Planet Python

Ready for some Python skill-building sales?

This is my seventh annual compilation of Python learning deals.

I’m publishing this post extra early this year, so bookmark this page and set a calendar event for yourself to check back on Friday November 29.

Currently live sales

Here are Python-related sales that are live right now:

• Python Jumpstart with Python Morsels: 50% off my brand new Python course, an introduction to Python that’s very hands-on ($99 instead of $199)
• Rodrigo 50% off Rodrigo’s all books bundle with code BF24
• The Python Coding Place: 40% off The Python Coding Book and 40% off a lifetime membership to The Python Coding Place with code black2024
• Sundeep Agarwal: ~50% off Sundeep’s all book and Python bundles with code FestiveOffer
• O'Reilly Media: 40% off the first year with code CYBERWEEK24 ($299 instead of $499)

Anticipated sales

Here are sales that will be live soon:

• Data School 40% off all Kevin’s courses or get a bundle with all 5 of his courses
• Mike Driscoll: 35% off Mike’s Python books and courses with code BF24

Here are some sales I expect to see, but which haven’t been announced yet:

• Talk Python: usually holds a sale on a variety of courses
• Brian Okken: often holds a sale on his pytest course
• Reuven Lerner: usually holds a sale
• Pragmatic Bookshelf: I’m guessing they’ll hold a 40% off sale with code turkeycode2024

Even more sales

Also see Adam Johnson’s Django-related Deals for Black Friday 2024 for sales on Adam’s books, courses from the folks at Test Driven, Django templates, and various other Django-related deals.

And for non-Python/Django Python deals, see the Awesome Black Friday / Cyber Monday deals GitHub repository and the BlackFridayDeals.dev website.

If you know of another sale (or a likely sale) please comment below or email me.

The NumPy library is a Python library used for scientific computing. It provides you with a multidimensional array object for storing and analyzing data in a wide variety of ways. In this tutorial, you’ll see examples of some features NumPy provides that aren’t always highlighted in other tutorials. You’ll also get the chance to practice your new skills with various exercises.

In this tutorial, you’ll learn how to:

• Create multidimensional arrays from data stored in files
• Identify and remove duplicate data from a NumPy array
• Use structured NumPy arrays to reconcile the differences between datasets
• Analyze and chart specific parts of hierarchical data
• Create vectorized versions of your own functions

If you’re new to NumPy, it’s a good idea to familiarize yourself with the basics of data science in Python before you start. Also, you’ll be using Matplotlib in this tutorial to create charts. While it’s not essential, getting acquainted with Matplotlib beforehand might be beneficial.

Get Your Code: Click here to download the free sample code that you’ll use to work through NumPy practical examples.

Take the Quiz: Test your knowledge with our interactive “NumPy Practical Examples: Useful Techniques” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

NumPy Practical Examples: Useful Techniques

This quiz will challenge your knowledge of working with NumPy arrays. You won't find all the answers in the tutorial, so you'll need to do some extra investigating. By finding all the answers, you're sure to learn some interesting things along the way.

Setting Up Your Working Environment

Before you can get started with this tutorial, you’ll need to do some initial setup. In addition to NumPy, you’ll need to install the Matplotlib library, which you’ll use to chart your data. You’ll also be using Python’s pathlib library to access your computer’s file system, but there’s no need to install pathlib because it’s part of Python’s standard library.

You might consider using a virtual environment to make sure your tutorial’s setup doesn’t interfere with anything in your existing Python environment.

Using a Jupyter Notebook within JupyterLab to run your code instead of a Python REPL is another useful option. It allows you to experiment and document your findings, as well as quickly view and edit files. The downloadable version of the code and exercise solutions are presented in Jupyter Notebook format.

The commands for setting things up on the common platforms are shown below:

• Windows
• Linux + macOS

Fire up a Windows PowerShell(Admin) or Terminal(Admin) prompt, depending on the version of Windows that you’re using. Now type in the following commands:

Windows PowerShell PS> python -m venv venv\ PS> venv\Scripts\activate (venv) PS> python -m pip install numpy matplotlib jupyterlab (venv) PS> jupyter lab Copied!

Here you create a virtual environment named venv\, which you then activate. If the activation is successful, then the virtual environment’s name will precede your Powershell prompt. Next, you install numpy and matplotlib into this virtual environment, followed by the optional jupyterlab. Finally, you start JupyterLab.

Note: When you activate your virtual environment, you may receive an error stating that your system can’t run the script. Modern versions of Windows don’t allow you to run scripts downloaded from the Internet as a security feature.

To fix this, you need to type the command Set-ExecutionPolicy RemoteSigned, then answer Y to the question. Your computer will now run scripts that Microsoft has verified. Once you’ve done this, the venv\Scripts\activate command should work.

Fire up a terminal and type in the following commands:

Shell $ python -m venv venv/ $ source venv/bin/activate (venv) $ python -m pip install numpy matplotlib jupyterlab (venv) $ jupyter lab Copied!

Here you create a virtual environment named venv/, which you then activate. If the activation is successful, then the virtual environment’s name will precede your command prompt. Next, you install numpy and matplotlib into this virtual environment, followed by the optional jupyterlab. Finally, you start JupyterLab.

You’ll notice that your prompt is preceded by (venv). This means that anything you do from this point forward will stay in this environment and remain separate from other Python work you have elsewhere.

Now that you have everything set up, it’s time to begin the main part of your learning journey.

NumPy Example 1: Creating Multidimensional Arrays From Files

When you create a NumPy array, you create a highly-optimized data structure. One of the reasons for this is that a NumPy array stores all of its elements in a contiguous area of memory. This memory management technique means that the data is stored in the same memory region, making access times fast. This is, of course, highly desirable, but an issue occurs when you need to expand your array.

Suppose you need to import multiple files into a multidimensional array. You could read them into separate arrays and then combine them using np.concatenate(). However, this would create a copy of your original array before expanding the copy with the additional data. The copying is necessary to ensure the updated array will still exist contiguously in memory since the original array may have had non-related content adjacent to it.

Constantly copying arrays each time you add new data from a file can make processing slow and is wasteful of your system’s memory. The problem becomes worse the more data you add to your array. Although this copying process is built into NumPy, you can minimize its effects with these two steps:

1. When setting up your initial array, determine how large it needs to be before populating it. You may even consider over-estimating its size to support any future data additions. Once you know these sizes, you can create your array upfront.

2. The second step is to populate it with the source data. This data will be slotted into your existing array without any need for it to be expanded.

Next, you’ll explore how to populate a three-dimensional NumPy array.

Populating Arrays With File Data

In this first example, you’ll use the data from three files to populate a three-dimensional array. The content of each file is shown below, and you’ll also find these files in the downloadable materials:

The first file has two rows and three columns with the following content:

CSV file1.csv 1.1, 1.2, 1.3 1.4, 1.5, 1.6 Copied! Read the full article at https://realpython.com/numpy-example/ »

[ 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 ]

Very often, backend devs don't want to write code.

For this, we use one trick : derive HTML widget for presentation, database access, REST endpoints from ONE SOURCE of truth and we call it MODEL.

A tradition, and I insist it's a conservative tradition, is to use a declarative model where we mad the truth of the model from python classes.

By declaring a class we will implicitly declare it's SQL structure, the HTML input form for human readable interaction and the REST endpoint to access a graph of objects which are all mapped on the database.

Since the arrival of pydantic it makes all the more sense when it comes to empower a strongly type approach in python.

But is it the only one worthy ?

I speak here as a veteran of the trenchline which job is to read a list of entries of customer in an xls file from a project manager and change the faulty value based on the retro-engineering of an HTML formular into whatever the freak the right value is supposed to be.

In this case your job is in fact to short circuit the web framework to which you don't have access to change values directly into the database.

More often than never is these real life case you don't have access to the team who built the framework (to much bureaucracy to even get a question answered before the situation gets critical) ... So you look at the form.

And you guess the name of the table that is impacted by looking at the « network tab » in the developper GUI when you hit the submit button.

And you guess the name of the field impacted in the table to guess the name of the columns.

And then you use your only magical tool which is a write access to the database to reflect the expected object with an automapper and change values.

You could do it raw SQL I agree, but sometimes you need to do a web query in the middle to change the value because you have to ask a REST service what is the new ID of the client.

And you see the more this experience of having to tweak into real life frameworks that often surprise users for the sake of the limitation of the source of truth, the more I want the HTML to be the source of truth.

The most stoïcian approach to full stack framework approach : to derive Everything from an HTML page.

The views, the controllers, the route, the model in such a true way that if you modify the HTML you modify in real time the database model, the routes, the displayed form.



What are the advantages of HTML as a declarative language ?

Here, one of the tradition is to prefere the human readable languages such as YAML and JSON, or machine readable as XML over HTML.

However, JSON and YAML are more limited in expressiveness of data structure than HTML (you can have a dict as a key in a dict in json ? Me I can.)

And on the other hand XML is quite a pain to read and write without mistakes.

HTML is just XML

HTML is a lax and lenient grammarless XML. No parsers will raise an exception because you wrote "<br>" instead of "<br/>" (or the opposite). You can add non existent attributes to tags and the parser will understand this easily without you having to redefine a full fledge grammar.

HTML is an XML YOU CAN SEE.

There are some tags that are related to a grammar of visual widget to which non computer people are familiar with.

If you use a FORM as a mapping to a database table, and all input inside has A column name you have already input drawn on your screen.



Modern « remote procedure call » are web based

Call it RPC, call it soap, call it REST, nowadays the web technologies trust 99% of how computer systems exchange data between each others.

You buy something on the internet, at the end you interact with a web formular or a web call. Hence, we can assert with strong convictions that 100% of web technologies can serve web pages. Thus, if you use your html as a model and present it, therefore you can deduce the data model from the form without needing a new pivoting language.

Proof of concept

For the convenience of « fun » we are gonna imagine a backend for « agile by micro blogging » (à la former twitter).

We are gonna assume the platform is structured micro blogging around where agile shines the most : not when things are done, but to move things on.

Things that are done will be called statements. Like : « software is delivered. Here is a factoid (a git url for instance) ». We will call this nodes in a graph and are they will be supposed to immutable states that can't be contested.

Each statement answers another statement's factoid like a delivery statement tends to follow a story point (at least should lead by the mean of a transition.

Hence in this application we will mirco-blog about the transition ... like on a social network with members of concerned group.
The idea of the application is to replace scrum meetings with micro blogging.

Are you blocked ? Do you need anything ? Can be answered on the mirco blogging platform, and every threads that are presented archived, used for machine learning (about what you want to hear as a good news) in a data form that is convenient for large language model.

As such we want to harvest a text long enough to express emotions, constricted to a laughingly small amount of characters so that finesse and ambiguity are tough to raise. That's the heart of the application : harvesting comments tagged with associated emotions to ease the work of tagging for Artificial Intelligence.

Hear me out, this is just a stupid idea of mine to illustrate a graph like structure described with HTML, not a real life idea. Me I just love to represent State Machine Diagram with everything that fall under my hands.

Here is the entity relationship diagram I have in mind :

Let's see what a table declaration might look like in HTML, let's say transition : <form action=/transition > <input type=number name=id /> <input type=number name=user_group_id nullable=false reference=user_group.id /> <textarea name=message rows=10 cols=50 nullable=false ></textarea> <input type=url name=factoid /> <select name="emotion_for_group_triggered" value=neutral > <option value="">please select a value</option> <option value=positive >Positive</option> <option value=neutral >Neutral</option> <option value=negative >Negative</option> </select> <input type=number name=expected_fun_for_group /> <input type=number name=previous_statement_id reference=statement.id nullable=false /> <input type=number name=next_statement_id reference=statement.id /> <unique_constraint col=next_statement_id,previous_statement_id name=unique_transition ></unique_constraint> <input type=checkbox name=is_exception /> </form> Through the use of additionnal tags of html and attributes we can convey a lot of informations usable for database construction/querying that are gonna be silent at the presentation (like unique_constraint). And with a little bit of javascript and CSS this html generate the following rendering (indicating the webservices endpoint as input type=submit :

Meaning that you can now serve a landing page that serve the purpose of human interaction, describing a « curl way » of automating interaction and a full model of your database.

Most startup think data model should be obfuscated to prevent being copied, most free software project thinks that sharing the non valuable assets helps adopt the technology.

