1,146 posts tagged “python”
The Python programming language.
2018
Trio Tutorial. Trio is a really nice async library for Python—a simpler alternative to asyncio, with some very clean API design. Best of all, the tutorial is fantastic—it provides a very clear explanation of async/await without diving into the intricacies of coroutines.
r1chardj0n3s/parse: Parse strings using a specification based on the Python format() syntax. (via) Really neat API design: parse() behaves almost exactly in the opposite way to Python’s built-in format(), so you can use format strings as an alternative to regular expressions for extracting specific data from a string.
kennethreitz/requests-html: HTML Parsing for Humans™ (via) Neat and tiny wrapper around requests, lxml and html2text that provides a Kenneth Reitz grade API design for intuitively fetching and scraping web pages. The inclusion of html2text means you can use a CSS selector to select a specific HTML element and then convert that to the equivalent markdown in a one-liner.
s3monkey: A Python library that allows you to interact with Amazon S3 Buckets as if they are your local filesystem. (via) A particularly devious hack by Kenneth Reitz—provides a context manager within which various Python filesystem APIs such as open() and os.listdir() are monkeypatched to operate against an S3 bucket instead. Kenneth built it to make it easier to work with files from apps running on Heroku. Under the hood it uses pyfakefs, a filesystem mocking library originally released by Google.
A Promenade of PyTorch. Useful overview of the PyTorch machine learning library from Facebook AI Research described as “a Python library enabling GPU-accelerated tensor computation”. Similar to TensorFlow, but where TensorFlow requires you to explicitly construct an execution graph PyTorch instead lets you write regular Python code (if statements, for loops etc) which PyTorch then uses to construct the execution graph for you.
Moving a large and old codebase to Python3 (via) Really interesting case study full of good ideas. The codebase in this case was 240,000 lines of Python and Django written over the course of 15 years. The team used Python-Modernize to aid their transition to a six-compatible codebase first.
Python & Async Simplified. Andrew Godwin: “Python’s async framework is actually relatively simple when you treat it at face value, but a lot of tutorials and documentation discuss it in minute implementation detail, so I wanted to make a higher-level overview that deliberately ignores some of the small facts and focuses on the practicalities of writing projects that mix both kinds of code.” This is really useful: clearly explains the two separate worlds of Python (sync and async functions) and describes Andrew’s clever sync_to_async and async_to_sync decorators as well.
Channels 2.0. Andrew just shipped Channels 2.0—a major rewrite and redesign of the Channels project he started back in 2014. Channels brings async to Django, providing a logical, standardized way of supporting things like WebSockets and asynchronous execution on top of a Django application. Previously it required you to run a separate Twisted server and redis/RabbitMQ queue, but thanks to Python 3 async everything can now be deployed as a single process. And the new ASGI spec means its turtles all the way down! Everything from URL routing to view functions to middleware can be composed together using the same ASGI interface.
Using setup.py in Your (Django) Project. Includes this neat trick: if you list manage.py in the setup(scripts=) argument you can call it from e.g. cron using the full path to manage.py within your virtual environment and it will execute in the correct context without needing to explicitly activate the environment first.
Datasette Demo (video) from the SF Python Meetup
I gave a short talk about Datasette last month at the SF Python Meetup Holiday Party. They’ve just posted the video, so here it is:
[... 63 words]Generating polygon representing a rough 100km circle around latitude/longitude point using Python. A question I posted to the GIS Stack Exchange—I found my own answer using a Python library called geog, then someone else posted a better solution using pyproj.
Notes on Kafka in Python. Useful review by Matthew Rocklin of the three main open source Python Kafka client libraries as of October 2017.
2017
Let your code type-hint itself: introducing open source MonkeyType. Instagram have open sourced their tool for automatically adding type annotations to your Python 3 code via runtime tracing. By default it logs the types it sees to a SQLite database, which means you can browse them with Datasette!
Python 3 Readiness (via) 345 of the 360 most popular Python packages are now compatible with Python 3. I’d love to see a version of this graph over time.
