#109 CPython byte code explorer

Python Bytes - Podcast tekijän mukaan Michael Kennedy and Brian Okken - Maanantaisin

kokeile Podimo ilmaiseksi 30 päivän ajan

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

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.

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.

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

Brian: