Planet Python

Last update: May 13, 2020 10:46 AM UTC

May 13, 2020

CubicWeb

A roadmap to Cubicweb 3.28 (and beyond)

Yesterday at Logilab we had a small meeting to discuss about a roadmap to Cubicweb 3.28 (and beyond), and we would like to report back to you from this meeting.

Cubicweb 3.28 will mainly bring the implementation of content negotiation. It means that Cubicweb will handle content negotiation and will be able to return RDF using Cubicweb's ontology when requested by a client.

The 3.28 will have other features as well (like a new variables attributes to ResultSet that contains the name of the projected variables, etc). Those features will be detailed the release changelog.

Before releasing this version, we would like to finish the migration to heptapod, to make sure that everything is ok. The remaining tasks are:

• fixing the CI (there is still some random failings, that need further investigation)
• migrate the jenkins job that pushes images to hub.docker.com on heptapod, to make everthing available from the forge. It will be explicit for everyone when a job is done, and what is its status.

Beside of releasing Cubicweb 3.28, its ecosystem will also be updated:

• logilab-common, a new version will be released very soon, which brings a refactoring of the deprecation system, and annotations (coming from pyannotate)
• yams, a new version is coming. This version:
• brings type annotation (manually done, a carefully checked);
• removes a lot of abbreviation to make the code clearer;
• removes some magic related to a object which used to behave like a string;

The goal of these two releases, is to have type annotations in the core libraries used by CubicWeb, and then to be able to bring type annotation into CubicWeb itself, in a future version.

On those projects, some “modernisation” has been started too ; (fixing flake8 when needed, repaint the code black). This “modernisation” step is still on going on the different projects related to CubicWeb (and achieved for yams, and logilab-common).

In the medium term, we would like to focus on the documentation of CubicWeb and it's ecosystem. We do know that it's really hard for newcomers (and even ourself sometime) to understand how to start, what each module is doing etc. An automatic documentation has been released for some modules (see 1, 2 or 3 for instance). It would be nice to automatize the update of the documentation on readthedocs, update the old examples, and add new ones about the new feature we are adding (like content negotiation, pyramid predicates, etc). This could be done in team Friday's sprint or hackathon for instance. CubicWeb would also need some modernisation (running black ? and above all, make all files flake8 compilant…).

Regarding CubicWeb development, all (or, at least a lot of) cubes and Cubicweb related projects moved from cubicweb.org's forge to our instance of heptapod (4 and 5). Some issues have been imported from cubicweb.org to heptapod. New issues should be opened on heptapod, and the review should also be done there. We hope that will ease the reappropriation of the code basis and stimulates new merge-requests :)

To end this report, we like to emphasis that we will try to make a « remote Cubicweb meeting » each Tuesday at 2 pm. If you would like to participate to this meeting, it's with great pleasure (if you need the webconference URL, contact one of us, we will provide it to you). We also created a #Cubicweb channel on matrix.logilab.org ; feel free to ask for an invitation if you'd like to discuss Cubicweb related things with us.

All the best, and… see you next Tuesday :)

May 13, 2020 09:03 AM UTC

Codementor

Create your first web scraper with ScrapingBee API and Python

Learn how to use cloud based Scraping API to scrape web pages without getting blocked.

May 13, 2020 07:20 AM UTC

Brett Cannon

Thoughts on where tools fit into a workflow

I am going to admit upfront that this is a thought piece, a brain dump, me thinking out loud. Do not assume there is a lesson here, nor some goal I have in mind. No, this blog post is providing me a place to write out what tools I use when in my ideal development workflow (and yes, this will have a bias towards the Python extension for  VS Code 😁).

While actively coding

The code-test-fix loop

Typically when I am coding I think about what problem I'm trying to solve, what  the API should look like, and then what it would take to test it. I then start to code up that solution, writing tests as I go. That means I have a virtual environment set up with the latest version of Python and all relevant required and testing-related dependencies installed into it. I am also regularly running the test I am currently working on or the related tests I have to prevent any regressions. But the key point is a tight development loop where I'm focusing on the code I'm actively working on.

The tools I'm using the most during this time is:

1. pytest
2. venv (although since virtualenv 20 now uses venv I should look into using virtualenv)

Making sure I didn't mess up

Once code starts to reach a steady state and the design seems "done", that's when I start to run linters and to expand the testing to other versions of Python. I also start to care about test coverage. I put this off until the code is "stable" to minimize churn and the overhead cost of running a wider amount of tools and have to await their results which slow down the development process.

Now, I should clarify that for me, linters are tools that you run to check your code for something which do not require running under a different version of Python. If you have to run something under every version of Python that you support then that's a test to me, not a lint. This allows me to group linters together and run them only once instead of under every version of Python with the tests, cutting the execution time down.

