Python dictionaries are a powerful built-in data type that allows you to store key-value pairs for efficient data retrieval and manipulation. Learning about them is essential for developers who want to process data efficiently. In this tutorial, you’ll explore how to create dictionaries using literals and the dict() constructor, as well as how to use Python’s operators and built-in functions to manipulate them.

By learning about Python dictionaries, you’ll be able to access values through key lookups and modify dictionary content using various methods. This knowledge will help you in data processing, configuration management, and dealing with JSON and CSV data.

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

• A dictionary in Python is a mutable collection of key-value pairs that allows for efficient data retrieval using unique keys.
• Both dict() and {} can create dictionaries in Python. Use {} for concise syntax and dict() for dynamic creation from iterable objects.
• dict() is a class used to create dictionaries. However, it’s commonly called a built-in function in Python.
• .__dict__ is a special attribute in Python that holds an object’s writable attributes in a dictionary.
• Python dict is implemented as a hashmap, which allows for fast key lookups.

To get the most out of this tutorial, you should be familiar with basic Python syntax and concepts such as variables, loops, and built-in functions. Some experience with basic Python data types will also be helpful.

Get Your Code: Click here to download the free sample code that you’ll use to learn about dictionaries in Python.

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

---

Interactive Quiz

Dictionaries in Python

Test your knowledge of Python's dict data type: how to create, access, and modify key-value pairs using built-in methods and operators.

Getting Started With Python Dictionaries

Dictionaries are one of Python’s most important and useful built-in data types. They provide a mutable collection of key-value pairs that lets you efficiently access and mutate values through their corresponding keys:

Python

>>> config = {
...     "color": "green",
...     "width": 42,
...     "height": 100,
...     "font": "Courier",
... }

>>> # Access a value through its key
>>> config["color"]
'green'

>>> # Update a value
>>> config["font"] = "Helvetica"
>>> config
{
    'color': 'green',
    'width': 42,
    'height': 100,
    'font': 'Helvetica'
}

A Python dictionary consists of a collection of key-value pairs, where each key corresponds to its associated value. In this example, "color" is a key, and "green" is the associated value.

Dictionaries are a fundamental part of Python. You’ll find them behind core concepts like scopes and namespaces as seen with the built-in functions globals() and locals():

Python

>>> globals()
{
    '__name__': '__main__',
    '__doc__': None,
    '__package__': None,
    ...
}

The globals() function returns a dictionary containing key-value pairs that map names to objects that live in your current global scope.

Python also uses dictionaries to support the internal implementation of classes. Consider the following demo class:

Python

>>> class Number:
...     def __init__(self, value):
...         self.value = value
...

>>> Number(42).__dict__
{'value': 42}

The .__dict__ special attribute is a dictionary that maps attribute names to their corresponding values in Python classes and objects. This implementation makes attribute and method lookup fast and efficient in object-oriented code.

You can use dictionaries to approach many programming tasks in your Python code. They come in handy when processing CSV and JSON files, working with databases, loading configuration files, and more.

Python’s dictionaries have the following characteristics:

• Mutable: The dictionary values can be updated in place.
• Dynamic: Dictionaries can grow and shrink as needed.
• Efficient: They’re implemented as hash tables, which allows for fast key lookup.
• Ordered: Starting with Python 3.7, dictionaries keep their items in the same order they were inserted.

The keys of a dictionary have a couple of restrictions. They need to be:

• Hashable: This means that you can’t use unhashable objects like lists as dictionary keys.
• Unique: This means that your dictionaries won’t have duplicate keys.

In contrast, the values in a dictionary aren’t restricted. They can be of any Python type, including other dictionaries, which makes it possible to have nested dictionaries.

Dictionaries are collections of pairs. So, you can’t insert a key without its corresponding value or vice versa. Since they come as a pair, you always have to insert a key with its corresponding value.

Note: In some situations, you may want to add keys to a dictionary without deciding what the associated value should be. In those cases, you can use the .setdefault() method to create keys with a default or placeholder value.

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

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In this quiz, you’ll test your understanding of Factory Method Pattern.

This quiz guides you through the Factory Method pattern: how it separates object creation from use, the roles of clients and products, when to apply it, and how to implement flexible, maintainable Python classes.

Test your ability to spot opportunities for the pattern and build reusable, decoupled object creation solutions.

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Today I’m very happy to share that Mario Zechner is joining Earendil.

First things first: I think you should read Mario’s post. This is his news more than it is ours, and he tells his side of it better than I could. What I want to do here is add a more personal note about why this matters so much to me, how the last months led us here, and why I am so excited to have him on board.

Last year changed the way many of us thought about software. It certainly changed the way I did. I spent much of 2025 building, probing, and questioning how to build software, and in many more ways what I want to do. If you are a regular reader of this blog you were along for the ride. I wrote a lot, experimented a lot, and tried to get a better sense for what these systems can actually do and what kinds of companies make sense to build around them. There was, and continues to be, a lot of excitement in the air, but also a lot of noise. It has become clear to me that it’s not a question of whether AI systems can be useful but what kind of software and human-machine interactions we want to bring into the world with them.

That is one of the reasons I have been so drawn to Mario’s work and approaches.

Pi is, in my opinion, one of the most thoughtful coding agents and agent infrastructure libraries in this space. Not because it is trying to be the loudest or the fastest, but because it is clearly built by someone who cares deeply about software quality, taste, extensibility, and design. In a moment where much of the industry is racing to ship ever more quickly, often at the cost of coherence and craft, Mario kept insisting on making something solid. That matters to me a great deal.

I have known Mario for a long time, and one of the things I admire most about him is that he does not confuse velocity with progress. He has a strong sense for what good tools should feel like. He cares about details. He cares about whether something is well made. And he cares about building in a way that can last. Mario has been running Pi in a rather unusual way. He exerts back-pressure on the issue tracker and the pull requests through OSS vacations and other means.

The last year has also made something else clearer to me: these systems are not only exciting, they are also capable of producing a great deal of damage. Sometimes that damage is obvious; sometimes it looks like low-grade degradation everywhere at once. More slop, more noise, more disingenuous emails in my inbox. There is a version of this future that makes people more distracted, more alienated, and less careful with one another.

That is not a future I want to help build.

At Earendil, Colin and I have been trying to think very carefully about what a different path might look like. That is a big part of what led us to Lefos.

Lefos is our attempt to build a machine entity that is more thoughtful and more deliberate by design. Not an agent whose main purpose is to make everything a little more efficient so that we can produce even more forgettable output, but one that can help people communicate with more care, more clarity, and joy.

Good software should not aim to optimize every minute of your life, but should create room for better and more joyful experiences, better relationships, and better ways of relating to one another. Especially in communication and software engineering, I think we should be aiming for more thought rather than more throughput. We should want tools that help people be more considerate, more present, and more human. If all we do is use these systems to accelerate the production of slop, we will have missed the opportunity entirely.

This is also why Mario joining Earendil feels so meaningful to me. Pi and Lefos come from different starting points. There was a year of distance collaboration, but they are animated by a similar instinct: that quality matters, that design matters, and that trust is earned through care rather than captured through hype.

I am very happy that Pi is coming along for the ride. Me and Colin care a lot about it, and we want to be good stewards of it. It has already played an important role in our own work over the last months, and I continue to believe it is one of the best foundations for building capable agents. We will have more to say soon about how we think about Pi’s future and its relationship to Lefos, but the short version is simple: we want Pi to continue to exist as a high-quality, open, extensible piece of software, and we want to invest in making that future real. As for our thoughts of Pi’s license, read more here and our company post here.

#729 – APRIL 7, 2026
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Events

Happy Pythoning!
This was PyCoder’s Weekly Issue #729.
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Reviewed by: Sumit Sarabhai

If you’ve been writing SQL in Python, you already know the debate: positional parameters (?) or named parameters (%(name)s)? Some developers swear by the conciseness of positional. Others prefer the clarity of named. With mssql-python, you no longer need to choose – we support both. 
 
