principle?” This post explains why “contravariance” is the underlying issue and how to deal with it.
QUANSIGHT.ORG • Shared by Marco Gorelli

Python for Nonprofits

This article and code demonstrates how to use Python to retrieve, analyze, visualize, and share nonprofit data. Even if you’re not dealing with nonprofits, the advice is useful to any data project.
GITHUB.COM/KBURCHFIEL

How to Migrate Your Python & Django Projects to uv

This post shows you one way of migrating an existing project to uv, including how to update your pyproject.toml file and what changes you might need to make if you use Docker.
TOBIAS MCNULTY

Dashboards With FastAPI, MongoDB, and WebSockets

This tutorial looks at how to develop a real-time order dashboard with FastAPI, MongoDB, and WebSockets to stream live data updates from the backend to the frontend.
ABDULAZEEZ ABDULAZEEZ ADESHINA • Shared by Mike Herman

Django Annual Impact Report

Every year the Django Software Foundation releases a report on its activities, including key milestones, community narratives, and what is coming in the future.
DJANGO SOFTWARE FOUNDATION

Python 3.14 Preview: Template Strings (T-Strings)

Python 3.14 introduces t-strings: a safer, more flexible alternative to f-strings. Learn how to process templates securely and customize string workflows.
REAL PYTHON

Quiz: Python 3.14 Preview: Template Strings (T-Strings)

REAL PYTHON

Implementing the Factory Method Pattern in Python

Learn how to use the Factory Method pattern in Python, when to apply it, how to refactor your code for it, and explore a reusable implementation.
REAL PYTHON course

Projects & Code

copier: Library and CLI for Rendering Project Templates

GITHUB.COM/COPIER-ORG

dataclasses-json: Serialize Data Classes to and From JSON

GITHUB.COM/LIDATONG

deltacycle: Discrete Event Simulation

GITHUB.COM/CJDRAKE

pyleak: Detect Leaked Asyncio Tasks, Threads, and More

GITHUB.COM/DEEPANKARM

ovld: Advanced Multiple Dispatch for Python Functions

GITHUB.COM/BREULEUX

Events

Weekly Real Python Office Hours Q&A (Virtual)

July 9, 2025
REALPYTHON.COM

Python Leiden User Group

July 10, 2025
PYTHONLEIDEN.NL

Python Atlanta

July 10 to July 11, 2025
MEETUP.COM

PyDelhi User Group Meetup

July 12, 2025
MEETUP.COM

DFW Pythoneers 2nd Saturday Teaching Meeting

July 12, 2025
MEETUP.COM

EuroPython 2025

July 14 to July 21, 2025
EUROPYTHON.EU

PyHEP.dev 2025

July 14 to July 18, 2025
CERN.CH

PyLadies Amsterdam: AI in Action: From ML Models to Games

July 15, 2025
MEETUP.COM

Happy Pythoning!
This was PyCoder’s Weekly Issue #689.
View in Browser »

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July 08, 2025 07:30 PM UTC

EuroPython

EuroPython 2025: Sponsor Job Postings

EuroPython is approaching, and as we prepare for another great edition, we’d like to take a moment to thank our community and sponsors for their continued support.

We&aposre also excited to share some fantastic job opportunities from our sponsors!

Bloomberg

Senior Software Engineer - Network Production Engineer


As a Network Production Engineer, you will be a critical member of the team responsible for the full lifecycle of our global network infrastructure that supports Bloomberg’s core products and services. This includes building and maintaining a network that is scalable, reliable and robust.
Our network is vast, connecting several large-scale Data Centers and over a hundred edge sites. It connects Bloomberg to hundreds of thousands of our clients, over 1,500 global exchanges and trading venues over private connectivity, Internet and Public Cloud. This is a unique opportunity to help build robust, highly scalable solutions that will power the future of how Bloomberg automates network infrastructure. You&aposll be trusted to design and work on tooling that builds on automation best practices and principles.


You need to have:

Learn more about this opportunity here.

Arm

ML/EDA DevOps Intern – Python Automation for AI in Hardware 

Location: Sophia Antipolis, France

Duration: 6 months

Start Date: Flexible (Autumn/Winter 2025) 

Description: Work at the cutting edge of ML and chip design automation. You&aposll help us integrate Python-based ML workflows into our EDA toolchain, improving the speed and accuracy of hardware design. Think: AI meets silicon. 

What you’ll do: 

If you&aposre excited about how Python can accelerate hardware design and enjoy playing with ML models, automation, and large-scale data, this is your space. 

Link to apply: https://careers.arm.com/job/sophia-antipolis/ml-eda-devops-intern/33099/82134400992 

Google Cloud

Find your next job at Google • Google Cloud • YouTube…

Create • design • code • build for everyone:

▶️ https://careers.google.com

We currently have 1,900+ job positions for Pythonistas worldwide:

▶️ https://careers.google.com/jobs/results?q=python

Numberly

Software Engineer - Backend / Fullstack

Numberly uses technology for marketing: we help clients better understand their customers by implementing systems to collect, analyze, and use data. With over 150 engineers (a third of Numberly&aposs talents), we work in autonomous teams to ensure everyone can influence technical and organizational choices. The NMP team develops and maintains the Numberly MarTech Platform, a suite of applications for campaign management across digital channels like email, SMS, push notifications, and social media. We prioritize code quality, modern technical stacks, and efficient processes. We seek a curious, autonomous individual with backend experience to contribute quickly to our team.

Responsibilities

Requirements

Benefits

Picnic Technologies

Senior Data Engineer

As a Data Engineer at Picnic, you are responsible for translating business needs into successful technical designs. You’ll work with large data sets, discover new insights and business opportunities, and promote business intelligence. 

It’s a role with plenty of freedom: think you&aposve spotted a way to ensure our large fleet of electric vehicles is used in the most efficient way? Go ahead and test, evaluate, and evolve your ideas alongside our Distribution team. 

More interested in customer behavior? Work on in-app analytics to ensure our mobile store remains smooth, speedy, and robust.

Join the ride: https://grnh.se/7elvekk21us

TravelPerk

 Senior Software Engineer (Full Stack and Backend) 

Required skills and experience:

The role is open in: London, Barcelona, Edinburgh and Berlin.

Interested? Apply here! https://grnh.se/mj0xp9zv1us

Apify

Open Source Engineer (TypeScript) 

What you’ll be working on? Apify has grown as the tool of choice for any Node.js/JS/TS engineer when it comes to web scraping and web automation. We are now also taking on the Python community. Our open-source tooling is used by tens of thousands of people worldwide. Check out the Crawlee library, our GitHub, and the vibrant community on Discord.

They say there are no perfect candidates, but you might be the one if you have:

Apply here

Ataccama

We&aposre on a mission to power a better future with data. Our platform helps all kinds of data professionals build high-quality, governed, reusable data products—earning us a spot as a Leader in the Gartner Magic Quadrant® and the backing of Bain Capital Tech Opportunities.

We&aposre aiming to lead in AI-powered cloud data management—and that means building a company where people love to work and grow. Our global team thrives on collaboration and lives by our values: Challenging Fun, ONE Team, Customer Centric, Candid and Caring, Aim High.

Join us as a Python Engineer and help transform Ataccama into a smarter, faster, more scalable SaaS platform.

Senior Python Senior Python Software Engineer

Visit here for more information

Snowflake

Commercial Solution Engineer

Amsterdam

Requirements

Preferred job requirements

July 08, 2025 06:31 PM UTC

Real Python

Exploring Protocols in Python

In Python, a protocol specifies the methods and attributes that a class must implement to be considered of a given type. Protocols are important in Python’s type hint system, which allows for static type checking through external tools, such as mypy, Pyright, and Pyre.

Before there were protocols, these tools could only check for nominal subtyping based on inheritance. There was no way to check for structural subtyping, which relies on the internal structure of classes. This limitation affected Python’s duck typing system, which allows you to use objects without considering their nominal types. Protocols overcome this limitation, making static duck typing possible.

In this video course, you’ll:

[ 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 ]

July 08, 2025 02:00 PM UTC

Python Insider

Python 3.14.0 beta 4 is here!

It’s the final 3.14 beta!

https://www.python.org/downloads/release/python-3140b4/

This is a beta preview of Python 3.14

Python 3.14 is still in development. This release, 3.14.0b4, is the last of four planned beta releases.

Beta release previews are intended to give the wider community the opportunity to test new features and bug fixes and to prepare their projects to support the new feature release.

We strongly encourage maintainers of third-party Python projects to test with 3.14 during the beta phase and report issues found to the Python bug tracker as soon as possible. While the release is planned to be feature-complete entering the beta phase, it is possible that features may be modified or, in rare cases, deleted up until the start of the release candidate phase (Tuesday 2025-07-22). Our goal is to have no ABI changes after beta 4 and as few code changes as possible after the first release candidate. To achieve that, it will be extremely important to get as much exposure for 3.14 as possible during the beta phase.

This includes creating pre-release wheels for 3.14, as it helps other projects to do their own testing. However, we recommend that your regular production releases wait until 3.14.0rc1, to avoid the risk of ABI breaks.

Please keep in mind that this is a preview release and its use is not recommended for production environments.

Major new features of the 3.14 series, compared to 3.13

Some of the major new features and changes in Python 3.14 are:

New features

Note that PEPs 734 and 779 are exceptionally new in beta 3!

(Hey, fellow core developer, if a feature you find important is missing from this list, let Hugo know.)

For more details on the changes to Python 3.14, see What’s new in Python 3.14. The next pre-release of Python 3.14 will be the first release candidate, 3.14.0rc1, scheduled for 2025-07-22.

Build changes

Incompatible changes, removals and new deprecations

Python install manager

The installer we offer for Windows is being replaced by our new install manager, which can be installed from the Windows Store or from its download page. See our documentation for more information. The JSON file available for download below contains the list of all the installable packages available as part of this release, including file URLs and hashes, but is not required to install the latest release. The traditional installer will remain available throughout the 3.14 and 3.15 releases.

More resources

And now for something completely different

All this talk of π and yet some say π is wrong. Tau Day (June 28th, 6/28 in the US) celebrates τ as the “true circle constant”, as the ratio of a circle’s circumference to its radius, C/r = 6.283185… The Tau Manifesto declares π “a confusing and unnatural choice for the circle constant”, in part because “2π occurs with astonishing frequency throughout mathematics”.

If you wish to embrace τ the good news is PEP 628 added math.tau to Python 3.6 in 2016:

When working with radians, it is trivial to convert any given fraction of a circle to a value in radians in terms of tau. A quarter circle is tau/4, a half circle is tau/2, seven 25ths is 7*tau/25, etc. In contrast with the equivalent expressions in terms of pi (pi/2, pi, 14*pi/25), the unnecessary and needlessly confusing multiplication by two is gone.

Enjoy the new release

Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organisation contributions to the Python Software Foundation.

Regards from a cloudy Helsinki, looking forward to Prague and EuroPython next week,

Your release team,
Hugo van Kemenade
Ned Deily
Steve Dower
Łukasz Langa

July 08, 2025 11:54 AM UTC

Python Software Foundation

Notice of Python Software Foundation Bylaws Change - Effective July 23, 2025

This post serves as notice that the Board of the Python Software Foundation has resolved to amend the Bylaws, effective July 23, 2025, to remove a condition of the Bylaws that would prevent the Foundation from complying with data privacy laws including those in effect in the European Union, the United Kingdom, and the State of California.

Section 3.8 of the Bylaws grants Voting Members the right to request the list of Voting Members’ names and email addresses. As written, this data must be given unconditionally, which would violate the previously mentioned laws. The amendment we are making grants the Foundation the ability to place conditions upon the use of that list in a way that allows us to comply with data privacy laws.

The full change can be found at https://github.com/psf/bylaws/pull/7/files

The Board determined that this change was time-sensitive and chose to amend the Bylaws without prior consultation with Voting Members. We greatly value the input of our membership in the governance of the Foundation. Therefore, we have opted to make only the most minimal change that will enable the Foundation to comply with data privacy laws and protect our members, while preserving the spirit of the text that the membership agreed to when adopting these Bylaws.

A future Bylaws update will be offered to the membership at a future election. As we are less than 3 months from the 2025 Board election, we are targeting the 2026 Board election to allow the membership to discuss further amendments for the membership to vote upon.

Thanks,

The Python Software Foundation Board

July 08, 2025 11:21 AM UTC

The Python Coding Stack

Python Backstage • Disassembling Python Code Using the `dis` Module

You're at the theatre. You're watching an impeccably produced play, and you're stunned at some of the visual effects. "How do they do that?", you're wondering.

But you're lucky to have a VIP pass, and you'll be touring backstage after the play to see what happens behind the scenes. The magic will be revealed.

What if you could go backstage at Python Theatre, too? What if you could see what happens behind the scenes when you run a Python program?

Well, you can.

Caveat: There are many levels of "behind the scenes" Python, ranging from understanding special methods and their connection to every Python operation to understanding the quirks of how functions, classes, data structures, and other operations work. But in this post, I'm going a bit further behind the scenes.

This post is relevant to CPython, the main Python implementation and almost certainly the one you're using, whether you know it or not.

I briefly pondered whether this article should be 3,000 or 30,000 words long. You'll be pleased to know that I opted for the shorter option!

Support The Python Coding Stack

Let's Go Backstage

Let's start with this short Python program:

All code blocks are available in text format at the end of this article • #1 • The code images used in this article are created using Snappify. [Affiliate link]

I'm sure you don't need me to explain this code. By the way, I’m showing line numbers in the code snippets in today’s post as these will be relevant later on.

But what happens when you run this script? The Python interpreter converts this code into an intermediate form called bytecode. This is a lower level of instructions, and it's what's executed to produce the desired output.

And you can peek at what this bytecode looks like using the dis module. This module allows you to disassemble this intermediate bytecode stage:

#2

You call the dis() function from the dis module and pass a string with the same code you had in the earlier program. Note that the indentation of the argument to dis, which is on multiple lines, is not the preferred format. This is needed because indentation matters in triple-quoted strings, and the triple-quoted string contains Python code, where indentation matters!

Here's the output from this code:

  0    RESUME       0
​
  2    LOAD_CONST   0 ('The Python Coding Stack')
       STORE_NAME   0 (publication)
​
  3    LOAD_NAME    1 (print)
       PUSH_NULL
       LOAD_NAME    0 (publication)
       CALL         1
       POP_TOP
       RETURN_CONST 1 (None)

"Gobbledegook. This is not Python", I can hear you shout at your screen. Let's explore what's happening here, without going too much down the rabbit hole. By the way, I'm using Python 3.13 in this article. Some instructions have changed in recent Python versions, so your output may be different if you're using older Python versions.

The output from dis.dis() is grouped into three segments separated by a blank line. This means there are three distinct sets of instructions in your program.

The first is the one we'll ignore for now. RESUME initialises the execution context for the code object. It’s used in all sorts of code, including simple scripts, but plays a more significant role in generators or async functions, where execution may pause and resume. In this case, "resume" means start from the beginning!

The second group contains two instructions:

  1. LOAD_CONST loads a constant to the top of the stack. The stack is the memory area used to store and manage data during code execution. The constant in this case is the string "The Python Coding Stack". This constant is the first one defined in this program, which is why there's a 0 after LOAD_CONST and before ('The Python Coding Stack) in the displayed output. Index 0 refers to the first item.

  2. STORE_NAME takes whatever is at the top of the stack, which is the string "The Python Coding Stack" that was placed there by the previous instruction, and stores it in the first name defined in the program. That's the identifier (name) publication. The 0 next to it shows it's the first name used in this program.

Python names are also called identifiers. I'll use these terms interchangeably in this post.

This first block, which consists of two instructions, refers to the first line in this program:

#3

Python performs two steps when executing this line.

The third group of instructions contains more steps:

  1. LOAD_NAME is similar to the LOAD_CONST instruction you saw earlier, but instead of a constant, it loads a name (identifier). It's now the turn of the name print. This is placed at the top of the stack. Note that there's a 1 between LOAD_NAME and (print) since print is the second identifier used in the program (index=1). The first one is publication used in the previous line of the program.

  2. PUSH_NULL is one you can safely ignore for now. It places a null value on the stack. It's a relatively new addition used to tidy up the stack. It provides consistency with other operations, such as instance method calls, which require self as the first argument.

  3. LOAD_NAME again. This time, the interpreter is loading the first identifier used in the code, publication, and places it on the stack. Recall that this refers to the string "The Python Coding Stack" following the first line of code.

  4. CALL, you guessed it, calls the function that's on the stack with one argument—that's the number 1 next to CALL. The argument is also on the stack following the previous instruction.

  5. POP_TOP is another instruction you can ignore. The previous step called print(), which returns None. Since this None value made it onto the top of the stack, POP_TOP removes it since it's not required.

  6. RETURN_CONST shows that this is the end of your script, which Python treats as a code object. It returns None to the Python interpreter running this script. This is the second constant used in this program, hence the 1 shown. The first constant is the string "The Python Coding Stack".

Those are plenty of steps for a two-line program!

There's one bit of the output from dis.dis() I haven't mentioned yet. This is the number in the first column shown at the start of each block of instructions.

Let's ignore the 0 ahead of RESUME. The 2 displayed before the first block of two instructions refers to the line number within the code. The code in this case is whatever is included in the triple-quoted string you pass to dis.dis(). But why isn't it 1 since publication = "The Python Coding Stack" is the first line of your mini two-line program?

Because it isn't the first line. The triple-quoted string starts right after the first """. But what's there right after the """? Here's your answer:

#4

There's a newline character, \n, right after the triple-quoted string. So, what appears as the first line is actually the second. The first line is blank. There's also a blank fourth line!

In my code, I wrote the following:

#5

I did this only to make the code more readable by placing the triple quotes on separate lines. You can remove the first and last blank lines in your mini-program if you prefer:

#6

Rerun this script with this change and you'll see the line numbers listed as 1 and 2 since the code block in the triple-quoted string now only has two lines—there are no blank lines.

