https://github.com/deathbeds/poser

dysfunctional programming in python
https://github.com/deathbeds/poser

composition functional-programming python syntactic-sugar

Last synced: about 1 month ago
JSON representation

dysfunctional programming in python

• Host: GitHub
• URL: https://github.com/deathbeds/poser
• Owner: deathbeds
• License: bsd-3-clause
• Created: 2018-01-29T00:40:08.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-10-21T18:03:02.000Z (about 2 years ago)
• Last Synced: 2024-11-05T20:19:24.891Z (about 2 months ago)
• Topics: composition, functional-programming, python, syntactic-sugar
• Language: Python
• Homepage:
• Size: 17.8 MB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

Awesome Lists containing this project

README

        

import toolz

import poser

from poser import λ, Λ, stars

[![Github Actions](https://github.com/deathbeds/poser/workflows/Python % 20package/badge.svg)](https: // github.com/deathbeds/poser/actions)

[![PyPI version](https://badge.fury.io/py/poser.svg)](https: // badge.fury.io/py/poser)

[![Binder](https://mybinder.org/badge_logo.svg)](https: // mybinder.org/v2/gh/deathbeds/poser/master)

a functional swiss army knife for python. `poser` provides python objects you didn't know you needed.

# Be a `poser`

`poser` is a API for lazy, (dys)functional python programming. It allows complex functions to be composed using fluent or symbollic interfaces.

```bash

pip install poser

```

> _dysfunctional programming_ == = Functional programming with all the side effects.

New to[functional programming]? Functional programming uses declarative functions to compose complex operations on arguments.

If you are familiar with python then[`toolz`][toolz] is a great starting point, [`poser`][poser] is a compact[API] for [`toolz`] and the python

[standard library].

`poser`

# `poser` API

`λ` is a composition that always returns a function. For example, below we create a list of numbers from 1 to a some value

>> > f = λ.range(1).list()

f

    λ(< class 'list' > , functools.partial( < class 'range' > , 1))

    >> > assert callable(f)

    >> > f(5), f(10)

    ([1, 2, 3, 4], [1, 2, 3, 4, 5, 6, 7, 8, 9])

`poser` can use forward references to lazily import modules.

>> > g = λ.range(1).enumerate().dict()['pandas.Series']

g

    λ(ForwardRef('pandas.Series'), < class 'dict' > , < class 'enumerate' > , functools.partial(< class 'range' > , 1))

    >> > λ(9) + g + type + ...

    

`Λ` is for object function composition where the

function represents symbollic or chained methods

>> > (Λ*10+2)(1)

12

>> > assert (Λ*10+2)(1) == 1*10+2

>> > s = "A formatted string :{}: with a %s"

... (Λ.format('foo') % '% formatted').upper()(s)

'A FORMATTED STRING :FOO: WITH A % FORMATTED'

>> > assert (Λ.format('foo') % '% formatted').upper()(s)\

    ... == (s.format('foo') % '% formatted').upper()

The `poser` API expresses all of the symbols in the python data model.  Refer to the tests for examples.

# juxtaposition

>> > assert isinstance(λ[range, type, str](10), tuple)

>> > assert isinstance(λ[[range, type, str]](10), list)

>> > assert isinstance(λ[{range, type, str}](10), set)

Value and keyword functions can be supplied to juxtapose functions across dictionaries.

λ[{'a': range, type: str}](10)

{'a': range(0, 10), int: '10'}

# Example recipes.

get = λ['functools.lru_cache']()(

    λ['requests.get'][λ[Λ.json()] ^ BaseException | Λ.text()])

get

read = λ**Λ.startswith('http') & get | λ.Path().read_text()