We’ve added dual parameter style support to mssql-python, enabling both qmark and pyformat parameter styles in Python applications that interact with SQL Server and Azure SQL. This feature is especially useful if you’re building complex queries, dynamically assembling filters, or migrating existing code that already uses named parameters with other DBAPI drivers.

What Are Parameter Styles? 

The DB-API 2.0 specification (PEP 249) defines several ways to pass parameters to SQL queries. The two most popular are: 

• qmark – Positional ? placeholders with a tuple/list of values. 
• pyformat – Named %(name)s placeholders with a dictionary of values.

  # qmark style 
  cursor.execute("SELECT * FROM users WHERE id = ? AND status = ?", (42, "active")) 
   
  # pyformat style 
  cursor.execute("SELECT * FROM users WHERE id = %(id)s AND status = %(status)s", 
                 {"id": 42, "status": "active"}) 

Business Requirement 

Previously, mssql-python only supported qmark. It works fine for simple queries, but as parameters multiply, tracking their order becomes error-prone: 

# Which ? corresponds to which value? 
cursor.execute( 
    "UPDATE users SET name=?, email=?, age=? WHERE id=? AND status=?", 
    (name, email, age, user_id, status) 
) 

Mix up the order and it’s easy to introduce subtle, hard to spot bugs. 

Why Named Parameters? 

• Self-documenting queries – No more guessing which ? maps to what: 

qmark — 6 parameters, which is which? 
cursor.execute( """INSERT INTO employees (first_name, last_name, email, department, salary, hire_date) VALUES (?, ?, ?, ?, ?, ?)""", ("Jane", "Doe", "jane.doe@company.com", "Engineering", 95000, "2025-03-01") ) 
pyformat — every value is labeled 
cursor.execute( """INSERT INTO employees (first_name, last_name, email, department, salary, hire_date) VALUES (%(first_name)s, %(last_name)s, %(email)s, %(dept)s, %(salary)s, %(hire_date)s)""", {"first_name": "Jane", "last_name": "Doe", "email": "jane.doe@company.com", "dept": "Engineering", "salary": 95000, "hire_date": "2025-03-01"} ) 

• Parameter reuse – Use the same value multiple times without repeating it: 

Audit log: record who made the change and when 
cursor.execute( """UPDATE orders SET status = %(new_status)s, modified_by = %(user)s, approved_by = %(user)s, modified_at = %(now)s, approved_at = %(now)s WHERE order_id = %(order_id)s""", {"new_status": "approved", "user": "admin@company.com", "now": datetime.now(), "order_id": 5042} ) 
3 unique values, used 5 times — no duplication needed 

• Dynamic query building – Add filters without tracking parameter positions:

def search_orders(customer=None, status=None, min_total=None, date_from=None): 
    query_parts = ["SELECT * FROM orders WHERE 1=1"] 
    params = {} 
  
    if customer: 
        query_parts.append("AND customer_id = %(customer)s") 
        params["customer"] = customer 
  
    if status: 
        query_parts.append("AND status = %(status)s") 
        params["status"] = status 
  
    if min_total is not None: 
        query_parts.append("AND total >= %(min_total)s") 
        params["min_total"] = min_total 
  
    if date_from: 
        query_parts.append("AND order_date >= %(date_from)s") 
        params["date_from"] = date_from 
  
    query_parts.append("ORDER BY order_date DESC") 
    cursor.execute(" ".join(query_parts), params) 
    return cursor.fetchall() 
  
# Callers use only the filters they need 
recent_big_orders = search_orders(min_total=500, date_from="2025-01-01") 
pending_for_alice = search_orders(customer=42, status="pending") 

• Dictionary Reuse Across Queries 

The same parameter dictionary can drive multiple queries:

report_params = {"region": "West", "year": 2025, "status": "active"} 
  
# Summary count 
cursor.execute( 
    """SELECT COUNT(*) FROM customers 
       WHERE region = %(region)s AND status = %(status)s""", 
    report_params 
) 
total = cursor.fetchone()[0] 
  
# Revenue breakdown 
cursor.execute( 
    """SELECT department, SUM(revenue) 
       FROM sales 
       WHERE region = %(region)s AND fiscal_year = %(year)s 
       GROUP BY department 
       ORDER BY SUM(revenue) DESC""", 
    report_params 
) 
breakdown = cursor.fetchall() 
  
# Top performers 
cursor.execute( 
    """SELECT name, revenue 
       FROM sales_reps 
       WHERE region = %(region)s AND fiscal_year = %(year)s AND status = %(status)s 
       ORDER BY revenue DESC""", 
    report_params 
) 
top_reps = cursor.fetchall() 
# Same dict, three different queries — change the filters once, all queries update 

The Solution: Automatic Detection 

mssql-python now detects which style you’re using based on the parameter type: 

• tuple/list → qmark (?) 

• dict → pyformat (%(name)s) 

No configuration needed. Existing qmark code requires zero changes. 

from mssql_python import connect 
 
# qmark - works exactly as before 
cursor.execute("SELECT * FROM users WHERE id = ?", (42,)) 
 
# pyformat - just pass a dict! 
cursor.execute("SELECT * FROM users WHERE id = %(id)s", {"id": 42})

How It Works 

When you pass a dict to execute(), the driver: 

1. Scans the SQL for %(name)s placeholders (context-aware – skips string literals, comments, and bracketed identifiers). 
2. Validates that every placeholder has a matching key in the dict. 
3. Builds a positional tuple in placeholder order (duplicating values for reused parameters). 
4. Replaces each %(name)s with ? and sends the rewritten query to ODBC. 

User Code                                  ODBC Layer 
─────────                                  ────────── 
cursor.execute(                            SQLBindParameter(1, "active") 
  "WHERE status = %(status)s               SQLBindParameter(2, "USA") 
   AND country = %(country)s",      →      SQLExecute( 
  {"status": "active",                       "WHERE status = ? 
   "country": "USA"}                          AND country = ?" 
)                                          ) 

The ODBC layer always works with positional ? placeholders. The pyformat conversion is purely a developer-facing convenience with zero overhead to database communication. 

Clear Error Messages 

Mismatched styles or missing parameters produce actionable errors – not cryptic database exceptions: 

cursor.execute("WHERE id = %(id)s AND name = %(name)s", {"id": 42}) 
# KeyError: Missing required parameter(s): 'name'. 
 
cursor.execute("WHERE id = ?", {"id": 42}) 
# TypeError: query uses positional placeholders (?), but dict was provided. 
 
cursor.execute("WHERE id = %(id)s", (42,)) 
# TypeError: query uses named placeholders (%(name)s), but tuple was provided.

Real-World Examples 

Example 1: Web Application

def add_user(name, email): 
    with connect(connection_string) as conn: 
        with conn.cursor() as cursor: 
            cursor.execute( 
                "INSERT INTO users (name, email) VALUES (%(name)s, %(email)s)", 
                {"name": name, "email": email} 
            ) 

Example 2: Batch Operations 

cursor.executemany( 
    "INSERT INTO users (name, age) VALUES (%(name)s, %(age)s)", 
    [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}] 
) 

Example 3: Financial Transactions 

def transfer_funds(from_acct, to_acct, amount): 
    with connect(connection_string) as conn: 
        with conn.cursor() as cursor: 
            cursor.execute( 
                "UPDATE accounts SET balance = balance - %(amount)s WHERE id = %(id)s", 
                {"amount": amount, "id": from_acct} 
            ) 
            cursor.execute( 
                "UPDATE accounts SET balance = balance + %(amount)s WHERE id = %(id)s", 
                {"amount": amount, "id": to_acct} 
            ) 
    # Automatic commit on success, rollback on failure 

Things to Keep in Mind 

• Don’t mix styles in one query. Use either ? or %(name)s, not both. The driver determines which style you’re using from the parameter type (tuple vs dict), not from the SQL text. If placeholders don’t match the parameter type, you’ll get a clear TypeError explaining the mismatch. If both placeholder types appear in the SQL, only one set gets substituted, leading to parameter count mismatches at execution time. 

