Immutability and Python
Introducing Pyrsistent
Disclaimer
Some of the stuff mentioned here is specific for CPython.
For other interpreters other rules may apply.
What is immutability?
Something is immutable if it cannot be changed once created.
Lets keep it simple:
Why immutability in programming?
- Cognitive offload for the developer
- Safe and simple invariance checking
- Safe and fast sharing, within and between threads
- Safe and fast reuse
- Hashable
- Runtime efficiency
Why not?
- Runtime efficiency
- Habits
- Language support
Immutability classes (WAT?)
- Immutable by convention
- Apparent immutability
- True immutability
Immutability in Python
No constant references
Base types
>>> x = 17
>>> y = 17
>>> x is y
True
>>> y = 1700
>>> x = 1700
>>> x is y
FalseNumbers and strings are immutable
Collections
tuple, frozenset and namedtuple, that's about it...
>>> tuple() is tuple()
True
>>> list() is list()
FalseGets messy and inefficient when you want to evolve the content
>>> t1 = (1, 2, 3, 4, 5)
>>> t2 = t1[:2] + tuple(17) + t1[3:]
# or
>>> l1 = list(t1)
>>> l1[2] = 17
>>> t2 = tuple(l1)Enter Pyrsistent
Immutable collections that are easy to evolve
a.k.a. persistent data structures
a.k.a. functional data structures
Pyrsistent eco system
a = apparent immutability
c = immutability by convention
Examples
>>> students = freeze([{'name': 'Ann', 'grade': 4.1}, {'name': 'Bob', 'grade': 2.1}])
>>> students_v2 = students.set_in((1, 'grade'), 1.9)
>>> students
pvector([pmap({'grade': 4.1, 'name': 'Ann'}), pmap({'grade': 2.1, 'name': 'Bob'})])
>>> students_v2
pvector([pmap({'grade': 4.1, 'name': 'Ann'}), pmap({'grade': 1.9, 'name': 'Bob'})])
>>> thaw(students_v2)
[{'grade': 4.1, 'name': 'Ann'}, {'grade': 1.9, 'name': 'Bob'}]
>>> Point = pclass('x, y', name='Point')
>>> p = Point(1, 2)
>>> p2 = p.set(x=3)
>>> p
Point(x=1, y=2)
>>> p2
Point(x=3, y=2)>>> v1 = pvector([1, 2, 3, 4])
>>> v1 == v(1, 2, 3, 4)
True
>>> v1[1]
2
>>> v1[1:3]
pvector([2, 3])
>>> v3 = v1.set(1, 5)
>>> v3
pvector([1, 5, 3, 4])
>>> v1
pvector([1, 2, 3, 4])
>>> pvector(x + 1 for x in v1)
pvector([2, 3, 4, 5])
>>> m1 = pmap({'a':1, 'b':2})
>>> m1 == m(a=1, b=2)
True
>>> m1['a']
1
>>> m1.b
2
>>> m1.items()
[('a', 1), ('b', 2)]
>>> m1.set('a', 3)
pmap({'a': 3, 'b': 2})
>>> m1
pmap({'a': 1, 'b': 2})
Structural reuse
>>> v1 = v(0, 1, 2, 3, 4, 5, 6, 7, 8)
>>> v1
pvector([0, 1, 2, 3, 4, 5, 6, 7, 8])
>>> v2 = v1.set(5, 'beef')
>>> v2
pvector([0, 1, 2, 3, 4, 'beef', 6, 7, 8])
>>> v1
pvector([0, 1, 2, 3, 4, 5, 6, 7, 8])
Get it:
Contribute:
Docs:
pip install pyrsistent
https://github.com/tobgu/pyrsistent
Thank you!
Immutability and Python
By Tobias Gustafsson
Immutability and Python
A brief introduction to Pythons take on (or rather lack of) immutability followed by a compressed description of Pyrsistent. Pyrsistent is a library that tries to provide some of the pieces missing in the Python standard library for convinient work with immutable data structures.