And thanks to this, I can now create my own test suite that is using the HTML form to work on a doppleganger of the real database by parsing the HTML served by the application service (pdca.py) and launch a perfectly functioning service out of it: from requests import post from html.parser import HTMLParser import requests import os from dateutil import parser from passlib.hash import scrypt as crypto_hash # we can change the hash easily from urllib.parse import parse_qsl, urlparse # heaviweight from requests import get from sqlalchemy import * from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session DB=os.environ.get('DB','test.db') DB_DRIVER=os.environ.get('DB_DRIVER','sqlite') DSN=f"{DB_DRIVER}://{DB_DRIVER == 'sqlite' and not DB.startswith('/') and '/' or ''}{DB}" ENDPOINT="http://127.0.0.1:5000" os.chdir("..") os.system(f"rm {DB}") os.system(f"DB={DB} DB_DRIVER={DB_DRIVER} python pdca.py & sleep 2") url = lambda table : ENDPOINT + "/" + table os.system(f"curl {url('group')}?_action=search") form_to_db = transtype_input = lambda attrs : { k: ( # handling of input having date/time in the name "date" in k or "time" in k and v and type(k) == str ) and parser.parse(v) or # handling of boolean mapping which input begins with "is_" k.startswith("is_") and [False, True][v == "on"] or # password ? "password" in k and crypto_hash.hash(v) or v for k,v in attrs.items() if v and not k.startswith("_") } post(url("user"), params = dict(id=1, secret_password="toto", name="jul2", email="j@j.com", _action="create"), files=dict(pic_file=open("./assets/diag.png", "rb").read())).status_code #os.system(f"curl {ENDPOINT}/user?_action=search") #os.system(f"sqlite3 {DB} .dump") engine = create_engine(DSN) metadata = MetaData() transtype_true = lambda p : (p[0],[False,True][p[1]=="true"]) def dispatch(p): return dict( nullable=transtype_true, unique=transtype_true, default=lambda p:("server_default",eval(p[1])), ).get(p[0], lambda *a:None)(p) transtype_input = lambda attrs : dict(filter(lambda x :x, map(dispatch, attrs.items()))) class HTMLtoData(HTMLParser): def __init__(self): global engine, tables, metadata self.cols = [] self.table = "" self.tables= [] self.enum =[] self.engine= engine self.meta = metadata super().__init__() def handle_starttag(self, tag, attrs): global tables attrs = dict(attrs) simple_mapping = { "email" : UnicodeText, "url" : UnicodeText, "phone" : UnicodeText, "text" : UnicodeText, "checkbox" : Boolean, "date" : Date, "time" : Time, "datetime-local" : DateTime, "file" : Text, "password" : Text, "uuid" : Text, #UUID is postgres specific } if tag in {"select", "textarea"}: self.enum=[] self.current_col = attrs["name"] self.attrs= attrs if tag == "option": self.enum.append( attrs["value"] ) if tag == "unique_constraint": self.cols.append( UniqueConstraint(*attrs["col"].split(','), name=attrs["name"]) ) if tag in { "input" }: if attrs.get("name") == "id": self.cols.append( Column('id', Integer, **( dict(primary_key = True) | transtype_input(attrs )))) return try: if attrs.get("name").endswith("_id"): table=attrs.get("name").split("_") self.cols.append( Column(attrs["name"], Integer, ForeignKey(attrs["reference"])) ) return except Exception as e: log(e, ln=line()) if attrs.get("type") in simple_mapping.keys() or tag in {"select",}: self.cols.append( Column( attrs["name"], simple_mapping[attrs["type"]], **transtype_input(attrs) ) ) if attrs["type"] == "number": if attrs.get("step","") == "any": self.cols.append( Columns(attrs["name"], Float) ) else: self.cols.append( Column(attrs["name"], Integer) ) if tag== "form": self.table = urlparse(attrs["action"]).path[1:] def handle_endtag(self, tag): global tables if tag == "select": # self.cols.append( Column(self.current_col,Enum(*[(k,k) for k in self.enum]), **transtype_input(self.attrs)) ) self.cols.append( Column(self.current_col, Text, **transtype_input(self.attrs)) ) if tag == "textarea": self.cols.append( Column( self.current_col, String(int(self.attrs["cols"])*int(self.attrs["rows"])), **transtype_input(self.attrs)) ) if tag=="form": self.tables.append( Table(self.table, self.meta, *self.cols), ) #tables[self.table] = self.tables[-1] self.cols = [] with engine.connect() as cnx: self.meta.create_all(engine) cnx.commit() HTMLtoData().feed(get("http://127.0.0.1:5000/").text) os.system("pkill -f pdca.py") #metadata.reflect(bind=engine) Base = automap_base(metadata=metadata) Base.prepare() with Session(engine) as session: for table,values in tuple([ ("user", form_to_db(dict( name="him", email="j2@j.com", secret_password="toto"))), ("group", dict(id=1, name="trolol") ), ("group", dict(id=2, name="serious") ), ("user_group", dict(id=1,user_id=1, group_id=1, secret_token="secret")), ("user_group", dict(id=2,user_id=1, group_id=2, secret_token="")), ("user_group", dict(id=3,user_id=2, group_id=1, secret_token="")), ("statement", dict(id=1,user_group_id=1, message="usable agile workflow", category="story" )), ("statement", dict(id=2,user_group_id=1, message="How do we code?", category="story_item" )), ("statement", dict(id=3,user_group_id=1, message="which database?", category="question")), ("statement", dict(id=4,user_group_id=1, message="which web framework?", category="question")), ("statement", dict(id=5,user_group_id=1, message="preferably less", category="answer")), ("statement", dict(id=6,user_group_id=1, message="How do we test?", category="story_item" )), ("statement", dict(id=7,user_group_id=1, message="QA framework here", category="delivery" )), ("statement", dict(id=8,user_group_id=1, message="test plan", category="test" )), ("statement", dict(id=9,user_group_id=1, message="OK", category="finish" )), ("statement", dict(id=10, user_group_id=1, message="PoC delivered",category="delivery")), ("transition", dict( user_group_id=1, previous_statement_id=1, next_statement_id=2, message="something bugs me",is_exception=True, )), ("transition", dict( user_group_id=1, previous_statement_id=2, next_statement_id=4, message="standup meeting feedback",is_exception=True, )), ("transition", dict( user_group_id=1, previous_statement_id=2, next_statement_id=3, message="standup meeting feedback",is_exception=True, )), ("transition", dict( user_group_id=1, previous_statement_id=2, next_statement_id=6, message="change accepted",is_exception=True, )), ("transition", dict( user_group_id=1, previous_statement_id=4, next_statement_id=5, message="arbitration",is_exception=True, )), ("transition", dict( user_group_id=1, previous_statement_id=3, next_statement_id=5, message="arbitration",is_exception=True, )), ("transition", dict( user_group_id=1, previous_statement_id=6, next_statement_id=7, message="R&D", )), ("transition", dict( user_group_id=1, previous_statement_id=7, next_statement_id=8, message="Q&A", )), ("transition", dict( user_group_id=1, previous_statement_id=8, next_statement_id=9, message="CI action", )), ("transition", dict( user_group_id=1, previous_statement_id=2, next_statement_id=10, message="situation unblocked", )), ("transition", dict( user_group_id=1, previous_statement_id=9, next_statement_id=10, message="situation unblocked", )), ]): session.add(getattr(Base.classes,table)(**values)) session.commit() os.system("python ./generate_state_diagram.py sqlite:///test.db > out.dot ;dot -Tpng out.dot > diag2.png; xdot out.dot") s = requests.session() os.system(f"DB={DB} DB_DRIVER={DB_DRIVER} python pdca.py & sleep 1") print(s.post(url("group"), params=dict(_action="delete", id=3,name=1)).status_code) print(s.post(url("grant"), params = dict(secret_password="toto", email="j@j.com",group_id=1, )).status_code) print(s.post(url("grant"), params = dict(_redirect="/group",secret_password="toto", email="j@j.com",group_id=2, )).status_code) print(s.cookies["Token"]) print(s.post(url("user_group"), params=dict(_action="search", user_id=1)).text) print(s.post(url("group"), params=dict(_action="create", id=3,name=2)).text) print(s.post(url("group"), params=dict(_action="delete", id=3)).status_code) print(s.post(url("group"), params=dict(_action="search", )).text) os.system("pkill -f pdca.py") Which give me a nice set of data to play with while I experiment on how to handle the business logic where the core of the value is.

SEGA Genesis & Mega Drive games and ROMs from Steam About • Blog • Cool URLs SEGA Genesis & Mega Drive games and ROMs from Steam

Published 2024-11-20 by Seth Larson
Reading time: minutes

TDLR: SEGA is discontinuing the "SEGA Mega Drive and Genesis Classics" on December 6th. This is an affordable way to purchase these games and ROMs compared to the original cartridges. Buy games you are interested in while you still can.

In particular, Dr. Robotnik's Mean Bean Machine is one of my favorite games. I created copy-cat games when I was first learning how to program computers. I already own this game twice over as a Genesis cartridge and in the Sonic Mega Collection for the GameCube, but neither of those formats are easy to find the ROM itself to be played elsewhere.

So I heard you like beans.
That's where the SEGA Mega Drive and Genesis Classics comes in. This launcher provides uncompressed ROMs that are easily accessible after purchasing the game. For the below instructions, I am using Ubuntu 24.04 as my operating system. Here's what I did:

• Download the Steam launcher for Linux.
• Purchase Dr. Robotnik's Mean Bean Machine on Steam for $4.99 USD.
• Download the "SEGA Mega Drive and Genesis Classics" launcher and the Dr. Robotnik's Mean Bean Machine "DLC". You don't have to launch the game through Steam.
• Navigate to ~/.steam/steam/steamapps/common/Sega\ Classics/uncompressed\ ROMs.
• ROM files can be found in this directory. Their file extension will be either .SGD or .68K. These can be changed to .bin to be recognized by emulators for Linux like Kega Fusion.

# How to mass-rename ROM extensions if you purchase multiple like I did: $ for f in *.68K; do mv -- "$f" "${f%.68K}.bin"; done $ for f in *.SGD; do mv -- "$f" "${f%.SGD}.bin"; done

From here, you should be able to load these ROMs into any emulator. Happy gaming!

Have thoughts or questions? Let's chat over email or social:

sethmichaellarson@gmail.com
@sethmlarson@fosstodon.org

Want more articles like this one? Get notified of new posts by subscribing to the RSS feed or the email newsletter. I won't share your email or send spam, only whatever this is!

Want more content now? This blog's archive has ready-to-read articles. I also curate a list of cool URLs I find on the internet.

Find a typo? This blog is open source, pull requests are appreciated.

Thanks for reading! ♡ This work is licensed under CC BY-SA 4.0

︎

#656 – NOVEMBER 19, 2024
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Formatting Floats Inside Python F-Strings

In this video course, you’ll learn how to use Python format specifiers within an f-string to allow you to neatly format a float to your required precision.
REAL PYTHON course

Package Compatibility With Free-Threading and Subinterpreters

This tracker tests the compatibility of the 500 most popular packages with Python 3.13’s free-threading and subinterpreter features.
PYTHON.TIPS • Shared by Vita Midori

Projects & Code chonkie: CHONK Your Texts With Chonkie

GITHUB.COM/BHAVNICKSM

seqlogic: Sequential Logic Simulator

GITHUB.COM/CJDRAKE

venvstacks: Virtual Environment Stacks for Python

GITHUB.COM/LMSTUDIO-AI

terminal-tree: Experimental Filesystem Navigator in Textual

GITHUB.COM/WILLMCGUGAN

chdb: An in-Process OLAP SQL Engine

GITHUB.COM/CHDB-IO

Events Weekly Real Python Office Hours Q&A (Virtual)

November 20, 2024
REALPYTHON.COM

PyData Bristol Meetup

November 21, 2024
MEETUP.COM

PyLadies Dublin

November 21, 2024
PYLADIES.COM

PyConAU 2024

November 22 to November 27, 2024
PYCON.ORG.AU

Plone Conference 2024

November 25 to December 1, 2024
PLONECONF.ORG

Code, Configure and Deploy a Market Making Bot

November 25, 2024
MEETUP.COM

PyCon Wroclaw 2024

November 30 to December 1, 2024
PYCONWROCLAW.COM

Happy Pythoning!
This was PyCoder’s Weekly Issue #656.
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Alpha 2? But Alpha 1 only just came out!

https://www.python.org/downloads/release/python-3140a2/

This is an early developer preview of Python 3.14

Major new features of the 3.14 series, compared to 3.13

Python 3.14 is still in development. This release, 3.14.0a2 is the second of seven planned alpha releases.

