NumPy Best Practices

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