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