# Mixing styles - raises TypeError 
cursor.execute( "SELECT * FROM users WHERE id = ? AND name = %(name)s", {"name": "Alice"} # Driver finds %(name)s but also sees unmatched ? ) 
# ODBC error: parameter count mismatch (2 placeholders, 1 value) 
# Pick one style and use it consistently 
cursor.execute( "SELECT * FROM users WHERE id = %(id)s AND name = %(name)s", {"id": 42, "name": "Alice"} ) 

• Extra dict keys are OK.  Unused parameters are silently ignored, this is by design to enable parameter dictionary reuse across different queries. 

• SQL injection safe. Both styles use ODBC parameter binding under the hood. Values are never interpolated into the SQL string, they are always safely bound by the driver. 

• Literal % in SQL. Use %% to escape if you need a literal %(…)s pattern in your query text. 

cursor.execute( 
    "SELECT * FROM users WHERE name LIKE %(pattern)s", 
    {"pattern": "%alice%"}  # The % inside the VALUE is fine 
) 
 
# But if you need a literal %(...)s in SQL text itself, use %% 
cursor.execute( 
    "SELECT '%%(example)s' AS literal WHERE id = %(id)s", 
    {"id": 42} 
)  

• mssql_python.paramstyle reports “pyformat”. The DB-API 2.0 spec only allows a single value for this module-level constant. We set it to pyformat because it’s the more expressive style and the one we recommend for new code. But qmark is fully supported at runtime, the driver accepts both styles transparently based on whether you pass a tuple or a dict. Think of paramstyle = “pyformat” as the advertised default, not a limitation. 

Compatibility at a Glance 

Feature	qmark (?)	pyformat (%(name)s)
cursor.execute()
cursor.executemany()
connection.execute()
Parameter reuse
Stored procedures
All SQL data types
Backward compatible with qmark paramstyle		N/A (new)

Takeaway 

Use ? for quick, simple queries. Use %(name)s for complex, multi-parameter queries where clarity and reuse matter. You don’t have to pick a side – use whichever fits the situation. The driver handles the rest. 

Whether you’re building dynamic queries, or simply want more readable SQL, dual paramstyle support makes mssql-python work the way you already think. 

Try It and Share Your Feedback! 

We invite you to:

1. Check-out the mssql-python driver and integrate it into your projects.
2. Share your thoughts: Open issues, suggest features, and contribute to the project.
3. Join the conversation: GitHub Discussions | SQL Server Tech Community.

The post Write SQL Your Way: Dual Parameter Style Benefits in mssql-python appeared first on Microsoft for Python Developers Blog.

Friendships, collaborations, and breakthroughs 

In accordance with our security release policy, the Django team is issuing releases for Django 6.0.4, Django 5.2.13, and Django 4.2.30. These releases address the security issues detailed below. We encourage all users of Django to upgrade as soon as possible.

Logging is a vital programming practice that helps you track, understand, and debug your application’s behavior. Loguru is a Python library that provides simpler, more intuitive logging compared to Python’s built-in logging module.

Good logging gives you insights into your program’s execution, helps you diagnose issues, and provides valuable information about your application’s health in production. Without proper logging, you risk missing critical errors, spending countless hours debugging blind spots, and potentially undermining your project’s overall stability.

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

• Logging in Python can be simple and intuitive with the right tools.
• Using Loguru lets you start logging immediately without complex configuration.
• You can customize log formats and send logs to multiple destinations like files, the standard error stream, or external services.
• Loguru provides powerful debugging capabilities that make troubleshooting easier.
• Loguru supports structured logging with JSON formatting for modern applications.

After watching this course, you’ll be able to quickly implement better logging in your Python applications. You’ll spend less time wrestling with logging configuration and more time using logs effectively to debug issues. This will help you build production-ready applications that are easier to troubleshoot when problems occur.

To get the most from this course, you should be familiar with Python concepts like functions, decorators, and context managers. You might also find it helpful to have some experience with Python’s built-in logging module, though this isn’t required.

Don’t worry if you’re new to logging in Python. This course will guide you through everything you need to know to get started with Loguru and implement effective logging in your applications.

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We are looking for the next group of organizers to own and lead the 2027 DjangoCon Europe conference. Could your town's football stadium, theatre, cinema, city hall, circus tent or a private island host this wonderful community event?

DjangoCon Europe is a major pillar of the Django community, as people from across the world meet and share. Many qualities make it a unique event: Unconventional and conventional venues, creative happenings, a feast of talks and a dedication to inclusion and diversity.

Hosting a DjangoCon is an ambitious undertaking. It's hard work, but each year it has been successfully run by a team of community volunteers, not all of whom have had previous experience - more important is enthusiasm, organizational skills, the ability to plan and manage budgets, time and people - and plenty of time to invest in the project.

For 2027, rest assured that we will be there to answer questions and put you in touch with previous organizers through the brand new DSF Events Support Working Group (a reboot of the previous DjangoCon Europe Support Working Group).

Step 1: Submit your expression of interest

If you're considering organizing DjangoCon Europe (🙌 great!), fill in our DjangoCon Europe 2027 expression of interest form with your contact details. No need to fill in all the information at this stage if you don't have it all already, we'll reach out and help you figure it out.

Express your interest in organizing

Step 2: We're here to help!

We've set up a DjangoCon Europe support working group of previous organizers that you can reach out to with questions about organizing and running a DjangoCon Europe.

The group will be in touch with everyone submitting the expression of interest form, or you can reach out to them directly: events-support@djangoproject.com

We'd love to hear from you as soon as possible, so your proposal can be finalized and sent to the DSF board by June 1st 2026.

Step 3: Submitting the proposal

The more detailed and complete your final proposal is, the better. Basic details include:

• Organizing committee members: You won't have a full team yet, probably, naming just some core team members is enough.
• The legal entity that is intended to run the conference: Even if the entity does not exist yet, please share how you are planning to set it up.
• Dates: See "What dates are possible in 2027?" below. We must avoid conflicts with major holidays, EuroPython, DjangoCon US, and PyCon US.
• Venue(s), including size, number of possible attendees, pictures, accessibility concerns, catering, etc.
• Transport links and accommodation: Can your venue be reached by international travelers?
• Budgets and ticket prices: Talk to the DjangoCon Europe Support group to get help with this, including information on past event budgets.

We also like to see:

• Timelines
• Pictures
• Plans for online participation, and other ways to make the event more inclusive and reduce its environmental footprint
• Draft agreements with providers
• Alternatives you have considered

Have a look at our proposed (draft, feedback welcome) DjangoCon Europe 2027 Licensing Agreement for the fine print on contractual requirements and involvement of the Django Software Foundation.

Submit your completed proposal by June 1st 2026 via our DjangoCon Europe 2027 expression of interest form, this time filling in as many fields as possible. We look forward to reviewing great proposals that continue the excellence the whole community associates with DjangoCon Europe.

Q&A

Can I organize a conference alone?

We strongly recommend that a team of people submit an application.

I/we don't have a legal entity yet, is that a problem?

Depending on your jurisdiction, this is usually not a problem. But please share your plans about the entity you will use or form in your application.

Do I/we need experience with organizing conferences?

The support group is here to help you succeed. From experience, we know that many core groups of 2-3 people have been able to run a DjangoCon with guidance from previous organizers and help from volunteers.

What is required in order to announce an event?

Ultimately, a contract with the venue confirming the dates is crucial, since announcing a conference makes people book calendars, holidays, buy transportation and accommodation etc. This, however, would only be relevant after the DSF board has concluded the application process. Naturally, the application itself cannot contain any guarantees, but it's good to check concrete dates with your venues to ensure they are actually open and currently available, before suggesting these dates in the application.

Do we have to do everything ourselves?

No. You will definitely be offered lots of help by the community. Typically, conference organizers will divide responsibilities into different teams, making it possible for more volunteers to join. Local organizers are free to choose which areas they want to invite the community to help out with, and a call will go out through a blog post announcement on djangoproject.com and social media.

