Lesson 28 of 55
10 mins readPython Parametric Types & Generic Data Structures
In Plain English
Parametric types in Julia allow writing generic algorithms and data structures that work across any type while retaining 100% concrete type specialization and zero performance penalty.
Deep Dive: How It Works
Parametric Structs: struct Point3D{T <: Real} x::T; y::T; z::T end limits T to subtypes of Real.
Type Specialization: Point3D{Float64} and Point3D{Int32} generate distinct native machine code layouts.
Parametric Methods: function get_val(b::Box{T})::T where T defines type constraints on generic functions.
Core Rules to Remember
Zero-Cost Generics: Julia generates specialized LLVM IR for every concrete type parameter without boxing.
Type Bounds with <:: Constrain type parameters using {T <: Number} to guarantee mathematical operations.
Live Interactive Example
Hit Run Code to see it liveParametric Point and Distance
Python 3.12
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Output Console
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How it works: The compiler specializes Vector2D for Int64 and Float64 individually.
Your Turn: Micro Challenge
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Micro Exercise
Create Generic Box Struct
- Define `struct Box{T} value::T end`.
- Create `b = Box("Compiler")`.
- Print `"Box type: $(typeof(b)), Value: $(b.value)"`.
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Sandbox Output
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