PyCon: Eighth Annual PyLadies Auction at PyCon 2019

Photo Courtesy of Mike Pirnat

PyLadies is an international mentorship community for women that use Python. Started with the help of a grant provided by The Python Software Foundation (PSF)  in 2011, PyLadies has continued to bring women into the Python community through a variety of methods, including hosting events in local PyLadies chapters and offering grant opportunities to attend PyCon. Their mission is to promote, educate and advance a diverse Python community through outreach, education, conferences, events, and social gatherings.

The Python Software Foundation (PSF) is proud to announce the Eighth Annual PyCon Charity Auction for 2019.

PyCon 2018’s auction was a huge success raising over $ 30K! More than 40 items from sponsors and fellow attendees were auctioned. Attendance was overwhelming and, rather than turn more people away for 2019, we have decided to increase capacity this year!

The PSF subsidizes this event each year by covering the cost of the venue, food, and beverages. In addition, the PSF adds a substantial donation to the event after everything is auctioned off.  

If you are interested in donating and item for the auction send the information to

Thinking about becoming a Sponsor?

We are hoping to find one or two companies who love what the PyLadies are doing and are willing to sponsor this wonderful event. It’s a great opportunity to let our community know that you support them. Sponsorships start at $ 7500. More information can be found here, or you can contact

Photo Courtesy of Mike Pirnat

PyLadies also aims to provide a friendly support network for women and a bridge to the larger Python world. Anyone with an interest in Python is encouraged to participate! Check out local meetups here.

The auction is a fun and entertaining way to support the PyLadies community.

We hope to see you in Cleveland!

Planet Python

Tryton News: Tryton Unconference 2019: In Marseille on the 6th & 7th of June

@nicoe wrote:

The Tryton Foundation is happy to announce the venue and date of the next Tryton Unconference.

We will go in the sunny city of Marseille in south of France on the 6th and 7th of June. Contrary to previous editions of the Tryton Unconferences the coding sprint will be organized during the two days preceding the conference.

Both events will take place at the École de Commerce et de Management. We will publish a website with more detailed informations shortly.

Many thanks to adiczion which is the organizer of this year event!

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PyPy Development: Düsseldorf Sprint Report 2019

Hello everyone!

We are happy to report a successful and well attended sprint that is wrapping up in Düsseldorf, Germany. In the last week we had eighteen people sprinting at the Heinrich-Heine-Universität Düsseldorf on various topics.

Totally serious work going on here constantly.

A big chunk of the sprint was dedicated to various discussions, since we did not manage to gather the core developers in one room in quite a while. Discussion topics included:

  • Funding and general sustainability of open source.
  • Catching up with CPython 3.7/3.8 – we are planning to release 3.6 some time in the next few months and we will continue working on 3.7/3.8.
  • What to do with VMprof
  • How can we support Cython inside PyPy in a way that will be understood by the JIT, hence fast.
  • The future of supporting the numeric stack on pypy – we have made significant progress in the past few years and most of the numeric stack works out of the box, but deployment and performance remain problems. Improving on those problems remains a very important focus for PyPy as a project.
  • Using the presence of a CPython developer (Łukasz Langa) and a Graal Python developer (Tim Felgentreff) we discussed ways to collaborate in order to improve Python ecosystem across implementations.
  • Pierre-Yves David and Georges Racinet from octobus gave us an exciting demo on Heptapod, which adds mercurial support to gitlab.
  • Maciej and Armin gave demos of their current (non-PyPy-related) project VRSketch.

Visiting the Landschaftspark Duisburg Nord on the break day

Some highlights of the coding tasks worked on:

  • Aarch64 (ARM64) JIT backend work has been started, we are able to run the first test! Tobias Oberstein from Crossbar GmbH and Rodolph Perfetta from ARM joined the sprint to help kickstart the project.
  • The long running math-improvements branch that was started by Stian Andreassen got merged after bugfixes done by Alexander Schremmer. It should improve operations on large integers.
  • The arcane art of necromancy was used to revive long dormant regalloc branch started and nearly finished by Carl Friedrich Bolz-Tereick. The branch got merged and gives some modest speedups across the board.
  • Andrew Lawrence worked on MSI installer for PyPy on windows.
  • Łukasz worked on improving failing tests on the PyPy 3.6 branch. He knows very obscure details of CPython (e.g. how pickling works), hence we managed to progress very quickly.
  • Matti Picus set up a new benchmarking server for PyPy 3 branches.
  • The Utf8 branch, which changes the internal representation of unicode might be finally merged at some point very soon. We discussed and improved upon the last few blockers. It gives significant speedups in a lot of cases handling strings.
  • Zlib was missing couple methods, which were added by Ronan Lamy and Julian Berman.
  • Manuel Jacob fixed RevDB failures.
  • Antonio Cuni and Matti Picus worked on 7.0 release which should happen in a few days.

Now we are all quite exhausted, and are looking forward to catching up on sleep.

Best regards, Maciej Fijałkowski, Carl Friedrich Bolz-Tereick and the whole PyPy team.

Planet Python

PyCoder’s Weekly: Issue #354 (Feb. 5, 2019)

#354 – FEBRUARY 5, 2019
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The PyCoder’s Weekly Logo

Wow, so much has happened in the Python world last week!

I don’t even know where to begin here…

This issue is chock-full of actual Python news and, as usual, we’ve also got some interesting articles and tutorials for you.

Happy Pythoning!

— Dan Bader, Editor

2019 Python Steering Council Election Results

Voting for the first Python steering council election following PEP 8100 closed on February 4. The members of the first steering council are: Barry Warsaw, Brett Cannon, Carol Willing, Guido van Rossum, Nick Coghlan. The council has broad authority to make decisions about the Python project, such as accepting or rejecting PEPs.

Python Development in Visual Studio Code

In this tutorial, you’ll learn how to use Visual Studio Code for Python development. By following examples, you’ll cover everything from how to install and configure Visual Studio Code for Python development to how to run tests and debug application, so you can use this powerful tool.

Use Your Python Skills to Practice Interviewing for the Job You Want (Practice for Free on Pramp)


Increase your chance of success: Practice your interview skills for data structures & algorithms, system design, data science, and behavioral interviews on Pramp, the world’s leading peer-to-peer mock interview platform. Pramp is 100% free. Schedule your first interview →
PRAMP sponsor

Python Developers Survey 2018 Results

Results of the official Python Developers Survey 2018 by the Python Software Foundation and JetBrains. More than 20k responses from more than 150 countries were collected. One takeaway as a teaser: Python 3 adoption hit 84%!

How to Write a “Complete Program” From Start to Finish

When is a program ever complete or finished? Inspiring article that shows you the process for writing programs of various “completion levels” for the same original use case. (This uses Ruby for the code examples but the same principles apply in Python.)

Dense Matrices Implementation in Python

Machine learning algorithms often use matrices to store data and compute operations such as multiplications or singular value decomposition. In this article you’ll see how matrices are implemented in Python: how the data is stored and how much memory it consumes.

PyCon 2019 Reminders and Information

“PyCon 2019 Early Bird tickets have sold out but registration at regular price is still available. If you are planning to attend, register soon as tickets will sell out and you don’t want to miss out on the largest Python Conference.”

Simple Dependent Types in Python

A dependent type is a type whose definition depends on a value. For example, a dependent function’s return type may depend on the value (not just type) of an argument. This article shows you how to implement type checking for dependent types with Mypy.

Conda 4.6 Released

Includes better Pip interoperability and PowerShell support on Windows.

CPython 3.8.0a1 Now Available for Testing

The most visible change so far is probably the implementation of PEP 572: Assignment Expressions with the := operator.


A Modern Version of Python?

What the creator of Flask (and other popular Python projects) wishes for Python’s future.

Smart Python Script Template

“When I start development of new code I always use a script that starts off with traceback and pdb

How to Toggle a Value in Python?

In case you ever wondered what the most efficient way was to toggle between 0 and 1.

Python Jobs

Python Web Developer (Remote)

Premiere Digital Services

Software Developer (Herndon, VA)


Tech Lead/Senior Software Engineer/Full Stack (Seattle, WA) Incubator

Python Software Engineer (London, UK)

Pole Star Space Applications Ltd.

Senior Engineer Python (Winterthur, Switzerland)


Sr Enterprise Python Developer (Toronto, Canada)


Senior Software Engineer (Santa Monica, CA)


Python Software Engineer (Palo Alto, CA)

Rhythm Diagnostic Systems, Inc

Software Engineer (Herndon, VA)

Charon Technologies

Web UI Developer (Herndon, VA)

Charon Technologies

More Python Jobs >>>

Articles & Tutorials

Python Quiz Collection

Use this collection of online Python quizzes as a fun way for you to check your learning progress and to test your skills. Each quiz takes you through a series of questions and you’ll receive a score at the end.

Tutorial for Writing a systemd Service in Python

Many Linux distributions use systemd to manage the system’s services (or daemons), for example to automatically start certain services in the correct order when the system boots. This tutorial will help you write your first systemd service using Python. Nice writeup!

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Hashable Objects Must Be Immutable

In Python, hashable objects must be immutable and mutable objects cannot be hashable. Why? Find out by reading the full article.
AL SWEIGART • Shared by Ricky White

Mock vs MagicMock in Python

What’s the difference between the two and when to use one over the other?
RYAN ERMITA • Shared by Ryan Ermita

Why Python Is Not My Favorite Language (2016)

Opinion-piece on some of Python’s flaws, as perceived by the author.