Alpha releases are intended to make it easier to test the current state of new features and bug fixes and to test the release process.

During the alpha phase, features may be added up until the start of the beta phase (2025-05-06) and, if necessary, may be modified or deleted up until the release candidate phase (2025-07-22). Please keep in mind that this is a preview release and its use is not recommended for production environments.

Many new features for Python 3.14 are still being planned and written. Among the new major new features and changes so far:

• PEP 649: deferred evaluation of annotations
• PEP 741: Python configuration C API
• PEP 761: Python 3.14 and onwards no longer provides PGP signatures for release artifacts. Instead, Sigstore is recommended for verifiers.
• Improved error messages
• (Hey, fellow core developer, if a feature you find important is missing from this list, let Hugo know.)

The next pre-release of Python 3.14 will be 3.14.0a3, currently scheduled for 2024-12-17.

More resources

• Online documentation
• PEP 745, 3.14 Release Schedule
• Report bugs at https://github.com/python/cpython/issues
• Help fund Python and its community

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 chilly Helsinki with snow on the way,

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

PyCharm 2024.3 comes with many improvements to JetBrains AI to help you code faster. I’m going to walk you through some of these updates in this blog post. 

Natural language inline AI prompt

You can now use JetBrains AI by typing straight into your editor in natural language without opening the AI Assistant tool window. If you use either IntelliJ IDEA or PyCharm, you might already be familiar with natural language AI prompts, but let me walk you through the process. 

If you’re typing in the gutter you can start typing your request straight into the editor, and then press Tab. Here’s an example of one such request:

write a script to capture a date input from a user and print it out prefixed by a message stating that their birthday is on that date.

You can then iterate on the initial input by clicking on the purple block in the gutter or by pressing ⌘\ or Ctrl+\ and pressing Enter:

add error handling so that when a birthday is in the future, we dont accept it

You can use  ⌘\ or Ctrl+\ to keep iterating until you’re happy with the result. For example, we can use the prompt:

print out the day of the week as well as their birthday date

And then: 

change the format of day_of_week to short

This feature is available for Python, JavaScript, TypeScript, JSON, and YAML files.

Let’s look at some more examples. We can get JetBrains AI Assistant to help us generate new code with a prompt like this:

Write code that lists the latest polls, shows poll details, handles voting, updates votes, and displays poll results, ensuring only published polls are accessible.

Or add some error handling to our code:

Add edge case handling to this code

Remember, context is everything. Where you start your natural language prompt is important, as PyCharm uses the placement of your caret to figure out the context. You don’t need to prefix your query with a ? or $ if you start typing in the gutter because the context is the file, but if your caret is indented, you’ll need to start your query with the ? or $ character so PyCharm knows you’re crafting a natural language query.

In this example, we want to refactor existing code, so we need to prefix our query with the ? character:

?create a dedicated function for printing the schedule and remove the code from here

Try JetBrains AI for free

Running code in the Python console

We know that JetBrains AI can generate code for you, but now you can run that code in the Python console without leaving the AI Assistant tool window by clicking the green run arrow.

For example, let’s say you have the following prompt:

Create a python script that asks for a birthday date in standard format yyy-MM-dd then converts it and prints it back out in a written format such as 22nd January 1991 

You can now click the green run arrow on the top-right of the code snippet to run it in your Python console:

Even more features

In addition to the new functionality for natural language and code completion for PyCharm highlighted above, there are several other improvements to JetBrains AI. 

Faster code completion

We have introduced a new model for faster cloud-based completion with AI Assistant which is showing very promising results.

Faster documentation

If documentation isn’t your thing, you can now hand off writing your Python docstrings to JetBrains AI. If you type either single or double quotes to enter a docstring and then press Return, you’ll see a prompt that says Generate with AI Assistant. Click that prompt and let JetBrains AI generate the documentation for you:

Help at your fingertips

We all need a little help now and again, and we can get JetBrains AI to help us here too. We’ve added a /docs prompt to the JetBrains AI tool window. This prompt will query the PyCharm documentation to save you from switching out of the context you’re working in!

Ability to choose your LLM

For AI Chat, you can now select a different LLM from the drop-down menu in the chat window itself. There are lots of options for you to choose from:

More context in Jupyter notebooks

We’ve also improved how JetBrains AI works for data scientists. JetBrains AI now recognizes DataFrames and variables in your notebook. You can prefix your DataFrame or variable with # so that JetBrains AI considers it as part of the context. 

Summary

JetBrains AI is available inside PyCharm, right where you need it. This release brings many improvements, from writing in natural language inside the editor and running AI-generated Python snippets in the console to generating documentation. 

Remember, if you’re in the gutter, you can start typing in natural language and then press Tab to get AI Assistant to generate the code. If you’re inside a method or function, you need to prefix your natural language query with either ? or $. You can then iterate on the generated code as many times as you like as you build out your new functionality and explore further.

Try JetBrains AI for free

TOML—Tom’s Obvious Minimal Language—is a reasonably new configuration file format that the Python community has embraced over the last couple of years. TOML plays an essential part in the Python ecosystem. Many of your favorite tools rely on TOML for configuration, and you’ll use pyproject.toml when you build and distribute your own packages.

In this video course, you’ll learn more about TOML and how you can use it. In particular, you’ll:

• Learn and understand the syntax of TOML
• Use tomli and tomllib to parse TOML documents
• Use tomli_w to write data structures as TOML
• Use tomlkit when you need more control over your TOML 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 ]

Textual is a great Python package for creating a lightweight, powerful, text-based user interface. That means you can create a GUI in your terminal with Python without learning curses! But what happens when you encounter some problems that require debugging your application? A TUI takes over your terminal, which means you cannot see anything from Python’s print() statement.

Wait? What about your IDE? Can that help? Actually no. When you run a TUI, you need a fully functional terminal to interact with it. PyCharm doesn’t work well with Textual. WingIDE doesn’t even have a terminal emulator. Visual Studio Code also doesn’t work out of the box, although you may be able to make it work with a custom json or yaml file. But what do you do if you can’t figure that out?

That is the crux of the problem and what you will learn about in this tutorial: How to debug Textual applications!

Getting Started

To get the most out of this tutorial, make sure you have installed Textual’s development tools by using the following command:

python -m pip install textual-dev --upgrade

Once you have the latest version of textual-dev installed, you may continue!

Debugging with Developer Mode

When you want to debug a Textual application, you need to open two terminal windows. On Microsoft Windows, you can open two Powershell or two Command Prompts. In the first terminal, run this command:

textual console

The Textual console will listen for any Textual application running in developer mode. But first, you need some kind of application to test with. Open up your favorite Python IDE and create a new file called hello_textual.py. Then enter the following code into it:

from textual.app import App, ComposeResult from textual.widgets import Button class WelcomeButton(App): def compose(self) -> ComposeResult: yield Button("Exit") def on_button_pressed(self) -> None: self.mount(Button("Other")) if __name__ == "__main__": app = WelcomeButton() app.run()

To run a Textual application, use the other terminal you opened earlier. The one that isn’t running Textual Console in it. Then run this command:

textual run --dev hello_textual.py

You will see the following in your terminal:

If you switch over to the other terminal, you will see a lot of output that looks something like this:

Now, if you want to test that you are reaching a part of your code in Textual, you can add a print() function now to your on_button_pressed() method. You can also use self.log.info() which you can read about in the Textual documentation.

Let’s update your code to include some logging:

from textual.app import App, ComposeResult from textual.widgets import Button class WelcomeButton(App): def compose(self) -> ComposeResult: yield Button("Exit") print("The compose() method was called!") def on_button_pressed(self) -> None: self.log.info("You pressed a button") self.mount(Button("Other")) if __name__ == "__main__": app = WelcomeButton() app.run()

Now, when you run this code, you can check your Textual Console for output. The print() statement should be in the Console without you doing anything other than running the code. You must click the button to get the log statement in the Console.

Here is what the log output will look like in the Console:

And here is an example of what you get when you print() to the Console:

There’s not much difference here, eh? Either way, you get the information you need and if you need to print out Python objects, this can be a handy debugging tool.

If you find the output in the Console to be too verbose, you can use -x or --exclude to exclude log groups. Here’s an example:

textual console -x SYSTEM -x EVENT -x DEBUG -x INFO

In this version of the Textual Console, you are suppressing SYSTEM, EVENT, DEBUG, and INFO messages.

Launch your code from earlier and you will see that the output in your Console is greatly reduced:

Now, let’s learn how to use notification as a debugging tool.

Debugging with Notification

If you like using print() statements then you will love that Textual’s App() class provides a notify() method. You can call it anywhere in your application using self.app.notify() , along with a message. If you are in your App class, you can reduce the call to simply self.notify().

Let’s take the example from earlier and update it to use the notify method instead:

from textual.app import App, ComposeResult from textual.widgets import Button class WelcomeButton(App): def compose(self) -> ComposeResult: yield Button("Exit") def on_button_pressed(self) -> None: self.mount(Button("Other")) self.notify("You pressed the button!") if __name__ == "__main__": app = WelcomeButton() app.run()

The notify() method takes the following parameters:

• message – The message you want to display in the notification
• title – An optional title to add to the message
• severity – The message’s severity, which translates to a different color for the notification. You may use “information”, “error” or “warning”
• timeout – The timeout in seconds for how long to show the message

Try editing the notification to use more of these features. For example, you could update the code above to use this instead:

self.notify("You pressed the button!", title="Info Message", severity="error")

Textual’s App class also provides a bell() method you can call to play the system bell. You could add this to really get the user’s attention, assuming they have the system bell enabled on their computer.

Wrapping Up

Debugging your TUI application successfully is a skill. You need to know how to find errors, and Textual’s dev mode makes this easier. While it would be great if a Python IDE had a fully functional terminal built into it, that is a very niche need. So it’s great that Textual included the tooling you need to figure out your code.

Give these tips a try, and you’ll soon be able to debug your Textual applications easily!

The post How to Debug Your Textual Application appeared first on Mouse Vs Python.

I posted a Python tidbit about how for loops can assign to other things than simple variables, and many people were surprised or even concerned:

params = {
    "query": QUERY,
    "page_size": 100,
}

# Get page=0, page=1, page=2, ...
for params["page"] in itertools.count():
    data = requests.get(SEARCH_URL, params).json()
    if not data["results"]:
        break
    ...

This code makes successive GET requests to a URL, with a params dict as the data payload. Each request uses the same data, except the “page” item is 0, then 1, 2, and so on. It has the same effect as if we had written it:

for page_num in itertools.count():
    params["page"] = page_num
    data = requests.get(SEARCH_URL, params).json()

One reply asked if there was a new params dict in each iteration. No, loops in Python do not create a scope, and never make new variables. The loop target is assigned to exactly as if it were an assignment statement.

As a Python Discord helper once described it,

While loops are “if” on repeat. For loops are assignment on repeat.

A loop like for <ANYTHING> in <ITER>: will take successive values from <ITER> and do an assignment exactly as this statement would: <ANYTHING> = <VAL>. If the assignment statement is ok, then the for loop is ok.

We’re used to seeing for loops that do more than a simple assignment:

for i, thing in enumerate(things):
    ...

for x, y, z in zip(xs, ys, zs):
    ...

These work because Python can assign to a number of variables at once:

i, thing = 0, "hello"
x, y, z = 1, 2, 3

Assigning to a dict key (or an attribute, or a property setter, and so on) in a for loop is an example of Python having a few independent mechanisms that combine in uniform ways. We aren’t used to seeing exotic combinations, but you can reason through how they would behave, and you would be right.

You can assign to a dict key in an assignment statement, so you can assign to it in a for loop. You might decide it’s too unusual to use, but it is possible and it works.

IMAP and OAuth2 Integrations with Microsoft 365 2024-11-19, by Dariusz Suchojad

Overview

This is the first in a series of articles about automation of and integrations with Microsoft 365 cloud products using Python and Zato.

We start off with IMAP automation by showing how to create a scheduled Python service that periodically pulls latest emails from Outlook using OAuth2-based connections.

IMAP and OAuth2

Microsoft 365 requires for all IMAP connections to use OAuth2. This can be challenging to configure in server-side automation and orchestration processes so Zato offers an easy way that lets you read and send emails without a need for getting into low-level OAuth2 details.

Consider a common orchestration scenario - a business partner sends automated emails with attachments that need to be parsed, some information needs to be extracted and processed accordingly.

Before OAuth2, an automation process would receive from Azure administrators a dedicated IMAP account with a username and password.