What kind of support can we expect from the Django Software Foundation?

The DSF regularly provides grant funding to DjangoCon organizers, to the extent of $6,000 in recent editions. We also offer support via specific working groups:

• The dedicated DjangoCon Europe support working group.
• The social media working group can help you promote the event.
• The Code of Conduct working group works with all event organizers.

In addition, a lot of Individual Members of the DSF regularly volunteer at community events. If your team aren't Individual Members, we can reach out to them on your behalf to find volunteers.

What dates are possible in 2027?

For 2027, DjangoCon Europe should happen between January 4th and April 26th, or June 3rd and June 27th. This is to avoid the following community events' provisional dates:

• PyCon US 2027: May 2027
• EuroPython 2027: July 2027
• DjangoCon US 2027: September - October 2027
• DjangoCon Africa 2027: August - September 2027

We also want to avoid the following holidays:

• New Year's Day: Friday 1st January 2027
• Chinese New Year: Saturday 6th February 2027
• Eid Al-Fitr: Tuesday 9th March 2027
• Easter: Sunday 28th March 2027
• Passover: Wednesday 21st - Thursday 29th April 2027
• Eid Al-Adha: Monday 17th - Thursday 20th May 2027
• Rosh Hashanah: Saturday 2nd - Monday 4th October 2027
• Yom Kippur: Monday 11th - Tuesday 12th October 2027

What cities or countries are possible?

Any city in Europe. This can be a city or country where DjangoCon Europe has happened in the past (Athens, Vigo, Edinburgh, Porto, Copenhagen, Heidelberg, Florence, Budapest, Cardiff, Toulon, Warsaw, Zurich, Amsterdam, Berlin), or a new locale.

References

Past calls

• Interested in organizing DjangoCon Europe 2016? | Weblog | Django
• Could you host DjangoCon Europe 2017? | Weblog | Django
• DjangoCon Europe 2019 - where will it be? | Weblog | Django
• Could you host DjangoCon Europe 2023? | Weblog | Django
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• Want to host DjangoCon Europe 2024? | Weblog | Django
• DjangoCon Europe 2025 Call for Proposals | Weblog | Django
• Last call for DjangoCon Europe 2025 organizers | Weblog | Django
• Could you host DjangoCon Europe 2026? Call for organizers | Weblog | Django

In this quiz, you’ll test your understanding of Building a Python GUI Application With Tkinter.

Test your Tkinter knowledge by identifying core widgets, managing layouts, handling text with Entry and Text widgets, and connecting buttons to Python functions.

This quiz also covers event loops, widget sizing, and file dialogs, helping you solidify the essentials for building interactive, cross-platform Python GUI apps.

[ 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 ]

In this quiz, you’ll test your understanding of Using Loguru to Simplify Python Logging.

By working through this quiz, you’ll revisit key concepts like the pre-configured logger, log levels, format placeholders, adding context with .bind() and .contextualize(), and saving logs to files.

[ 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 ]

This is a guest post from Iulia Feroli, founder of the Back To Engineering community on YouTube.

TensorFlow is a powerful open-source framework for building machine learning and deep learning systems. At its core, it works with tensors (a.k.a multi‑dimensional arrays) and provides high‑level libraries (like Keras) that make it easy to transform raw data into models you can train, evaluate, and deploy.

TensorFlow helps you handle the full pipeline: loading and preprocessing data, assembling models from layers and activations, training with optimizers and loss functions, and exporting for serving or even running on edge devices (including lightweight TensorFlow Lite models on Raspberry Pi and other microcontrollers). 

If you want to make data-driven applications, prototyping neural networks, or ship models to production or devices, learning TensorFlow gives you a consistent, well-supported toolkit to go from idea to deployment.

If you’re brand new to TensorFlow, start by watching the short overview video where I explain tensors, neural networks, layers, and why TensorFlow is great for taking data → model → deployment, and how all of this can be explained with a LEGO-style pieces sorting example. 

In this blog post, I’ll walk you through a first, stripped-down TensorFlow implementation notebook so we can get started with some practical experience. You can also watch the walkthrough video to follow along.

We’ll be exploring a very simple use case today: load the Fashion MNIST dataset, build two very simple Keras models, train and compare them, then dig into visualizations (predictions, confidence bars, confusion matrix). I kept the code minimal and readable so you can focus on the ideas – and you’ll see how PyCharm helps along the way.

Training TensorFlow models step by step

Getting started in PyCharm

We’ll be leveraging PyCharm’s native Notebook integration to build out our project. This way, we can inspect each step of the pipeline and use some supporting visualization along the way. We’ll create a new project and generate a virtual environment to manage our dependencies. 

If you’re running the code from the attached repo, you can install directly from the requirements file. If you wish to expand this example with additional visualizations for further models, you can easily add more packages to your requirements as you go by using the PyCharm package manager helpers for installing and upgrading.

Load Fashion MNIST and inspect the data

Fashion MNIST is a great starter because the images are small (28×28 pixels), visually meaningful, and easy to interpret. They represent various garment types as pixelated black-and-white images, and provide the relevant labels for a well-contained classification task. We can first take a look at our data sample by printing some of these images with various matplotlib functions:

```
fig, axes = plt.subplots(2, 5, figsize=(10, 4))
for i, ax in enumerate(axes.flat):
    ax.imshow(x_train[i], cmap='gray')
    ax.set_title(class_names[y_train[i]])
    ax.axis('off')
plt.show()
```
# Two simple models (a quick experiment)
```
model1 = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])
model2 = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])
```

Compile and train your first model

From here, we can compile and train our first TensorFlow model(s). With PyCharm’s code completion features and documentation access, you can get instant suggestions for building out these simple code blocks.

For a first try at TensorFlow, this allows us to spin up a working model with just a few presses of Tab in our IDE. We’re using the recommended standard optimizer and loss function, and we’re tracking for accuracy. We can choose to build multiple models by playing around with the number or type of layers, along with the other parameters. 

```
model1.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)
model1.fit(x_train, y_train, epochs=10)
model2.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)
model2.fit(x_train, y_train, epochs=15)
```

Evaluate and compare your TensorFlow model performance

```
loss1, accuracy1 = model1.evaluate(x_test, y_test)
print(f'Accuracy of model1: {accuracy1:.2f}')
loss2, accuracy2 = model2.evaluate(x_test, y_test)
print(f'Accuracy of model2: {accuracy2:.2f}')
```

Once the models are trained (and you can see the epochs progressing visually as each cell is run), we can immediately evaluate the performance of the models.

In my experiment, model1 sits around ~0.88 accuracy, and while model2 is a little higher than that, it took 50% longer to train. That’s the kind of trade‑off you should be thinking about: Is a tiny accuracy gain worth the additional compute and complexity? 

We can dive further into the results of the model run by generating a DataFrame instance of our new prediction dataset. Here we can also leverage built-in functions like `describe` to quickly get some initial statistical impressions:

```
predictions = model1.predict(x_test)
import pandas as pd
df_pred = pd.DataFrame(predictions, columns=class_names)
df_pred.describe()
```

However, the most useful statistics will compare our model’s prediction with the ground truth “real” labels of our dataset. We can also break this down by item category:

```
y_pred = model1.predict(x_test).argmax(axis=1)
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8,6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()
print('Classification report:')
print(classification_report(y_test, y_pred, target_names=class_names))
```

From here, we can notice that the accuracy differs quite a bit by type of garment. A possible interpretation of this is that trousers are quite a distinct type of clothing from, say, t-shirts and shirts, which can be more commonly confused. 

