The Pattern Matching for Python you always dreamed of
Project description
Pampy: Pattern Matching for Python
Pampy is pretty small (150 lines), reasonably fast, and often makes your code more readable and hence easier to reason about. There is also a JavaScript version, called Pampy.js.
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, (range, 10))) # => 45
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",
_, "anything 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 maintains insertion key order by default
You can match class hierarchies
class Pet: pass
class Dog(Pet): pass
class Cat(Pet): pass
class Hamster(Pet): pass
def what_is(x):
return match(x,
Dog, 'dog',
Cat, 'cat',
Pet, 'any other pet',
_, 'this is not a pet at all',
)
what_is(Cat()) # => 'cat'
what_is(Dog()) # => 'dog'
what_is(Hamster()) # => 'any other pet'
what_is(Pet()) # => 'any other pet'
what_is(42) # => 'this is not a pet at all'
All the things you can match
As Pattern you can use any Python type, any class, or any Python value.
The operator _
and built-in 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" |
nothing | any other value |
None |
only None |
None |
nothing | 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. And any object that extends MyClass. | MyClass() |
that instance | any other object |
_ |
Any object (even None) | that value | ||
ANY |
The same as _ |
that value | ||
(int, int) |
A tuple made of any two integers | (1, 2) |
1 and 2 |
(True, False) |
[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} |
re.compile('(\w+)-(\w+)-cat$') |
Any string that matches that regular expression expr | "my-fuffy-cat" |
"my" and "puffy" |
"fuffy-dog" |
Pet(name=_, age=7) |
Any Pet dataclass with age == 7 |
Pet('rover', 7) |
['rover'] |
Pet('rover', 8) |
Using strict=False
By default match()
is strict. If no pattern matches, it raises a MatchError
.
You can prevent it using strict=False
. In this case match
just returns False
if nothing matches.
>>> match([1, 2], [1, 2, 3], "whatever")
MatchError: '_' not provided. This case is not handled: [1, 2]
>>> match([1, 2], [1, 2, 3], "whatever", strict=False)
False
Using Regular Expressions
Pampy supports Python's Regex. You can pass a compiled regex as pattern, and Pampy is going to run patter.search()
, and then pass to the action function the result of .groups()
.
def what_is(pet):
return match(pet,
re.compile('(\w+)-(\w+)-cat$'), lambda name, my: 'cat '+name,
re.compile('(\w+)-(\w+)-dog$'), lambda name, my: 'dog '+name,
_, "something else"
)
what_is('fuffy-my-dog') # => 'dog fuffy'
what_is('puffy-her-dog') # => 'dog puffy'
what_is('carla-your-cat') # => 'cat carla'
what_is('roger-my-hamster') # => 'something else'
Using Dataclasses
Pampy supports Python 3.7 dataclasses. You can pass the operator _
as arguments and it will match those fields.
@dataclass
class Pet:
name: str
age: int
pet = Pet('rover', 7)
match(pet, Pet('rover', _), lambda age: age) # => 7
match(pet, Pet(_, 7), lambda name: name) # => 'rover'
match(pet, Pet(_, _), lambda name, age: (name, age)) # => ('rover', 7)
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
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