The tools that I am using during this time are:

1. coverage.py
2. Black
3. mypy
4. I should probably start using Pyflakes (or flake8 --ignore=C,E,W)

Running these three tools all the time can be a bit time-consuming. I have to remember to do it and they don't necessarily run quickly. Luckily I can amortize the costs of running linters thanks to support in the Python extension for VS Code. If I set up the linters to run when I save, I can have them running regularly in the background and not have to do the work they will ask me to do later. Since the results show up as I work without having to wait for a manual run it makes it much cheaper to run linters. Sames goes for setting up formatters (which also act as linters when you're enforcing style).

The problem is not everyone uses VS Code. To handle the issue of not remembering what to run, people often set up tox or nox which also has the benefit of making it easier to run tests against other versions of Python. Another option is you can also set up pre-commit so as to not forget and get the benefit of linting for other things like trailing whitespace, well-formed JSON, etc. So there's overlap between tox/nox and pre-commit, but also differentiators. This leads some people to set up tox/nox to execute pre-commit for linting to get the most that they can out of all the tools.

So tools people use to run linters:

1. tox or nox
2. pre-commit

But then there is also the situation where people have their own editors that they want to set up to using these linters. And so when using build tools like poetry and flit they have the concept of development dependencies. That way everyone working on the project get the same tools installed and they can set them up however they want to fit their workflow.

Proposing a change

When getting ready to create a pull request, I want the tests and linters to run against all supported versions of Python and OSs via continuous integration. To make things easier to debug when CI flags a problem, I want my CI to be nothing more than running something I could run locally if I had the appropriate setup. I am also of the opinion that people proposing PRs should do as much testing locally as possible which requires being able to replicate CI runs locally (I hold this view because people very often don't pay attention to whether CI for their PR goes green or not and making the maintainer have to message you saying your PR is failing CI adds delays and takes up time).

There is one decision to make about tooling updates. Obviously tools like the linters that you  rely on will make new releases and chances are you want to use them (improved error detection, bugfixes, etc.). There are two ways of handling this.

One is to leave the development dependencies unpinned. Unfortunately that can lead to an unsuspecting contributor having CI fail on their PR simply because a development dependency changed. To avoid that I can run a CI cron job at some frequency to try and pick up those sorts of failures early on.

The other option is to pin my  development dependencies (and I truly mean pin; I have had micro releases break CI because a project added a warning and a flag was set to make warnings be considered errors). This has the side-effect that in order to get those bugfixes and improvements from the tools I will need to occasionally check for updates. It's possible to use tools like Dependabot to update pinned dependencies in an automated fashion to alleviate the burden.

Tools for CI:

1. GitHub Actions
2. Dependabot

Preparing for a release

I want to make sure CI testing against the wheel that you would be uploading to PyPI (setuptools users will know why this is important thanks to MANIFEST.in). I want the same OS test coverage as testing a PR request. For Python versions, I will test against all supported versions plus the in-development version of Python where I allow for failures (see my blog post on why this is helpful and how to do it on Travis).

With testing and linting being clean, that leaves release-only prep work. I have to update the version if I haven't been doing that continuously. The changelog will also require updating if I haven't been doing it after every commit. With all of this in place I should be ready to build the sdist and wheel(s) and uploading them to PyPI. Finally, the release needs to be tagged in git.

Conclusion (?)

Let's take setting up Blak for formatting. That would mean:

1. List Black as a development dependency
2. Set up VS Code to run Black
3. Set up pre-commit to enforce Black
4. Set up tox or nox to run pre-commit
5. Set up GitHub Actions to lint using tox or nox

What about mypy?

1. List mypy as a development dependency
2. Set up VS Code to run mypy
3. Set up pre-commit to enforce mypy

Repeat as necessary for other linters. There's a bit of repetition, especially considering how I set up Black will probably be the same as all of my other projects and very similar to other people. And if there is an update to a linter?

1. Update pre-commit
2. Potentially update development dependency pin

There's also another form of repetition when you add support for a new version of Python:

1. Update your Python requirement for build back-end
2. Update your trove classifiers
3. Update tox or nox
4. Update GitHub Actions

Once again, how I do this is very likely the same for all of my projects and lots of other people.

So if I'm doing the same basic thing for the same tools, how can I cut down on this repetition? I could use Cookiecutter to stamp out new repositories with all of this already set up. That does have the drawback of not being able to update things later. Feels like I want a Dependabot for linters and new Python versions.

