#109: CPython byte code explorer

Sponsored by DigitalOcean: pythonbytes.fm/digitalocean

Brian #1: Python Descriptors Are Magical Creatures

• an excellent discussion of understanding @property and Python’s descriptor protocol.
• discussion includes getter, setter, and deleter methods you can override.

Michael #2: Data Science Survey 2018 JetBrains

• JetBrains polled over 1,600 people involved in Data Science and based in the US, Europe, Japan, and China, in order to gain insight into how this industry sector is evolving
• Key Takeaways
  • Most people assume that Python will remain the primary programming language in the field for the next 5 years.
  • Python is currently the most popular language among data scientists.
  • Data Science professionals tend to use Keras and Tableau, while amateur data scientists are more likely to prefer Microsoft Azure ML.
• Most common activities among pros and amateurs:
  • Data processing
  • Data visualization
• Main programming language for data analysis
  • Python 57%
  • R 15%
  • Julia 0%
• IDEs and Editors
  • Jupyter 43%
  • PyCharm 38%
  • RStudio 23%
  • …

Brian #3: cache.py

• cache.py is a one file python library that extends memoization across runs using a cache file.
• memoization is an incredibly useful technique that many self taught or on the job taught developers don’t know about, because it’s not obvious.

• example:

      import cache
  
      @cache.cache()
      def expensive_func(arg, kwarg=None):
        # Expensive stuff here
        return arg
  

• The @cache.cache() function can take multiple arguments.

  • @cache.cache(timeout=20) - Only caches the function for 20 seconds.
  • @cache.cache(fname="my_cache.pkl") - Saves cache to a custom filename (defaults to hidden file .cache.pkl)
  • @cache.cache(key=cache.ARGS[KWARGS,NONE]) - Check against args, kwargs or neither of them when doing a cache lookup.

Michael #4: Setting up the data science tools

• part of a larger video series
• set up. Tools to keras ultimately
• Tools
  • anaconda
  • tensorflow
  • Jupyter
  • Keras
• good for true beginners
• setup and activate a condo venv
• Start up a notebook and switch envs
• use conda, rather than pip

Brian #5: chartify

• “Python library that makes it easy for data scientists to create charts.”
• from the docs:
  • Consistent input data format: Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format.
  • Smart default styles: Create pretty charts with very little customization required.
  • Simple API: We've attempted to make to the API as intuitive and easy to learn as possible.
  • Flexibility: Chartify is built on top of Bokeh, so if you do need more control you can always fall back on Bokeh's API.

Michael #6: CPython byte code explorer

• JupyterLab extension to inspect Python Bytecode
• via Anton Helm
• by Jeremy Tuloup
• You’ll see exactly what it’s about if you watch the GIF movie at the github repo.
• Can’t think of a better way to understand Python bytecode quickly than to play a little with this
• Comparing versions of CPython: If you have several versions of Python installed on your machine (let's say in different conda environments), you can use the extension to check how the bytecode might differ.
• Nice visualization of different performance aspects of while vs. for at the end

Extras:

Brian:

• “How the Internet is made.”