Mridu Bhatnagar - Object Internals

Python

Explore the internal workings of Python objects, including mutable and immutable types, shallow and deep copying, and integer caching, to gain a deeper understanding of the language's underlying mechanics.

Key takeaways

In Python, objects can be either mutable (changeable) or immutable (unchangeable).
Mutable objects can be modified, while immutable objects cannot.
In Python, objects have a type, value, and location (memory address).
A shallow copy of an object is a new object that references the same embedded objects as the original.
A shallow copy changes the original object when modifications are made, because they share the same memory references.
Deep copy of an object is a new object that contains its own copies of the embedded objects.
Deep copy does not share the same memory references as the original, so changes do not affect the original object.
Mutable objects include lists, dictionaries, sets, and some custom objects.
Immutable objects include integers, floats, booleans, strings, and some built-in types.
Integer caching is used to optimize memory by reusing integers within the range of -5 to 256.
Strings are interned, which means they are stored only once in memory, and all references to the same string are the same instance.
The is operator compares the memory addresses of two objects, while the == operator compares the values.
The id() function returns the memory address of an object.
copy.copy() is used to create a shallow copy, and copy.deepcopy() is used to create a deep copy of an object.
sort() and sorted() are two different functions, with different uses and behaviors.

Mridu Bhatnagar - Object Internals

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