numpy

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