Skill: NumPy

Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization.

When to Use

Apply this skill when doing numerical computing with NumPy — arrays, broadcasting, linear algebra, random sampling.

Arrays

• Use explicit dtypes (np.float64, np.int32) when creating arrays.
• Prefer np.zeros, np.ones, np.empty, np.arange, np.linspace over list-based construction.
• Use structured arrays or separate arrays instead of object arrays.

Vectorization

• Replace Python loops with vectorized NumPy operations wherever possible.
• Use broadcasting rules to operate on arrays of different shapes without explicit expansion.
• Use np.where() for conditional element-wise operations.

Memory

• Use np.float32 instead of np.float64 when precision is not critical to halve memory.
• Use views (reshape, slicing) instead of copies when data doesn't need mutation.
• Use np.memmap for arrays too large to fit in RAM.

Random

• Use np.random.default_rng(seed) (new Generator API) instead of np.random.seed().
• Always seed random generators in tests for reproducibility.

Pitfalls

• Don't compare floats with ==; use np.allclose() or np.isclose().
• Beware of silent integer overflow in integer arrays.
• Avoid np.matrix — it's deprecated; use 2D np.ndarray.