> Markdown version of [/videos/618-how-to-implement-convenient-python-bindings-to-c](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# How to implement convenient Python bindings to C++

Integrating strict C++ into Python doesn't have to be sluggish. Master pybind11 to achieve zero-copy NumPy integrations and seamless type hinting. Build high-performance, idiomatic backtesting pipelines.

- **Speakers:** Konstantin Bespalov
- **Event:** World Congress 2023
- **Published:** August 11, 2023
- **Duration:** 22:13
- **URL:** https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c

## Summary

Integrating a strict, high-performance C++ library into a dynamic Python environment presents significant architectural challenges, especially when serving quantitative analysts performing complex backtesting and research. An initial attempt to expose a native C++ derivative pricing engine using `pythonnet` to interact with existing .NET assemblies proved heavily inefficient. The wrapper suffered from slow serialization overhead, forced the exposure of cumbersome .NET types in Python scripts, and completely lacked the IDE type hinting necessary for a streamlined developer experience.

Addressing these processing and usability bottlenecks required migrating to pybind11, a header-only framework that seamlessly binds C++ directly to Python while minimizing boilerplate. This approach bypassed the native C API, allowing engineers to utilize buffer protocols that securely convert custom collections into NumPy arrays without additional memory copying. Furthermore, bridging via pybind11 natively supported C++ function and method overloading—a foundational requirement inherently missing from Python's architectural design.

However, exposing C++ functionality directly is not enough; the resulting library must feel idiomatic to end-users. Instead of confusing business analysts by forcing them to wrap simple values in opaque C++ data properties, developers implemented `std::variant` types to automatically map standard Python `datetime` objects and strings into their native C++ counterparts. To emulate native Python behavior further, the team implemented internal collection magic methods, ensuring custom C++ data collections natively supported syntax sugar for standard `for` loops and `in` keyword containment checks.

Finalizing the integration meant resolving strict type-checking and multiprocessing requirements. The team adopted PEP 484 by generating `.pyi` stub files, which restored robust type hinting and code completion functionality in IDEs like PyCharm without bearing any runtime impact. Because rigorous backtesting necessitates concurrent processing, the encapsulated C++ objects were made fundamentally picklable for cross-process memory boundaries; this was achieved by attaching existing native serialization engines to Python's `copyreg` module. Ultimately, while meticulous hand-coding works for modest packages, scaling these deep integrations across massive enterprise repositories is best managed through dedicated automated code generation models.

**Keywords:** c++ python bindings, pybind11 implementation, pythonnet performance limitations, pyi stub files, pep 484 type hinting, ide code completion, numpy buffer protocols, std::variant type mapping, c++ function overloading, custom pybind11 types, python magic methods, syntax sugar implementation, multiprocessing object pickling, copyreg module serialization, automated binding generation

## Chapters

1. **Overview of the derivative pricing library architecture** (00:44) — The context and architecture of a C++ analytical library for pricing foreign exchange derivatives.
1. **Initial bindings using pythonnet and their limitations** (04:08) — Why wrapping a dotnet assembly with pythonnet resulted in poor performance and missing type hints.
1. **Evaluating pybind11 against direct Python C API** (06:40) — How pybind11 reduces boilerplate compared to the native Python C API while supporting function overloading and buffers.
1. **Defining classes and building packages with pybind11** (09:11) — A walkthrough of exposing C++ classes to Python and compiling the extension module.
1. **Adding IDE type hints using stub files** (10:26) — Implementing PEP 484 stub files to enable autocomplete and static type checking in editors.
1. **Simplifying interfaces with C++ variant types** (14:55) — Using variant types and mapping functions to accept simple Python datatypes instead of custom wrapped objects.
1. **Implementing magic methods for Python collections** (16:30) — Enabling native collection behaviors like iteration and membership testing by adding magic methods.
1. **Supporting multiprocessing with custom copyreg pickling** (17:24) — Registering serialization functions via the copyreg module to make C++ objects picklable for multiprocessing.
1. **Summary of Python bindings and audience QA** (19:02) — A review of the pybind11 implementation steps and answers to audience questions on Python extension methodologies.

## Related Moments

- [The expanded CUDA ecosystem and native Python support](https://www.wearedevelopers.com/videos/100221-cuda-python-gpu-programming-for-the-modern-developer) (from "CUDA Python: GPU programming for the modern developer")
- [Bridging Fortran and Python in modern computing](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) (from "Coffee with Developers - Stephen Jones - NVIDIA")
- [Bridging Python prototyping environments and embedded systems languages](https://www.wearedevelopers.com/videos/367-intelligent-data-selection-for-continual-learning-of-ai-functions) (from "Intelligent Data Selection for Continual Learning of AI Functions")
- [Evaluating absolute performance thresholds between Python and procedural syntaxes](https://www.wearedevelopers.com/videos/859-accelerating-python-on-gpus) (from "Accelerating Python on GPUs")
- [Simplifying parallel programming with the CUDA ecosystem](https://www.wearedevelopers.com/videos/859-accelerating-python-on-gpus) (from "Accelerating Python on GPUs")
- [Bridging dynamic typing paradigms for Python cross-language libraries](https://www.wearedevelopers.com/videos/1026-how-aws-generates-polyglot-libraries-based-on-a-single-typescript-codebase) (from "How AWS Generates Polyglot Libraries Based on a Single TypeScript Codebase")

## Related Articles

- [What’s the latest in NVIDIA CUDA Python](https://www.wearedevelopers.com/magazine/568-what-s-the-latest-in-nvidia-cuda-python)
- [Liuba Gonta and Yuliya Khadasevic - GitHub Copilot Beyond the Basics - 10 Ways to Elevate Your Coding](https://www.wearedevelopers.com/magazine/490-liuba-gonta-and-yuliya-khadasevic-github-copilot-beyond-the-basics-10-ways-to-elevate-your-coding)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [The 13 Best Python Libraries for Developers in 2025](https://www.wearedevelopers.com/magazine/371-the-13-best-python-libraries-for-developers-in-2025)

## Related Jobs

- [Software Engineer](https://www.wearedevelopers.com/jobs/ext/1304791-software-engineer) at **Bitpanda**
- [Backend Engineer, Python (Web3)](https://www.wearedevelopers.com/jobs/ext/1934515-backend-engineer-python-web3) at **Bitpanda**
- [Senior Software Engineer,Billing](https://www.wearedevelopers.com/jobs/ext/1991843-senior-software-engineer-billing) at **GitHub**
- [Principal Software Engineer, Database Infrastructure](https://www.wearedevelopers.com/jobs/ext/1465908-principal-software-engineer-database-infrastructure) at **GitHub**
- [Principal Software Engineer, Enterprise AI Platform](https://www.wearedevelopers.com/jobs/ext/1467292-principal-software-engineer-enterprise-ai-platform) at **GitHub**
- [Staff Software Engineer, Copilot Experiences](https://www.wearedevelopers.com/jobs/ext/164361-staff-software-engineer-copilot-experiences) at **GitHub**