numpy-best-practices

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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