Disassembling Functions

Let's place your two-line program in a function:

#7

Note that this time, you pass the function name directly to dis.dis() and not a string with the code. This disassembles the function:

  3    RESUME       0
​
  4    LOAD_CONST   1 ('The Python Coding Stack')
       STORE_FAST   0 (publication)
​
  5    LOAD_GLOBAL  1 (print + NULL)
       LOAD_FAST    0 (publication)
       CALL         1
       POP_TOP
       RETURN_CONST 0 (None)

Let's look at the differences from the example presented earlier in this post.

The line numbers now refer to the lines within the whole script. So, the first line is line 3, which is the line that includes def do_something(). And the two blocks of instructions now refer to lines 4 and 5 within the script.

The second block of instructions (if you count RESUME as the first block) is nearly identical to the earlier one. The interpreter loads the constant "The Python Coding Stack" and places it at the top of the stack, and then it stores it in publication. But there are two differences:

  1. The first difference is not really important, but here it is anyway. The two constants in the code are still the string "The Python Coding Stack" and None, but the compiler stores them in a different order, so the string is the second constant, the one with index 1.

  2. The instruction to store the value by linking it to an identifier is now STORE_FAST rather than STORE_NAME. The identifier publication is a local variable within the function, so the interpreter has a more efficient process to store the data.

How about the final block, the one that refers to the final line of code in the function?

  1. In the previous example, when you ran the two-line program as a script, the first two instructions in the final block were LOAD_NAME 1 (print) and PUSH_NULL. Now, these are replaced by LOAD_GLOBAL 1 (print + NULL). The name print is a global variable, so the LOAD_GLOBAL instruction deals with this specific case. It also pushes a null value on the stack so that there's no need for an explicit PUSH_NULL. But you can ignore this null value.

  2. The second instruction is LOAD_FAST rather than LOAD_NAME. Once again, LOAD_FAST deals with loading a local variable, which is more efficient than loading a global variable.

The rest is the same as in the previous example, except that None is now the first constant (index=0) rather than the second one.

Some Optimisation You'll (Nearly) Never Need To Do

Let's look at this code now:

#8

The function do_something() finds the maximum value within the list numbers 100,000 times. This is, of course, a waste of time since the answer will remain the same value, 8, but bear with me…

Here's the human-readable representation of the bytecode that's output by dis.dis():

  5     RESUME             0
​
  6     LOAD_GLOBAL        1 (range + NULL)
        LOAD_CONST         1 (100000)
        CALL               1
        GET_ITER
    L1: FOR_ITER           18 (to L2)
        STORE_FAST         0 (_)
​
  7     LOAD_GLOBAL        3 (max + NULL)
        LOAD_GLOBAL        4 (numbers)
        CALL               1
        POP_TOP
        JUMP_BACKWARD      20 (to L1)
​
  6 L2: END_FOR
        POP_TOP
        RETURN_CONST       0 (None)

We'll proceed more quickly now that you're familiar with some of these terms. Ignore RESUME and let's focus on the first line of code within the function, which is on line 6. This is the for statement:

  1. LOAD_GLOBAL loads the global name range (and also pushes a null value on the stack, which you don't care about much).

  2. LOAD_CONST loads the constant integer 100000, which is the argument you pass to range() in the for loop statement.

  3. CALL shows it's now time to call range() with one argument.

  4. GET_ITER represents the start of the iteration process. Python creates an iterator from the iterable used in the for statement. In this case, it creates a range_iterator object from the iterable object range. You can read more about iterables, iterators, the iterator protocol, and .__iter__() in these posts:

  5. FOR_ITER tries to fetch the next item from the iterator that's just been placed on the stack in the previous step. The L1 shown next to FOR_ITER is a label used to show different parts of the for loop. There are two options for what happens next:

    • If the iterator returns a value, then the interpreter moves on to the next instruction, STORE_FAST, which you'll get to soon in this bulleted list.

    • If the iterator is exhausted and raises a StopIteration exception (see links above to find out more about iteration), then it jumps forward to the label L2. This refers to the instructions needed to end the for loop. The 18 next to FOR_ITER refers to the number of bytes to jump forward within the bytecode. Typically, each instruction is two bytes, but some need more bytes. So don't worry too much about this number. The human-readable output shows you the correct point using the label L2.

  6. STORE_FAST stores the value yielded by the iterator to a local variable. Recall that the _FAST subscript refers to local variables within functions. In this case, this is stored in the local variable _.

The body of this for loop includes just one line, which is line 7 in the script. Let's speed through this by combining some instructions into the same bullet point:

And when FOR_ITER cannot fetch any more values from the iterator, as you may recall from bullet point 5 above, it jumps to the instruction labelled L2. Note that this also refers to line 6 in the code:

  1. END_FOR: Do you need me to explain this? This is the instruction that actually gets rid of the exhausted iterator object from the stack.

  2. POP_TOP: This is used for consistency and to tidy up the stack. You can safely ignore it!

  3. RETURN_CONST: This is the end of the code object, which in this case is the function definition. So, it returns None to the Python interpreter.

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And now, for the (possibly useless) optimisation

There are three LOAD_GLOBAL instructions in the bytecode for this function. But you’ve come across the LOAD_FAST instruction earlier in this article, which, as the name implies, is faster. Can we replace some or all of the LOAD_GLOBAL instructions with LOAD_FAST?

Let's start by timing this code first. The changes are highlighted in green:

#9

The timeit.repeat() call calls the function do_something() 1,000 times and repeats this process five times by default. Therefore, timings is a list showing five times, each one showing how long it took to call do_something() 1,000 times. You can then take an average of these five readings:

[6.457241459000215,
 6.45811324999886,
 6.480874916000175,
 6.472584875002212,
 6.502984124999784]
Average time: 6.47435972500025 seconds

The average time to call do_something() 1,000 times is 6.47 seconds. Of course, your mileage may vary depending on your computer and what else is running in the background.

Now, you can replace the global list numbers with a local list. The easiest way to do this is to include a parameter in the function definition and then pass the list as an argument when you call the function:

#10

First, let's see the output from dis.dis():

  6     RESUME             0
​
  7     LOAD_GLOBAL        1 (range + NULL)
        LOAD_CONST         1 (100000)
        CALL               1
        GET_ITER
    L1: FOR_ITER           14 (to L2)
        STORE_FAST         1 (_)
​
  8     LOAD_GLOBAL        3 (max + NULL)
        LOAD_FAST          0 (data)
        CALL               1
        POP_TOP
        JUMP_BACKWARD      16 (to L1)
​
  7 L2: END_FOR
        POP_TOP
        RETURN_CONST       0 (None)

Ignoring RESUME, the first block of instructions is almost identical. The line number is different—it's now line 7, since an additional import in this script has pushed everything down by a line. The number of bytes in FOR_ITER is also different, but let's ignore this.

In the second main block, linked to line 8 of the code, there's one important change. The second instruction is now LOAD_FAST instead of LOAD_GLOBAL. This loads data, which is now a local variable since data is the variable created when you pass the global list numbers as an argument assigned to the parameter data.

And here's the output from the call to timeit.repeat():

[6.2737918330021785,
 6.286419416999706,
 6.250972666999587,
 6.234414000002289,
 6.241118416997779]
Average time: 6.257343266800308 seconds

The average time is down to about 6.26 seconds from the previous 6.47 seconds. It's not a huge difference, but it shows how accessing local variables using LOAD_FAST is more efficient than accessing global variables using LOAD_GLOBAL.

But there are two more LOAD_GLOBAL instructions in the bytecode. But, I hear you say, these are references to the built-in names range and max. How can you bypass this limitation?

Let's start with max. Have a look at this code:

#11

You define a local variable max_ and make it equal to the built-in max. This way, each time you need to refer to max_ when you call it in the for loop, you use a local variable instead of a global one:

  6     RESUME             0
​
  7     LOAD_GLOBAL        0 (max)
        STORE_FAST         1 (max_)
​
  8     LOAD_GLOBAL        3 (range + NULL)
        LOAD_CONST         1 (100000)
        CALL               1
        GET_ITER
    L1: FOR_ITER           11 (to L2)
        STORE_FAST         2 (_)
​
  9     LOAD_FAST          1 (max_)
        PUSH_NULL
        LOAD_FAST          0 (data)
        CALL               1
        POP_TOP
        JUMP_BACKWARD      13 (to L1)
​
  8 L2: END_FOR
        POP_TOP
        RETURN_CONST       0 (None)

Note how in the block of instructions linked to line 9, you no longer have any LOAD_GLOBAL. Both instructions that need to load data are now LOAD_FAST, since both max_ and data are local variables.

However, you now have an additional block, the one linked to line 7 in your code (max_ = max), which is not included in the original code. Here, you still need to use LOAD_GLOBAL and also have an additional STORE_FAST to store the max function object in the local variable max_. But you only need to do this once!

