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Episode #182: PSF Survey is out!

Published Tue, May 19, 2020, recorded Wed, May 13, 2020.

Sponsored by Datadog: pythonbytes.fm/datadog

Michael #1: PSF / JetBrains Survey

  • via Jose Nario
  • Let’s talk results:
  • 84% of people who use Python do so as their primary language [unchanged]
  • Other languages: JavaScript (down), Bash (down), HTML (down), C++ (down)
  • Web vs Data Science languages:
    • More C++ / Java / R / C# on Data Science side
    • More SQL / JavaScript / HTML
  • Why do you mainly use Python? 58% work and personal
  • What do you use Python for?
    • Average answers was 3.9
    • Data analysis [59% / 59% — now vs. last year]
    • Web Development [51% / 55%]
    • ML [40% / 39%]
    • DevOps [39% / 43%]
  • What do you use Python for the most?
    • Web [28% / 29%]
    • Data analysis [18% / 17%]
    • Machine Learning [13% / 11%]
  • Python 3 vs Python 2: 90% Python 3, 10% Python 2
  • Widest disparity of versions (pro 3) is in data science.
  • Web Frameworks:
    • Flask [48%]
    • Django [44%]
  • Data Science
    • NumPy 63%
    • Pandas 55%
    • Matplotlib 46%
  • Testing
    • pytest 49%
    • unittest 30%
    • none 34%
  • Cloud
    • AWS 55%
    • Google 33%
    • DigitalOcean 22%
    • Heroku 20%
    • Azure 19%
  • How do you run code in the cloud (in the production environment)
    • Containers 47%
    • VMs 46%
    • PAAS 25%
  • Editors
    • PyCharm 33%
    • VS Code 24%
    • Vim 9%
  • tool use
    • version control 90%
    • write tests 80%
    • code linting 80%
    • use type hints 65%
    • code coverage 52%

Brian #2: Hypermodern Python

  • Claudio Jolowicz, @cjolowicz
  • An opinionated and fun tour of Python development practices.
  • Chapter 1: Setup
    • Setup a project with pyenv and Poetry, src layout, virtual environments, dependency management, click for CLI, using requests for a REST API.
  • Chapter 2: Testing
    • Unit testing with pytest, using coverage.py, nox for automation, pytest-mock. Plus refactoring, handling exceptions, fakes, end-to-end testing opinions.
  • Chapter 3: Linting
    • Flake8, Black, import-order, bugbear, bandit, Safety. Plus more on managing dependencies, and using pre-commit for git hooks.
  • Chapter 4: Typing
    • mypy and pytype, adding annotations, data validation with Desert & Marshmallow, Typeguard, flake8-annotations, adding checks to test suite
  • Chapter 5: Documentation
    • docstrings, linting docstrings, docstrings in nox sessions and test suites, darglint, xdoctest, Sphinx, reStructuredText, and autodoc
  • Chapter 6: CI/CD
    • CI with GithHub Actions, reporting coverage with Codecov, uploading to PyPI, Release Drafter for release documentation, single-sourcing the package version, using TestPyPI, docs on RTD
  • The series is worth it even for just the artwork.
  • Lots of fun tools to try, lots to learn.

Michael #3: Open AI Jukebox

  • via Dan Bader
  • Listen to the songs under “Curated samples.”
  • A neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles.
  • Code is available on github.
  • Dataset: To train this model, we crawled the web to curate a new dataset of 1.2 million songs (600,000 of which are in English), paired with the corresponding lyrics and metadata from LyricWiki.
  • The top-level transformer is trained on the task of predicting compressed audio tokens. We can provide additional information, such as the artist and genre for each song.
  • Two advantages: first, it reduces the entropy of the audio prediction, so the model is able to achieve better quality in any particular style; second, at generation time, we are able to steer the model to generate in a style of our choosing.

Brian #4: The Curious Case of Python's Context Manager

  • Redowan Delowar, @rednafi
  • A quick tour of context managers that goes deeper than most introducitons.
  • Writing custom context managers with __init__, __enter__, __exit__.
  • Using the decorator contextlib.contextmanager
  • Then it gets even more fun
    • Context managers as decorators
    • Nesting contexts within one with statement.
    • Combining context managers into new ones
  • Examples
    • Context managers for SQLAlchemy sessions
    • Context managers for exception handling
    • Persistent parameters across http requests

Michael #5: nbstripout

  • via Clément Robert
  • In the latest episode, you praised NBDev for having a git hook that strips out notebook outputs.
  • strip output from Jupyter and IPython notebooks
  • Opens a notebook, strips its output, and writes the outputless version to the original file.
  • Useful mainly as a git filter or pre-commit hook for users who don’t want to track output in VCS.
  • This does mostly the same thing as the Clear All Output command in the notebook UI.
  • Has a nice youtube tutorial right in the pypi listing
  • Just do nbstripout --``install in a git repo!

Brian #6: Write ups for The 2020 Python Language Summit

Also, another way to get involved is to become a member of the PSF board of directors

Extras:

Michael:

  • Updated search engine for better result ranking
  • Windel Bouwman wrote a nice little script for speedscope https://github.com/windelbouwman/pyspeedscope (follow up from Austin profiler)

Jokes:

  • “Due to social distancing, I wonder how many projects are migrating to UDP and away from TLS to avoid all the handshakes?” - From Sviatoslav Sydorenko
  • “A chef and a vagrant walk into a bar. Within a few seconds, it was identical to the last bar they went to.” - From Benjamin Jones, crediting @lufcraft
  • Understanding both of these jokes is left as an exercise for the reader.

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