pyimmutable

pyimmutable

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API documentation <https://spacedentist.github.io/pyimmutable/>_ is hosted on GitHub pages.

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Introduction

pyimmutable offers two immutable data types for Python3.6+: a mapping (similar to dict, but, obviously immutable) and a sequence (like list). It is implemented in C++, using the faboulous "immer <https://github.com/arximboldi/immer>__" library for efficiently implementing the immutable data structures.

When your Python code passes an ImmutableDict or an ImmutableList to some function, you do not have to worry that function may alter the contents of that object. A reference to such an immutable data structure is guaranteed to forever point to the same data. If you do want to change the contents of your data, you have to update the reference. So instead of data["foo"] = "bar" you will have to write data = data.set("foo", "bar"). As a result, your data variable will be updated, but any other variable that referenced data before the change will keep referencing the old data---the one before the update.

Immutable data structures like this allow for some interesting optimization features. For example, two ImmutableDict objects with identical content are not actually two objects but one and the same object, and the same is true for ImmutableList. If any operation returns an ImmutableDict or an ImmutableList that is identical to one that has been constructed earlier, the existing one is returned instead of a new one being constructed. This entails that e.g. two ImmutableDict objects, even if they represent big trees of data, can be compared using the Python is operation.

pyimmutable has a stricter requirement for equality than the usual Python convention. In a nutshell, for pyimmutable, the integer 3 and the float 3.0 are not equal. An ImmutableDict can store different values for the keys 3 and 3.0, whereas a normal Python dict cannot. You can use objects of any type as keys and values, but only identical objects are considered equal. There is an exception to this rule for a small set of built-in types (int, float, str, bytes, tuple---all of which are immutable themselves), which are considered equal when they contain the same data.

Example

from pyimmutable import ImmutableDict d1 = ImmutableDict()

d1 is an empty immutable dictionary.

d2 = d1.set("foo", "bar")

d2 contains one key ("foo") whose value is "bar". d1 is remains an empty immutable dictionary.

d3 = d2.set("fox", "baz")

d3 now contains two keys ("foo", "fox") with values "bar" and "baz", respectively. d1 and d2 remain unchanged.

d4 = ImmutableDict(foo="bar", fox="baz")

d4 is a newly constructed ImmutableDict, initialized with values foo and fox that happen to be the same as in d3. In such a case, pyimmutable does not actually construct a new ImmutableDict for us, but returns the d3 object from earlier.

d3 is d4 True

This is fine, because ImmutableDict objects are immutable, and since the objects referred to by d3 and d4 can never be mutated and therefore will always have the same contents, d3 and d4 might as well refer to the same object.