What about the time it takes? Drum roll…

[6.103223165999225,
 6.050973375000467,
 6.056473458000255,
 6.06607754200013,
 6.083692415999394]
Average time: 6.072087991399894 seconds

The average time is now 6.07 seconds, down from 6.25 seconds when you used the global max() within the for loop. Even though you still have LOAD_GLOBAL linked to line 7 and the additional overhead of storing this to max_, each iteration of the for loop is a bit quicker since you can now use LOAD_FAST each time you call max_().

The difference in performance is not huge, but it was even more noticeable in older versions of Python.

Can you use the same trick with range? Yes, you can, but you won't gain any speed advantage. Can you see why?

Whereas your code needs to use LOAD_FAST in each iteration of the for loop when referring to max_ and data, it only uses LOAD_GLOBAL once to refer to range. Therefore, it doesn't make sense to replace range with a local variable equivalent. You still need to use LOAD_GLOBAL once to load the built-in range, but then you waste time reassigning it to a local variable. So let's not do this! You can try it out if you wish.

[I've been writing this article for a while, on and off, and as I was writing, my friend and fellow author wrote a somewhat related article that dives even deeper into this topic. This last comparison was inspired by his article: Why This Python Performance Trick Doesn’t Matter Anymore]

Final Words

There's so much more we could explore by disassembling code. But I promised a short-ish article, so I'll stop here. Perhaps I'll post about this again in the future.

Disassembling code using dis.dis() is helpful in understanding Python better and writing more efficient code. In some cases, you can look for ways to optimise your code by looking at the bytecode. In some other use cases, you may have a stubborn bug you can't expose, and the bytecode may give you a different perspective.

If you want to explore a bit further on your own, you can start with the dis module's documentation code at dis — Disassembler for Python bytecode, and specifically at the list of possible instructions further down the page: https://docs.python.org/3/library/dis.html#dis.Instruction.

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Code in this article uses Python 3.13

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Support The Python Coding Stack

For more Python resources, you can also visit Real Python—you may even stumble on one of my own articles or courses there!

Also, are you interested in technical writing? You’d like to make your own writing more narrative, more engaging, more memorable? Have a look at Breaking the Rules.

And you can find out more about me at stephengruppetta.com

Further reading related to this article’s topic:

Appendix: Code Blocks

Code Block #1
publication = "The Python Coding Stack"
print(publication)
Code Block #2
import dis

dis.dis(
"""
publication = "The Python Coding Stack"
print(publication)
"""
)
Code Block #3
publication = "The Python Coding Stack"
Code Block #4
"""
"first line?"
"second line?"
"""
# '\n"first line?"\n"second line?"\n'
Code Block #5
import dis

dis.dis(
"""
publication = "The Python Coding Stack"
print(publication)
"""
)
Code Block #6
import dis

dis.dis(
"""publication = "The Python Coding Stack"
print(publication)"""
)
Code Block #7
import dis

def do_something():
    publication = "The Python Coding Stack"
    print(publication)

dis.dis(do_something)
Code Block #8
import dis

numbers = [2, 4, 6, 8]

def do_something():
    for _ in range(100_000):
        max(numbers)

dis.dis(do_something)
Code Block #9
import dis
import timeit

numbers = [2, 4, 6, 8]

def do_something():
    for _ in range(100_000):
        max(numbers)

dis.dis(do_something)

timings = timeit.repeat(
    "do_something()",
    number=1_000,
    globals=globals(),
)

print(timings)
print(f"Average time: {sum(timings) / len(timings)} seconds")
Code Block #10
import dis
import timeit

numbers = [2, 4, 6, 8]

def do_something(data):
    for _ in range(100_000):
        max(data)

dis.dis(do_something)

timings = timeit.repeat(
    "do_something(numbers)",
    number=1_000,
    globals=globals(),
)

print(timings)
print(f"Average time: {sum(timings) / len(timings)} seconds")
Code Block #11
# ...

def do_something(data):
    max_ = max
    for _ in range(100_000):
        max_(data)

# ...

For more Python resources, you can also visit Real Python—you may even stumble on one of my own articles or courses there!

Also, are you interested in technical writing? You’d like to make your own writing more narrative, more engaging, more memorable? Have a look at Breaking the Rules.

And you can find out more about me at stephengruppetta.com

July 08, 2025 08:51 AM UTC

Seth Michael Larson

Setting Discord status from physical GameCube console

Have you ever seen one of your friends playing a game or listening to music in their Discord “status”? That feature is called “Rich Presence”.

What if you want to show your Discord friends that you're playing your GameCube? and I don't mean an emulator like Dolphin, I'm talking about a physical console from 2001.

We can do just that with a simple Python script and a Memcard Pro GC. The Memcard Pro GC is an internet-connected GameCube Memory Card virtualizer. The Memcard Pro GC automatically switches to a virtual Memory Card (VMC) for specific games when launched with a “Game ID”-aware launcher like Swiss or the FlippyDrive.

We can use this automatic VMC switching feature to “detect” when a game is being played on a physical GameCube and update Discord with a new Rich Presence.

Configuring the Memcard Pro GC

You can buy a Black or White Memcard Pro GC from 8BitMods for ~$45 USD plus shipping. They are oftentimes sold out, so you may need to be patient for a re-stock.

There is a documented first-time setup page. If you're on Ubuntu or Linux like me, you can use mkfs to format the microSD filesystem to exFAT:

# Find your microSD card. Make sure it's the right size
# and device so you don't accidentally overwrite your
# storage drive(s) / boot drive. You want the /dev directory
sudo fdisk -l

# Might need to fiddle with microSD card, un-and-remount,
# make sure it's not in read-only mode on the adapter.
sudo apt-get install exfat-fuse
sudo mkfs.exfat -n memcardprogc </dev/...>

You should see something like this when you're done:

Writing volume boot record: done
Writing backup volume boot record: done
Fat table creation: done
Allocation bitmap creation: done
Upcase table creation: done
Writing root directory entry: done
Synchronizing...
exFAT format complete!

After that put all the files on the new filesystem and plug the card into a GameCube or Wii, power the console on, and let the device install the firmware.

NOTE: When I set up the Memcard Pro GC I wasn't able to get the latest firmware at the time (v2.0.4) to work without the device endlessly boot-looping. I tried downgrading to an earlier version of the firmware (v2.0.0) and the device worked flawlessly. Maybe I'll try again if the firmware is updated again and see what happens.

Once the device is online do the setup for connecting the device to WiFi. From here you can access the “Settings” page on your phone or computer.

For automatic detection to work as expected we need to create a default memory card that isn't associated with a Game ID (I used MemoryCard1) and to disable the “Load Last Card when MemCard Boots” feature. Now on boot-up the Memcard Pro GC will use the MemoryCard1 instead of whatever your last played game was by default.

Now we can create VMCs for every game which the Memcard Pro GC will automatically do when we launch a new game through our Game ID launcher. This can be done quickly in CubeBoot on the FlippyDrive by selecting games in your library one-by-one but not launching into the game completely. This is enough to trigger the Memcard Pro GC to load the VMC for a given Game ID.

NOTE: If you plan to do anything with the FTP server with the Memcard Pro GC you need to configure a username and password, enable the server, and crucially completely power off and power on the console for the FTP server to start on boot. I lost around 30 minutes trying to get FTP to work to this oversight.

Downloading assets

So we'll need two different assets from the GameTDB. First we'll need a list of valid Game IDs so our program can distinguish between a VMC not associated with an actual game and the cover artwork for the games we own.

Download the wiidb.txt database in your preferred language (I am using English) and “Covers” for each region of games that your collection contains. I own games from the USA (NSTC) and Japan (NSTC-J) regions, so I downloaded those covers for those regions.

The covers from GameTDB are 160x224 pixels which is below the minimums that Discord requires for Art Assets (512x512) so we can scale them up with ImageMagick after we've unzipped the images into a directory.

First we need to isolate only the cover artworks for games in our library. For this we can query the VMCs on our Memcard Pro GC after we've setup VMCs for each game. Replace the IP address (192.168.0.43) with the one used for your own Memcard Pro GC:

$ curl -X POST \
  --data '{"limit":100,"start":0}' \
  http://192.168.0.43/api/query | \
  pcregrep -o1 'gameID":\s*"([A-Z0-9]{6})'

Example list of Game IDs

GPVE01
GC6E01
G8MJ01
G4SE01
GAFE01
GEZE8P
GKYE01
GLME01
GM4E01
GP5E01
GP6E01
GP7E01
GFTE01
GMPE01
G8ME01
GPIE01
GSNE8P
GZLE01
GALE01
GMSE01
GS8E7D
GXSE8P
GSOE8P
G9SE8P
G2XE8P
PZLE01
GPVJ01

Save this list into a file and make sure there's a trailing newline. Now we can use the list we generated to select and resize only the game cover artworks we plan to use. Assuming we have a directory of the original images named covers/ and an empty directory named discord-icons/

sudo apt-get install imagemagick

cat gameids.txt | while read gameid 
do
   convert covers/$gameid.png -scale 400% discord-icons/$gameid.png
done

We don't want ImageMagick to blur or interpolate the images, so we use -scale instead of -resize. At this point you should have a directory full of upscaled images to use with your Discord application.