HFT-like Trading Algorithm in 300 Lines of Python

A complete Python trading system example, using asyncio and 300 lines of code.

Practical Machine Learning With Python and Keras

An introduction to machine learning with Python. Learn how to use the Keras library to train a simple neural network that recognizes handwritten digits.

API/REST Testing Like Chuck Norris With pytest-play

See how to write HTTP API tests with Pytest using YAML files thanks to pytest-play.

Single Linked Lists in Python, With Examples

See the different types of linked lists, how to traverse a linked list, how to insert and remove elements from a linked list, the different techniques to sort a linked list, how to reverse a linked list, and more.

Instagram Street Art Dataset and Detection Model

How to build your own deep learning dataset and detection model using public Instagram photos and the TensorFlow and Keras frameworks.

Why I Chose Flask to Build’s Mini-Services

“There are frameworks which are smaller still – but I think Flask hits a sweet spot. If you’re making a monolithic web app and can live within the on-rails Django experience, you might want to use it.”

Formatting Numbers With Thousands Separators

Quick tip: You can use f"{number:,}" to thousands-format an integer to a string.

Projects & Code

A Neural Network Framework in 25 LOC

“Framework” is a big word here, but this is a neat minimal example for a Python neural network.

RustPython: A Python Interpreter Written in Rust

Implements a basic Python 3.5+ interpreter in the Rust programming language.


PyLadies LA

February 5, 2019

Python Miami

February 9 to February 10, 2019

PyTennessee 2019

February 9 to February 11, 2019

DjangoGirls Bangalore

February 9 to February 10, 2019

Python Atlanta

February 14, 2019

PyCon Belarus 2019

February 15 to February 17, 2019

Happy Pythoning!
This was PyCoder’s Weekly Issue #354.
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Planet Python

Tryton News: Newsletter February 2019

@ced wrote:

Tryton continues its road of improvements for more performance and more scaling.


Changes For The User

The arrows on columns are now always synchronized with the actual order. If the order is not on a single column then all arrows are displayed.

The records created by XML files in modules are by default protected against modification and deletion. But if they have the attribute noupdate set, they can be modified. Now they can also be deleted and updating the database will not recreate them.

On the wizard that allows to pay multiple lines at once, we added back a field to define the date of the payment.

Refining a search in a long list can lead to no results on the actual page of the pagination.
This can be astonishing and annoying because the user may think that there is no result at all. To prevent this, now the client automatically reduces the pagination until it finds a result.

New Modules


The module allows rules to be defined to complete statement lines from imported files. When the “Apply Rule” button is clicked on a statement, each rule is tested in order, against each origin that does not have any lines, until one is found that matches. Then the rule found is used to create the statement lines linked to the origin. Get the account_statement_rule module.

Changes For The Developer

We added two tables ir.calendar.month and which store the translations of months and week days. This allowed to replace the hard-coded values to format time with locale and re-use the translation infrastructure.
In addition, it provides also a common way for modules to store month or day like in the payment term, instead of duplicate many times the same selections. All standard modules have been migrated.

An old constraint inherited from TinyERP was removed from analytic account. It checked that debit and credit were always positive. We finally remove it to follow the same design as the general accounting.

We use by default soffice to convert report into different formats. But sometime (rarely), soffice command does not stop and so it blocks the request for ever. In order to release the locks of the request transaction, we added a default timeout of 5 minutes to execute the conversion.

We added the option to have ModuleTestCase, the generic test case for a module, to run with extra modules installed. This is useful for module that have extra_depends so the depending code is also tested.

We have speed the startup time of trytond for about 10% by improving the depends computation of the fields.

The plugins for clients are small piece of code that are added to the client in order to preform some specific actions (usually to interact locally with the OS or to define a new widget). We can now define such plugins on the web client too.

Tryton supports a minimal cross-origin resource sharing mechanism. You just have to list the authorized origin in the configuration. For more complex rules, we advise to use a front-end proxy like nginx.

Thanks to the CORS support, we can now redirect the request for the bus to a different host or service. This allows to reduce the load on the main server.

We can now search on keys of Dict of fields using the Tryton’s ORM. On PostgreSQL back-end, Dict fields can be stored as a JSON. In this case, the database can use indexes to speed-up the query.
It is also possible to order the search result based on the keys of Dict fields.

The cache management has been improved to be more transactional. It has now a more transactional-like API by using sync, commit and rollback. Only committed data can be stored in it.

Some times it may be needed to lock a record or a list of records for the transaction. To simplify this task, we added a dualmethod ModelSQL.lock which takes care of the different ways to lock depending of the back-end.

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