I also need to automate my release workflow. I've taken a stab at it, but it's not working quite yet. If ditched SemVer for all my projects it would greatly simplify everything. 🤔

May 13, 2020 03:40 AM UTC

May 12, 2020

PyCoder’s Weekly

Issue #420 (May 12, 2020)

#420 – MAY 12, 2020
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May 12, 2020 07:30 PM UTC

Python Engineering at Microsoft

Python in Visual Studio Code – May 2020 Release

We are pleased to announce that the May 2020 release of the Python Extension for Visual Studio Code is now available. You can download the Python extension from the Marketplace, or install it directly from the extension gallery in Visual Studio Code. If you already have the Python extension installed, you can also get the latest update by restarting Visual Studio Code. You can learn more about  Python support in Visual Studio Code in the documentation.  

In this release we addressed 42 issues, and it includes the ability to browse for or enter an interpreter path on selection. If you’re interested, you can check the full list of improvements in our changelog. 

Ability to browse for interpreter path  

To make selecting or changing interpreter easier, you now have the option to browse for a Python interpreter in your file system. You can also set an interpreter by manually entering its path:

Theme: Eva

Coming Next: moving python.pythonPath out of settings.json 

One change that is coming relates to how the Python extension handles Python interpreter selection. Currently the path to the selected interpreter is stored in the workspace settings. This can be a problem when you share VS Code workspace settings in a GitHub repo, for example, as reported in our issue tracker. 

In order to make the interpreter information system agnostic and prevent sharing the interpreter path (which commonly won’t be the same across different machines), we’re going to deprecate the python.pythonPath setting in the Python extension.   

These changes will be added gradually as an experiment. If you’re interested to try it ahead of time, you can opt into this functionality by adding the following line to your User settings (View > Command Palette… and run Preferences: Open Settings (JSON)) and then reloading the window (View > Command Palette… and run Developer: Reload Window): 

"python.experiments.optInto": ["DeprecatePythonPath - experiment"]

To see if you are part of an experiment, you can check the first lines in the Python extension output channel. If you wish to opt-out of A/B testing in general, you can open the user settings.json file and set the “python.experiments.enabled” setting to false. 

We also have some additional announcements coming soon, so stay tuned! 

Other Changes and Enhancements 

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

• CVE-2020-1171: Do not perform pipenv interpreter discovery on extension activation. (#11127) 
• CVE-2020-1192: Setting “Data Science: Run Startup Commands” is now limited to being a User scope only setting.
• Performance improvements when executing multiple cells in Notebook and Interactive Window using ipywidgets. (#11576) 
• Fix for opening the interactive window when no workspace is open. (#11291) 
• Update Jedi 0.17 (thanks Peter Law) (#11221)

Be sure to download the Python extension for Visual Studio Code now to try out the above improvements. If you run into any problems, please file an issue on the Python VS Code GitHub page. 

The post Python in Visual Studio Code – May 2020 Release appeared first on Python.

May 12, 2020 05:47 PM UTC

Mike Driscoll

Learn How to Log with Python (Video)

Learn how to use Python’s logging module in this screencast:

You will learn about the following:

• Creating a log
• Logging Levels
• Logging Handlers
• Logging Formatters
• Logging to Multiple Locations
• and more!

The post Learn How to Log with Python (Video) appeared first on The Mouse Vs. The Python.

May 12, 2020 02:17 PM UTC

Real Python

Improve Your Tests With the Python Mock Object Library

When you’re writing robust code, tests are essential for verifying that your application logic is correct, reliable, and efficient. However, the value of your tests depends on how well they demonstrate these criteria. Obstacles such as complex logic and unpredictable dependencies make writing valuable tests difficult. The Python mock object library, unittest.mock, can help you overcome these obstacles.

By the end of this course, you’ll be able to:

• Create Python mock objects using Mock
• Assert that you’re using objects as you intended
• Inspect usage data stored on your Python mocks
• Configure certain aspects of your Python mock objects
• Substitute your mocks for real objects using patch()
• Avoid common problems inherent in Python mocking

You’ll begin by seeing what mocking is and how it will improve your tests!

[ 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 ]

May 12, 2020 02:00 PM UTC

Python Software Foundation

Python’s migration to GitHub - Request for Project Manager Resumes

Timeline

• May 4 - Requests for resumes opens
• June 4 - Requests for resumes closes
• June 12 - Final decision will be made on proposals received
• June 22 - Work will begin

