namedtuple in a post-dataclasses world
namedtuple has been around since forever,1 and over time, its convenience saw it used far outside its originally intended purpose. With dataclasses now covering part of those use cases, what should one use named tuples for? In this article, we take a look at exactly that, with a few examples from real code. Contents What are named tuples used for? The problems with named tuples What are named tupl
Async Python is not faster
50th and 99th percentile response times are in milliseconds, throughput is in requests per second. The table is ordered by P99, which I think is perhaps the most important real world statistic. Note that: The best performers are sync frameworks but Flask has lower throughput than others The worst performers are all async frameworks Async frameworks have far worse latency variation Uvloop-based opt
Dataclasses and attrs: when and why
Python 3.7 introduced dataclasses (PEP557). Dataclasses can be a convenient way to generate classes whose primary goal is to contain values. The design of dataclasses is based on the pre-existing attr.s library. In fact Hynek Schlawack, the very same author of attrs, helped with the writing of PEP557. Basically dataclasses are a slimmed-down version of attrs. Whether this is an improvement or not
Python 3 Module of the Week — PyMOTW 3
Python 3 Module of the Week¶ PyMOTW-3 is a series of articles written by Doug Hellmann to demonstrate how to use the modules of the Python 3 standard library. It is based on the original PyMOTW series, which covered Python 2.7. See About Python Module of the Week for details including the version of Python and tools used.
Clean Architectures in Python
The clean architecture is the opposite of spaghetti code, where everything is interlaced and there are no single elements that can be easily detached from the rest and replaced without the whole system collapsing. The main point of the clean architecture is to make clear "what is where and why", and this should be your first concern while you design and implement a software system, whatever archit
GitHub - getredash/redash: Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
Redash is designed to enable anyone, regardless of the level of technical sophistication, to harness the power of data big and small. SQL users leverage Redash to explore, query, visualize, and share data from any data sources. Their work in turn enables anybody in their organization to use the data. Every day, millions of users at thousands of organizations around the world use Redash to develop
YOLO: Real-Time Object Detection
How It Works Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections. We use a totally different approach. We apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilitie
PEP 505 -- None-aware operators | Python.org
PEP 505 – None-aware operators Author: Mark E. Haase <mehaase at gmail.com>, Steve Dower <steve.dower at python.org> Status: Deferred Type: Standards Track Created: 18-Sep-2015 Python-Version: 3.8 Table of Contents Abstract Syntax and Semantics Specialness of None Grammar changes The coalesce rule The maybe-dot and maybe-subscript operators Reading expressions Examples Standard Library jsonify Gra
Pylint - code analysis for Python | www.pylint.org
Coding Standard checking line-code's length, checking if variable names are well-formed according to your coding standard checking if imported modules are used Python's PEP8 style guide Error detection checking if declared interfaces are truly implemented checking if modules are imported and much more (see the complete check list) Full list of codes (wiki) Fully customizable Modify your pylintrc t
Pyre | Pyre
FastBuilt from the ground up to support gradual typing and deliver responsive incremental checks. Performant on large codebases with millions of lines of Python. IntegratedDesigned to help improve code quality and development speed by flagging type errors interactively in your terminal or live in your favorite editor. Fully FeaturedFollows the typing standards introduced in PEPs 484, 526, 612, and
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Vyper by Example
Flask-SeaSurf — SeaSurf 1.1.1 documentation
Flit 1.2.1 — Flit 1.2.1 documentation
GitHub - OlafenwaMoses/ImageAI: A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
Welcome to MySQL-AutoXtrabackup’s documentation! — MySQL-AutoXtrabackup 1.5.4 documentation
Introduction — Uplink 0.5.5 documentation
GitHub - linkedin/shiv: shiv is a command line utility for building fully self contained Python zipapps as outlined in PEP 441, but with all their dependencies included.
GitHub - RussBaz/enforce: Python 3.5+ runtime type checking for integration testing and data validation
GitHub - matthew-sochor/assert_types
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