This is, of course, the type of nuance that, as humans, we can pick up by looking at the images, but the model only has access to a matrix of pixel values. The data does seem, however, to confirm our intuition. We can further build a more comprehensive visualization to test this hypothesis. 

```
import numpy as np
import matplotlib.pyplot as plt
# pick 8 wrong examples
y_pred = predictions.argmax(axis=1)
wrong_idx = np.where(y_pred != y_test)[0][:8]  # first 8 mistakes
n = len(wrong_idx)
fig, axes = plt.subplots(n, 2, figsize=(10, 2.2 * n), constrained_layout=True)
for row, idx in enumerate(wrong_idx):
    p = predictions[idx]
    pred = int(np.argmax(p))
    true = int(y_test[idx])
    axes[row, 0].imshow(x_test[idx], cmap="gray")
    axes[row, 0].axis("off")
    axes[row, 0].set_title(
        f"WRONG  P:{class_names[pred]} ({p[pred]:.2f})  T:{class_names[true]}",
        color="red",
        fontsize=10
    )
    bars = axes[row, 1].bar(range(len(class_names)), p, color="lightgray")
    bars[pred].set_color("red")
    axes[row, 1].set_ylim(0, 1)
    axes[row, 1].set_xticks(range(len(class_names)))
    axes[row, 1].set_xticklabels(class_names, rotation=90, fontsize=8)
    axes[row, 1].set_ylabel("conf", fontsize=9)
plt.show()
```

This table generates a view where we can explore the confidence our model had in a prediction: By exploring which weight each class was given, we can see where there was doubt (i.e. multiple classes with a higher weight) versus when the model was certain (only one guess). These examples further confirm our intuition: top-types appear to be more commonly confused by the model. 

Conclusion

And there we have it! We were able to set up and train our first model and already drive some data science insights from our data and model results. Using some of the PyCharm functionalities at this point can speed up the experimentation process by providing access to our documentation and applying code completion directly in the cells. We can even use AI Assistant to help generate some of the graphs we’ll need to further evaluate the TensorFlow model performance and investigate our results.

You can try out this notebook yourself, or better yet, try to generate it with these same tools for a more hands-on learning experience.

Where to go next

This notebook is a minimal, teachable starting point. Here are some practical next steps to try afterwards:

• Replace the dense baseline with a small CNN (Conv2D → MaxPooling → Dense).
• Add dropout or batch normalization to reduce overfitting.
• Apply data augmentation (random shifts/rotations) to improve generalization.
• Use callbacks like EarlyStopping and ModelCheckpoint so training is efficient, and you keep the best weights.
• Export a SavedModel for server use or convert to TensorFlow Lite for edge devices (Raspberry Pi, microcontrollers).

Frequently asked questions

When should I use TensorFlow?

TensorFlow is best used when building machine learning or deep learning models that need to scale, go into production, or run across different environments (cloud, mobile, edge devices). 

TensorFlow is particularly well-suited for large-scale models and neural networks, including scenarios where you need strong deployment support (TensorFlow Serving, TensorFlow Lite). For research prototypes, TensorFlow is viable, but it’s more commonplace to use lightweight frameworks for easier experimentation.

Can TensorFlow run on a GPU?

Yes, TensorFlow can run GPUs and TPUs. Additionally, using a GPU can significantly speed up training, especially for deep learning models with large datasets. The best part is, TensorFlow will automatically use an available GPU if it’s properly configured.

What is loss in TensorFlow?

Loss (otherwise known as loss function) measures how far a model’s predictions are from the actual target values. Loss in TensorFlow is a numerical value representing the distance between predictions and actual target values. A few examples include: 

• MSE (mean squared error), used in regression tasks.
• Cross-entropy loss, often used in classification tasks.

How many epochs should I use?

There’s no set number of epochs to use, as it depends on your dataset and model. Typical approaches cover: 

• Starting with a conservative number (10–50 epochs).
• Monitoring validation loss/accuracy and adjusting based on the results you see.
• Using early stopping to halt training when improvements decrease.

An epoch is one full pass through your training data. Not enough passes through leads to underfitting, and too many can cause overfitting. The sweet spot is where your model generalizes best to unseen data. 

About the author

Iulia Feroli

Iulia’s mission is to make tech exciting, understandable, and accessible to the new generation.

With a background spanning data science, AI, cloud architecture, and open source, she brings a unique perspective on bridging technical depth with approachability.

She’s building her own brand, Back To Engineering, through which she creates a community for tech enthusiasts, engineers, and makers. From YouTube videos on building robots from scratch, to conference talks or keynotes about real, grounded AI, and technical blogs and tutorials – Iulia shares her message worldwide on how to turn complex concepts into tools developers can use every day.

In this tutorial, you will learn how to use Google's Gemini AI model through its API in Python.

Follow the steps below to access the Gemini API and then use it in python.

1. Visit Google AI Studio website.
2. Sign in using your Google account.
3. Create an API key.
4. Install the Google AI Python library for the Gemini API using the command below :
   

   pip install google-genai

   .

Ce livre Python que je voulais juste mettre à jour

En août dernier, j’annonçais la relance de ce livre avec une certaine naïveté : j’avais retrouvé mon PDF de 2014, extrait le Markdown avec Docling, et assemblé un pipeline Longform → Pandoc → Typst. Je me disais que ce serait l’affaire de quelques semaines — mettre à jour les versions, ajouter quelques chapitres, boucler.

Huit mois plus tard, le périmètre a triplé, la chaîne d’outils a été réécrite, et la façon dont je travaille a complètement changé. Ce n’est pas ce que j’avais prévu. C’est mieux.

Finding AI insights and education at PyCon US 2026

Did you know that you can customize the Python debugger (PDB) by creating custom aliases within a .pdbrc file in your home directory or Python’s current working directory?

I recently learned this and I’d like to share a few helpful aliases that I now have access to in my PDB sessions thanks to my new ~/.pdbrc file.

The aliases in my ~/.pdbrc file

Here’s my new ~/.pdbrc file:

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# Custom PDB aliases

# dir obj: print non-dunder attributes and methods
alias dir print(*(f"%1.{n} = {v!r}" for n, v in __import__('inspect').getmembers(%1) if not n.startswith("__")), sep="\n")

# attrs obj: print non-dunder data attributes
alias attrs import inspect as __i ;; print(*(f"%1.{n} = {v!r}" for n, v in __i.getmembers(%1, lambda v: not __i.isroutine(v)) if not n.startswith("__")), sep="\n") ;; del __i

# vars obj: print instance variables (object must have __dict__)
alias vars print(*(f"%1.{k} = {v!r}" for k, v in vars(%1).items()), sep="\n")

# src obj: print source file, line number, and code where a class/function was defined
alias src import inspect as __i;; print(f"{__i.getsourcefile(%1)} on line {__i.getsourcelines(%1)[1]}:\n{''.join(__i.getsource(%1))}") ;; del __i

# loc: print local variables from current frame
alias loc print(*(f"{name} = {value!r}" for name, value in vars().items() if not name.startswith("__")), sep="\n")

This allows me to use:

• dir, attrs, and vars to inspect Python objects
• src to see the source code of a particular function/class
• loc to see the local variables

You might wonder “Can’t I use dir(x) instead of dir x and vars(x) instead of vars x and locals() instead of loc?”

You can!… but those aliases print things out in a nicer format.