Submitting a proposal

Goal

Tasks

• Create a timeline for the project with the Python team and GitHub team
• Find out from the community the context behind GitHub search limitations, why bugs.python.org search is sometimes preferred.
• Research the Contributor License Agreement (CLA) process and how it can be achieved outside of bugs.python.org. Work with interested contractors, volunteers, and the PSF’s Director of Infrastructure on a solution.
• Work with GitHub’s migration team and Python’s community on how mapping of fields should work from bugs.python.org to GitHub
• Work with GitHub’s migration team on the transition from bugs.python.org and be the Python point of contact for GitHub. This includes helping field questions from GitHub to the Steering Council/core devs and vice versa.
• Assist the Python community with creating guidelines on how people are promoted to Python’s triage team.
• Obtain from GitHub a list of projects that have bots built that may help Python with "nosy lists"
• Oversee the creation of the new workflow on GitHub
• Assist with the creation of GitHub labels and templates when necessary 
• Oversee the creation of the sandbox issue tracker on GitHub to experiment and test the new workflow
• Ensure that the sandbox received adequate testing from the Python team
• Update the devguide with that new process ahead of the migration and communicate it to the core developers
• Communicate with PSF staff on a regular basis when necessary and provide monthly reports via email.

Estimated budget

Necessary Skills

May 12, 2020 12:48 PM UTC

Catalin George Festila

Python 3.8.3 : Create shortut and add python to the Context Menu.

The tutorial for today is a simple script for python user. If you run this python script in your folder you will get an python shortcut to the python and the python to the Context Menu in Windows 10. The Context Menu can be used with right click from your mouse, see the screenshot. This script can also be used with any executable if you make some changes. import os import pythoncom from

May 12, 2020 10:43 AM UTC

Talk Python to Me

#264 10 tips every Flask developer should know

Are you a web developer who uses Flask? It has become the most popular Python web framework. Even if you have used it for years, I bet we cover at least one thing that will surprise you and make your Flask code better. <br/> <br/> Join me as I speak with Miguel Grinberg about his top 10 list for tips and tricks in the Flask world. They're great!<br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Miguel on Twitter</b>: <a href="https://twitter.com/miguelgrinberg" target="_blank" rel="noopener">@miguelgrinberg</a><br/> <b>Miguel's blog</b>: <a href="http://blog.miguelgrinberg.com" target="_blank" rel="noopener">blog.miguelgrinberg.com</a><br/> <br/> <b>python-dotenv package</b>: <a href="https://pypi.org/project/python-dotenv/" target="_blank" rel="noopener">pypi.org</a><br/> <b>httpie package</b>: <a href="https://httpie.org/" target="_blank" rel="noopener">httpie.org</a><br/> <b>Quart</b>: <a href="https://pgjones.gitlab.io/quart/" target="_blank" rel="noopener">pgjones.gitlab.io</a><br/> <b>Talk Python episode on Quart</b>: <a href="https://talkpython.fm/episodes/show/147/quart-flask-but-3x-faster" target="_blank" rel="noopener">talkpython.fm</a><br/> <b>secure.py package</b>: <a href="https://github.com/TypeError/secure.py" target="_blank" rel="noopener">github.com</a><br/></div><br/> <strong>Sponsors</strong><br/> <br/> <a href='https://talkpython.fm/sentry'>Sentry Error Monitoring</a><br> <a href='https://talkpython.fm/linode'>Linode</a><br> <a href='https://talkpython.fm/training'>Talk Python Training</a>

May 12, 2020 08:00 AM UTC

Wing Tips

Moving the Program Counter in Wing's Python Debugger

This Wing Tip describes how to move the program counter while debugging Python code in Wing Personal and Wing Pro. This is a good way to go back and re-execute previously visited Python code, in order to trace through to the cause of a bug without having to restart the debug process.

To move the program counter, the debugger must be running and paused or stopped at a breakpoint. Then right-click on the target line in the editor and select Move Program Counter Here:

Shown above: Right-click to select Move Program Counter Here, then continue stepping with Step Over and Step Into in the toolbar.

Limitations: Due to the way Python is implemented, the program counter can only be moved within the current inner-most stack frame and it may not be moved within an exception handler, after an exception has been raised but not yet handled.

That's it for now! We'll be back soon with more Wing Tips for Wing Python IDE.

As always, please don't hesitate to email support@wingware.com if you run into problems or have any questions.

May 12, 2020 01:00 AM UTC

May 11, 2020

Daniel Roy Greenfeld

Two Scoops of Django 3.x Released

We just released the early release (alpha) of the fifth edition of our book, titled Two Scoops of Django 3.x. The 3.x means we are supporting Django 3.0, 3.1, and 3.2 Long Term Support (LTS) releases, ensuring the content will be valid until April of 2024.

So long as it is May 11, 2020, anywhere on planet Earth, the e-book version sells for just US$42.95!

On May 12th, 2020, the price goes up to $49.95. Hurry up and get your book!

For now, the e-book is just in PDF format and will be expanded to epub and mobi formats in the days to come. Readers of this alpha version get all the updates and have the opportunity to help us shape the direction of the book through their feedback, and to be credited as contributors.

If you bought the 1.11 e-book in 2020 you'll receive an email on May 11th with a discount code covering the cost of the new edition.

