---name: numpy-best-practicesdescription: Best practices for NumPy array programming, numerical computing, and performance optimization in Python---

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

Indexing and Slicing

• Use advanced indexing with boolean arrays for conditional selection
• Prefer views over copies when possible to save memory
• Use np.where() for conditional element selection
• Understand the difference between fancy indexing (creates copy) and basic slicing (creates view)

Data Types

• Specify appropriate data types explicitly using dtype parameter
• Use np.float32 for memory-efficient computations when full precision is not needed
• Be aware of integer overflow with fixed-size integer types
• Use np.asarray() for type conversion without unnecessary copies

Performance Optimization

Vectorization

• Always prefer vectorized operations over Python loops
• Use NumPy universal functions (ufuncs) for element-wise operations
• Leverage np.einsum() for complex tensor operations
• Use np.dot() or @ operator for matrix multiplication

Memory Management

• Use np.ndarray.flags to check memory layout (C-contiguous vs Fortran-contiguous)
• Prefer in-place operations with out parameter when possible
• Use memory-mapped arrays (np.memmap) for large datasets
• Be mindful of array copies vs views

Computation Efficiency

• Use np.sum(), np.mean(), np.std() with axis parameter for aggregations
• Leverage np.cumsum(), np.cumprod() for cumulative operations
• Use np.searchsorted() for efficient sorted array operations

Error Handling and Validation

• Validate input shapes and data types before computations
• Use assertions for dimension checking with informative messages
• Handle NaN and Inf values appropriately with np.isnan(), np.isinf()
• Use np.errstate() context manager for controlling floating-point error handling

Random Number Generation

• Use np.random.default_rng() for modern random number generation
• Set seeds for reproducibility: rng = np.random.default_rng(seed=42)
• Prefer the new Generator API over legacy np.random functions
• Use appropriate distributions: rng.normal(), rng.uniform(), rng.choice()

Linear Algebra

• Use np.linalg for linear algebra operations
• Leverage np.linalg.solve() instead of computing inverse for linear systems
• Use np.linalg.eig(), np.linalg.svd() for decompositions
• Check matrix condition with np.linalg.cond() before inversion

Testing and Documentation

• Write unit tests using pytest with np.testing assertions
• Use np.testing.assert_array_equal() for exact comparisons
• Use np.testing.assert_array_almost_equal() for floating-point comparisons
• Include comprehensive docstrings following NumPy docstring format

Key Conventions

• Import as import numpy as np
• Use snake_case for variables and functions
• Document array shapes in docstrings
• Profile code with %timeit to identify bottlenecks