Creating the Discord Application

To use “Rich Presence” you need a Discord application. There are plenty of tutorials on how to create one of these. I named the application “GameCube” so the Discord status will say “Seth is playing GameCube”. You'll need to upload all the newly resized game cover artwork images under the Rich Presence > Art Assets section.

NOTE: Uploading tons of Art Assets to Discord applications is kinda annoying. Super aggressive rate-limiting, upload them in small batches and take your time. Duplicates aren't a huge issue so don't sweat it!

Copy your Discord application ID into the script below.

Querying the Memcard Pro GC

The Memcard Pro GC provides a simple JSON HTTP API that can be queried for the current state of the VMC. Requesting GET /api/currentState returns a JSON body including the current Game ID and game name for the VMC:

{
  "gameName": "Paper Mario: The Thousand-Year Door",
  "gameID": "G8ME0100",
  "currentChannel": 1,
  "rssi": -40
}

We can periodically call this API and then, using the pypresence library, we can update our Discord Rich Status. We need to have fairly lax timeouts and retries to avoid unnecessarily setting and clearing the Rich Status due to things like the memory card not responding fast enough, the Memcard Pro GC is a fairly low powered device:

Full Python script source code

import urllib3
import pypresence
import time
import re
import pathlib

ROOT_DIR = pathlib.Path(__file__).absolute().parent
with (ROOT_DIR / "wiitdb.txt").open(mode="r") as f:
    GAME_ID_TO_NAMES = {
        gid: name
        for gid, name in re.findall(
            r"^([A-Z0-9]{6}) = (.+?)$",
            f.read(),
            re.DOTALL | re.MULTILINE,
        )
    }


class MemcardGCPresence:
    def __init__(
        self, memcardgc_host: str, discord_app_id: str
    ):
        self.host = memcardgc_host
        self.http = urllib3.HTTPConnectionPool(
            memcardgc_host
        )
        self.discord = pypresence.Presence(
            client_id=discord_app_id
        )
        self.discord.connect()
        self.active_game_id: str | None = None
        self.consecutive_errors = 0

    def poll_forever(self):
        try:
            while True:
                poll_start = time.time()
                self.poll_once()

                # Only set Discord status every 15 seconds.
                poll_duration = time.time() - poll_start
                time.sleep(max([0, 15 - poll_duration]))
        finally:
            self.reset()
            self.close()

    def poll_once(self) -> None:
        try:
            resp = self.http.request(
                "GET",
                "/api/currentState",
                timeout=3,
                redirect=False,
                retries=False,
            )
            if resp.status != 200:
                raise ValueError("Invalid HTTP status")
            data = resp.json()
            game_id_with_revision = data["gameID"]
            # We use the GameID without the revision
            # to determine the game cover artwork.
            game_id = game_id_with_revision[:6]
            game_name = data["gameName"]
        except (
            urllib3.exceptions.HTTPError,
            ValueError,
            KeyError,
        ):
            self.consecutive_errors += 1
            if (
                self.active_game_id
                and self.consecutive_errors > 3
            ):
                self.reset()
            return

        # Game ID isn't a known game. Might be the default
        # memory card or a ROM hack that we don't know about.
        if game_id not in GAME_ID_TO_NAMES:
            self.reset()
            return

        # New game, set Rich Presence.
        if game_id_with_revision != self.active_game_id:
            self.discord.update(
                activity_type=pypresence.ActivityType.PLAYING,
                state=game_name,
                start=int(time.time()),
                # Discord lowercases all filenames.
                large_image=game_id.lower(),
            )
            self.active_game_id = game_id_with_revision

    def reset(self) -> None:
        if self.active_game_id is not None:
            print(f"Stopped playing {self.active_game_id}")
        self.consecutive_errors = 0
        self.active_game_id = None
        self.discord.clear()

    def close(self) -> None:
        self.http.close()
        self.http = None
        try:
            self.discord.clear()
        except Exception:
            pass
        self.discord.close()
        self.discord = None


if __name__ == "__main__":
    memcardgc = MemcardGCPresence(
        # Default IP address for the Memcard Pro GC.
        # Update if necessary. Include your own
        # Discord App ID.
        memcardgc_host="192.168.0.43",
        discord_app_id="<Discord App ID>",
    )
    memcardgc.poll_forever()

The above script is open source under the MIT license.

So now when you're playing your GameCube at home you can run this script, and you should see your Discord status change to the game you are playing. Magic!

Let me know what games you still play on your GameCube! :)

July 08, 2025 12:00 AM UTC

July 07, 2025

Real Python

Free-Threaded Python Unleashed and Other Python News for July 2025

Last month was a watershed moment for Python. Python 3.14 beta 3, released mid-June, isn’t just another pre-release. It’s the first build in which the long-awaited free-threaded or “no-GIL” variant is officially supported. That’s right: a no-GIL Python is finally taking shape.

The interpreter isn’t the only thing making headlines, though.

Below you’ll find a curated tour of the latest Python developments, broken down by area so you can zero in on your favorite topic.

Join Now: Click here to join the Real Python Newsletter and you'll never miss another Python tutorial, course, or news update.

Core Python Development

CPython’s core improved in concurrency and stability last month. The third beta of Python 3.14 now officially supports the no-GIL build for CPython, signaling a new era of multi-core Python. That same release also brought multiple interpreters into the standard library, while earlier in June, the core team rolled out a coordinated batch of security patches across all maintained versions to keep your deployments safe.

Python 3.14 Beta 3 Lands Free-Threaded Support

On June 17, the release team pushed Python 3.14.0b3 to the mirrors. What sets this beta apart is the acceptance of PEP 779, which promotes the GIL-less build from experimental to supported. That single status change means binary wheels, continuous integration images, and even hosting platforms can start treating free-threaded Python as a first-class citizen.

Why is that a big deal? The Global Interpreter Lock (GIL) has long limited Python’s ability to use multiple CPU cores effectively. With a supported no-GIL build, data science and high-throughput web workloads can reach for true multithreading without resorting to subprocess orchestration.

While this is exciting, many caveats remain. Native extensions must be rebuilt, and performance trade-offs still exist. Still, the road to production looks increasingly well paved, and the Python community can start preparing for Phase III, which would make the free-threaded build the default.

Multiple Interpreters Join the Standard Library

Beta 3 also finalizes another language proposal with PEP 734 – Multiple Interpreters in the Stdlib. This PEP supersedes PEP 554 and adds Python subinterpreters to the standard library via the concurrent.interpreters module. This addition hoists the ability to run multiple interpreters from the C API up to the standard library, paving the way to making this approach to concurrency more popular in the Python ecosystem.

Together with earlier additions like t-string literals, Python 3.14 is shaping up to be a feature-packed release when it ships this October.

Coordinated Security Releases Ensure Safety

If you maintain anything on a Long-Term Support (LTS) Python version, then block off some time to upgrade. Earlier last month, the core team released fixes for multiple tarfile CVE identifiers and an ipaddress memory bug across all supported branches: 3.13.4, 3.12.11, 3.11.13, 3.10.18, and 3.9.23. If you think this might affect you, then read the full details on the Python Insider blog.

And—as it sometimes goes in life and software—just a week later, Python 3.13.5 shipped to fix regressions introduced by 3.13.4. These included, most notably, a Windows build failure for C extensions and unintended delays when raising TypeError exceptions in generators. If you’re on the 3.13 series, then you can upgrade directly to 3.13.5.

Library and Tooling Highlights

From data science to web development, Python’s ecosystem is keeping pace with the core language’s progress.

Read the full article at https://realpython.com/python-news-july-2025/ »

