numpy-best-practices

SKILL.md

NumPy Best Practices

Expert guidelines for NumPy development, focusing on array programming, numerical computing, and performance optimization.

Code Style and Structure

Write concise, technical Python code with accurate NumPy examples

Prefer vectorized operations over explicit loops for performance

Use descriptive variable names reflecting data content (e.g., weights, gradients, input_array)

Follow PEP 8 style guidelines for Python code

Use functional programming patterns when appropriate

Array Creation and Manipulation

Use appropriate array creation functions: np.array(), np.zeros(), np.ones(), np.empty(), np.arange(), np.linspace()

Prefer np.zeros() or np.empty() for pre-allocation when array size is known

Use np.concatenate(), np.vstack(), np.hstack() for combining arrays

Leverage broadcasting for operations on arrays with different shapes