A demo of these 5 aliases

Let’s use -m pdb -m calendar to launch Python’s calendar module from the command line while dropping into PDB immediately:

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$ python -m pdb -m calendar
> /home/trey/.local/share/uv/python/cpython-3.15.0a3-linux-x86_64-gnu/lib/python3.15/calendar.py(1)<module>()
-> """Calendar printing functions
(Pdb)

Then we’ll set a breakpoint after lots of stuff has been defined and continue to that breakpoint:

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(Pdb) b 797
Breakpoint 1 at /home/trey/.local/share/uv/python/cpython-3.15.0a3-linux-x86_64-gnu/lib/python3.15/calendar.py:797
(Pdb) c
> /home/trey/.local/share/uv/python/cpython-3.15.0a3-linux-x86_64-gnu/lib/python3.15/calendar.py(797)<module>()
-> firstweekday = c.getfirstweekday
(Pdb) l
792
793
794     # Support for old module level interface
795     c = TextCalendar()
796
797 B-> firstweekday = c.getfirstweekday
798
799     def setfirstweekday(firstweekday):
800         if not MONDAY <= firstweekday <= SUNDAY:
801             raise IllegalWeekdayError(firstweekday)
802         c.firstweekday = firstweekday

The string representation for that c variable doesn’t tell us much:

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(Pdb) !c
<__main__.TextCalendar object at 0x7416de93af90>

If we use the dir alias, we’ll see every attribute that’s accessible on c printed in a pretty friendly format:

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(Pdb) dir c
c._firstweekday = 0
c.firstweekday = 0
c.formatday = <bound method TextCalendar.formatday of <__main__.TextCalendar object at 0x7416de93af90>>
c.formatmonth = <bound method TextCalendar.formatmonth of <__main__.TextCalendar object at 0x7416de93af90>>
c.formatmonthname = <bound method TextCalendar.formatmonthname of <__main__.TextCalendar object at 0x7416de93af90>>
c.formatweek = <bound method TextCalendar.formatweek of <__main__.TextCalendar object at 0x7416de93af90>>
c.formatweekday = <bound method TextCalendar.formatweekday of <__main__.TextCalendar object at 0x7416de93af90>>
c.formatweekheader = <bound method TextCalendar.formatweekheader of <__main__.TextCalendar object at 0x7416de93af90>>
c.formatyear = <bound method TextCalendar.formatyear of <__main__.TextCalendar object at 0x7416de93af90>>
c.getfirstweekday = <bound method Calendar.getfirstweekday of <__main__.TextCalendar object at 0x7416de93af90>>
c.itermonthdates = <bound method Calendar.itermonthdates of <__main__.TextCalendar object at 0x7416de93af90>>
c.itermonthdays = <bound method Calendar.itermonthdays of <__main__.TextCalendar object at 0x7416de93af90>>
c.itermonthdays2 = <bound method Calendar.itermonthdays2 of <__main__.TextCalendar object at 0x7416de93af90>>
c.itermonthdays3 = <bound method Calendar.itermonthdays3 of <__main__.TextCalendar object at 0x7416de93af90>>
c.itermonthdays4 = <bound method Calendar.itermonthdays4 of <__main__.TextCalendar object at 0x7416de93af90>>
c.iterweekdays = <bound method Calendar.iterweekdays of <__main__.TextCalendar object at 0x7416de93af90>>
c.monthdatescalendar = <bound method Calendar.monthdatescalendar of <__main__.TextCalendar object at 0x7416de93af90>>
c.monthdays2calendar = <bound method Calendar.monthdays2calendar of <__main__.TextCalendar object at 0x7416de93af90>>
c.monthdayscalendar = <bound method Calendar.monthdayscalendar of <__main__.TextCalendar object at 0x7416de93af90>>
c.prmonth = <bound method TextCalendar.prmonth of <__main__.TextCalendar object at 0x7416de93af90>>
c.prweek = <bound method TextCalendar.prweek of <__main__.TextCalendar object at 0x7416de93af90>>
c.pryear = <bound method TextCalendar.pryear of <__main__.TextCalendar object at 0x7416de93af90>>
c.setfirstweekday = <bound method Calendar.setfirstweekday of <__main__.TextCalendar object at 0x7416de93af90>>
c.yeardatescalendar = <bound method Calendar.yeardatescalendar of <__main__.TextCalendar object at 0x7416de93af90>>
c.yeardays2calendar = <bound method Calendar.yeardays2calendar of <__main__.TextCalendar object at 0x7416de93af90>>
c.yeardayscalendar = <bound method Calendar.yeardayscalendar of <__main__.TextCalendar object at 0x7416de93af90>>

If we use attrs we’ll see just the non-method attributes:

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(Pdb) attrs c
c._firstweekday = 0
c.firstweekday = 0

And vars will show us just the attributes that live as proper instance attributes in that object’s __dict__ dictionary:

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(Pdb) vars c
c._firstweekday = 0

The src alias can be used to see the source code for a given method:

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(Pdb) src c.prmonth
/home/trey/.local/share/uv/python/cpython-3.15.0a3-linux-x86_64-gnu/lib/python3.15/calendar.py on line 404:
    def prmonth(self, theyear, themonth, w=0, l=0):
        """
        Print a month's calendar.
        """
        print(self.formatmonth(theyear, themonth, w, l), end='')

And the loc alias will show us all the local variables defined in the current scope:

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(Pdb) loc
APRIL = __main__.APRIL
AUGUST = __main__.AUGUST
Calendar = <class '__main__.Calendar'>
DECEMBER = __main__.DECEMBER
Day = <enum 'Day'>
FEBRUARY = __main__.FEBRUARY
FRIDAY = __main__.FRIDAY
HTMLCalendar = <class '__main__.HTMLCalendar'>
IllegalMonthError = <class '__main__.IllegalMonthError'>
IllegalWeekdayError = <class '__main__.IllegalWeekdayError'>
IntEnum = <enum 'IntEnum'>
JANUARY = __main__.JANUARY
JULY = __main__.JULY
JUNE = __main__.JUNE
LocaleHTMLCalendar = <class '__main__.LocaleHTMLCalendar'>
LocaleTextCalendar = <class '__main__.LocaleTextCalendar'>
MARCH = __main__.MARCH
MAY = __main__.MAY
MONDAY = __main__.MONDAY
Month = <enum 'Month'>
NOVEMBER = __main__.NOVEMBER
OCTOBER = __main__.OCTOBER
SATURDAY = __main__.SATURDAY
SEPTEMBER = __main__.SEPTEMBER
SUNDAY = __main__.SUNDAY
THURSDAY = __main__.THURSDAY
TUESDAY = __main__.TUESDAY
TextCalendar = <class '__main__.TextCalendar'>
WEDNESDAY = __main__.WEDNESDAY
_CLIDemoCalendar = <class '__main__._CLIDemoCalendar'>
_CLIDemoLocaleCalendar = <class '__main__._CLIDemoLocaleCalendar'>
_get_default_locale = <function _get_default_locale at 0x7416de7c0930>
_locale = <module 'locale' from '/home/trey/.local/share/uv/python/cpython-3.15.0a3-linux-x86_64-gnu/lib/python3.15/locale.py'>
_localized_day = <class '__main__._localized_day'>
_localized_month = <class '__main__._localized_month'>
_monthlen = <function _monthlen at 0x7416de7c0720>
_nextmonth = <function _nextmonth at 0x7416de7c0880>
_prevmonth = <function _prevmonth at 0x7416de7c07d0>
_validate_month = <function _validate_month at 0x7416de7c05c0>
c = <__main__.TextCalendar object at 0x7416de93af90>
datetime = <module 'datetime' from '/home/trey/.local/share/uv/python/cpython-3.15.0a3-linux-x86_64-gnu/lib/python3.15/datetime.py'>
day_abbr = <__main__._localized_day object at 0x7416de941090>
day_name = <__main__._localized_day object at 0x7416de93acf0>
different_locale = <class '__main__.different_locale'>
error = <class 'ValueError'>
global_enum = <function global_enum at 0x7416dee17480>
isleap = <function isleap at 0x7416dea92770>
leapdays = <function leapdays at 0x7416de7c0460>
mdays = [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
month_abbr = <__main__._localized_month object at 0x7416de9411d0>
month_name = <__main__._localized_month object at 0x7416de93ae40>
monthrange = <function monthrange at 0x7416de7c0670>
repeat = <class 'itertools.repeat'>
standalone_month_abbr = <__main__._localized_month object at 0x7416de954fc0>
standalone_month_name = <__main__._localized_month object at 0x7416de941310>
sys = <module 'sys' (built-in)>
weekday = <function weekday at 0x7416de7c0510>

~/.pdbrc isn’t as powerful as PYTHONSTARTUP

I also have a custom PYTHONSTARTUP file, which is launched every time I start a new Python REPL (see Handy Python REPL modifications). A PYTHONSTARTUP file is just Python code, which makes it easy to customize.

A ~/.pdbrc file is not Python code… it’s a very limited custom file format.

You may notice that every alias line defined in my ~/.pdbrc file is a bunch of code shoved all on one line. That’s because there’s no way to define an alias over multiple lines.

