Python is widely used in machine learning, data science, and general programming.
Libraries such as NumPy, SciPy, and Pandas provide established tools for scientific computing and data manipulation.
Calling Python from Nelson makes Python libraries available from Nelson code.
Why call Python from Nelson?
- Library access: Python has libraries for tasks ranging from statistical analysis to machine learning.
Calling Python from Nelson keeps Nelson functions available while using Python libraries.
- Python integration: Nelson can call external functions and exchange data with Python code.
This allows Nelson users to call Python libraries from Nelson while preserving their usual numerical computing environment.
- Specialized libraries: Nelson includes many built-in functions, while some domain-specific tasks are better served by Python libraries.
For example, deep learning tasks can use Python libraries such as TensorFlow or PyTorch from Nelson workflows.
- Rapid prototyping and development: Python is often used for prototyping because of its concise syntax and mature library ecosystem.
Calling Python from Nelson lets users combine Python prototyping libraries with Nelson's numerical computing environment.
- Community practices: Integrating Python into Nelson workflows makes it possible to use libraries and practices from both ecosystems.
Calling Python from Nelson combines Python libraries with Nelson's numerical computing environment.
Python integration gives Nelson workflows access to specialized libraries and prototyping tools for scientific computing and data analysis.