The book will also be printed, but for several reasons that won't happen until hopefully August of this year. When we get closer to that date we'll take pre-orders and send everyone who ordered an e-book a big discount code.

Due to popular demand, we are selling group licenses of Two Scoops of Django 3.x, in 10 developer, 50 developer, and 250 developer packages. These can be found as options in the product selection dropdown.

Learn more and order the 5th edition of our venerable series about Django best practices.

May 11, 2020 04:30 PM UTC

Podcast.__init__

Managing Distributed Teams In The Age Of Remote Work

More of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.

May 11, 2020 04:20 PM UTC

Real Python

Python eval(): Evaluate Expressions Dynamically

eval(expression[, globals[, locals]])

>>> eval("2 ** 8")
256
>>> eval("1024 + 1024")
2048
>>> eval("sum([8, 16, 32])")
56
>>> x = 100
>>> eval("x * 2")
200

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

[ 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 ]

May 11, 2020 02:00 PM UTC

Catalin George Festila

Python Qt5 - Simple text editor with QPlainTextEdit.

I haven't played python in a long time. It can be seen after the last article. Today I installed Python 3.8.3 and my favorite PyQt5 and started to see how much I forgot from what I knew. Installing PyQt5 python mode was simple. pip3 install PyQt5 Collecting PyQt5 Downloading PyQt5-5.14.2-5.14.2-cp35.cp36.cp37.cp38-none-win_amd64.whl (52.9 MB) |████████████████████████████████| 52.9 MB 80

May 11, 2020 12:17 PM UTC

Julien Danjou

Interview: The Performance of Python

Earlier this year, I was supposed to participate to dotPy, a one-day Python conference happening in Paris. This event has unfortunately been cancelled due to the COVID-19 pandemic.

Both Victor Stinner and me were supposed to attend that event. Victor had prepared a presentation about Python performances, while I was planning on talking about profiling.

Rather than being completely discouraged, Victor and I sat down (remotely) with Anne Laure from Behind the Code (a blog ran by Welcome to the Jungle, the organizers of the dotPy conference).

We discuss Python performance, profiling, speed, projects, problems, analysis, optimization and the GIL.

You can read the interview here.

May 11, 2020 11:40 AM UTC

PyCharm

Interview: PyCharm helping developers to navigate complexity and be more productive

Developing in Python is the dream job of many developers out there. According to the latest survey from StackOverflow, Python is the second most ‘loved’ language, and the ‘most wanted’ one. Now, imagine working for a Python software house where you have the opportunity to be part of many different projects, sometimes even simultaneously? It sounds amazing, right? But, of course, it also brings some challenges. You need to communicate more, be aware of different project scopes, take decisions under different technology requirements, and much more. Complexity is also closely related to productivity. While some people excel in this scenario others spend more time finding their way around.

A type of tool that helps developers to find their way around and navigate complexity easily are IDEs (Integrated Development Environment). Today we will chat with two professional Python developers that, despite very different backgrounds, chose PyCharm as their main Python IDE. Łukasz Wyszomirski and Łukasz Ćwikliński work at STX Next, a European software house specialized in Python, and will share their experiences with us.

– Hi, nice to meet you both! Could you start by telling us a little bit about yourself and your journey to becoming a Python developer?

Łukasz Wyszomirski: I started my journey to become a programmer at university. Originally I wanted to be an accountant, but I ended up getting interested in programming. Back in secondary school, our teacher took us to an education fair and after learning more about it, I chose programming studies in Gdańsk. In my third year at university, I got my first job as a Ruby on Rails intern.

Łukasz Ćwikliński: After a couple of years as Head of Sales in a company of a dozen or so, I came to a realization that I’m no longer learning new things in that industry. I decided to look for something new and exciting. I quickly learned that IT is something I’m interested in—that’s how my journey with programming started. In my first year, I programmed in PHP, but quickly after that I moved on to Python.

– Can you describe your team at STX Next?

Łukasz W.: Currently I’m working in a team consisting of 5 members: a product owner, a QA specialist, 2 frontend developers, and one backend developer (me). I have two main roles. I’m responsible for building the entire backend in the project and, as I currently have the most experience in commercial projects I also help the other members of the team, even if I’m not an expert in a particular technology.

Łukasz Ć.: I have worked in several teams so far, and the size of each team was always different. It varied from being a one-man army to a team of 7. In the latter, there were such roles as a QA specialist, backend and frontend developers, a product owner, and DevOps. As for my role, I am a full-stack Python developer.

– What kind of projects do you usually work on?

Łukasz W.: I have experience in big projects from the health and banking sectors but I have also worked on MVP projects. Currently, my team is working on a project in the area of logistics. I can’t really talk about many of the projects we work on, but to give you an idea, you can check out our portfolio.

