sysconfig Project Structure Setup Script Distribution Archives Distribution sysconfig The process of bundling Python code into a format that eases distribution and sharing. First up, I find it helps to get a concrete understanding of how the specific python distro I’m working with is configured, of paricular interest are the various system paths that will be visited for package dependencies. The built-in sysconfig module neatly manages and surfaces this information.

A modified version of excellent original checklist by Paul Wolf General Code is blackened with black ruff has been run with no errors mypy has been run with no errors Function complexity problems have been resolved using the default complexity index of flake8. Important core code can be loaded in iPython, ipdb easily. There is no dead code Comprehensions or generator expressions are used in place of for loops where appropriate Comprehensions and generator expressions produce state but they do not have side effects within the expression.

Background Using asyncio will not make your code multi-threaded. That is, it will not cause multiple Python instructions to be executed at the same time, and it will not in any way allow you to side step the so-called “global interpreter lock” (GIL). Some processes are CPU-bound: they consist of a series of instructions which need to be executed one after another until the result has been computed. Most of their time is spent making heavy use of the processor.

Start with the docs and the Type hints cheat sheet Topics for consideration: syntax shorthands e.g. | for Union or Optional Self If you are using the typing library then there is an abstract type class provided for asynchronous context managers AsyncContextManager[T], where T is the type of the object which will be bound by the as clause of the async with statement. mypy If you are using typing then there is an abstract class Awaitable which is generic, so that Awaitable[R] for some type R means anything which is awaitable, and when used in an await statement will return something of type R.

An important part of becoming “good” at a language is becoming familiar with its library eco-system. The official Python Standard Library reference manual rocks. Module Category Description argparse functions for parsing command line arguments atexit allows you to register functions for your program to call when it exits bisect bisection algorithms for sorting lists (see Chapter 10) calendar a number of date-related functions codecs functions for encoding and decoding data collections a variety of useful data structures concurrent asynchronous computation copy functions for copying data csv functions for reading and writing CSV files datetime classes for handling dates and times fileinput file access iterate over lines from multiple files or input streams fnmatch functions for matching Unix-style filename patterns glob functions for matching Unix-style path patterns io functions for handling I/O streams and StringIO, which allows you to treat strings as files.

Special methods (dunders) Foundational Iterators Compariable classes Serializable classes Classes with computed attributes Classes that are callable Classes that act like sets Classes that act like dictionaries Classes that act like numbers Classes that can be used in a with block Esoteric behavior Design Patterns As I learn more about Pythons idioms reflect on its unique approach to object based programming. In combination with duck typing its approach to objects feels distrubingly flexible.

There are many ways to write unit tests in Python. unittest Here the focus is living off the land with built-in unittest. unittest is both a framework and test runner, meaning it can execute your tests and return the results. In order to write unittest tests, you must: Write your tests as methods within classes These TestCase classes must subclass unittest.TestCase Names of test functions must begin with test_ Import the code to be tested Use a series of built-in assertion methods Basic example import unittest class TestStringMethods(unittest.

Cool new features in 3.11. Performance 1.2x faster generally, thanks to an adaptive interpreter (PEP659) that optimises byte-code based on observed behaviour and usage. Take for example the LOAD_ATTR instruction, which under 3.11 can be replaced by LOAD_ATTR_ADAPTIVE. This will replace the call to the most optimised instruction based on what is being done, such as: LOAD_ATTR_INSTANCE_VALUE LOAD_ATTR_MODULE LOAD_ATTR_SLOT Disassembling some code: def feet_to_meters(feet): return 0.3048 * feet for feet in (1.

Forked from 101t/python-cheatsheet.md RTFM The Python Standard Library Built-in Functions Python Enhancement Propsoals (PEPs) The Zen of Python never far away in the REPL import this Contents Getting started: CPython, Easter eggs, Import paths, venv Collections: List, Dictionary, Set, Tuple, Range, Enumerate, Iterator, Generator Functions: Functions, Modules Types: Type, String, Regular_Exp, Format, Numbers, Combinatorics, Datetime Syntax: Args, Splat, Inline, Closure, Decorator, Class, Duck_Type, Enum, Exception System: Exit, Print, Input, Command_Line_Arguments, Open, Path, OS_Commands Data: JSON, Pickle, CSV, SQLite, Bytes, Struct, Array, Memory_View, Deque Advanced: Threading, Operator, Introspection, Metaprograming, Eval, Coroutines Libraries: Progress_Bar, Plot, Table, Curses, Logging, Scraping, Web, Profile, NumPy Packaging and Tools: Real app, Bytecode disassembler, Poetry, Gems, Resources CPython Most distros lag behind the latest releases of python.