pytest plugins are an amazing way to supercharge your test suites, leveraging great solutions from people solving test problems all over the world. In this episode Michael and I discuss 15 favorite plugins that you should know about. We also discuss fixtures and plugins and other testing tools that work great with pytest tox GitHub Actions Coverage.py Selenium + splinter with pytest-splinter Hypothesis And then our list of pytest plugins: pytest-sugar pytest-cov pytest-stress pytest-repeat pytest-instafail pytest-metadata pytest-randomly pytest-xdist pytest-flake8 pytest-timeout pytest-spec pytest-picked pytest-freezegun pytest-check fluentcheck That last one isn't a plugin, but we also talked about pytest-splinter at the beginning. So I think it still counts as 15.Special Guest: Michael Kennedy.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:pytest-sugar — changes the default look and feel of pyptest (e.g. progressbar, show tests that fail instantly)pytest-cov — run coverage.py from pytestpytest-stress — allows you to loop tests for a user defined amount of time.pytest-repeat — for repeating test executionpytest-instafail — shows failures and errors instantly instead of waiting until the end of test session.pytest-metadata — for accessing test session metadatapytest-randomly — randomly order tests and control random.seedpytest-xdist — distributed testingpytest-flake8 — pytest plugin to run flake8pytest-timeout — terminate tests after a certain timeoutpytest-spec — display test execution output like a specificationpytest-picked — run the tests related to the changed files (according to Git)pytest-freezegun — easily freeze timepytest-check — allows multiple failures per testfluentcheck — fluent assertionsepisode 104 — Top 28 pytest plugins with Anthony SottilePython Testing with pytest — The easiest way to get up to speed with pytest fast. There's also a chapter dedicated to plugins that also teaches you how to write and test your own plugins.toxGitHub ActionsCoverage.pypytest-splinter — provides a set of fixtures to use splinter for browser testingsplinter — makes it easy to write automated tests of web applicationshypothesis — property-based testingTalk Python Episode #267 — This episode is a cross post with Talk Python ★ Support this podcast on Patreon ★

One of the great things about attending in person coding conferences, such as PyCon, is the hallway track, where you can catch up with people you haven't seen for possibly a year, or maybe even the first time you've met in person. Nina is starting something like the hallway track, online, on twitch, and it's already going, so check out the first episode of Python Tea. Interesting coincidence is that this episode is kind of like a hallway track discussion between Nina and Brian. We've had Nina on the show a couple times before, but it's been a while. In 2018, we talked about Mentoring on episode 44. In 2019, we talked about giving Memorable Tech Talks in episode 71. In this episode, we catch up with Nina, find out what she's doing, and talk about a bunch of stuff, including: Live Coding Online Conferences Microsoft Python team Python Tea, an online hallway track Q&A with Python for VS Code team Python on hardware Adafruit Device Simulator Express CircuitPython Tricking out your command prompt Zsh and Oh My Zsh Emacs vs vi key bindings for shells Working from home Special Guest: Nina Zakharenko.Sponsored By:Python Morsels: Level up your Python skills with challenges and expert advice.ConfigCat.com: Release features faster with less risk with ConfigCat feature flags. Promo Code: TESTANDCODELinks:nnjaio - TwitchNina Zakharenko 💜🐍 (@nnja) / TwitterDevice Simulator Express - Visual Studio MarketplaceInitial code for Microsoft's PyBadge at PyCon 2020Goodbye Print, Hello Debugger! - Nina Zakharenko - PyCon 2020 Talk 𝙽𝚒𝚗𝚊 𝚉𝚊𝚔𝚑𝚊𝚛𝚎𝚗𝚔𝚘 💜🐍 - DEV.toPython Tea AnnouncementThe Live Coders Conference ★ Support this podcast on Patreon ★

"The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers." That's a lot of responsibility, and to that end, the PSF Board Directors help out quite a bit. If you want to be a part of the board, you can. There's an election coming up right around the corner and you gotta get your nomination in by May 31. You can also join the PSF if you want to vote for who gets to be part of the board. But what does it really mean to be on the Board, and what are some of the things the PSF does? To help answer those questions, I've got Ewa Jodlowska, the PSF Executive Director, and Christopher Neugebauer, a current board member, on the show today. I've also got some great links in the show notes if we don't answer your questions and you want to find out more.Special Guests: Christopher Neugebauer and Ewa Jodlowska.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:Latest PSF Board Elections DiscussionPython Software FoundationOverview of ElectionsDuties and Responsibilities of DirectorsLife as a Python Software Foundation Director - YouTube ★ Support this podcast on Patreon ★

Technical debt has to be dealt with on a regular basis to have a healthy product and development team. The impacts of technical debt include emotional drain on engineers and slowing down development and can adversely affect your hiring ability and retention. But really, what is technical debt? Can we measure it? How do we reduce it, and when? James Smith, the CEO of Bugsnag, joins the show to talk about technical debt and all of these questions.Special Guest: James Smith.Sponsored By:AcePythonInterviews.com: Get all the confidence you need to ace your next interview.ConfigCat.com: Release features faster with less risk with ConfigCat feature flags. Promo Code: TESTANDCODE ★ Support this podcast on Patreon ★

"Code is read much more often than it is written." - Guido van Rossum This is true for both production code and test code. When you are trying to understand why a test is failing, you'll be very grateful to the test author if they've taken the care to make it readable. David Seddon came up with 6 principles to help us write more readable tests. We discuss these, as well as more benefits of readable tests. David's 6 Principles of Readable Tests: Profit from the work of others Put naming to work Show only what matters Don’t repeat yourself Arrange, act, assert Aim high Special Guest: David Seddon.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:How to write readable tests (presentation) · David SeddonHow to write readable tests (slides)pytestWebTest factory_boydjango-webtest ★ Support this podcast on Patreon ★

In both unittest and pytest, when a test function hits a failing assert, the test stops and is marked as a failed test. What if you want to keep going, and check more things? There are a few ways. One of them is subtests. Python's unittest introduced subtests in Python 3.4. pytest introduced support for subtests with changes in pytest 4.4 and a plugin, called pytest-subtests. Subtests are still not really used that much. But really, what are they? When could you use them? And more importantly, what should you watch out for if you decide to use them? That's what Paul Ganssle and I will be talking about today.Special Guest: Paul Ganssle.Sponsored By:ConfigCat.com: Release features faster with less risk with ConfigCat feature flags. Promo Code: TESTANDCODEWeeklyPythonExercise.com: Become a more fluent Python developer.Links:Subtests in Python — Paul's article on subtestssubtests in unittest - Python docs pytest-subtests: plugin to support subtests in pytestpytest-check: A pytest plugin that allows multiple failures per test. ★ Support this podcast on Patreon ★

Django supports testing out of the box with some cool extensions to unittest. However, many people are using pytest for their Django testing, mostly using the pytest-django plugin. Adam Parkin, who is known online as CodependentCodr, joins us to talk about migrating an existing Django project from unittest to pytest. Adam tells us just how easy this is.Special Guest: Adam Parkin.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:Django Tutorial, on testingThe Django docs on testing & the test databaseThe Django docs on the TestCase classpytest-django docs ★ Support this podcast on Patreon ★

Financial services have their own unique testing development challenges. But they also have lots of the same challenges as many other software projects. Eric Bergemann joins Brian Okken to discuss: Specific testing challenges in the financial services domain CI/CD : Continuous Integration, Continuous Deployment TDD : Test Driven Development Confidence from testable applications Testing strategies to add coverage to legacy systems Testing the data and test cases themselves DevOps Continuous testing Manual testing procedures BDD & Gherkin Hiring in vs training industry knowledge Special Guest: Eric Bergemann.Sponsored By:ConfigCat.com: Release features faster with less risk with ConfigCat feature flags. Promo Code: TESTANDCODELinks:ParagonThe Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win ★ Support this podcast on Patreon ★

Apache Spark is a unified analytics engine for large-scale data processing. PySpark blends the powerful Spark big data processing engine with the Python programming language to provide a data analysis platform that can scale up for nearly any task. Johnathan Rioux, author of "PySpark in Action", joins the show and gives us a great introduction of Spark and PySpark to help us decide how to get started and decide whether or not to decide if Spark and PySpark are right you.Special Guest: Jonathan Rioux.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:PySpark in ActionSparkPySpark documentationJoel Grus, livecoding ★ Support this podcast on Patreon ★

Hypothesis is the Python tool used for property based testing. Hypothesis claims to combine "human understanding of your problem domain with machine intelligence to improve the quality of your testing process while spending less time writing tests." In this episode Alexander Hultnér introduces us to property based testing in Python with Hypothesis. Some topics covered: What is property based testing Thinking differently for property based testing Using hypothesis / property based testing in conjunction with normal testing Failures saved and re-run What parts of development/testing is best suited for hypothesis / property based testing Comparing function implementations Testing against REST APIs that use Open API / Swagger with schemathesis Changing the number of tests in different test environments System, integration, end to end, and unit tests Special Guest: Alexander Hultnér.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:Hypothesis homeHypothesis docsTest Fast, Fix More - Property based testing with Hypothesis by Alexander Hultnér - YouTubeQuickcheck, Grandfather of property based testingBeyond Unit Tests, Hillel Wayne, PyCon 2018Better Testing With Less Code, Matt Bachmann, PyCon 2016Choosing properties for property-based testing (F#)schemathesis: Hypothesis + Open API / Swagger for testing web applications ★ Support this podcast on Patreon ★