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July 07, 2025 02:00 PM UTC

Python Bytes

#439 That Astral Episode

<strong>Topics covered in this episode:</strong><br> <ul> <li><em>* <a href="https://docs.astral.sh/ty?featured_on=pythonbytes">ty documentation site and uv migration guide</a></em>*</li> <li><em>* <a href="https://pydevtools.com/blog/uv-build-backend/?featured_on=pythonbytes">uv build backend is now stable</a> + other Astral news</em>*</li> <li><em>* <a href="https://www.pythonmorsels.com/refactoring-boolean-expressions/?featured_on=pythonbytes">Refactoring long boolean expressions</a></em>*</li> <li><em>* <a href="https://github.com/eightBEC/fastapi-ml-skeleton?featured_on=pythonbytes">fastapi-ml-skeleton</a></em>*</li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=fClLeYVRhvc' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="439">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p><strong>Sponsored by</strong> Sentry: <a href="http://pythonbytes.fm/sentry">pythonbytes.fm/sentry</a></p> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href="https://fosstodon.org/@mkennedy">@mkennedy@fosstodon.org</a> / <a href="https://bsky.app/profile/mkennedy.codes?featured_on=pythonbytes">@mkennedy.codes</a> (bsky)</li> <li>Brian: <a href="https://fosstodon.org/@brianokken">@brianokken@fosstodon.org</a> / <a href="https://bsky.app/profile/brianokken.bsky.social?featured_on=pythonbytes">@brianokken.bsky.social</a></li> <li>Show: <a href="https://fosstodon.org/@pythonbytes">@pythonbytes@fosstodon.org</a> / <a href="https://bsky.app/profile/pythonbytes.fm">@pythonbytes.fm</a> (bsky)</li> </ul> <p>Join us on YouTube at <a href="https://pythonbytes.fm/stream/live"><strong>pythonbytes.fm/live</strong></a> to be part of the audience. Usually <strong>Monday</strong> at 10am PT. Older video versions available there too.</p> <p>Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to <a href="https://pythonbytes.fm/friends-of-the-show">our friends of the show list</a>, we'll never share it.</p> <p><strong>Michael #1: <a href="https://docs.astral.sh/ty?featured_on=pythonbytes">ty documentation site and uv migration guide</a></strong></p> <ul> <li>via Skyler Kasko</li> <li>Astral created <a href="https://docs.astral.sh/ty?featured_on=pythonbytes">a documentation site for ty</a> (PR <a href="https://github.com/astral-sh/ty/pull/744?featured_on=pythonbytes">#744</a> in release <a href="https://github.com/astral-sh/ty/releases/tag/0.0.1-alpha.13?featured_on=pythonbytes">0.0.1-alpha.13</a>).</li> <li>Astral added <a href="https://docs.astral.sh/uv/guides/migration/pip-to-project?featured_on=pythonbytes">a page on migrating from pip to a uv project</a> in the uv documentation. (PR <a href="https://github.com/astral-sh/uv/pull/12382?featured_on=pythonbytes">#12382</a> in release <a href="https://github.com/astral-sh/uv/releases/tag/0.7.19?featured_on=pythonbytes">0.7.19</a>).</li> <li><a href="https://talkpython.fm/episodes/show/506/ty-astrals-new-type-checker-formerly-red-knot?featured_on=pythonbytes">Talk Python episode on ty</a>.</li> </ul> <p><strong>Brian #2: <a href="https://pydevtools.com/blog/uv-build-backend/?featured_on=pythonbytes">uv build backend is now stable</a> + other Astral news</strong></p> <ul> <li><p><a href="https://pydevtools.com/blog/uv-build-backend/?featured_on=pythonbytes"><strong>The uv build backend is now stable</strong></a></p> <ul> <li>Tim Hopper via Python Developer Tooling Handbook</li> </ul></li> <li><p><a href="https://bsky.app/profile/crmarsh.com/post/3lszmqo27b224?featured_on=pythonbytes">From Charlie Marsh</a></p> <ul> <li>“The uv build backend is now stable, and considered ready for production use. An alternative to setuptools, hatchling, etc. for pure Python projects, with a focus on good defaults, user-friendly error messages, and performance. When used with uv, it's 10-35x faster.”</li> <li><p>“(In a future release, we'll make this the default.)”</p> <div class="codehilite"> <pre><span></span><code><span class="k">[build-system]</span> <span class="n">requires</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;uv_build&gt;=0.7.19,&lt;0.8.0&quot;</span><span class="p">]</span> <span class="n">build-backend</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;uv_build&quot;</span> </code></pre> </div></li> <li><p>I believe it’s faster, but I agree with <a href="https://bsky.app/profile/snarky.ca/post/3lt3ozeg7jk27?featured_on=pythonbytes">Brett Canno</a>n in asking “What's being benchmarked? I'm not sure what a "backend sync" is referring to other than maybe installing the build back-end?”</p></li> </ul></li> <li><p>See also: <a href="https://www.youtube.com/watch?v=TiBIjouDGuI">uv: Making Python Local Workflows FAST and BORING in 2025</a> - Hynek</p></li> </ul> <p><strong>Brian #3: <a href="https://www.pythonmorsels.com/refactoring-boolean-expressions/?featured_on=pythonbytes">Refactoring long boolean expressions</a></strong></p> <ul> <li><p>Trey Hunner</p></li> <li><p>This is applied boolean logic, and even folks who learned this in a CS program probably did so early on, and may have forgotten it.</p></li> <li><p>How can you improve the readability of long Boolean expressions in Python?</p> <ul> <li><p>Put parens around the whole expression and separate clauses onto different lines</p></li> <li><p>Where to put boolean operators between clauses? at the end of the line or the beginning?</p> <ul> <li>PEP8 recommends the beginning <div class="codehilite"> <pre><span></span><code><span class="k">if</span> <span class="p">(</span><span class="n">expression1</span> <span class="ow">and</span> <span class="n">expression2</span> <span class="ow">and</span> <span class="n">expression3</span><span class="p">):</span> <span class="o">...</span> </code></pre> </div></li> </ul></li> <li><p>Naming sub-expressions with variables</p> <ul> <li>Odd downside that wouldn’t occur to me. All expressions are evaluated, thus not taking advantage of expression short-circuiting.</li> </ul></li> <li><p>Naming operations with functions</p> <ul> <li>Less readable, but takes advantage of short-circuiting</li> </ul></li> <li><p>Using De Morgan’s Law : replacing a compound expression with a similar (and hopefully easier to read) expression</p> <div class="codehilite"> <pre><span></span><code><span class="c1"># neither: we want both to be false</span> <span class="ow">not</span> <span class="p">(</span><span class="n">a</span> <span class="ow">or</span> <span class="n">b</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="ow">not</span> <span class="n">a</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">b</span><span class="p">)</span> <span class="c1"># never_both: at least one false</span> <span class="ow">not</span> <span class="p">(</span><span class="n">a</span> <span class="ow">and</span> <span class="n">b</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="ow">not</span> <span class="n">a</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">b</span><span class="p">)</span> </code></pre> </div></li> </ul></li> </ul> <p><strong>Michael #4: <a href="https://github.com/eightBEC/fastapi-ml-skeleton?featured_on=pythonbytes">fastapi-ml-skeleton</a></strong></p> <ul> <li>FastAPI Skeleton App to serve machine learning models production-ready.</li> <li>This repository contains a skeleton app which can be used to speed-up your next machine learning project.</li> <li>The code is fully tested and provides a preconfigured <code>tox</code> to quickly expand this sample code.</li> <li>A sample regression model for house price prediction is included in this project.</li> <li>Short write up on "<a href="https://blobs.pythonbytes.fm/what-does-set-a-do.html">What does set -a do?</a>"</li> </ul> <p><strong>Extras</strong></p> <p>Brian:</p> <ul> <li><a href="https://www.oregoncountryfair.org?featured_on=pythonbytes">OCF</a></li> </ul> <p>Michael:</p> <ul> <li><p>via Wei Lee</p></li> <li><p>Extra Airflow ruff rules:</p> <p>Starting from Ruff version 0.11.13, most changes from Airflow 2 to Airflow 3 can be automated using AIR3. (It’s still in preview so a “—-preview” flag is needed)</p> <p>e.g., if you have the following Airflow 2 code</p> <div class="codehilite"> <pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span> <span class="kn">from</span><span class="w"> </span><span class="nn">airflow.models</span><span class="w"> </span><span class="kn">import</span> <span class="n">DAG</span> <span class="kn">from</span><span class="w"> </span><span class="nn">airflow.operators.empty</span><span class="w"> </span><span class="kn">import</span> <span class="n">EmptyOperator</span> <span class="k">with</span> <span class="n">DAG</span><span class="p">(</span> <span class="n">dag_id</span><span class="o">=</span><span class="s2">&quot;my_dag_name&quot;</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">schedule_interval</span><span class="o">=</span><span class="s2">&quot;@daily&quot;</span><span class="p">,</span> <span class="p">):</span> <span class="n">EmptyOperator</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">&quot;task&quot;</span><span class="p">)</span> </code></pre> </div> <p>it can be fixed with <code>uvx ruff check --select AIR3 --fix --unsafe-fixes --preview</code></p> <div class="codehilite"> <pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span> <span class="kn">from</span><span class="w"> </span><span class="nn">airflow.sdk</span><span class="w"> </span><span class="kn">import</span> <span class="n">DAG</span> <span class="kn">from</span><span class="w"> </span><span class="nn">airflow.providers.standard.operators.empty</span><span class="w"> </span><span class="kn">import</span> <span class="n">EmptyOperator</span> <span class="k">with</span> <span class="n">DAG</span><span class="p">(</span> <span class="n">dag_id</span><span class="o">=</span><span class="s2">&quot;my_dag_name&quot;</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">schedule</span><span class="o">=</span><span class="s2">&quot;@daily&quot;</span><span class="p">,</span> <span class="p">):</span> <span class="n">EmptyOperator</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">&quot;task&quot;</span><span class="p">)</span> </code></pre> </div> <p>which works with Airflow 3.</p></li> </ul> <p><strong>Joke:</strong></p> <ul> <li><a href="https://social.chinwag.org/@mike/114800294387410694?featured_on=pythonbytes">Front Toward Enemy</a></li> </ul>

