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