1. JS Party: JavaScript & Web Dev

    A community celebration of JavaScript and the web. This show records LIVE on Thursdays at 1pm US/Eastern time. Panelists include Suz Hinton, Feross Aboukhadijeh, Kevin Ball, Emma Wedekind, Jerod Santo, Nick Nisi, Divya Sasidharan, Mikeal Rogers, and Chris Hiller. Topics discussed include the web platform (Chrome, Safari, Edge, Firefox, Brave, etc), front-end frameworks (React, Ember, Angular, Vue, etc), Node.js, web animation, SVG, robotics, IoT, and much more. If JavaScript and/or the web touch your life, this show’s for you. Some people search for JSParty and can't find the show, so now the string JSParty is in our description too.

    Latest episode: We got confs on lockdown

    on 24.04.2020

    Emma, Divya, and Suz are joined by Quincy Larson from freeCodeCamp where they chat about virtual conferences. Are they better than in-person conferences? What are the differences? Let’s find out!

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  2. Python Bytes

    Python Bytes is a weekly podcast hosted by Michael Kennedy and Brian Okken. The show is a short discussion on the headlines and noteworthy news in the Python, developer, and data science space.

    Latest episode: #178 Build a PyPI package from a Jupyter notebook

    on 22.04.2020

    This episode is brought to you by Digital Ocean: pythonbytes.fm/digitalocean YouTube is going strong over at pythonbytes.fm/youtube Michael #1: Python String Format Website by Lachlan Eagling Have you ever forgotten the arguments to datetime.str``f``time()? Quick: What’s the format for Wed April 15, 10:30am? I don’t know but the site says '%a %B %H, %M:%Sam' and it’s right! Brian #2: Pandas-Bokeh Suggested by Jack McKew “Pandas-Bokeh provides a Bokeh plotting backend for Pandas, GeoPandas and Pyspark DataFrames, similar to the already existing Visualization feature of Pandas. Importing the library adds a complementary plotting method plot_bokeh() on DataFrames and Series.” “With Pandas-Bokeh, creating stunning, interactive, HTML-based visualization is as easy as calling: df.plot_bokeh()" You can also switch the default plotting of pandas to Bokeh with pd.set_option('plotting.backend', 'pandas_bokeh') This interface looks a lot easier to me, instead of frames and plots and shows and such. Lots of options, and all collected in parameters to the plot call. Can also export a notebook or a standalone html file. Plus, the combined install of pip install pandas-bokeh pulls in everything you need. Michael #3: NBDev nbdev is a library that allows you to fully develop a library in Jupyter Notebooks, putting all your code, tests and documentation in one place. That is: you now have a true literate programming environment, as envisioned by Donald Knuth back in 1983! This seems to be a massive upgrade for notebooks and related tooling Creates Python packages out of a notebook Creates documentation from the notebook Solves the git perma-conflict issues with git pre-commit hooks Use #export to declare a cell should become a function in the package Manages the boilerplate issues for creating Python packages (setup.py, etc) Makes testing possible inside notebooks Navigate and edit your code in a standard text editor or IDE, and sync any changes automatically back into your notebooks (reverse basically) Follow getting started instructions. Docs render slightly better at nbdev.fast.ai Brian #4: Stop naming your python modules “utils” Sebastian Buczyński, @EnforcerPL Lots of projects, public and private, end up having a utils.py. “utils is arguably one of the worst names for a module because it is very blurry and imprecise. Such a name does not say what is the purpose of code inside. On the contrary, a utils module can as well contain almost anything. By naming a module utils, a software developer lays down perfect conditions for an incohesive code blob. Since the module name does not hint team members if something fits there or not, it is likely that unrelated code will eventually appear there, as more utils.” one occurrence of misbehavior invites more of them I have seen this in action. I’ve put 2-3 hard to classify methods, but used in lots of modules, into a utils.py, only to come back in a few months and see a couple dozen completely unrelated methods, now that the team has a junk drawer to throw things in. Excuses: It’s just one function There is no other place to put this code I need a place for company commons But Django does it Instead: Try naming based on role of the code or group functions by theme. If you see a utils.py crop up in a code review, request that it be renamed or split and renamed. Michael #5: Scalene A high-performance, high-precision CPU and memory profiler for Python It runs orders of magnitude faster than other profilers while delivering far more detailed information. Scalene is fast. It uses sampling instead of instrumentation or relying on Python's tracing facilities. Its overhead is typically no more than 10-20% (and often less). Scalene is precise. Unlike most other Python profilers, Scalene performs CPU profiling at the line level, pointing to the specific lines of code that are responsible for the execution time in your program. Scalene separates out time spent running in Python from time spent in native code (including libraries). Scalene profiles memory usage. In addition to tracking CPU usage, Scalene also points to the specific lines of code responsible for memory growth. It accomplishes this via an included specialized memory allocator. Requires special install, not just pip (see brew install instructions for the docs) Scalene profiles copying volume, making it easy to spot inadvertent copying, especially due to crossing Python/library boundaries (e.g., accidentally converting numpy arrays into Python arrays, and vice versa). See the performance comparison chart. Would be nice to have integrated in the editors (PyCharm and VS Code) Brian #6: From 1 to 10,000 test cases in under an hour: A beginner's guide to property-based testing Carolyn Stransky, @carolynstran Excellent intro to property based testing and hypothesis Starts with a unit test that uses example based testing. Before showing similar test using hypothesis, she talks about the different mindset of testing for properties instead of exact examples. Like not the exact sorted list you should but instead, the length should be the same the contents should contain the same things, for instance, using set for that assertion you could element-wise walk the list and make sure i <= i+1 She walks through the hypothesis decorators to come up with input and shows how to use some.lists and some.integers and max_examples Goes on to discuss coming up with properties to test for, which really is the hard part of property based testing. Checking for expected exceptions Using a naive method technique, useful in property based testing, to compare two versions of a method. This is super useful for refactoring and testing new vs old versions on tons of input data. json5 lib Extras John Conway, inventor of the Game of Life, has died of COVID-19 GitHub is now free for all teams (and individuals) including 2,000 Actions minutes/month unlimited collaborators, even on private repos GitLab has a similar free tier PyCon US 2020 Online Lots of talks already up, more on the way. Joke PyJoke delivers: How many QAs does it take to change a lightbulb? They noticed that the room was dark. They don't fix problems, they find them.