Now, however, in addition to creating an IMAP account, administrators will need to create and configure a few more resources that the orchestration service will use. Note that the password to the IMAP account will never be used.

Administrators need to:

• Register an Azure client app representing your service that uses IMAP
• Grant this app a couple of Microsoft Graph application permissions:
• Mail.ReadWrite
• Mail.Send

Next, administrators need to give you a few pieces of information about the app:

• Application (client) ID
• Tenant (directory) ID
• Client secret

Additionally, you still need to receive the IMAP username (an e-mail address). It is just that you do not need its corresponding password.

In Dashboard

The first step is to create a new connection in your Zato Dashboard - this will establish an OAuth2-using connection that Zato will manage and your Python code will not have to do anything else, all the underlying OAuth2 tokens will keep refreshing as needed, the platform will take care of everything.

Having received the configuration details from Azure administrators, you can open your Zato Dashboard and navigate to IMAP connections:

Fill out the form as below, choosing "Microsoft 365" as the server type. The other type, "Generic IMAP" is used for the classical case of IMAP with a username and password:

Change the secret and click Ping to confirm that the connection is configured correctly:

In Python

Use the code below to receive emails. Note that it merely needs to refer to a connection definition by its name and there is no need for any usage of OAuth2 here:

# -*- coding: utf-8 -*- # Zato from zato.server.service import Service class MyService(Service): def handle(self): # Connect to a Microsoft 365 IMAP connection by its name .. conn = self.email.imap.get('My Automation').conn # .. get all messages matching filter criteria ("unread" by default).. for msg_id, msg in conn.get(): # .. and access each of them. self.logger.info(msg.data)

This is everything that is needed for integrations with IMAP using Microsoft 365 although we can still go further. For instance, to create a scheduled job to periodically invoke the service, go to the Scheduler job in Dashboard:

In this case, we decide to have a job that runs once per hour:

As expected, clicking OK will suffice for the job to start in background. It is as simple as that.

More resources

➤ Python API integration tutorial
➤ What is an integration platform?
➤ Python Integration platform as a Service (iPaaS)
➤ What is an Enterprise Service Bus (ESB)? What is SOA?

More blog posts➤

Python's pathlib module is the tool to use for working with file paths. See pathlib quick reference tables and examples.

Table of contents

1. A pathlib cheat sheet
2. The open function accepts Path objects
3. Why use a pathlib.Path instead of a string?
4. The basics: constructing paths with pathlib
5. Joining paths
6. Current working directory
7. Absolute paths
8. Splitting up paths with pathlib
9. Listing files in a directory
10. Reading and writing a whole file
11. Many common operations are even easier
12. No need to worry about normalizing paths
13. Built-in cross-platform compatibility
14. A pathlib conversion cheat sheet
15. What about things pathlib can't do?
16. Should strings ever represent file paths?
17. Use pathlib for readable cross-platform code

A pathlib cheat sheet

Below is a cheat sheet table of common pathlib.Path operations.

The variables used in the table are defined here:

>>> import pathlib >>> path = Path("/home/trey/proj/readme.md") >>> relative = Path("readme.md") >>> base = Path("/home/trey/proj") >>> new = Path("/home/trey/proj/sub") >>> target = path.with_suffix(".txt") # .md -> .txt >>> pattern = "*.md" >>> name = "sub/f.txt" Path-related task pathlib approach Example Read all file contents path.read_text() 'Line 1\nLine 2\n' Write file contents path.write_text('new') Writes new to file Get absolute file path relative.resolve() Path('/home/trey/proj/readme.md') Get the filename path.name 'readme.md' Get parent directory path.parent Path('home/trey/proj') Get file extension path.suffix '.md' Get suffix-free name path.stem 'readme' Ancestor-relative path path.relative_to(base) Path('readme.md') Verify path is a file path.is_file() True Make new directory new.mkdir() Makes new directory Get current directory Path.cwd() Path('/home/trey/proj') Get home directory Path.home() Path('/home/trey') Get all ancestor paths path.parents [Path('/home/trey/proj'), ...] List files/directories base.iterdir() [Path('home/trey/proj/readme.md'), ...] Find files by pattern base.glob(pattern) [Path('/home/trey/proj/readme.md')] Find files recursively base.rglob(pattern) [Path('/home/trey/proj/readme.md')] Join path parts base / name Path('/home/trey/proj/sub/f.txt') Get file size (bytes) path.stat().st_size 14 Walk the file tree base.walk() Iterable of (path, subdirs, files) Rename file to new path path.rename(target) Path object for new path Remove file path.unlink()

Note that iterdir, glob, rglob, and walk all return iterators. The examples above show lists for convenience.

The open function accepts Path objects

What does Python's open function …

Read the full article: https://www.pythonmorsels.com/pathlib-module/

There are multiple ways of interacting with Python, and each can be useful for different scenarios. You can quickly explore functionality in Python’s interactive mode using the built-in Read-Eval-Print Loop (REPL), or you can write larger applications to a script file using an editor or Integrated Development Environment (IDE).

In this tutorial, you’ll learn how to:

• Use Python interactively by typing code directly into the interpreter
• Execute code contained in a script file from the command line
• Work within a Python Integrated Development Environment (IDE)
• Assess additional options, such as the Jupyter Notebook and online interpreters

Before working through this tutorial, make sure that you have a functioning Python installation at hand. Once you’re set up with that, it’s time to write some Python code!

Get Your Code: Click here to get the free sample code that you’ll use to learn about interacting with Python.

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

Interactive Quiz

Interacting With Python

In this quiz, you'll test your understanding of the different ways of interacting with Python. By working through this quiz, you'll revisit key concepts related to Python interaction in interactive mode using the REPL, through Python script files, and within IDEs and code editors.

Hello, World!

There’s a long-standing custom in computer programming that the first code written in a newly installed language is a short program that displays the text Hello, World! to the console.

In Python, running a “Hello, World!” program only takes a single line of code:

Python print("Hello, World!") Copied!

Here, print() will display the text Hello, World! in quotes to your screen. In this tutorial, you’ll explore several ways to execute this code.

Running Python in Interactive Mode

The quickest way to start interacting with Python is in a Read-Eval-Print Loop (REPL) environment. This means starting up the interpreter and typing commands to it directly.

When you interact with Python in this way, the interpreter will:

• Read the command you enter
• Evaluate and execute the command
• Print the output (if any) to the console
• Loop back and repeat the process

The interactive session continues like this until you instruct the interpreter to stop. Using Python in this interactive mode is a great way to test short snippets of Python code and get more familiar with the language.

• Windows
• Linux + macOS

When you install Python using an installer, the Start menu shows a program group labeled Python 3.x. The label may vary depending on the particular installation you chose. Click on that item to start the Python interpreter.

Alternatively, you can open your Command Prompt or PowerShell application and type the py command to launch it:

Windows PowerShell PS> py Copied!

To start the Python interpreter, open your Terminal application and type python3 to launch it from the command line:

Shell $ python3 Copied!

If you’re unfamiliar with this application, then you can use your operating system’s search function to find it.

After pressing Enter, you should see a response from the Python interpreter similar to the one below:

Python Python 3.13.0 (main, Oct 14 2024, 10:34:31) [Clang 15.0.0 (clang-1500.3.9.4)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> Copied!

If you’re not seeing the >>> prompt, then you’re not talking to the Python interpreter. This could be because Python is either not installed or not in the path of your terminal window session.

Note: If you need additional help to get to this point, then you can check out the How to Install Python on Your System: A Guide tutorial.

If you’re seeing the prompt, then you’re off and running! With these next steps, you’ll execute the statement that displays "Hello, World!" to the console:

1. Ensure that Python displays the >>> prompt, and that you position your cursor after it.
2. Type the command print("Hello, World!") exactly as shown.
3. Press the Enter key.

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

[ 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 ]

The Python Software Foundation (PSF) is the charitable organization behind Python, dedicated to advancing, supporting, and protecting the Python programming language and the community that sustains it. That mission and cause are more than just words we believe in. Our tiny but mighty team works hard to deliver the projects and services that allow Python to be the thriving, independent, community-driven language it is today. Some of what the PSF does includes producing PyCon US, hosting the Python Packaging Index (PyPI), awarding grants to Python initiatives worldwide, maintaining critical community infrastructure, and more.

To build the future of Python and sustain the thriving community that its users deserve, we need your help. By backing the PSF, you’re investing in Python’s growth and health, and your contributions directly impact the language's future. Is your community, work, or hobby powered by Python? Join this year’s drive and power Python’s future with us by donating or becoming a Supporting Member today.

There are three ways to join in:

Save on PyCharm! JetBrains is once again supporting the PSF by providing a 30% discount on PyCharm and ALL proceeds will go to the PSF! You can take advantage of this discount by clicking the button on the PyCharm promotion page and the discount will be automatically applied when you check out. The promotion will only be available through November 30th, 2024, so make sure to grab the deal today!

Donate to the PSF! Your donation is a direct way to support and power the future of the Python programming language and community you love. Every dollar makes a difference.

Become a Supporting member! When you sign up as a Supporting Member of the PSF, you become a part of the PSF, are eligible to vote in PSF elections and help us sustain what we do with your annual support. You can sign up as a Supporting Member at the usual annual rate($99 USD), or you can take advantage of our sliding scale option (starting at $25 USD)! We don't want cost to be a barrier to you being a part of the PSF or to your voice helping direct our future. Every PSF member makes the Python community stronger!

Your donations:

• Keep Python thriving 
• Support CPython and PyPI progress 
• Increase security across the Python ecosystem 
• Bring the global Python community together 
• Make our community more diverse and robust every year

Highlights from 2024:

• A record-making PyCon US - We produced the 21st PyCon US, in Pittsburgh, US, and online, and it was a huge success! For the first time post-2020, PyCon US 2024 sold out with over 2,500 in-person attendees.
• Advances in our Grants Program - 2024 has been a year of change and reflection for the Grants Program, starting with the addition of Marie Nordin to the grants administration team who has supported the PSF in launching several new grants initiatives. We set up Grants Program Office Hours, published a Grants Program Transparency Report for 2022 and 2024, invested in a third-party retrospective, launched a major refresh of all areas of our Grants program and updated our Grants Workgroup Charter. With more changes to come, we are thrilled to share that we awarded a record-breaking amount of grant funds in 2024!
• Empowering the Python community through Fiscal Sponsorship - We are proud to continue supporting our 20 fiscal sponsoree organizations with their initiatives and events all year round. The PSF provides 501(c)(3) tax-exempt status to fiscal sponsorees such as PyLadies and Pallets, and provides back office support so they can focus on their missions. Consider donating to your favorite PSF Fiscal Sponsoree and check out our Fiscal Sponsorees page to learn more about what each of these awesome organizations is all about!
• Connecting directly through Office Hours - The current PSF Board has decided to invest more in connecting and serving the global Python community by establishing a forum to have regular conversations. The board members of the PSF with the support of PSF staff are now holding monthly PSF Board Office Hours on the PSF Discord. The Office Hours are sessions where folks from the community can share with us how we can help your regional community, express perspectives, and provide feedback for the PSF.
• Paying more engineers to work directly on Python, PyPI, and security - We welcomed Petr Viktorin, Deputy Developer in Residence (DiR), and Serhiy Storchaka, Supporting DiR. It’s been exciting to begin to realize the full vision of the DiR program, with special thanks to Bloomberg for making it possible for us to bring Petr on board. The DiR team is taking an active role in shaping the development of the language, and with three people on the team each DiR can now also spend a percentage of their time on feature work aligned with their interests.
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• New PSF Staff dedicated to critical infrastructure - We established the PyPI Support Specialist role, filled by Maria Ashna. Over the past 23 years, PyPI has seen essentially exponential growth in traffic and users, relying for the most part on volunteers to support it. The load far outstretched volunteers and prior staff capacity, so we are very excited to have Maria on board. We also filled our Infrastructure Engineer role, welcoming Jacob Coffee to the team, to ensure PSF-maintained systems and services are running smoothly.
  

