Pampy: Pattern Matching for Python

Pampy is pretty small, reasonably fast, and often makes your code more readable, and easier to reason about.

You can write many patterns

Patterns are evaluated in the order they appear.

You can write Fibonacci

The operator _ means "any other case I didn't think of".

from pampy import match, _

def fibonacci(n):
    return match(n,
        1, 1,
        2, 1,
        _, lambda x: fibonacci(x-1) + fibonacci(x-2)
    )

You can write a Lisp calculator in 5 lines

from pampy import match, REST, _

def lisp(exp):
    return match(exp,
        int,                lambda x: x,
        callable,           lambda x: x,
        (callable, REST),   lambda f, rest: f(*map(lisp, rest)),
        tuple,              lambda t: list(map(lisp, t)),
    )

plus = lambda a, b: a + b
minus = lambda a, b: a - b
from functools import reduce

lisp((plus, 1, 2))                 # => 3
lisp((plus, 1, (minus, 4, 2)))     # => 3
lisp((reduce, plus, (1, 2, 3))     # => 6

You can match so many things!

match(x,
    3,              "this matches the number 3",

    int,            "matches any integer",

    (str, int),     lambda a, b: "a tuple (a, b) you can use in a function",

    [1, 2, _],      "any list of 3 elements that begins with [1, 2]",

    {'x': _},       "any dict with a key 'x' and any value associated",

    _,              "anthing else"
)

You can match [HEAD, TAIL]

from pampy import match, HEAD, TAIL, _

x = [1, 2, 3]

match(x, [1, TAIL],     lambda t: t)            # => [2, 3]

match(x, [HEAD, TAIL],  lambda h, t: (h, t))    # => (1, [2, 3])

TAIL and REST actually mean the same thing.

You can nest lists and tuples

from pampy import match, _

x = [1, [2, 3], 4]

match(x, [1, [_, 3], _], lambda a, b: [1, [a, 3], b])       # => [1, [2, 3], 4]

You can nest dicts. And you can use _ as key!

pet = { 'type': 'dog', 'details': { 'age': 3 } }

match(pet, { 'details': { 'age': _ } }, lambda age: age)        # => 3

match(pet, { _ : { 'age': _ } },        lambda a, b: (a, b))    # => ('details', 3)

It feels like putting multiple _ inside dicts shouldn't work. Isn't ordering in dicts not guaranteed ? But it does because in Python 3.7, dict is an OrderedDict by default

All the things you can match

As Pattern you can use any Python type, any class, or any Python value.

The operator _ and types like int or str, extract variables that are passed to functions.

Types and Classes are matched via instanceof(value, pattern).

Iterable Patterns match recursively through all their elements. The same goes for dictionaries.

Pattern Example	What it means	Matched Example	Arguments Passed to function	NOT Matched Example
"hello"	only the string "hello" matches	"hello"	"hello"	any other value
int	Any integer	42	42	any other value
float	Any float number	2.35	2.35	any other value
str	Any string	"hello"	"hello"	any other value
tuple	Any tuple	(1, 2)	(1, 2)	any other value
list	Any list	[1, 2]	[1, 2]	any other value
MyClass	Any instance of MyClass	MyClass()	that instance	any other object instance
_	Any object (even None)
ANY	The same as _
(int, int)	A tuple made of any two integers	(1, 2)
[1, 2, _]	A list that starts with 1, 2 and ends with any value	[1, 2, 3]	3	[1, 2, 3, 4]
[1, 2, TAIL]	A list that start with 1, 2 and ends with any sequence	[1, 2, 3, 4]	[3, 4]	[1, 7, 7, 7]
{'type':'dog', age: _ }	Any dict with type: "dog" and with an age	{"type":"dog", "age": 3}	3	{"type":"cat", "age":2}
{'type':'dog', age: int }	Any dict with type: "dog" and with an int age	{"type":"dog", "age": 3}	3	{"type":"dog", "age":2.3}

Using strict=False

By default match() is strict. If no pattern matches, it raises a MatchError.

You can prevent it using strict=False. In this case match just returns False if nothing matches.

>>> match([1, 2], [1, 2, 3], "whatever")
MatchError: '_' not provided. This case is not handled:

>>> match([1, 2], [1, 2, 3], "whatever", strict=False)
False

Install

Currently it works only in Python > 3.6 Because dict matching can work only in the latest Pythons.

I'm currently working on a backport with some minor syntax changes for Python2.

To install it:

$ pip install pampy

or $ pip3 install pampy