Also any variables assigned in an alias will leak into the surrounding scope… so I have a del statement in a couple of those aliases to clean up a stray variable assignment (from an import).

See the documentation on alias and the top of the debugger commands for more on how ~/.pdbrc files work.

lazy import json
lazy from datetime import timedelta

# The json module isn't loaded yet, so no startup cost

# Later, when you actually use it:
data = json.loads(payload)  # Now it loads

import textwrap

# Before: awkward indentation or textwrap.dedent() wrapper
def get_query():
    return textwrap.dedent("""\
        SELECT name, email
        FROM users
        WHERE active = true
    """)

# With d-strings: clean and readable
def get_query():
    return d"""
        SELECT name, email
        FROM users
        WHERE active = true
    """

Read the full article at https://realpython.com/python-news-april-2026/ »

[ 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 ]

Test your understanding of For Loops in Python (Definite Iteration).

You’ll revisit Python loops, iterables, and how iterators behave. You’ll also explore set iteration order and the effects of the break and continue statements.

[ 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 ]

• Migrating from mypy to ty: Lessons from FastAPI
• Oxyde ORM
• Typeshedded CPython docs
• Raw+DC Database Pattern: A Retrospective
• Extras
• Joke

About the show

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Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too.

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Brian #1: Migrating from mypy to ty: Lessons from FastAPI

• Tim Hopper
• I saw this post by Sebastián Ramírez about all of his projects switching to ty
  • FastAPI, Typer, SQLModel, Asyncer, FastAPI CLI
• SqlModel is already ty only - mypy removed
• This signals that ty is ready to use
• Tim lists some steps to apply ty to your own projects
  • Add ty alongside mypy
  • Set error-on-warning = true
  • Accept the double-ignore comments
  • Pick a smaller project to cut over first
  • Drop mypy when the noise exceeds the signalAdd ty alongside mypy
• Related anecdote:
  • I had tried out ty with pytest-check in the past with difficulty
  • Tried it again this morning, only a few areas where mypy was happy but ty reported issues
  • At least one ty warning was a potential problem for people running pre-releases of pytest,
  • Not really related: packaging.version.parse is awesome

Michael #2: Oxyde ORM

• Oxyde ORM is a type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core.
• Note: Oxyde is a young project under active development. The API may evolve between minor versions.
• No sync wrappers or thread pools. Oxyde is async from the ground up
• Includes oxyde-admin
• Features
  • Django-style API - Familiar Model.objects.filter() syntax
  • Pydantic v2 models - Full validation, type hints, serialization
  • Async-first - Built for modern async Python with asyncio
  • Rust performance - SQL generation and execution in native Rust
  • Multi-database - PostgreSQL, SQLite, MySQL support
  • Transactions - transaction.atomic() context manager with savepoints
  • Migrations - Django-style makemigrations and migrate CLI

Brian #3: Typeshedded CPython docs

• Thanks emmatyping for the suggestion
• Documentation for Python with typeshed types
• Source: typeshedding_cpython_docs

Michael #4: Raw+DC Database Pattern: A Retrospective

• A new design pattern I’m seeing gain traction in the software space: Raw+DC: The ORM pattern of 2026
• I’ve had a chance to migrate three of my most important web app.
• Thrilled to report that yes, the web app is much faster using Raw+DC
• Plus, this was part of the journey to move from 1.3 GB memory usage to 0.45 GB (more on this next week)

Extras

Brian:

• Lean TDD 0.5 update
  • Significant rewrite and focus

Michael:

• pytest-just (for just command file testing), by Michael Booth
• Something going on with Encode
  • httpx: Anyone know what's up with HTTPX? And forked
  • starlette and uvicorn: Transfer of Uvicorn & Starlette
  • mkdocs: The Slow Collapse of MkDocs
  • django-rest-framework: Move to django commons?
• Certificates at Talk Python Training

Joke:

• Neue Rich

Behind every flawless talk, engaging workshop, and perfectly timed coffee break at EuroPython is a crew of unsung heroes—our volunteers! 🌟 Not just organizers, but dream enablers: printer ninjas, registration magicians, social butterflies, and even salsa instructors (yeah, that happened!) 

We’re the quiet force turning chaos into community, one sprint at a time. 💻✨ 

Curious who really makes the magic happen? Today we’d like to introduce George Zisopoulos, member of the Operations team at EuroPython 2025. 

EP: What first inspired you to volunteer for EuroPython? And which edition of the conference was it?

I was inspired because I gave a presentation in 2020, and after that I wanted to experience the conference from the other side, as part of the volunteers. It was amazing to see how much work all these people had done for us as attendees, and I wanted to be a part of that.

So I applied and became an online volunteer in 2022 in Dublin, and the following year I joined EuroPython 2023 as an on-site volunteer. Once you start, you can’t stop doing it.

EP: Have you learned new skills while contributing to EuroPython? If so, which ones?

It’s less about learning new skills and more about discovering the ones you already have. With guidance and a supportive team, you feel confident using them and even pushing a bit past your comfort zone.

EP: What's your favorite memory from volunteering at the conference?

My favorite part is walking into the conference and unexpectedly running into someone you met at previous years’ editions. It’s like a little déjà vu. They hug you like you just saw them yesterday, even if it’s been a whole year.

EP: Did you make any lasting friendships or professional connections through volunteering?

Yes, I’ve made a few lasting friendships. We stay in touch all year, even though we live in different cities or countries. We visit each other, and often end up meeting in other countries while traveling.

EP: Any unexpected or funny experiences during the conference which you’d like to share?

I love coffee, so during the conference I’m usually wandering around with a cup in hand. Two years ago, thanks to some playful hits from friends, I ended up destroying three t-shirts with coffee during the conference! Now every year they wonder… How many shirts will I sacrifice this time?

EP: Would you volunteer again, and why?

I would say what I used to say last year: Summer without EuroPython just doesn’t really feel like a summer 😉 See you all there!

EP: Thank you for your contribution, George!

As a hobbyist consumer of OSM data to render maps, I find wrong tags annoying. Bad values mean that the resulting map is wrong or incomplete, so less useful. I decided to attack the most egregious ones, which include typos, street names instead of type and some other errors. The idea is to attack the long tail first, so I'm not blocked because the next batch of errors (objects with exactly the same error) looks too big (yes, OCD).

So I hacked a small python script to help me find and edit them:

#! /usr/bin/env python3

import os

import psycopg2


def main():
    db = psycopg2.connect(dbname='europe')
    cursor = db.cursor()

    cursor.execute('''
        SELECT
            count(*) AS count,
            highway
        FROM planet_osm_line
        WHERE
            highway IS NOT NULL
        GROUP BY highway
        ORDER BY count ASC
    ''')
    data = cursor.fetchall()

    for count, highway in data:
        print(f"next {count}: {highway}")

        cursor.execute('''
            SELECT osm_id
            FROM planet_osm_line
            WHERE
                highway = %s
        ''', (highway, ))

        for (osm_id, ) in cursor.fetchall():
            if osm_id < 0:
                # in rendering DBs, this is a relation
                os.system(f"librewolf -P default 'https://www.openstreetmap.org/edit?relation={-osm_id}'")
            else:
                os.system(f"librewolf -P default 'https://www.openstreetmap.org/edit?way={osm_id}'")


if __name__ == '__main__':
    main()

It is quite inefficient, but what I want is to edit the errors, not to write a script :) This requires a rendering database, which I already have locally :)

From here the workflow is:

• Analyze the type of error.
  • This includes looking at the history of the object.
• Search the wiki.
• Correct the error or leave a note.
• Find the original changeset and leave a note if needed.
• Add any details to te changeset if needed.

In my machine, finding the long tail, and finding each set of errors takes one minute, so I was launching two at the same time. One of the things to notice is that if the object you try to edit does no exists anymore, you get and edit view of the whole planet.