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<strong>Topics covered in this episode:</strong><br> <ul> <li><strong><a href="https://buttondown.com/carlton/archive/thoughts-on-djangos-core/?featured_on=pythonbytes">Thoughts on Django’s Core</a></strong></li> <li><strong><a href="https://pypi.org/project/futurepool/?featured_on=pythonbytes">futurepool</a></strong></li> <li><strong><a href="https://snarky.ca/dont-use-named-tuples-in-new-apis/?featured_on=pythonbytes">Don't return named tuples in new APIs</a></strong></li> <li><strong><a href="https://ziglang.org/news/migrate-to-self-hosting/?featured_on=pythonbytes">Ziglang: Migrating from AWS to Self-Hosting</a></strong></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=j-q31u9G3Ds' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="410">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by us! Support our work through:</p> <ul> <li>Our <a href="https://training.talkpython.fm/?featured_on=pythonbytes"><strong>courses at Talk Python Training</strong></a></li> <li><a href="https://courses.pythontest.com/p/the-complete-pytest-course?featured_on=pythonbytes"><strong>The Complete pytest Course</strong></a></li> <li><a href="https://www.patreon.com/pythonbytes"><strong>Patreon Supporters</strong></a></li> </ul> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href="https://fosstodon.org/@mkennedy"><strong>@mkennedy@fosstodon.org</strong></a> <strong>/</strong> <a href="https://bsky.app/profile/mkennedy.codes?featured_on=pythonbytes"><strong>@mkennedy.codes</strong></a></li> <li>Brian: <a href="https://fosstodon.org/@brianokken"><strong>@brianokken@fosstodon.org</strong></a> <strong>/</strong> <a href="https://bsky.app/profile/brianokken.bsky.social?featured_on=pythonbytes"><strong>@brianokken.bsky.social</strong></a></li> <li>Show: <a href="https://fosstodon.org/@pythonbytes"><strong>@pythonbytes@fosstodon.org</strong></a> <strong>/</strong> <a href="https://bsky.app/profile/pythonbytes.bsky.social"><strong>@pythonbytes.bsky.social</strong></a></li> </ul> <p>Join us on YouTube at <a href="https://pythonbytes.fm/stream/live"><strong>pythonbytes.fm/live</strong></a> to be part of the audience. Usually <strong>Monday</strong> at 10am PT. Older video versions available there too.</p> <p>Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to <a href="https://pythonbytes.fm/friends-of-the-show">our friends of the show list</a>, we'll never share it. </p> <p><strong>Brian #1:</strong> <a href="https://buttondown.com/carlton/archive/thoughts-on-djangos-core/?featured_on=pythonbytes">Thoughts on Django’s Core</a></p> <ul> <li>Carlton Gibson</li> <li>Great discussion on <ul> <li>Django and Core vs Plugins</li> <li>Sustainability with limited people</li> <li>Keeping core small</li> <li>The release cycle</li> <li>eembrace plugins vs endorsing plugins.</li> </ul></li> </ul> <p><strong>Michael #2:</strong> <a href="https://pypi.org/project/futurepool/?featured_on=pythonbytes">futurepool</a></p> <ul> <li>via Pat Decker</li> <li>Takes the concept of multiprocessing Pool to the async/await world.</li> <li><p>Create a pool then delegate the work:</p> <pre><code>async with FuturePool(2) as fp: result = await fp.map(async_pool_fn, range(10)) </code></pre></li> <li><p>I would LOVE to see something like this in a broader background asyncio worker pool concept.</p></li> <li>But that concept doesn’t exist in asyncio in Python and that’s a failing of the framework IMO.</li> </ul> <p><strong>Brian #3:</strong> <a href="https://snarky.ca/dont-use-named-tuples-in-new-apis/?featured_on=pythonbytes">Don't return named tuples in new APIs</a></p> <ul> <li>Brett Cannon</li> <li>First off, I’m grateful for any post that talks about APIs and the API is a module, class, or package API and not a Web/REST API. The term API existed long before the internet.</li> <li>“e.g., get_mouse_position() very likely has a two-item tuple of X and Y coordinates of the screen”</li> <li>“it actually makes your API more complex for both you and your users <em>to use</em>. For you, it doubles the data access API surface for your return type as you have to now support index-based and attribute-based data access forever (or until you choose to break your users and change your return type so it doesn't support both approaches)”</li> <li>“… you probably don't want people doing with your return type, like slicing, iterating over all the items …”</li> <li>Alternatives <ul> <li>class</li> <li>dataclass</li> <li>dictionary</li> <li>TypedDict</li> <li>SimpleNamespace</li> </ul></li> <li>“My key point in all of this is to prefer readability and ergonomics over brevity in your code. That means avoiding named tuples except where you are expanding to tweaking an existing API where the named tuple improves over the plain tuple that's already being used.”</li> </ul> <p><strong>Michael #4:</strong> <a href="https://ziglang.org/news/migrate-to-self-hosting/?featured_on=pythonbytes">Ziglang: Migrating from AWS to Self-Hosting</a></p> <ul> <li>The Rust Foundation for example, reports that they spent $404,400 on infrastructure costs in 2023.</li> <li>Zig lang has decided to use a single big cloud machine + mirrors</li> </ul> <p><strong>Extras</strong> </p> <p>Brian:</p> <ul> <li>Changing the Python Test community <ul> <li>Was started to answer questions for Test &amp; Code listeners years ago. </li> <li>Primarily pytest questions</li> <li>Used to be Slack. Then moved to Podia forum. </li> <li>Now I’m trying to work out a Discord solution that is both sustainable and usable.</li> </ul></li> </ul> <p>Michael:</p> <ul> <li><a href="https://bsky.app/profile/wang.social/post/3lb346uyzdc2r?featured_on=pythonbytes">PWang Bsky essay</a></li> <li><a href="https://theworkitem.com/blog/building-a-business-from-python-expertise-michael-kennedy/?featured_on=pythonbytes">Building A Business From Python Expertise - Michael Kennedy on Work Item Podcast</a></li> <li>Subscribe to package releases, just put .atom on the end of their releases URL, for example: <ul> <li><a href="https://github.com/mikeckennedy/jinja_partials/releases?featured_on=pythonbytes">github.com/mikeckennedy/jinja_partials/releases</a> ← add .atom for RSS</li> </ul></li> <li><a href="https://pypi.org/project/pytest-bdd/8.0.0/#data">pytest-bdd 8.0.0</a> was just released via Jamie Thomson <ul> <li>The big feature (in Jamie’s opinion) is the addition of data tables https://github.com/pytest-dev/pytest-bdd/blob/master/CHANGES.rst#800---2024-11-14</li> </ul></li> </ul> <p><strong>Joke:</strong> <a href="https://devhumor.com/media/breaking-javascript-developer-commits-to-framework-for-record-breaking-3-weeks?featured_on=pythonbytes">Breaking: JavaScript Developer Commits to Framework for Record-Breaking 3 Weeks</a></p>

Version numbering is hard, and there are lots of popular schemes out there for how to do it. Today I want to talk about a system I’ve settled on for my own Django-related packages, and which I’m calling “DjangoVer”, because it ties the version number of a Django-related package to the latest Django version that package supports.

But one quick note to start with: this is not really “introducing” the idea of DjangoVer, because I know I’ve used the name a few times already in other places. I’m also not the person who invented this, and I don’t know for certain who did — I’ve seen several packages which appear to follow some form of DjangoVer and took inspiration from them in defining my own take on it.

Django’s version scheme: an overview

The basic idea of DjangoVer is that the version number of a Django-related package should tell you which version of Django you can use it with. Which probably doesn’t help much if you don’t know how Django releases are numbered, so let’s start there. In brief:

• Django issues a “feature release” — one which introduces new features — roughly once every eight months. The current feature release series of Django is 5.1.
• Django issues “bugfix releases” — which fix bugs in one or more feature releases — roughly once each month. As I write this, the latest bugfix release for the 5.1 feature release series is 5.1.3 (along with Django 5.0.9 for the 5.0 feature release series, and Django 4.2.16 for the 4.2 feature release series).
• The version number scheme is MAJOR.FEATURE.BUGFIX, where MAJOR, FEATURE, and BUGFIX are integers.
• The FEATURE component starts at 0, then increments to 1, then to 2, then MAJOR is incremented and FEATURE goes back to 0. BUGFIX starts at 0 with each new feature release, and increments for the bugfix releases for that feature release.
• Every feature release whose FEATURE component is 2 is a long-term support (“LTS”) release.

This has been in effect since Django 2.0 was released, and the feature releases have been: 2.0, 2.1, 2.2 (LTS); 3.0, 3.1, 3.2 (LTS); 4.0, 4.1, 4.2 (LTS); 5.0, 5.1. Django 5.2 (LTS) is expected in April 2025, and then eight months later (if nothing is changed) will come Django 6.0.

I’ll talk more about SemVer in a bit, but it’s worth being crystal clear that Django does not follow Semantic Versioning, and the MAJOR number is not a signal about API compatibility. Instead, API compatibility runs LTS-to-LTS, with a simple principle: if your code runs on a Django LTS release and raises no deprecation warnings, it will run unmodified on the next LTS release. So, for example, if you have an application that runs without deprecation warnings on Django 4.2 LTS, it will run unmodified on Django 5.2 LTS (though at that point it might begin raising new deprecation warnings, and you’d need to clear them before it would be safe to upgrade any further).

DjangoVer, defined

In DjangoVer, a Django-related package has a version number of the form DJANGO_MAJOR.DJANGO_FEATURE.PACKAGE_VERSION, where DJANGO_MAJOR and DJANGO_FEATURE indicate the most recent feature release series of Django supported by the package, and PACKAGE_VERSION begins at zero and increments by one with each release of the package supporting that feature release of Django.

Since the version number only indicates the newest Django feature release supported, a package using DjangoVer should also use Python package classifiers to indicate the full range of its Django support (such as Framework :: Django :: 5.1 to indicate support for Django 5.1 — see examples on PyPI).

But while Django takes care to maintain compatibility from one LTS to the next, I do not think DjangoVer packages need to do that; they can use the simpler approach of issuing deprecation warnings for two releases, and then making the breaking change. One of the stated reasons for Django’s LTS-to-LTS compatibility policy is to help third-party packages have an easier time supporting Django releases that people are actually likely to use; otherwise, Django itself generally just follows the “deprecate for two releases, then remove it” pattern. No matter what compatibility policy is chosen, however, it should be documented clearly, since DjangoVer explicitly does not attempt to provide any information about API stability/compatibility in the version number.

That’s a bit wordy, so let’s try an example:

• If you started a new Django-related package today, you’d (hopefully) support the most recent Django feature release, which is 5.1. So the DjangoVer version of your package should be 5.1.0.
• As long as Django 5.1 is the newest Django feature release you support, you’d increment the third digit of the version number. As you add features or fix bugs you’d release 5.1.1, 5.1.2, etc.
• When Django 5.2 comes out next year, you’d (hopefully) add support for it. When you do, you’d set your package’s version number to 5.2.0. This would be followed by 5.2.1, 5.2.2, etc., and then eight months later by 6.0.0 to support Django 6.0.
• If version 5.1.0 of your package supports Django 5.1, 5.0, and 4.2 (the feature releases receiving upstream support from Django at the time of the 5.1 release), it should indicate that by including the Framework :: Django, Framework :: Django :: 4.2, Framework :: Django :: 5.0, and Framework :: Django :: 5.1 classifiers in its package metadata.

Why another version system?

Some of you probably didn’t even read this far before rushing to instantly post the XKCD “Standards” comic as a reply. Thank you in advance for letting the rest of us know we don’t need to bother listening to or engaging with you. For everyone else: here’s why I think in this case adding yet another “standard” is actually a good idea.

The elephant in the room here is Semantic Versioning (“SemVer”). Others have written about some of the problems with SemVer, but I’ll add my own two cents here: “compatibility” is far too complex and nebulous a concept to be usefully encoded in a simple value like a version number. And if you want my really cynical take, the actual point of SemVer in practice is to protect developers of software from users, by providing endless loopholes and ways to say “sure, this change broke your code, but that doesn’t count as a breaking change”. It’ll turn out that the developer had a different interpretation of the documentation than you did, or that the API contract was “underspecified” and now has been “clarified”, or they’ll just throw their hands up, yell “Hyrum’s Law” and say they can’t possibly be expected to preserve that behavior.

A lot of this is rooted in the belief that changes, and especially breaking changes, are inherently bad and shameful, and that if you introduce them you’re a bad developer who should be ashamed. Which is, frankly, bullshit. Useful software almost always evolves and changes over time, and it’s unrealistic to expect it not to. I wrote about this a few years back in the context of the Python 2/3 transition:

Though there is one thing I think gets overlooked a lot: usually, the anti-Python-3 argument is presented as the desire of a particular company, or project, or person, to stand still and buck the trend of the world to be ever-changing.