Łukasz Ć.: So far I have worked on some fintech projects, some internal projects (for instance the company intranet, website, and RMS), and on projects related to A/B tests.

– Among those projects, is there one (or more) that you are really proud of?

Łukasz W.: Working on projects in the health sector is especially gratifying. In my previous job, I had an opportunity to develop a platform for mobile surveys used in developing countries. It was an app used by workers who traveled to villages in those countries, gathering data from pregnant women and newborns. The data was then used to combat the problem of low childbirth survival of women and newborns.

Łukasz Ć.: Yes, I’m very proud of one of the fintech projects I worked on. It started about 2 years ago and it’s still ongoing. Many fintech projects have to be stopped because of problems with funds or competition, but this one is still going strong. On top of that, the code itself is interesting in this project, and the technology stack is very broad, which is a plus, too.

– What are the main challenges that you face while working on multiple projects?

Łukasz W.: The main challenge of working on multiple projects is having to switch the context between them, while still keeping up with the new requirements. However, that’s an issue only if the domain sectors of the projects are different. If the projects are in the same sector, it’s not that big of a deal. There’s also the issue of time and how to divide it to make sure everything is done efficiently.

Łukasz Ć.: I think that the greatest challenge is having to switch contexts between the projects. Another challenge is related to all the changes that happen in a given project when I’m not working on it. Sometimes when I spend a week focusing on one of the projects new features are introduced in the second one and, later on, when I go back to the first project, I need to find out what’s been happening there. It takes a while to catch up and get on the same page with everyone else.

– What technologies do you use for your Python development?

Łukasz W.: It depends on what we need in the given project. Currently, in each project I’m using Celery, a database, and the Django-rest-framework. When we need a good analytics system I tend to use the ELK stack. I also prefer Django over Flask. In terms of tools in general, I also use Docker containers. In all honesty, though, most of the tools I use are in PyCharm — the terminal, the connection to the database, the Python console — this way I have all that and more just one click away.

– PyCharm is your primary Python IDE, can you recall when you first decided to use it?

Łukasz W.: I mentioned before that my first programming job was as a Ruby on Rails intern. Back then I didn’t know which IDE was good, so my teammates suggested me to use RubyMine. Later on, I had to start programming in Java, so, as far as IDEs go, I had only one choice: IntelliJ. After around a year of that, I had an opportunity to start a new project which would be written in Python and another system, which I worked on in Java. Back at the university, I had a chance to check out other Python IDEs and I had bad memories of those, so after RubyMine and IntelliJ I wanted to try PyCharm.

Łukasz Ć: About 3.5 years ago I started working with STX Next, and I had the possibility to use part of my training budget on tools for my work. I’ve used JetBrains tools before, so this was a perfect opportunity to purchase the license.

– Which PyCharm features increase your productivity the most?

Łukasz W.: There’s a bunch of those. The main one I use a lot is definitely the debugger. It allows me to connect to the project and stop the application from running at a given point and verify the variables or contexts. I also appreciate the fact that I can configure the project in a way that lets me launch it from PyCharm, either with the debugger or not. I also use the file watchers a lot. That allows me to define which files PyCharm is supposed to watch, change, and launch linters. All of us try to keep good practices in mind when writing code, but this way I don’t have to worry about them too much — PyCharm will worry about them for me. For example, it can add commas if I end up forgetting about them. Finally, I appreciate that PyCharm provides the actual connection to the database.

Łukasz Ć: I’d say this is my list: project interpreter, open recent, jump to the declaration, setting server configuration, autosuggestion, and visual guides.

– Have you ever encouraged other members of your team to use PyCharm?

Łukasz W.: I’m the only Python developer in my current team, but before that using PyCharm was a standard in my team. A colleague from another team helped us in one of the projects recently and I recommended PyCharm to him. Now he uses it whenever he’s working with Python. In fact, as far as I know, when he works on the frontend he uses WebStorm — another JetBrains IDE, but for JavaScript.

– Thank you for all your answers! Is there anything else you would like to share with us today?

Łukasz W.: You guys are doing a really good job developing software, not only for Python. I had a chance to use your other products like RubyMine, IntelliJ, dotPeek, etc., and it was a good experience.

Łukasz Ć.: Keep doing a good job! I work with 2-3 tools that were developed by you guys. Sometimes I do frontend, and then I use WebStorm. I also use Java at university and then I stick to IntelliJ.

Read this blog post to learn more about what our interviewees and their colleagues think of the different IDEs in the market and why they chose PyCharm as number one.

