Part 2 | How Python Lists REALLY Work: Internal Representation & Memory Layout
🧠 *Python list() Constructor & Range: How Lists Are Stored in Memory! | NeuralAICodeCraft*
Ever wondered how Python actually stores lists in memory? In Part 2, we go deep into the internal representation of lists - what happens when you call `list()` or use `[]` brackets.
📌 *What you'll learn:*
*THE list() & range() CONSTRUCTORS (Deep Dive)*
▸ `list` is a TYPE, not a function [citation:1]
▸ `range` is a TYPE, not a function [citation:1]
▸ What "callable" means in Python
*HOW LISTS ARE STORED IN MEMORY*
▸ Lists store POINTERS to objects, not the objects themselves
▸ The internal struct: `PyListObject`
▸ Memory layout: array of pointers
▸ Over-allocation for performance
*list() vs [] - Performance Differences*
▸ Bytecode comparison: `BUILD_LIST` vs `LOAD_GLOBAL` + `CALL_FUNCTION` [citation:5]
▸ When to use list() vs []
▸ Performance implications for large lists
*range() INTERNAL REPRESENTATION*
▸ range stores start, stop, step
▸ Lazy evaluation - values computed on demand
▸ Converting range to list - memory implications
📌 *Timestamps:*
0:00 - Recap of Part 1
2:00 - list() is a Type, Not a Function
4:30 - range() is a Type, Not a Function
7:00 - Internal Representation of Lists
11:00 - list() vs [] - Performance Differences
15:00 - range() Internal Representation
17:00 - Summary & Practice Problems
💻 *Code from this video:* [GitHub link]
🔔 *Subscribe for more Python tutorials:* @NeuralAICodeCraft
#Python #listConstructor #rangeFunction #PythonInternals #MemoryManagement #NeuralAICodeCraft
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