But really they’re asking for the inverse of that. Rather than being a fixed point in a constantly-changing world, what they really seem to want is to be the only ones still moving in a world that has become static around them. If only the Python team would stop fiddling with the language! If only the maintainers of popular frameworks would stop evolving their APIs! Then we could finally stop worrying about our dependencies and get on with our real work! Of course, it’s logically impossible for each one of those entities to be the sole mover in a static world, but pointing that out doesn’t always go well.

But that’s a rant for another day and another full post all its own. For now it’s enough to just say I don’t believe SemVer can ever deliver on what it promises. So where does that leave us?

Well, if the version number can’t tell you whether it’s safe to upgrade from one version to another, perhaps it can still tell you something useful. And for me, when I’m evaluating a piece of third-party software for possible use, one of the most important things I want to know is: is someone actually maintaining this? There are lots of potential signals to look for, but some version schemes — like CalVer — can encode this into the version number. Want to know if the software’s maintained? With CalVer you can guess a package’s maintenance status, with pretty good accuracy, from a glance at the version number.

Over the course of this year I’ve been transitioning all my personal non-Django packages to CalVer for precisely this reason. Compatibility, again, is something I think can’t possibly be encoded into a version number, but “someone’s keeping an eye on this” can be. Even if I’m not adding features to something, Python itself does a new version every year and I’ll push a new release to explicitly mark compatibility (as I did recently for the release of Python 3.13). That’ll bump the version number and let anyone who takes a quick glance at it know I’m still there and paying attention to the package.

For packages meant to be used with Django, though, the version number can usefully encode another piece of information: not just “is someone maintaining this”, but “can I use this with my Django installation”. And that is what DjangoVer is about: telling you at a glance the maintenance and Django compatibility status of a package.

DjangoVer in practice

All of my own personal Django-related packages are now using DjangoVer, and say so in their documentation. If I start any new Django-related projects they’ll do the same thing.

A quick scroll through PyPI turns up other packages doing something that looks similar; django-cockroachdb and django-snowflake, for example, versioned their Django 5.1 packages as “5.1”, and explicitly say in their READMEs to install a package version corresponding to the Django version you use (they also have a maintainer in common, who I suspect of having been an early inventor of what I’m now calling “DjangoVer”).

If you maintain a Django-related package, I’d encourage you to at least think about adopting some form of DjangoVer, too. I won’t say it’s the best, period, because something better could always come along, but in terms of information that can be usefully encoded into the version number, I think DjangoVer is the best option I’ve seen for Django-related packages.

It's been a few years since I wrote about my challenges with async/await-based systems and how they just seem to not support back pressure well. A few years later, I do not think that this problem has subsided much, but my thinking and understanding have perhaps evolved a bit. I'm now convinced that async/await is, in fact, a bad abstraction for most languages, and we should be aiming for something better instead and that I believe to be thread.

In this post, I'm also going to rehash many arguments from very clever people that came before me. Nothing here is new, I just hope to bring it to a new group of readers. In particular, you should really consider these who highly influential pieces:

• Bob Nystrom's What Color is Your Function post, which makes a very strong case that having two types of functions, which are only compatible in one direction, causes problems.
• Ron Pressler's Please stop polluting our imperative languages with pure concepts which I think is probably the single most important talk on that topic.
• Nathaniel J. Smith's Notes on structured concurrency, or: Go statement considered harmful which does a really good job laying out the motivation for structured concurrency.

Your Child Loves Actor Frameworks

As programmers, we are so used to how things work that we make some implicit assumptions that really cloud our ability to think freely. Let me present you with a piece of code that demonstrates this:

def move_mouse(): while mouse.x < 200: mouse.x += 5 sleep(10) def move_cat(): while cat.x < 200: cat.x += 10 sleep(10) move_mouse() move_cat()

Read that code and then answer this question: do the mouse and cat move at the same time, or one after another? I guarantee you that 10 out of 10 programmers will correctly state that they move one after another. It makes sense because we know Python and the concept of threads, scheduling and whatnot. But if you speak to a group of children familiar with Scratch, they are likely to conclude that mouse and cat move simultaneously.

The reason is that if you are exposed to programming via Scratch you are exposed to a primitive form of actor programming. The cat and the mouse are both actors. In fact, the UI makes this pretty damn clear, just that the actors are called “sprites”. You attach logic to a sprite on the screen and all these pieces of logic run at the same time. Mind-blowing. You can even send messages from sprite to sprite.

The reason I want you to think about this for a moment is that I think this is rather profound. Scratch is a very, very simple system and it's intended to teaching programming to young kids. Yet the model it promotes is an actor system! If you were to foray into programming via a traditional book on Python, C# or some other language, it's quite likely that you will only learn about threads at the very end. Not just that, it will likely make it sound really complex and scary. Worse, you will probably only learn about actor patterns in some advanced book that will bombard you with all the complexities of large scale applications.

There is something else though you should keep in mind: Scratch will not talk about threads, it will not talk about monads, it will not talk about async/await, it will not talk about schedulers. As far as you are concerned as a programmer, it's an imperative (though colorful and visual) language with some basic “syntax” support for message passing. Concurrency comes natural. A child can program it. It's not something to be afraid of.

Imperative Programming Is Not Inferior

The second thing I want you to take away is that imperative languages are not inferior to functional ones.

While probably most of us are using imperative programming languages to solve problems, I think we all have been exposed to the notion that it's inferior and not particularly pure. There is this world of functional programming, with monads and other things. This world have these nice things involving composition, logic and maths and fancy looking theorems. If you program in that, you're almost transcending to a higher plane and looking down to the folks who are stitching together if statements, for loops, make side effects everywhere, and are doing highly inappropriate things with IO.

Okay, maybe it's not quite as bad, but I don't think I'm completely wrong with those vibes. And look, I get it. I feel happy chaining together lambdas in Rust and JavaScript. But we should also be aware that these constructs are, in many languages, bolted on. Go, for instance, gets away without most of this, and that does not make it an inferior language!

So what you should keep in mind here is that there are different paradigms, and mentally you should try to stop thinking for a moment that functional programming has all its stuff figured out, and imperative programming does not.

Instead, I want to talk about how functional languages and imperative languages are dealing with “waiting”.

The first thing I want to back to is the example from above. Both of the functions (for the cat and the mouse) can be seen as separate threads of execution. When the code calls sleep(10) there's clearly an expectation by the programmer that the computer will temporarily pause the execution and continue later. I don't want to bore you with monads, so as my “functional” programming language, I will use JavaScript and promises. I think that's an abstraction that most readers will be sufficiently familiar with:

function moveMouseBlocking() { while (mouse.x < 200) { mouse.x += 5; sleep(10); // a blocking sleep } } function moveMouseAsync() { return new Promise((resolve) => { function iterate() { if (mouse.x < 200) { mouse.x += 5; sleep(10).then(iterate); // non blocking sleep } else { resolve(); } } iterate(); }); }

You can immediately see a challenge here: it's very hard to translate the blocking example into a non blocking example because all the sudden we need to find a way to express our loop (or really any control flow). We need to manually decompose it into a form of recursive function calling and we need the help of a scheduler and executor here to do the waiting.

This style obviously eventually became annoying enough to deal with that async/await was introduced to mostly restore the sanity of the old code. So it now can look more like this:

async function moveMouseAsync() { while (mouse.x < 200) { mouse.x += 5; await sleep(10); } }

Behind the scenes though, nothing has really changed, and in particular, when you call that function, you just get an object that encompasses the “composition of the computation”. That object is a promise which will eventually hold the resulting value. In fact, in some languages like C#, the compiler will really just transpile this into chained function calls. With the promise in hand, you can await the result, or register a callback with then which gets invoked if this thing ever runs to completion.

For a programmer, I think async/await is clearly understood as some sort of neat abstraction — an abstraction over promises and callbacks. However strictly speaking, it's just worse than where we started out, because in terms of expressiveness, we have lost an important affordance: we cannot freely suspend.

In the original blocking code, when we invoked sleep we suspended for 10 milliseconds implicitly; we cannot do the same with the async call. Here we have to “await” the sleep operation. This is the crucial aspect of why we're having these “colored functions”. Only an async function can call another async function, as you cannot await in a sync function.

Halting Problems

The above example shows another problem that async/await causes: what if we never resolve? A normal function call eventually returns, the stack unwinds, and we're ready to receive the result. In an async world, someone has to call resolve at the very end. What if that is never called? Now in theory, that does not seem all that different from someone calling sleep() with a large number to suspend for a very long time, or waiting on a pipe that never gets data sent into. But it is different! In one case, we keep the call stack and everything that relates to it alive; in another case, we just have a promise and are waiting for independent garbage collection with everything already unwound.

Contract wise, there is absolutely nothing that says one has to call resolve. As we know from theory the halting problem is undecidable so it's going to be actually impossible to know if someone will call resolve or not.

That sounds pedantic, but it's very important because promises/futures and async/await are making something strictly worse than not having them. Let's consider a JavaScript promise to be the most canonical example of what this looks like. A promise is created by an anonymous function, that is invoked to eventually call resolve. Take this example:

let neverSettle = new Promise((resolve) => { // this function ends, but we never called resolve });

Let me clarify first that this is not a JavaScript specific problem, but it's nice to show it this way. This is a completely legal thing! It's a promise, that never resolves. That is not a bug! The anonymous function in the promise itself will return, the stack will unwind, and we are left with a “pending” promise that will eventually get garbage collected. That is a bit of a problem because since it will never resolve, you can also never await it.

Think of the following example, which demonstrates this problem a bit. In practice you might want to reduce how many things can work at once, so let's imagine a system that can handle up to 10 things that run concurrently. So we might want to use a semaphore to give out 10 tokens so up to 10 things can run at once; otherwise, it applies back pressure. So the code looks like this:

const semaphore = new Semaphore(10); async function execute(f) { let token = await semaphore.acquire(); try { await f(); } finally { await semaphore.release(token); } }

But now we have a problem. What if the function passed to the execute function returns neverSettle? Well, clearly we will never release the semaphore token. This is strictly worse compared to blocking functions! The closest equivalent would be a stupid function that calls a very long running sleep. But it's different! In one case, we keep the call stack and everything that relates to it alive; in the other case case we just have a promise that will eventually get garbage collected, and we will never see it again. In the promise case, we have effectively decided that the stack is not useful.

There are ways to fix this, like making promise finalization available so we can get informed if a promise gets garbage collected etc. However I want to point out that as per contract, what this promise is doing is completely acceptable and we have just caused a new problem, one that we did not have before.

And if you think Python does not have that problem, it does too. Just await Future() and you will be waiting until the heat death of the universe (or really when you shut down your interpreter).

The promise that sits there unresolved has no call stack. But that problem also comes back in other ways, even if you use it correctly. The decomposed functions calling functions via the scheduler flow means that now you need extra affordances to stitch these async calls together into full call stacks. This all creates extra problems that did not exist before. Call stacks are really, really important. They help with debugging and are also crucial for profiling.

Blocking is an Abstraction

Okay, so we know there is at least some challenge with the promise model. What other abstractions are there? I will make the argument that a function being able to “suspend” a thread of execution is a bloody great capability and abstraction. Think of it for a moment: no matter where I am, I can say I need to wait for something and continue later where I left off. This is particularly crucial to apply back-pressure if you decide to need it later. The biggest footgun in Python asyncio remains that write is non blocking. That function will stay problematic forever and you need to follow up with await s.drain() to avoid buffer bloat.

In particular it's an important abstraction because in the real world we have constantly faced with things in fact not being async all the time, and some of the things we think might not block, will in fact block. Just like Python did not think that write should be able to block when it was designed. I want to give you a colorful example of this. Why is the following code blocking, and what is?

def decode_object(idx): header = indexes[idx] object_buf = buffer[header.start:header.start + header.size] return brotli.decompress(object_buf)

It's a bit of a trick question, but not really. The reason it's blocking is because memory access can be blocking! You might not think of it this way, but there are many reasons why just touching a memory region can take time. The most obvious one is memory-mapped files. If you're touching a page that hasn't been loaded yet, the operating system will have to shovel it into memory before returning back to you. There is no “await touching this memory” expression, because if there were, we would have to await everywhere. That might sound petty but blocking memory reads were at the source of a series of incidents at Sentry [1].

The trade-off that async/await makes today is that the idea is that not everything needs to block or needs to suspend. The reality, however, has shown me that many more things really want to suspend, and if a random memory access is a case for suspending, then is the abstraction worth anything?