About five months ago I wrote about Absurd, a durable execution system we built for our own use at Earendil, sitting entirely on top of Postgres and Postgres alone. The pitch was simple: you don’t need a separate service, a compiler plugin, or an entire runtime to get durable workflows. You need a SQL file and a thin SDK.

Since then we’ve been running it in production, and I figured it’s worth sharing what the experience has been like. The short version: the design held up, the system has been a pleasure to work with, and other people seem to agree.

A Quick Refresher

Absurd is a durable execution system that lives entirely inside Postgres. The core is a single SQL file (absurd.sql) that defines stored procedures for task management, checkpoint storage, event handling, and claim-based scheduling. On top of that sit thin SDKs (currently TypeScript, Python and an experimental Go one) that make the system ergonomic in your language of choice.

The model is straightforward: you register tasks, decompose them into steps, and each step acts as a checkpoint. If anything fails, the task retries from the last completed step. Tasks can sleep, wait for external events, and suspend for days or weeks. All state lives in Postgres.

If you want the full introduction, the original blog post covers the fundamentals. What follows here is what we’ve learned since.

What Changed

The project got multiple releases over the last five months. Most of the changes are things you’d expect from a system that people actually started depending on: hardened claim handling, watchdogs that terminate broken workers, deadlock prevention, proper lease management, event race conditions, and all the edge cases that only show up when you’re running real workloads.

A few things worth calling out specifically.

Decomposed steps. The original design only had ctx.step(), where you pass in a function and get back its checkpointed result. That works well for many cases but not all. Sometimes you need to know whether a step already ran before deciding what to do next. So we added beginStep() / completeStep(), which give you a handle you can inspect before committing the result. This turned out to be very useful for modeling intentional failures and conditional logic. This in particular is necessary when working with “before call” and “after call” type hook APIs.

Task results. You can now spawn a task, go do other things, and later come back to fetch or await its result. This sounds obvious in hindsight, but the original system was purely fire-and-forget. Having proper result inspection made it possible to use Absurd for things like spawning child tasks from within a parent workflow and waiting for them to finish. This is particularly useful for debugging with agents too.

absurdctl. We built this out as a proper CLI tool. You can initialize schemas, run migrations, create queues, spawn tasks, emit events, retry failures from the command line. It’s installable via uvx or as a standalone binary. This has been invaluable for debugging production issues. When something is stuck, being able to just absurdctl dump-task --task-id=<id> and see exactly where it stopped is a very different experience from digging through logs.

Habitat. A small Go application that serves up a web dashboard for monitoring tasks, runs, checkpoints, and events. It connects directly to Postgres and gives you a live view of what’s happening. It’s simple, but it’s the kind of thing that makes the system more enjoyable for humans.

Agent integration. Since Absurd was originally built for agent workloads, we added a bundled skill that coding agents can discover and use to debug workflow state via absurdctl. There’s also a documented pattern for making pi agent turns durable by logging each message as a checkpoint.

What Held Up

The thing I’m most pleased about is that the core design didn’t need to change all that much. The fundamental model of tasks, steps, checkpoints, events, and suspending is still exactly what it was initially. We added features around it, but nothing forced us to rethink the basic abstractions.

Putting the complexity in SQL and keeping the SDKs thin turned out to be a genuinely good call. The TypeScript SDK is about 1,400 lines. The Python SDK is about 1,900 but most of this comes from the complexity of supporting colored functions. Compare that to Temporal’s Python SDK at around 170,000 lines. It means the SDKs are easy to understand, easy to debug, and easy to port. When something goes wrong, you can read the entire SDK in an afternoon and understand what it does.

The checkpoint-based replay model also aged well. Unlike systems that require deterministic replay of your entire workflow function, Absurd just loads the cached step results and skips over completed work. That means your code doesn’t need to be deterministic outside of steps. You can call Math.random() or datetime.now() in between steps and things still work, because only the step boundaries matter. In practice, this makes it much easier to reason about what’s safe and what isn’t.

Pull-based scheduling was the right choice too. Workers pull tasks from Postgres as they have capacity. There’s no coordinator, no push mechanism, no HTTP callbacks. That makes it trivially self-hostable and means you don’t have to think about load management at the infrastructure level.

What Might Not Be Optimal

I had some discussions with folks about whether the right abstraction should have been a durable promise. It’s a very appealing idea, but it turns out to be much more complex to implement in practice. It’s however in theory also more powerful. I did make some attempts to see what absurd would look like if it was based on durable promises but so far did not get anywhere with it. It’s however an experiment that I think would be fun to try!

What We Use It For

The primary use case is still agent workflows. An agent is essentially a loop that calls an LLM, processes tool results, and repeats until it decides it’s done. Each iteration becomes a step, and each step’s result is checkpointed. If the process dies on iteration 7, it restarts and replays iterations 1 through 6 from the store, then continues from 7.

But we’ve found it useful for a lot of other things too. All our crons just dispatch distributed workflows with a pre-generated deduplication key from the invocation. We can have two cron processes running and they will only trigger one absurd task invocation. We also use it for background processing that needs to survive deploys. Basically anything where you’d otherwise build your own retry-and-resume logic on top of a queue.

What’s Still Missing

Absurd is deliberately minimal, but there are things I’d like to see.

There’s no built-in scheduler. If you want cron-like behavior, you run your own scheduler loop and use idempotency keys to deduplicate. That works, and we have a documented pattern for it, but it would be nice to have something more integrated.

There’s no push model. Everything is pull. If you need an HTTP endpoint to receive webhooks and wake up tasks, you build that yourself. I think that’s the right default as push systems are harder to operate and easier to overwhelm but there are cases where it would be convenient. In particular there are quite a few agentic systems where it would be super nice to have webhooks natively integrated (wake on incoming POST request). I definitely don’t want to have this in the core, but that sounds like the kind of problem that could be a nice adjacent library that builds on top of absurd.

The biggest omission is that it does not support partitioning yet. That’s unfortunate because it makes cleaning up data more expensive than it has to be. In theory supporting partitions would be pretty simple. You could have weekly partitions and then detach and delete them when they expire. The only thing that really stands in the way of that is that Postgres does not have a convenient way of actually doing that.

The hard part is not partitioning itself, it’s partition lifecycle management under real workloads. If a worker inserts a row whose expires_at lands in a month without a partition, the insert fails and the workflow crashes. So you need a separate maintenance loop that always creates future partitions far enough ahead for sleeps/retries, and does that for every queue.

On the delete side, the safe approach is DETACH PARTITION CONCURRENTLY, but getting that to run from pg_cron doesn’t work because it cannot be run within a transaction, but pg_cron runs everything in one.

I don’t think it’s an unsolvable problem, but it’s one I have not found a good solution for and I would love to get input on.

Does Open Source Still Matter?

This brings me a bit to a meta point on the whole thing which is what the point of Open Source libraries in the age of agentic engineering is. Durable Execution is now something that plenty of startups sell you. On the other hand it’s also something that an agent would build you and people might not even look for solutions any more. It’s kind of … weird?

I don’t think a durable execution library can support a company, I really don’t. On the other hand I think it’s just complex enough of a problem that it could be a good Open Source project void of commercial interests. You do need a bit of an ecosystem around it, particularly for UI and good DX for debugging, and that’s hard to get from a throwaway implementation.

I don’t think we have squared this yet, but it’s already much better to use than a few months ago.

If you’re using Absurd, thinking about it, or building adjacent ideas, I’d love your feedback. Bug reports, rough edges, design critiques, and contributions are all very welcome—this project has gotten better every time someone poked at it from a different angle.

In this quiz, you’ll test your understanding of How to Add Features to a Python Project With Codex CLI.

By working through this quiz, you’ll revisit how to install, configure, and use Codex CLI to implement and refine features in a Python project using natural language prompts.

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In this quiz, you’ll test your understanding of Class Concepts: Object-Oriented Programming in Python.

By working through this quiz, you’ll revisit how to define classes, use instance and class attributes, write different types of methods, and apply the descriptor protocol through properties.

You can also deepen your knowledge with the tutorial Python Classes: The Power of Object-Oriented Programming.

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