numpy-best-practices

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Best practices for NumPy array programming, numerical computing, and performance optimization in Python

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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

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