So maybe to allow any function call block and suspend really was the right abstraction to begin with.

But then we need to talk about spawning threads next, because a single thread is not worth much. The one affordance that async/await system gives you that you don't have otherwise, is actually telling two things to run concurrently. You get that by starting the async operation and deferring the awaiting to later. This is where I will have to concede that async/await has something going for it. It moves the reality of concurrent execution right into the language. The reason concurrency comes so natural to a Scratch programmer is that it's right there, so async/await solves a very similar purpose here.

In a traditional imperative language based on threads, the act of spawning a thread is usually hidden behind a (often convoluted) standard library function. More annoyingly threads very much feel bolted on and completely inadequate to even to the most basic of operations. Because not only do we want to spawn threads, we want to join on them, we want to send values across thread boundaries (including errors!). We want to wait for either a task to be done, or a keyboard input, messages being passed etc.

Classic Threading

So lets focus on threads for a second. As said before, what we are looking for is the ability for any function to yield / suspend. That's what threads allow us to do!

When I am talking about “threads” here, I'm not necessarily referring to a specific kind of implementation of threads. Think of the example of promises from above for a moment: we had the concept of “sleeping”, but we did not really say how that is implemented. There is clearly some underlying scheduler that can enable that, but how that takes places is outside the scope of the language. Threads can be like that. They could be real OS threads, they could be virtual and be implemented with fibers or coroutines. At the end of the day, we don't necessarily have to care about it as developer if the language gets it right.

The reason this matters is that when I talk about “suspending” or “continuing somewhere else,” immediately the thought of coroutines and fibers come to mind. That's because many languages that support them give you those capabilities. But it's good to step back for a second and just think about general affordances that we want, and not how they are implemented.

We need a way to say: run this concurrently, but don't wait for it to return, we want to wait later (or never!). Basically, the equivalent in some languages to call an async function, but to not await. In other words: to schedule a function call. And that is, in essence, just what spawning a thread is. If we think about Scratch: one of the reasons concurrency comes natural there is because it's really well integrated, and a core affordance of the language. There is a real programming language that works very much the same: go with its goroutines. There is syntax for it!

So now we can spawn, and that thing runs. But now we have more problems to solve: synchronization, waiting, message passing and all that jazz are not solved. Even Scratch has answers to that! So clearly there is something else missing to make this work. And what even does that spawn call return?

A Detour: What is Async Even

There is an irony in async/await and that irony is that it exists in multiple languages, it looks completely the same on the surface, but works completely different under the hood. Not only that, the origin stories of async/await in different languages are not even the same.

I mentioned earlier that code that can arbitrary block is an abstraction of sorts. That abstraction for many applications really only makes sense is if the CPU time while you're blocking can be used in other useful ways. On the one hand, because the computer would be pretty bored if it was only doing things in sequence, on the other hand, because we might need things to run in parallel. At times as programmers we need to do two things to make progress simultaneously before we can continue. Enter creating more threads. But if threads are so great, why all that talking about coroutines and promises that underpins so much of async/await in different languages?

I think this is the point where the story actually becomes confusing quickly. For instance JavaScript has entirely different challenges than Python, C# or Rust. Yet somehow all those languages ended up with a form of async/await.

Let's start with JavaScript. JavaScript is a single threaded language where a function scope cannot yield. There is no affordance in the language to do that and threads do not exist. So before async/await, the best you could do is different forms of callback hell. The first iteration of improving that experience was adding promises. async/await only became sugar for that afterward. The reason that JavaScript did not have much choice here is that promises was the only thing that could be accomplished without language changes, and async/await is something that can be implemented as a transpilation step. So really; there are no threads in JavaScript. But here is an interesting thing that happens: JavaScript on the language level has the concept of concurrency. If you call setTimeout, you tell the runtime to schedule a function to be called later. This is crucial! In particular it also means that a promise created, will be scheduled automatically. Even if you forget about it, it will run!

Python on the other hand had a completely different origin story. In the days before async/await, Python already had threads — real, operating system level threads. What it did not have however was the ability for multiple of those threads to run in parallel. The reason for this obviously the GIL (Global Interpreter Lock). However that “just” makes things not to scale to more than one core, so let's ignore that for a second. Because it had threads, it also rather early had people experiment with implementing virtual threads in Python. Back in the day (and to some extend today) the cost of an OS level thread was pretty high, so virtual threads were seen as a fast way to spawn more of these concurrent things. There were two ways in which Python got virtual threads. One was the Stackless Python project, which was an alternative implementation of Python (many patches for cpython rather) that implemented what's called a “stackless VM” (basically a VM that does not maintain a C stack). In short, what that enabled is implementing something that stackless called “tasklets” which were functions that could be suspended and resumed. Stackless did not have a bright future because the stackless nature meant that you could not have interleaving Python -> C -> Python calls and suspend with them on the stack.

There was a second attempt in Python called “greenlet”. The way greenlet worked was implementing coroutines in a custom extension module. It is pretty gnarly in its implementation, but it does allow for cooperative multi tasking. However, like stackless, that did not win out. Instead, what actually happened is that the generator system that Python had for years was gradually upgraded into a coroutine system with syntax support, and the async system was built on top of that.

One of the consequences of this is that it requires syntax support to suspend from a coroutine. This meant that you cannot implement a function like sleep that, when called, yields to a scheduler. You need to await it (or in earlier times you could use yield from). So we ended up with async/await because of how coroutines work in Python under the hood. The motivation for this was that it was seen as a positive thing that you know when something suspends.

One interesting consequence of the Python coroutine model is that at least on the coroutine model it can transcend OS level threads. I could make a coroutine on one thread, ship it off to another, and continue it there. In practice, that does not work because once hooked up with the IO system, it cannot travel to another event loop on anther thread any more. But you can already see that fundamentally it does something quite different to JavaScript. It can travel between threads at least in theory; there are threads; there is syntax to yield. A coroutine in Python will also start out with not running, unlike in JavaScript where it's effectively always scheduled. This is also in parts because the scheduler in python can be swapped out, and there are competing and incompatible implementations.

Lastly let's talk about C#. Here the origin story is once again entirely different. C# has real threads. Not only does it have real threads, it also has per-object locks and absolutely no problems with dealing with multiple threads running in parallel. But that does not mean that it does not have other issues. The reality is that threads alone are just not enough. You need to synchronize and talk between threads quite often and sometimes you just need to wait. For instance you need to wait for user input. You still want to do something, while you're stuck there processing that input. So over time .NET introduced “tasks” which are an abstraction over async operations. They are part of the .NET threading system and the way you interact with them is that you write your code in there, you can suspend from tasks with syntax. .NET will run the task on the current thread, and if you do some blocking you stay blocked. This is in that sense, quite different from JavaScript where while no new “thread” is created, you pend the execution in the scheduler. The reason it works this way in .NET is that some of the motivation of this system was to allow UI triggered code to access the main UI thread without blocking it. But the consequence again is, that if you block for real, you just screwed something up. That however is also why at least at one point what C# did was just to splice functions into chained closures whenever it hit an await. It just decomposes one logical piece of code into many separate functions.

I really don't want to go into Rust, but Rust's async system is probably the weirdest of them all because it's polling-based. In short: unless you actively “wait” for a task to complete, it will not make progress. So the purpose of a scheduler there is to make sure that a task actually can make progress. Why did rust end up with async/await? Primarily because they wanted something that works without a runtime and a scheduler and the limitations of the borrow checker and memory model.

Of all those languages, I think the argument for async/await is the strongest for Rust and JavaScript. Rust because it's a systems language and they wanted a design that works with a limited runtime. JavaScript to me also makes sense because the language does not have real threads, so the only alternative to async/await is callbacks. But for C# the argument seems much weaker. Even the problem of having to force code to run on the UI thread could be just used by having a scheduling policy for virtual threads. The worst offender here in my mind is Python. async/await has ended up with a really complex system where the language now has coroutines and real threads, different synchronization primitives for each and async tasks that end up being pinned to one OS thread. The language even has different futures in the standard library for threads and async tasks!

The reason I wanted you to understand all this is that all these different languages share the same syntax, yet what you can do with it is completely different. What they all have in common is that async functions can only be called by async functions (or the scheduler).

What Async Isn't

Over the years I heard a lot of arguments about why for instance Python ended up with async/await and some of the arguments presented don't hold up to scrutiny from my perspective. One argument that I have heard repeatedly is that if you control when you suspend, you don't need to deal with locking or synchronization. While there is some truth to that (you don't randomly suspend), you still end up with having to lock. There is still concurrency so you need to still protect all your stuff. In Python in particular this is particularly frustrating because not only do you have colored functions, you also have colored locks. There are locks for threads and there are locks for async code, and they are different.

There is a very good reason why I showed the example above of the semaphore: semaphores are real in async programming. They are very often needed to protect a system from taking on too much work. In fact, one of the core challenges that many async/await-based programs suffer from is bloating buffers because there is an inability to exert back pressure (I once again point you to my post on that). Why can they not? Because unless an API is async, it is forced to buffer or fail. What it cannot do, is block.

Async also does not magically solve the issues with GIL in Python. It does not magically make real threads appear in JavaScript, it does not solve issues when random code starts blocking (and remember, even memory access can block). Or you very slowly calculate a large Fibonacci number.

Threads are the Answer, Not Coroutines

I already alluded to this above a few times, but when we think about being able to “suspend” from an arbitrary point in time, we often immediately think of coroutines as a programmers. For good reasons: coroutines are amazing, they are fun, and every programming language should have them!

Coroutines are an important building block, and if any future language designer is looking at this post: you should put them in.

But coroutines should be very lightweight, and they can be abused in ways that make it very hard to follow what's going on. Lua, for instance, gives you coroutines, but it does not give you the necessary structure to do something with them easily. You will end up building your own scheduler, your own threading system, etc.

So what we really want is where we started out with: threads! Good old threads!

The irony in all of this is, that the language that I think actually go this right is modern Java. Project Loom in Java has coroutines and all the bells and whistles under the hood, but what it exposes to the developer is good old threads. There are virtual threads, which are mounted on carrier OS threads, and these virtual threads can travel from thread to thread. If you end up issuing a blocking call on a virtual thread, it yields to the scheduler.

Now I happen to think that threads alone are not good enough! Threads require synchronization, they require communication primitives etc. Scratch has message passing! So there is more that needs to be built to make them work well.

I want to follow up on an another blog post about what is needed to make threads easier to work with. Because what async/await clearly innovated is bringing some of these core capabilities closer to the user of the language, and often modern async/await code looks easier to read than traditional code using threads is.

Structured Concurrency and Channels

Lastly I do want to say something nice about async/await and celebrate the innovations that it has brought up. I believe that this language feature singlehandedly drove some crucial innovation about concurrent programming by making it widely accessible. In particular it moved many developers from a basic “single thread per request” model to breaking down tasks into smaller chunks, even in languages like Python. For me, the biggest innovation here goes to Trio, which introduced the concept of structured concurrency via its nursery. That concept has eventually found a home even in asyncio with the concept of the TaskGroup API and is finding its way into Java.

I recommend you to read Nathaniel J. Smith's Notes on structured concurrency, or: Go statement considered harmful for a much better introduction. However if you are unfamiliar with it, here is my attempt of explaining it:

• There is a clear start and end of work: every thread or task has a clear beginning and end, which makes it easier to follow what each thread is doing. All threads spawned in the context of a thread, are known to that thread. Think of it like creating a small team to work on a task: they start together, finish together, and then report back.
• Threads don't outlive their parent: if for whatever reason the parent is done before the children threads, it automatically awaits before returning.
• Error propagate and cause cancellations: If something goes wrong in one thread, the error is passed back to the parent. But more importantly, it also automatically causes other child threads to cancel. Cancellations are a core of the system!

I believe that structured concurrrency needs to become a thing in a threaded world. Threads must know their parents and children. Threads also need fo find convenient ways to ways to pass their success values back. Lastly context should flow from thread to thread implicity through context locals.

The second part is that async/await made it much more apparent that tasks / threads need to talk with each other. In particular the concept of channels and selecting on channels became more prevalent. This is an essential building block which I think can be further improved upon. As food for thought: if you have structured concurrency, in principle each thread's return value really can be represented as a buffered channel attached to the thread, holding up to a single value (successful return value or error) that you can select on.

Today, although no language has perfected this model, thanks to many years of experimentation, the solution seems clearer than ever, with structured concurrency at its core.

