NumPyfriendly

NumPyfriendly is a term used in software development to describe code, libraries, or APIs that are designed to integrate smoothly with NumPy, the foundational Python library for numerical computing. A NumPyfriendly component accepts and returns NumPy arrays and adheres to NumPy-like semantics, including broadcasting, universal functions, and dtype awareness. The aim is to maximize interoperability with existing NumPy code and minimize friction when combining tools in data processing pipelines.

Core principles include array-centric design, explicit handling of array-like inputs, and predictable broadcasting behavior. NumPyfriendly code typically avoids Python-level loops over data whenever possible, favoring vectorized operations, in-place updates, and memory-efficient practices such as views and careful use of contiguous array layouts. Functions are expected to accept array-like inputs, either as ndarray objects or objects convertible via np.asarray, and to return arrays that align with NumPy conventions regarding shape, dtype, and ordering.

Implementation guidelines often emphasize: clear input normalization, preserve or clearly cast dtypes, raise informative errors for incompatible shapes, and provide consistent broadcasting and scalar expansion. Documentation resembles NumPy's own style, with examples that show inputs and outputs as arrays and shapes.

NumPyfriendly design is common in scientific computing, data processing, and machine learning pipelines that rely on NumPy for core computations and desire seamless compatibility with other NumPy-based tools. Limitations include potential performance trade-offs for certain non-numeric tasks and the need to maintain compatibility across multiple NumPy versions.