About the company

STX Next is a European software house specialized in Python. Over 200 developers stand ready to empower different projects with extraordinary code and a results-driven Agile process. Apart from Python, STX is also an expert in JavaScript development. Their toolbox of frameworks includes Django, Flask, Angular, and React, each one chosen to create reliable solutions in short order. Their team consists of over 350 professionals, from software developers to UX designers, to automatic QA testers, to communication experts, all of them ready to ensure smooth cooperation with STX Next’s partners.

May 11, 2020 10:04 AM UTC

Codementor

🤖 Interactive Machine Learning Experiments

This is a collection of interactive machine-learning experiments. Each experiment consists of 🏋️ Jupyter/Colab notebook (to see how a model was trained) and 🎨 demo page (to see a model in action right in your browser).

May 11, 2020 05:06 AM UTC

Mike Driscoll

PyDev of the Week: Jan Giacomelli

This week we welcome Jan Giacomelli (@jangiacomelli) as our PyDev of the Week. Jan is an entrepreneur and blogs about Python. You can see what projects Jan contributes to over on Github.

Let’s spend a few minutes and get to know Jan better!

Can you tell us a little about yourself (hobbies, education, etc):

I’ve been programming for a while. I started when I was a high school senior – I made scraper for online betting webpages. After that, I studied electrical engineering and finished my MsC degree. Since my student years I’ve been working as a software engineer.

I’ve been training alpine skiing for almost a decade. After that, I also got a ski instructor license. Therefore in the winter ski centers are the place to go. I also love windsurfing and squash. Besides sports I also like to play guitar and cook.

Why did you start using Python?

At my first programming job there was a guy who really loved Python. He introduced me to it when we were working together on the project. I immediately loved it. Then I used it during my university studies for ML and math tasks. A also used for my master thesis where I was building a model of guitar amplifier
with neural networks. It was more natural to me than, for example, Matlab. I continue using it ever since.

What other programming languages do you know and which is your favorite?

I also worked in javascript, Java, PHP, C and C#. I enjoy the most when working with Python. While most of the time I work on ML projects I also use it the most. All in all I try to use the right tool for the job. I won’t use Python for real-time applications such as audio recording.

What projects are you working on now?

I am Chief software architect/engineer at typless. We are building AI services for data extraction from documents such as invoices, receipts, declarations, reports,… I write a blog with development stories and tutorials. I also tweet about software development.

Which Python libraries are your favorite (core or 3rd party)?

From 3rd party libraries I definitely love Django and Django REST framework. I also enjoy using scikit, keras, numpy.

How did you become an entrepreneur?

In May 2017 I attended DragonHack hackathon. In 24 hours we developed a mobile app. User was able to take a photo of the document (book page for example) and the app converted it into editable a Word document with the same layout (headers, paragraphs, positions, …). After that, we started thinking about starting our own company. Firstly, we got a project for some industrial optimization. We use money from this project to start developing an data extraction AI service. This service, called typless, can be now trained to extract data from any document.

Do you have any advice for other people who would like to start a business?

Firstly, you must strongly believe that you will succeed. Develop MVP as fast as you can and then start selling it. Sell as fast as you can and be creative at it. Don’t take investors who have only money – a startup is like a family.

Is there anything else you’d like to say?

The more you know about programming the less you think you know.

Thanks for doing the interview, Jan!

The post PyDev of the Week: Jan Giacomelli appeared first on The Mouse Vs. The Python.

May 11, 2020 05:05 AM UTC

The Three of Wands

Building Pyrseia III: Server Middleware, Client Senders, CLI and InApp Validators

async def logging_middleware(ctx, call: Call, next):
    log.info(f"Processing {call.name}")
    try:
        return await next(ctx, call)
    finally:
        log.info(f"Processed {call.name}")

from aiohttp import web
from pyrseia.aiohttp import create_aiohttp_app
from tests.calculator import Calculator

serv = server(Calculator, web.Request, middleware=[logging_middleware])
app = create_aiohttp_app(serv)
web.run_app(app)

from msgpack import dumps, loads
from cattr import Converter

converter = Converter()

async def s(session: ClientSession, call: Call, type):
     async with session.post(
         url,
         data=dumps(converter.unstructure(call)),
         timeout=client_timeout,
      ) as resp:
           return converter.structure(loads(await resp.read()), type)

class AppStoreVerifier:
    @rpc
    async def verify_receipt(
        self,
        receipt_b64_data: str,
        password: Optional[str],
        exclude_old_transactions: bool,
    ) -> ResponseBody:
        ...

$ python -m pyrseia contrib.apple:create_verifier "verify_receipt('<receipt>', None, True)"

ResponseBody(status=<Status.SUCCESS: 0>, latest_receipt=None, latest_receipt_info=[], pending_renewal_info=[], receipt=ResponseBody.Receipt(<omitted>), environment=<Environment.PRODUCTION: 'Production'>, is_retryable=None)

class GooglePlayDeveloperApi:
    @rpc
    async def get_purchases_products(
        self, package_name: str, product_id: str, token: str
    ) -> ProductPurchase:
        ...