Object models (via) Extremely comprehensive and readable discussion of the object models of Python, JavaScript, Lua and Perl 5. I learned something new about every one of those languages.
pillow-simd (via) A “friendly fork” of the Python Pillow image library that takes advantage of SIMD operations on certain CPUs to obtain massive speed-ups—they claim 16 to 40 times faster than ImageMagick.
Exploring Line Lengths in Python Packages. Interesting exploration of the impact if the 79 character length limit rule of thumb on various Python packages—and a thoroughly useful guide to histogram plotting in Jupyter, pandas and matplotlib.
dhash (via) Python library to calculate the perceptual difference hash for an image. Delightfully simple algorithm that’s fully explained in the README—it works by scaling the image to 8x8 grayscale and then creating a bitmap representing of each pixel is lighter or darker than the previous one.
Eager Execution: An imperative, define-by-run interface to TensorFlow. Lets you evaluate TensorFlow expressions interactively in Python without needing to constantly run tf.Session().run(variable).
TensorFlow 101. Concise, readable introduction to TensorFlow, with Python examples you can execute (and visualize) in Jupyter.
spaCy. “Industrial-strength Natural Language Processing in Python”. Exciting alternative to nltk—spaCy is mostly written in Cython, makes bold performance claims and ships with a range of pre-built statistical models covering multiple different languages. The API design is clean and intuitive and spaCy even includes an SVG visualizer that works with Jupyter.
Pull request #4120 · python/cpython. I just had my first ever change merged into Python! It was a one sentence documentation improvement (on how to cancel SQLite operations) but it was fascinating seeing how Python’s GitHub flow is set up—clever use of labels, plus a bot that automatically checks that you have signed a copy of their CLA.
walrus. Fascinating collection of Python utilities for working with Redis, by Charles Leifer. There are a ton of interesting ideas in here. It starts with Python object wrappers for Redis so you can interact with lists, sets, sorted sets and Redis hashes using Python-like objects. Then it gets really interesting: walrus ships with implementations of autocomplete, rate limiting, a graph engine (using a sorted set hexastore) and an ORM-style models mechanism which manages secondary indexes and even implements basic full-text search.
Try hosting on PyPy by simonw. I had a go at hosting my blog on PyPy. Thanks to the combination of Travis CI, Sentry and Heroku it was pretty easy to give it a go—I had to swap psycopg2 for psycopg2cffi and switch to the currently undocumented pypy3-5.8.0 Heroku runtime (pypy3-5.5.0 is only compatible with Python 3.3, which Django 2.0 does not support). I ran it in production for a few minutes and didn’t get any Sentry errors but did end up using more Heroku dyno memory than I’m comfortable with—see the graph I posted in a comment. I’m going to stick with CPython 3.6 for the moment. Amusingly I did almost all of the work on this on my phone! Travis CI means it’s easy to create and test a branch through GitHub’s web UI, and deploying a tested branch to Heroku is then just a button click.
Super Fast String Matching in Python (via) Interesting technique for calculating string similarity at scale in Python, with much better performance than Levenshtein distances. The trick here uses TF/IDF against N-Grams, plus a CSR (Compressed Sparse Row) scipy matrix to run the calculations. Includes clear explanations of each of these concepts.
Connecting to Google Sheets with Python. Useful guide to interacting with Google Sheets via the gspread python library, including how to work with Google’s unintuitive “service account keys”.
How Adversarial Attacks Work. Adversarial attacks against machine learning classifiers involve constructing an input that deliberately produces the wrong classification. This article shows how these can be constructed, and includes examples generated using PyTorch which produce a sports car that gets identified as a toaster and a photo of Sylvester Stallone that gets classified as Keanu Reeves.
A Minimalist Guide to SQLite. Pretty comprehensive actually—covers the sqlite3 command line app, importing CSVs, integrating with Python, Pandas and Jupyter notebooks, visualization and more.
Exploring United States Policing Data Using Python. Outstanding introduction to data analysis with Jupyter and Pandas.
Fast GeoSpatial Analysis in Python. Some clever advanced performance tricks with Cython and Dask, but it also introduced me to GeoPandas.