July 07, 2025 08:00 AM UTC

July 06, 2025

Anwesha Das

Creating Pull request with GitHub Action

---
name: Testing Gha
on:
  workflow_dispatch:
    inputs:
      GIT_BRANCH:
        description: The git branch to be worked on
        required: true

jobs:
  test-pr-creation:
    name: Creates test PR
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
      contents: write
    env:
      GIT_BRANCH: ${{ inputs.GIT_BRANCH }}
    steps:
      - uses: actions/checkout@v4
      - name: Updates README
        run: echo date >> README.md

      - name: Set up git
        run: |
          git switch --create "${GIT_BRANCH}"
          ACTOR_NAME="$(curl -s https://api.github.com/users/"${GITHUB_ACTOR}" | jq --raw-output &apos.name // .login&apos)"
          git config --global user.name "${ACTOR_NAME}"
          git config --global user.email "${GITHUB_ACTOR_ID}+${GITHUB_ACTOR}@users.noreply.github.com"

      - name: Add README
        run: git add README.md

      - name: Commit
        run: >-
          git diff-index --quiet HEAD ||
          git commit -m "test commit msg"
      - name: Push to the repo
        run: git push origin "${GIT_BRANCH}"

      - name: Create PR as draft
        env:
          GITHUB_TOKEN: ${{ github.token }}
        run: >-
          gh pr create
          --draft
          --base main
          --head "${GIT_BRANCH}"
          --title "test commit msg"
          --body "pr body"

      - name: Retrieve the existing PR URL
        id: existing-pr
        env:
          GITHUB_TOKEN: ${{ github.token }}
        run: >
          echo -n pull_request_url= >> "${GITHUB_OUTPUT}"

          gh pr view
          --json &aposurl&apos
          --jq &apos.url&apos
          --repo &apos${{ github.repository }}&apos
          &apos${{ env.GIT_BRANCH }}&apos
          >> "${GITHUB_OUTPUT}"
      - name: Select the actual PR URL
        id: pr
        env:
          GITHUB_TOKEN: ${{ github.token }}
        run: >
          echo -n pull_request_url=
          >> "${GITHUB_OUTPUT}"

          echo &apos${{steps.existing-pr.outputs.pull_request_url}}&apos
          >> "${GITHUB_OUTPUT}"

      - name: Log the pull request details
        run: >-
           echo &aposPR URL: ${{ steps.pr.outputs.pull_request_url }}&apos | tee -a "${GITHUB_STEP_SUMMARY}"


      - name: Instruct the maintainers to trigger CI by undrafting the PR
        env:
          GITHUB_TOKEN: ${{ github.token }}
        run: >-
            gh pr comment
            --body &aposPlease mark the PR as ready for review to trigger PR checks.&apos
            --repo &apos${{ github.repository }}&apos
            &apos${{ steps.pr.outputs.pull_request_url }}&apos

The above is an example of how to create a draft PR via GitHub Actions. We need to give permissions to the GitHub action to create PR in a repository (workflow permissions in the settings).

workflow_permissions.png

Hopefully, this blogpost will help my future self.

July 06, 2025 06:22 PM UTC

July 04, 2025

PyPy

PyPy v7.3.20 release

PyPy v7.3.20: release of python 2.7, 3.11

The PyPy team is proud to release version 7.3.20 of PyPy after the previous release on Feb 26, 2025. The release fixes some subtle bugs in ctypes and OrderedDict and makes PyPy3.11 compatible with an upcoming release of Cython.

The release includes two different interpreters:

The interpreters are based on much the same codebase, thus the double release. This is a micro release, all APIs are compatible with the other 7.3 releases.

We recommend updating. You can find links to download the releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work. If PyPy is helping you out, we would love to hear about it and encourage submissions to our blog via a pull request to https://github.com/pypy/pypy.org

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: bug fixes, PyPy and RPython documentation improvements, or general help with making RPython's JIT even better.

If you are a python library maintainer and use C-extensions, please consider making a HPy / CFFI / cppyy version of your library that would be performant on PyPy. In any case, cibuildwheel supports building wheels for PyPy.

What is PyPy?

PyPy is a Python interpreter, a drop-in replacement for CPython It's fast (PyPy and CPython performance comparison) due to its integrated tracing JIT compiler.

We also welcome developers of other dynamic languages to see what RPython can do for them.

We provide binary builds for:

PyPy supports Windows 32-bit, Linux PPC64 big- and little-endian, Linux ARM 32 bit, RISC-V RV64IMAFD Linux, and s390x Linux but does not release binaries. Please reach out to us if you wish to sponsor binary releases for those platforms. Downstream packagers provide binary builds for debian, Fedora, conda, OpenBSD, FreeBSD, Gentoo, and more.

What else is new?

For more information about the 7.3.20 release, see the full changelog.

Please update, and continue to help us make pypy better.

Cheers, The PyPy Team

July 04, 2025 12:00 PM UTC

Real Python

The Real Python Podcast – Episode #256: Solving Problems and Saving Time in Chemistry With Python

What motivates someone to learn how to code as a scientist? How do you harness the excitement of solving problems quickly and make the connection to the benefits of coding in your scientific work? This week on the show, we speak with Ben Lear and Christopher Johnson about their book "Coding For Chemists."

[ 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 ]

July 04, 2025 12:00 PM UTC

July 03, 2025

Mike Driscoll

Python eBook Fourth of July Sale

Happy Fourth of July! I am hosting a sale for the 4th of July weekend, where you can get 25% off most of my books and courses.

Here are the books included in the sale and the direct links with the 25% coupon already applied:

I hope you’ll check out the sale, but even if you don’t, I hope you have a great holiday weekend!

The post Python eBook Fourth of July Sale appeared first on Mouse Vs Python.

July 03, 2025 01:32 PM UTC

Armin Ronacher

Tools: Code Is All You Need

If you've been following me on Twitter, you know I'm not a big fan of MCP (Model Context Protocol) right now. It's not that I dislike the idea; I just haven't found it to work as advertised. In my view, MCP suffers from two major flaws:

  1. It isn’t truly composable. Most composition happens through inference.
  2. It demands too much context. You must supply significant upfront input, and every tool invocation consumes even more context than simply writing and running code.

A quick experiment makes this clear: try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker.

But MCP is the Future!

I want to address some of the feedback I've received on my stance on this. I evaluated MCP extensively in the context of agentic coding, where its limitations were easiest to observe. One piece of feedback is that MCP might not make a ton of sense for general code generation, because models are already very good at that but they make a lot of sense for end-user applications, like, say, automating a domain-specific task in a financial company. Another one is that I need to look at the world of the future, where models will be able to reach many more tools and handle much more complex tasks.

My current take is that my data indicates that current MCP will always be harder to use than writing code, primarily due to the reliance on inference. If you look at the approaches today for pushing towards higher tool counts, the proposals all include a layer of filtering. You pass all your tools to an LLM and ask it to filter it down based on the task at hand. So far, there hasn't been much better approaches proposed.

The main reason I believe this will most likely also hold true — that you shouldn't be using MCP in its current form even for non-programming, domain-specific tasks — is that even in those cases code generation just is the better choice because of the ability to compose.

Replace Yourself With A Shellscript

The way to think about this problem is that when you don't have an AI, and you're solving a problem as a software engineer, your tool of choice is code. Perhaps as a non-software engineer, code is out of reach. Many many tasks people do by hand are actually automatable through software. The challenge is finding someone to write that software. If you're working in a niche environment and you're not a programmer yourself, you might not pick up a programming book to learn how to code, and you might not find a developer willing to provide you with a custom piece of software to solve your specific problem. And yes, maybe your task requires some inference, but many do need them all the time.

There is a reason we say “to replace oneself with a shell script”, it's because that has been happening for a long time. With LLMs and programming, the idea is that rather than replacing yourself with a shell script, you're replacing yourself with an LLM. But you run into three problems: cost, speed, and general reliability. All these problems are what we need to deal with before we can even think of tool usage or MCP. We need to figure out how to ensure that our automated task actually works correctly at scale.

Automation at Scale

The key to automation is really to automate things that will happen over and over. You're not going to automate a one-shot change that will never recur. You're going to start automating the things where the machine can truly give you a productivity boost because you're going to do it once or twice, figure out how to make it work, and then have the machine repeat it a thousand times. For that repetition, there's a very strong argument to be made for always using code. That's because if we instruct the machine to use inference to do it, it might work, particularly for small tasks, but it requires validation which can take almost the same time as doing it in the first place. Getting an LLM to calculate for you sort of works, but it's much better for the LLM to write the Python code to do the calculation. Why? First, you can review the formula, not the calculated result. We can write it ourselves or we can use the LLM as a judge to figure out if the approach is correct. Don't really have to validate that Python calculates correct, you can rely on that. So, by opting for code generation for task solving, we get a little closer to being able to verify and validate the process ourselves, rather than hoping the LLM inferred correctly.