    @rpc
    async def get_voided_purchases(
        self,
        package_name: str,
        start_time: Optional[int],
        end_time: Optional[int],
        token: Optional[str],
        type: int,
    ) -> VoidedPurchasesResponse:
        ...

May 11, 2020 12:25 AM UTC

Matt Layman

Episode 5 - How To Use Forms

On this episode, we will learn about HTML forms and Django’s form system to use when collecting input from users. Listen at djangoriffs.com. Last Episode On the previous episode, we looked at templates, the primary tool that Django provides to build user interfaces in your Django app. Web Forms 101 HTML can describe the type of data that you may want your users to send to your site. Collecting this data is done with a handful of tags.

May 11, 2020 12:00 AM UTC

May 10, 2020

Abhijeet Pal

How To Upload Images With Django

One of the most common requirement in any modern web application is the ability to take images or pictures from the users as input and save them on the server however Letting users upload files can have big security implications. In this article, we will learn how to upload images in a Django application. Uploading Images in Django Django has two model fields that allow user uploads FileField and ImageField basically ImageField is a specialized version of  FileField that uses Pillow to confirm that a file is an image. Let’s, start by creating models. models.py from django.db import models class Image(models.Model): title = models.CharField(max_length=200) image = models.ImageField(upload_to='images') def __str__(self): return self.title The image column is an ImageField field that works with the Django’s file storage API, which provides a way to store and retrieve files, as well as read and write them. The upload_to parameters specify the location where images will be stored which for this model is MEDIA_ROOT/images/ Setting dynamic paths for the pictures is also possible. image = models.ImageField(upload_to='users/%Y/%m/%d/', blank=True) This will store the images in date archives …

The post How To Upload Images With Django appeared first on Django Central.

May 10, 2020 03:55 PM UTC

Python Software Foundation

The 2020 Python Language Summit

The Python Language Summit is a small gathering of Python language implementers (both the core developers of CPython and alternative Pythons), as well third-party library authors and other Python community members. The summit features short presentations followed by group discussions. In 2020, the Summit was held over two days by videoconference; questions were asked by a combination of voice and chat.

Summaries of all presentations will be posted here as they are completed.

Thanks to MongoDB for sponsoring the Python Language Summit.

Day 1

May 10, 2020 03:24 PM UTC

CPython on Mobile platforms - Python Language Summit 2020

"We've got very big news on Android," Russell Keith-Magee told the Language Summit. "We're close to having a full set of BeeWare tools that can run on Android."

The BeeWare project aims to let programmers write apps in Python for Android, iOS, and other platforms using native UI widgets. Keith-Magee reported that BeeWare has made good progress since his Summit presentation last year. On iOS, "Python worked well before, it works well now," and BeeWare has added Python 3.8 support. Until recently, however, Python was struggling to make inroads on Android. BeeWare's Android strategy was to compile Python to Java bytecode, but Android devices are now fast enough, and the Android kernel permissive enough, to run CPython itself. With funding from the PSF, BeeWare hired Asheesh Laroia to port CPython to Android.

Read more 2020 Python Language Summit coverage.

May 10, 2020 03:23 PM UTC

EuroPython

EuroPython 2020: First part of the program available

Our program work group (WG) has been working hard over the last week to select the first batch of sessions for EuroPython 2020, based on your talk voting and our diversity criteria.

We’re now happy to announce the first 60 talks, brought to you by 61 speakers.

EuroPython 2020 Session List

We will have over 80 sessions for EP2020

Tomorrow, we will open the second CFP to fill the additional slots we have added for the Americas, India/Asian/Pacific time zones. This will then complete the program for EP2020, with over 80 sessions by more than 80 speakers waiting for you — from all over the world !

Waiting List

Some talks are still in the waiting list. We will inform all speakers who have submitted talks about the selection status by email.

Full Schedule

The full schedule will be available shortly after we have completed the second CFP, later in May.

Conference Tickets

Conference tickets are available on our registration page. We have simplified and greatly reduced the prices for the EP2020 online edition.

As always, all proceeds from the conference will go into our grants budget, which we use to fund financial aid for the next EuroPython edition, special workshops and other European conferences and projects:

EuroPython Society Grants Program

We hope to see lots of you at the conference in July. Rest assured that we’ll make this a great event again — even within the limitations of running the conference online.

Enjoy,
–
EuroPython 2020 Team
https://ep2020.europython.eu/
https://www.europython-society.org/

May 10, 2020 03:07 PM UTC

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