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  3. Talk Python To Me

    Talk Python to Me is a weekly podcast hosted by Michael Kennedy. The show covers a wide array of Python topics as well as many related topics. Our goal is to bring you the human story behind the Python packages and frameworks you know and love.

    Latest episode: #261 Monitoring and auditing machine learning

    on 25.04.2020

    Traditionally, when we have depended upon software to make a decision with real-world implications, that software was deterministic. It had some inputs, a few if statements, and we could point to the exact line of code where the decision was made. And the same inputs lead to the same decisions.

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  4. The Changelog: Software Dev & Open Source

    Conversations with the hackers, leaders, and innovators of software development. Hosts Adam Stacoviak and Jerod Santo face their imposter syndrome so you don’t have to. Expect in-depth interviews with the best and brightest in software engineering, open source, and leadership. This is a polyglot podcast. All programming languages, platforms, and communities are welcome. Open source moves fast. Keep up.

    Latest episode: Work from home SUPERCUT

    on 22.04.2020

    Today we’re featuring conversations from different perspectives on working from home from our JS Party, Go Time, and Brain Science podcasts here on Changelog.com. Because, hey…if you didn’t know we have 6 active podcasts in our portfolio of shows. Head to changelog.com/podcasts to collect them all!

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  5. The WIRED Podcast

    The award-winning WIRED UK Podcast with James Temperton and the rest of the team. Listen every week for the an informed and entertaining rundown of latest technology, science, business and culture news. New episodes every Friday.

    Latest episode: The coronavirus startup crisis

    on 24.04.2020

    We explain why coronavirus is hitting startups so hard, investigate the crisis facing Airbnb and its hosts and find out how researchers have cracked a secret paedophile code

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  6. Troy Hunt's Weekly Update Podcast

    This is the audio podcast version of Troy Hunt's weekly update video published here: https://www.troyhunt.com/tag/weekly-update/

    Latest episode: Weekly Update 188

    on 25.04.2020

    Life Returning to Normal (Kinda); NDC Workshop & Pluralsight #TechSkillsDay; IoT & Nanoleafs; Nintendo Credential Stufing; More Breaches; Sponsored by Varonis https://www.troyhunt.com/weekly-update-188/

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