#9: Walking with async coroutines, diving deep into requests, and a universe of options (for AIs)

This is Python Bytes, Python headlines and news deliver directly to your earbuds: episode 9, recorded on Tuesday, January 17th. In this episode we discuss walking with async coroutines, diving deep into requests, and a universe of options (for AIs).

#1 (Brian): Python Asynchronous I/O Walkthrough

• In July, there was an open source book published called 500 Lines or Less.
• One of the chapters was called A Web Crawler With asyncio Coroutines, written by A. Jesse Jiryu Davis and Guido van Rossum. It explains async networking, showing how non-blocking sockets work and how Python 3’s coroutines improve asynchronous network programs.
• As mentioned recently on A. Jesse Jiryu Davis’s blog emptysqua.re, the chapter may be difficult for a novice or intermediate developer to follow.
• This month, Philip Guo, an Assistant Professor at UC San Diego, has released an 8 part video series, 90 minutes total, so on the order of 10 minutes each, that walks through the article, including some coding examples that he walks through.
• Since async is something that takes a while to get your head around, I appreciate the multi-sensory education experience. Listening to Philip talk about it, watching him walk through the code and talk about the article, and having the article as a reference, is super helpful.
• Talk Python #22: CPython Internals and Learning Python with pythontutor.com
• Talk Python #69: Write an Excellent Programming Blog

#2 (Michael): 4 likely future twists for Python by Serdar Yegulalp

1. Python 2.x may live on

   • Python 2.x might also get a continued lease on life if independent developers decide to keep the branch going on their own.
   • At least one such effort exists -- Naftali Harris's "Python 2.8" project, which backports improvements and bug fixes from Python 3 into the Python 2.x branch.
   • it makes sense to make the 3.x leap, but it's likely we'll see a lot of keep-the-2.x-flame-alive efforts

2. Requirements.txt may be replaced with something better

   • Pipfile has been proposed as a possible replacement by the folks at the Python Packaging Authority, which is "the working group that maintains many of the relevant projects in Python packaging."
   • Pipfile will be superior to requirements.txt file in a number of ways:
     1. TOML syntax for declaring all types of Python dependencies.
     2. One Pipfile (as opposed to multiple requirements.txt files).
     3. Existing requirements files tend to proliferate into multiple files - e.g. dev-requirements.txt, test-requirements.txt, etc. - but a Pipfile will allow seamlessly specifying groups of dependencies in one place.
     4. This will be surfaced as only two built-in groups (default & development). (see note below)
     5. Fully specified (and deterministic) environments in the form of Pipfile.freeze. A deployed application can then be completely redeployed with the same exact versions of all recursive dependencies, by referencing the Pipfile.freeze file.

   • Example pipfile:

     [[source]] url = 'https://pypi.org/' verify_ssl = true

     [requires] python_version = '2.7'

     [packages] requests = { extras = ['socks'] } Django = '>1.10' pinax = { git = 'git://github.com/pinax/pinax.git', ref = '1.4', editable = true }

     [dev-packages] nose = '*'

3. Python could get more enterprise editions

   • As the language has gained traction across the board, it's also appearing in versions aimed specifically at solving enterprise-grade problems.
   • Intel, for instance, elected to repackage the Anaconda science-and-math distribution of Python after outfitting it with extensions that give it a speed boost, albeit only on Intel processors.
   • Anaconda is itself produced by Continuum Analytics, no stranger to enterprise data-analysis needs.
4. Python's new software repository system could lead to enterprise-friendly Python package management
   • One possibility being floated in this vein is the concept of an enterprise-grade package index for Python, as discussed by Cristian Medina of Nimble Storage:
   • Businesses always have a need for an on-premises, secure, encrypted and highly available distribution mechanism of compiled binaries. Together with setuptools providing various install capabilities that can cover non-Python code just as well, it seems like we could put together a decent product. Something like a Docker Trusted Registry.
   • I’m working on something here too that come out of the discussion of Talk Python #84

#3 (Brian) From Python to Numpy

• Nicolas P. Rougier - labri.fr
• “Nicolas P. Rougier is a full-time research scientist at Inria which is the French national institute for research in computer science and control.”
• Creative commons book for people who are intermediate level Python developers and want to use numpy for science or engineering.

#4 (Michael): openai/universe

• Universe: a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications. https://universe.openai.com
• Universe allows anyone to train and evaluate AI agents on an extremely wide range of real-time, complex environments.
• Universe makes it possible for any existing program to become an OpenAI Gym environment, without needing special access to the program's internals, source code, or APIs.
  • It does this by packaging the program into a Docker container
  • presenting the AI with the same interface a human uses:
    • sending keyboard
    • mouse events
    • receiving screen pixels
  • Our initial release contains over 1,000 environments in which an AI agent can take actions and gather observations.

#5: (Brian): Python requests deep dive

• Anthony Shaw - on medium.com
• Converted a large project from httplib to requests
• Apache Libcloud
  • needed to provide a set of base classes that would handle HTTP and HTTPS REST/JSON, REST/XML and various other bizarre HTTP APIs.
  • Libcloud has over 80 client libraries for every major cloud service out there.
  • single Connection class that handles encoding and decoding of JSON, XML or Raw data.
• Anthony walks through the types of changes made, including authentication, session handling, testing, prepared requests, streams, …
• uses requests-mock
  • very cool API

#6 (Michael): What's the community favorite for developing OSX desktop applications with Python?

• bangeron: I've been using PyQt for all my projects with a GUI: - It has a native look and feel - It's cross-platform (I do everything on macOS) - It's a whole framework, including modules for plotting charts, networking, SQL, etc. - It has excellent documentation. - It has a GUI editor (Qt Creator) that is pretty good, once you figure it out.
• Omnius: Same but using pyside to avoid license fees.
• spinwizard69: Use TK for simple apps! It is included with Python and avoids dependency hell.
• EssaAlshammri: Take a look at Toga its idea is great.
• Ruditorres: Kivy hands down, it is pythonic, easy, and beautiful
• bangeron: Kivy is anything but beautiful. At least, not without spending some time creating your own theme.
• Personally, I want py_electron (modeled after electronjs).
• Notable mention, related: cx_Freeze released (Version 5.0.1 (January 2017))