This obviously goes way beyond calculation. Take, for instance, this blog. I converted this entire blog from reStructuredText to Markdown recently. I put this conversion off for a really long time, partly because I was a little too lazy. But also, when I was lazy enough to consider deploying an LLM for it, I just didn't trust it to do the conversion itself without regressing somewhere. I was worried that if it ran out of context, it might start hallucinating text or change wording slightly. It's just that I worried about subtle regressions too much.

I still used an LLM for it, but I asked it to do that transformation in a different way: through code.

LLM to Code to LLM

  1. I asked the LLM to perform the core transformation from reStructuredText to Markdown but I also asked it to do this in a way that uses the underlying AST (Abstract Syntax Tree). So, I instructed it to parse the reStructuredText into an actual reStructuredText AST, then convert that to a Markdown AST, and finally render it to HTML, just like it did before. This gave me an intermediate transformation step and a comparable end result.

  2. Then, I asked it to write a script that compares the old HTML with the new HTML, performs the diffing after some basic cleanup it deemed necessary for comparison. I asked it to consider what kind of conversion errors were actually acceptable. So, it read through its own scripts to see where it might not match the original output due to known technical limitations (e.g., footnotes render differently between the Markdown library I'm using and the reStructuredText library, so even if the syntax matches correctly, the HTML would look different). I asked it to compensate for this in that script.

  3. After that was done, I asked it to create a third script, which I could run over the output of hundreds of files to analyze the differece to go back into the agentic loop for another iteration tep.

Then I kicked this off in a loop. I did not provide all the posts, I started with 10 until differences were low and then had it do it for all. It did this for maybe 30 minutes or so until I came back to it and found it in a pretty acceptable state.

What's key about this transformation is not so much that the LLM was capable of pulling it off, but that I actually trusted this process at the end because I could review the approach. Not only that, I also tried to ask another LLM what it thinks of the code that another LLM wrote, and the changes. It gave me much higher confidence that what was going on would not lose data. It felt right to me. It felt like a mechanical process that was fundamentally correct, and I was able to observe it and do spot checks. At worst, the regressions were minor Markdown syntax errors, but the text itself wouldn't have been corrupted.

Another key here is also that because the inference is rather constant, the cost of inference in this process scales with the number of iteration steps and the sample size, but it doesn't depend on how many documents I'm wanting to convert overall. Eventually, I just had it run over all documents all the time but running it over 15 docs vs 150 docs is more or less the same effort, because the final LLM based analysis step did not have that many more things to review (it already skipped over all minor differences in the files).

MCP Cannot Do That

This is a long-winded way of saying that this entire transformation went through code. It's a pipeline that starts with human input, produces code, does an LLM as a judge step and iterates. And you can take this transformation and apply it to a general task as well.

To give an example, one MCP you might be using is Playwright. I find it very hard to replace Playwright with a code approach for all cases because what you're essentially doing is remotely controlling your browser. The task you're giving it largely involves reading the page, understanding what's on it, and clicking the next button. That's the kind of scenario where it's very hard to eliminate inference at each step.

However, if you already know what the page is — for instance, if you're navigating your own app you're working on — then you can actually start telling it to write a Playwright Python script instead and run that. This script can perform many of those steps sequentially without any inference. I've noticed that this approach is significantly quicker, and because it understands your code, it still generally produces correct results. It doesn't need to navigate, read page contents, find a button, or press an input in real-time. Instead, it will write a single Python script that automates the entire process in one go, requiring very little context by comparison.

This process is repeatable. Once the script is written, I can execute it 100, 200, or even 300 times without requiring any further inference. This is a significant advantage that an MCP typically cannot offer. It's incredibly challenging to get an LLM to understand generic, abstract MCP tool calls. I wish I could, for example, embed an MCP client directly into a shell script, allowing me to run remote MCP services efficiently via code generation, but actually doing that is incredibly hard because the tools are not written with non inference based automation in mind.

Also, as ironic as it is: I'm a human, not an MCP client. I can run and debug a script, I cannot even figure out how to reliably do MCP calls. It's always a gamble and incredibly hard to debug. I love using the little tools that Claude Code generates while generating code. Some of those I had it convert into long term additions to my development process.

Where does this take us?

I don't know. But it's an interesting moment to think what we could potentially do to make code generation for purposeful agentic coding better. The weird thing is that MCP is actually pretty great when it works. But it feels in the current form too much like a dead end that cannot be scaled up, particularly to automation at scale because it relies on inference too much.

So maybe we need to look at ways to find a better abstraction for what MCP is great at, and code generation. For that that we might need to build better sandboxes and maybe start looking at how we can expose APIs in ways that allow an agent to do some sort of fan out / fan in for inference. Effectively we want to do as much in generated code as we can, but then use the magic of LLMs after bulk code execution to judge what we did.

I can also imagine that it might be quite interesting to do code generation in a way that also provides enough context for an LLM to explain in human language to a non programmer what the script is doing. That might enable these flows to be used by human users that are not developers themselves.

In any case I can only encourage people to bypass MCP and to explore what else is possible. LLMs can do so much more if you give them the power to write code.

Further Reading

Here are some more posts you might want to read or videos you might want to watch:

July 03, 2025 12:00 AM UTC

Quansight Labs Blog

Escaping Contravariance Hell

Protocols and TypeVars

July 03, 2025 12:00 AM UTC

July 02, 2025

Real Python

Python 3.14 Preview: Template Strings (T-Strings)

Python 3.14’s t-strings allow you to intercept and transform input values before assembling them into a final representation. Unlike f-strings, which produce a str object, t-strings resolve to a Template instance, allowing you to safely process and customize dynamic content.

One of the key benefits of t-strings is their ability to help prevent security vulnerabilities like SQL injection and XSS attacks. They’re also valuable in other fields that rely on string templates, such as structured logging.

By the end of this tutorial, you’ll understand that:

  • Python t-strings are a generalization of f-strings, designed to safely handle and process input values.
  • The main components of a t-string include static string parts and interpolations, which are accessible through the Template class.
  • You process t-strings by iterating over their components, using attributes such as .strings, .interpolations, and .values for safe and customized handling.

Python t-strings enhance both security and flexibility in string processing tasks. This tutorial will guide you through understanding t-strings, comparing them with f-strings, and exploring their practical use cases in Python programming.

Get Your Code: Click here to download the free sample code that shows you how to use Python 3.14’s new template strings.

Take the Quiz: Test your knowledge with our interactive “Python 3.14 Preview: Template Strings (T-Strings)” quiz. You’ll receive a score upon completion to help you track your learning progress:

Python 3.14 Preview: Template Strings (T-Strings)

Interactive Quiz

Python 3.14 Preview: Template Strings (T-Strings)

Evaluate your grasp of Python's t-strings, which provide a structured and secure way to handle string templates.

Exploring String Templates Before Python 3.14

Creating string templates that you can populate with specific values dynamically is a common requirement in programming. A string template is a string that contains placeholders—special markers representing variable values—that you can dynamically replace at runtime.

You’ll often use templates to generate text or structured content by filling these placeholders with actual data. Before Python 3.14, the language provided several tools that allowed you to interpolate and format values in your strings:

You can use all these tools to create and process string templates. Of course, each has its own unique strengths and weaknesses.

The String Formatting Operator (%)

The string formatting operator (%), inspired by C’s printf() syntax, is the oldest string formatting and interpolation tool in Python. Here’s a quick example of how you can use this operator to create and process templates:

Python 
>>> city = "Vancouver"
>>> temperature = 52
>>> template = "The temperature in %s is %d F."

>>> template % (city, temperature)
'The temperature in Vancouver is 52 F.'
Copied!

In this example, you have two variables containing data. The first contains a string, and the second holds an integer value. Then, you define a string template using the %s and %d syntax to define placeholders or replacement fields. The s means that the first field must be filled with a string, and the d indicates that the field accepts decimal integer values. These are known as conversion types.

Finally, you use the % operator to dynamically interpolate the variables’ content into the template and build a new string.

This operator also allows you to apply formatting rules to the input values. For example, here’s how you can format currency values:

Python 
>>> template = "$%.2f"

>>> template % 2456.5673
'$2456.57'
Copied!

In this example, the template contains the literal dollar sign ($) to indicate that the formatted value represents a USD amount. The $ character is not part of the formatting syntax itself but part of the output.

Then, you have a replacement field that starts with the string formatting operator (%) followed by the string ".2f". This string is a format specifier that formats any input number as a floating-point value with a precision of two digits.

Note: To learn more about formatting strings, check out the following tutorials:

You can format a string inline using the % operator by passing the values directly. This approach combines the template and the data in a single step, but it doesn’t allow you to reuse the template later on:

Python 
>>> "$%.2f" % 2456.5673
'$2456.57'
Copied!

When you have a complex template, the string formatting operator’s syntax can become cumbersome and hard to read:

Read the full article at https://realpython.com/python-t-strings/ »

[ 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 ]

July 02, 2025 02:00 PM UTC

Quiz: Python 3.14 Preview: Template Strings (T-Strings)

Evaluate your grasp of Python’s t-strings, which provide a structured and secure way to handle string templates.

[ 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 ]

July 02, 2025 12:00 PM UTC