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OO: Python to Rust

This course is part of Rust for Data Engineering Specialization

Instructor: Noah Gift

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply a receipt-driven, three-mode workflow (Discover, Refactor, Translate) to move object-oriented Python code into idiomatic Rust.
Translate Python class hierarchies into Rust structs, traits, enums, and the type-state pattern using composition over inheritance.
Score Python-to-Rust translation pull requests using a breakeven analysis and complexity-claim review rubric.

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This course is part of the Rust for Data Engineering Specialization

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There are 5 modules in this course

OO: Python to Rust is a hands-on course on object-oriented design for engineers moving from Python (a class-based, dynamic OO language) to Rust (a struct-and-trait-based language with no inheritance and strict ownership). You will learn how Rust models the four classical OO pillars - encapsulation, abstraction, polymorphism, and code reuse - without classes or inheritance, using structs, methods (impl blocks), traits, trait objects, generics, enums, and the type state pattern. The course translates common Python OO patterns (dataclasses, dunder methods, ABCs, mixins, descriptors, protocols, the singleton, factory, observer, strategy, and decorator patterns) into idiomatic Rust, and explains why some of them simply don't apply once you have algebraic data types and ownership. You will refactor a non-trivial Python OO codebase into Rust, learn when composition beats inheritance, when an enum beats a class hierarchy, and how to design APIs that leverage Rust's compile-time guarantees. By the end of the course, you will be able to read OO Python code and produce a correct, idiomatic Rust translation, and justify your design decisions on a code review. Part of the Rust for Data Engineering specialization.

Module details

Every Big-O claim in this course ships with three independent receipts: an EMPIRICAL bench (criterion measures the curve), a STRUCTURAL guard (Rust types and proptest enforce it), and a FORMAL theorem (Lean 4 machine-checked, displayed as a [verified] stamp). Complexity is a CLAIM about how runtime or memory scales with input size. In this module you learn the c24 workflow: write the claim as a contract, bind it to your Rust pub item, then attach the three receipts. A CLAIM of O(N) survives only if the slope on a log-log plot stays linear from N=10 to N=1,000,000.

What's included

3 videos7 readings

3 videosTotal 4 minutes

What "Complexity" Means + Three Modes of Proof1 minute
Reading a Complexity Claim - Falsifiability1 minute
Depyler - the Transpiler Shortcut1 minute

7 readingsTotal 70 minutes

About This Course10 minutes
Key Terms: What "Complexity" Means + Three Modes of Proof10 minutes
Reflection: What "Complexity" Means + Three Modes of Proof10 minutes
Key Terms: Reading a Complexity Claim - Falsifiability10 minutes
Reflection: Reading a Complexity Claim - Falsifiability10 minutes
Key Terms: Depyler - the Transpiler Shortcut10 minutes
Reflection: Depyler - the Transpiler Shortcut10 minutes

In this module you will master Empirical Wins — the speedups you can measure with cargo bench. You will translate three Python patterns to Rust idioms that drop runtime and memory: list comprehensions become fused iterator chains (filter+map in one pass, O(1) memory vs O(N)); x in dict becomes HashMap::contains_key with hasher choice as a c24 receipt (FxHash, AHash, or default SipHash, each measured); and sorted() becomes sort_unstable for cases where stability is not required. Every claim ships with criterion HTML reports as the EMPIRICAL receipt, a STRUCTURAL guard from Rust's type system, and a [verified] stamp from a FORMAL theorem.

What's included

3 videos6 readings

3 videosTotal 4 minutes

From Python List Comprehensions to Rust Iterator Chains1 minute
From Python Dict Lookup to Rust HashMap1 minute
From Python sorted() to Rust sort_unstable1 minute

6 readingsTotal 60 minutes

Key Terms: List Comprehension → Iterator10 minutes
Reflection: List Comprehension → Iterator10 minutes
Key Terms: x in dict → HashMap Lookup10 minutes
Reflection: x in dict → HashMap Lookup10 minutes
Key Terms: sorted() → sort_unstable10 minutes
Reflection: sorted() → sort_unstable10 minutes

Python Optional[T] becomes Rust Option<T> with proptest sweeping every combinator chain; Python try/except becomes Result<T, E> with @semantic: total promoting every fallible function — the structural receipt that makes silent failure impossible.

What's included

3 videos6 readings

3 videosTotal 3 minutes

From Python Optional to Rust Option1 minute
From Python try/except to Rust Result1 minute
From Python Mutable Default to Rust Ownership1 minute

6 readingsTotal 60 minutes

Key Terms: Optional[T] → Option<T>10 minutes
Reflection: Optional[T] → Option<T>10 minutes
Key Terms: try/except → Result<T, E>10 minutes
Reflection: try/except → Result<T, E>10 minutes
Key Terms: Mutable Default Arg → Ownership10 minutes
Reflection: Mutable Default Arg → Ownership10 minutes

Systems-level translation makes the runtime trade-off visible. Python generators allocate per-call frame state and pay GIL overhead; Rust Iterator is monomorphized into the caller's stack — the structural receipt is the criterion bench showing a ~50x tight-loop gap. subprocess.run with sh -c re-introduces CWE-78 shell injection; Command::new().arg() invokes execve directly and removes the attack surface entirely. threading on CPython is serialized by the GIL; rayon::par_iter() distributes work across all cores via a Send + Sync work-stealing scheduler whose data-race bugs are compile errors. Each translation in this module is a runtime claim with a measurable receipt.

What's included

3 videos6 readings

3 videosTotal 3 minutes

From Python yield Generator to Rust Iterator1 minute
From Python subprocess to Rust Command1 minute
From Python threading to Rust rayon Parallel Iterator1 minute

6 readingsTotal 60 minutes

Key Terms: Python generator → Rust Iterator10 minutes
Reflection: Python generator → Rust Iterator10 minutes
Key Terms: subprocess → Command10 minutes
Reflection: Exhaustive Match as a Completeness Receipt10 minutes
Key Terms: threading → rayon Parallel Iterator10 minutes
Reflection: When to Pick ADT vs Trait10 minutes

The capstone closes the loop with a five-step playbook — transpile, inspect, refine, bench, prove — that you can apply to any hot Python loop and ship a receipt-backed Rust translation. Every Pull Request lands four receipts: criterion HTML report, proptest run, contract pv check, and a Lean status: proved stamp. The second lesson is the inverse: when the receipt-driven math says keep Python. Breakeven analysis, BREAKEVEN.md, and a final graded quiz that synthesizes Modules 1–5.