Conclusion

I hope I was able to demonstrate to you that async/await has been a mixed bag. It brought some relief from callback hell, but it also saddled us with new issues like colored functions, new back-pressure challenges, and introduced new problems all entirely such as promises that can just sit around forever without resolving. It has also taken away a lot of utility that call stacks brought, in particular for debugging and profiling. These aren't minor hiccups; they're real obstacles that get in the way of the straightforward, intuitive concurrency we should be aiming for.

If we take a step back, it seems pretty clear to me that we have veered off course by adopting async/await in languages that have real threads. Innovations like Java's Project Loom feel like the right fit here. Virtual threads can yield when they need to, switch contexts when blocked, and even work with message-passing systems that make concurrency feel natural. If we free ourselves from the idea that the functional, promise system has figured out all the problems we can look at threads properly again.

However at the same time async/await has moved concurrent programming to the forefront and has resulted in real innovation. Making concurrency a core feature of the language (via syntax even!) is a good thing. Maybe the increased adoption and people struggling with it, was what made structured concurrency a real thing in the Python async/await world.

Future language design should rethink concurrency once more: Instead of adopting async/await, new languages should model themselves more like Java's Project Loom but with more user friendly primitives. But like Scratch, it should give programmers really good APIs that make concurrency natural. I don't think actor frameworks are the right fit, but a combination of structured concurrency, channels, syntax support for spawning/joining/selecting will go a long way. Watch this space for a future blog post about some things I found to work better than others.

[1]Sentry works with large debug information files such as PDB or DWARF. These files can be gigabytes in size and we memory map terabytes of preprocessed files into memory during processing. Memory mapped files can block is hardly a surprise, but what we learned in the process is that thanks to containerization and memory limits, you can easily navigate yourself into a situation where you spend much more time on page faults than you expected and the system crawls to a halt.

The 2025 DSF Board Election has closed, and the following candidates have been elected:

• Abigail Gbadago
• Jeff Triplett
• Paolo Melchiorre
• Tom Carrick

They will all serve two years for their term.

Directors elected for the 2024 DSF Board, Jacob, Sarah, and Thibaud are continuing with one year left to serve on the board.

Therefore, the combined 2025 DSF Board of Directors are:

• Jacob Kaplan-Moss
• Sarah Abderemane
• Thibaud Colas
• Abigail Gbadago*
• Jeff Triplett*
• Paolo Melchiorre*

• Tom Carrick*

• Elected to a two (2) year term

Congratulations to our winners, and a huge thank you to our departing board members Çağıl Uluşahin Sonmez, Chaim Kirby, Kátia Yoshime Nakamura, Katie McLaughlin.

Thank you again to everyone who nominated themselves. Even if you were not successful, you gave our community the chance to make their voices heard in who they wanted to represent them.

 

Best seen on a larger than landscape phone

Someone blogged about a particular problem:

From: https://theweeklychallenge.org/blog/perl-weekly-challenge-294/#TASK1
Given an unsorted array of integers, `ints`Write a script to return the length of the longest consecutive elements sequence.Return -1 if none found. *The algorithm must run in O(n) time.*

The solution they blogged used a sort which meant it could not be O(n) in time, but the problem looked good so I gave it some thought.

Sets! sets are O(1) in Python and are good for looking things up.

What if when looking at the inputted numbers, one at a time, you also looked for other ints in the input that would extend the int you have to form a longer  range?  Keep tab of the longest range so far and if you remove ints from the pool as they form ranges, when the pool is empty, you should know the longest range.

I added the printout of the longest range too.

My codedef consec_seq(ints) -> tuple[int, int, int]:    "Extract longest_seq_length, its_min, its_max"    pool = set(ints)    longest, longest_mn, longest_mx = 0, 1, 0    while pool:        this = start = pool.pop()        ln = 1        # check down        while (this:=(this - 1)) in pool:            ln += 1            pool.remove(this)        mn = this + 1        # check up        this = start        while (this:=(this + 1)) in pool:            ln += 1            pool.remove(this)        mx = this - 1        # check longest        if ln > longest:            longest, longest_mn, longest_mx = ln, mn, mx
    return longest,longest_mn,longest_mx
def _test():    for ints in[(),            (69,),            (-20, 78, 79, 1, 100),            (10, 4, 20, 1, 3, 2),            (0, 6, 1, 8, 5, 2, 4, 3, 0, 7),            (10, 30, 20),            (2,4,3,1,0, 10,12,11,8,9),  # two runs of five            (10,12,11,8,9, 2,4,3,1,0),  # two runs of five - reversed            (2,4,3,1,0,-1, 10,12,11,8,9),  # runs of 6 and 5            (2,4,3,1,0, 10,12,11,8,9,7),   # runs of 5 and 6            ]:        print(f"Input {ints = }")        longest, longest_mn, longest_mx = consec_seq(ints)
        if longest <2:            print("  -1")        else:            print(f"  The/A longest sequence has {longest} elements {longest_mn}..{longest_mx}")

# %%if __name__ == '__main__':    _test()

Sample outputInput ints = ()  -1Input ints = (69,)  -1Input ints = (-20, 78, 79, 1, 100)  The/A longest sequence has 2 elements 78..79Input ints = (10, 4, 20, 1, 3, 2)  The/A longest sequence has 4 elements 1..4Input ints = (0, 6, 1, 8, 5, 2, 4, 3, 0, 7)  The/A longest sequence has 9 elements 0..8Input ints = (10, 30, 20)  -1Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9)  The/A longest sequence has 5 elements 0..4Input ints = (10, 12, 11, 8, 9, 2, 4, 3, 1, 0)  The/A longest sequence has 5 elements 0..4Input ints = (2, 4, 3, 1, 0, -1, 10, 12, 11, 8, 9)  The/A longest sequence has 6 elements -1..4Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9, 7)  The/A longest sequence has 6 elements 7..12Another Algorithm

What if, you kept and extended ranges untill you amassed all ranges then chose the longest? I need to keep the hash lookup. dict key lookup should also be O(1).  What to look up? Look up ints that would extend a range!

If you have an existing (integer) range, say 1..3 inclusive of end points then finding 0 would extend the range to 0..3 or finding one more than the range maximum, 4 would extend the original range to 1..4 

So if you have ranges then they could be extended by finding rangemin - 1 or rangemax +1. I call then extends

If you do find that the next int from the input ints is also an extends value then you need to find the range that it extends, (by lookup), so you can modify that range. - use a dict to map extends to their range and checking if an int is in the extends dict keys should also take O(1) time.

I took that sketch of an algorithm and started to code. It took two evenings to finally get something that worked and I had to work out several details that were trying. The main problem was what about coalescing ranges? if you have ranges 1..2 and 4..5 what happens when you see a 3? the resultant is the single range 1..5. It took particular test cases and extensive debugging to work out that the extends2range mapping should map to potentially more than one range and that you need to combine ranges if two of them are present for any extend value being hit.

So for 1..2 the extends being looked for are 0 and 3. For 4..5 the extends being looked for are 3, again, and 6. The extends2ranges data structure for just this should look like:

{0: [[1, 2]], 3: [[1, 2], [4, 5]], 6: [[4, 5]]}

 The Code #2from collections import defaultdict

def combine_ranges(min1, max1, min2, max2):    "Combine two overlapping ranges return the new range as [min, max], and a set of limits unused in the result"    assert (min1 <= max1 and min2 <= max2          # Well formed            and ( min1 <= max2 and min2 <= max1 )) # and ranges touch or overlap    range_limits = set([min1, max1, min2, max2])    new_mnmx = [min(range_limits), max(range_limits)]    unused_limits = range_limits - set(new_mnmx)
    return new_mnmx, unused_limits
def consec_seq2(ints) -> tuple[int, int, int]:    "Extract longest_seq_length, its_min, its_max"    if not ints:        return -1, 1, -1    seen = set()  # numbers seen so far    extends2ranges = defaultdict(list)  # map extends to its ranges    for this in ints:        if this in seen:            continue        else:            seen.add(this)
        if this not in extends2ranges:            # Start new range            mnmx = [this, this]    # Range of one int            # add in the extend points            extends2ranges[this + 1].append(mnmx)            extends2ranges[this - 1].append(mnmx)        else:            # Extend an existing range            ranges = extends2ranges[this]  # The range(s) that could be extended by this            if len(ranges) == 2:                # this joins the two ranges                extend_and_join_ranges(extends2ranges, this, ranges)            else:                # extend one range, copied                extend_and_join_ranges(extends2ranges, this, [ranges[0], ranges[0].copy()])
    all_ranges = sum(extends2ranges.values(), start=[])    longest_mn, longest_mx = max(all_ranges, key=lambda mnmx: mnmx[1] - mnmx[0])
    return (longest_mx - longest_mn + 1), longest_mn, longest_mx
def extend_and_join_ranges(extends2ranges, this, ranges):    mnmx, mnmx2 = ranges    mnmx_orig, mnmx2_orig = mnmx.copy(), mnmx2.copy() # keep copy of originals    mn, mx = mnmx    mn2, mx2 = mnmx2    if this == mn - 1:        mnmx[0] = mn = this  # Extend lower limit of the range    if this == mn2 - 1:        mnmx2[0] = mn2 = this  # Extend lower limit of the range    if this == mx + 1:        mnmx[1] = mx = this  # Extend upper limit of the range    if this == mx2 + 1:        mnmx2[1] = mx2 = this  # Extend lower limit of the range    new_mnmx, _unused_limits = combine_ranges(mn, mx, mn2, mx2)
    remove_merged_from_extends(extends2ranges, this, mnmx, mnmx2)    add_combined_range_to_extends(extends2ranges, new_mnmx)

def add_combined_range_to_extends(extends2ranges, new_mnmx):    "Add in the combined of two ranges's extends"    new_mn, new_mx = new_mnmx    for extend in (new_mn - 1, new_mx + 1):        r = extends2ranges[extend]  # ranges at new limit extension        if new_mnmx not in r:            r.append(new_mnmx)
def remove_merged_from_extends(extends2ranges, this, mnmx, mnmx2):    "Remove original ranges that were merged from extends"    for lohi in (mnmx, mnmx2):        lo, hi = lohi        for extend in (lo - 1, hi + 1):            if extend in extends2ranges:                r = extends2ranges[extend]                for r_old in (mnmx, mnmx2):                    if r_old in r:                        r.remove(r_old)                if not r:                    del extends2ranges[extend]    # remove joining extend, this    del extends2ranges[this]

def _test():    for ints in[            (),            (69,),            (-20, 78, 79, 1, 100),            (4, 1, 3, 2),            (10, 4, 20, 1, 3, 2),            (0, 6, 1, 8, 5, 2, 4, 3, 0, 7),            (10, 30, 20),            (2,4,3,1,0, 10,12,11,8,9),  # two runs of five            (10,12,11,8,9, 2,4,3,1,0),  # two runs of five - reversed            (2,4,3,1,0,-1, 10,12,11,8,9),  # runs of 6 and 5            (2,4,3,1,0, 10,12,11,8,9,7),   # runs of 5 and 6            ]:        print(f"Input {ints = }")        longest, longest_mn, longest_mx = consec_seq2(ints)
        if longest <2:            print("  -1")        else:            print(f"  The/A longest sequence has {longest} elements {longest_mn}..{longest_mx}")

# %%if __name__ == '__main__':    _test()

Its Output
 Input ints = ()  -1Input ints = (69,)  -1Input ints = (-20, 78, 79, 1, 100)  The/A longest sequence has 2 elements 78..79Input ints = (4, 1, 3, 2)  The/A longest sequence has 4 elements 1..4Input ints = (10, 4, 20, 1, 3, 2)  The/A longest sequence has 4 elements 1..4Input ints = (0, 6, 1, 8, 5, 2, 4, 3, 0, 7)  The/A longest sequence has 9 elements 0..8Input ints = (10, 30, 20)  -1Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9)  The/A longest sequence has 5 elements 0..4Input ints = (10, 12, 11, 8, 9, 2, 4, 3, 1, 0)  The/A longest sequence has 5 elements 8..12Input ints = (2, 4, 3, 1, 0, -1, 10, 12, 11, 8, 9)  The/A longest sequence has 6 elements -1..4Input ints = (2, 4, 3, 1, 0, 10, 12, 11, 8, 9, 7)  The/A longest sequence has 6 elements 7..12

This second algorithm gives correct results but is harder to develop and explain. It's a testament to my stubbornness as I thought their was a solution there, and debugging was me flexing my skills to keep them honed.

END.

My thoughts on my election as a member of the Django Software Foundation (DSF) board of directors.