numpysave

Numpysave is commonly used to refer to the NumPy function numpy.save, a utility for persisting a single NumPy array to disk in the binary .npy format. It preserves essential metadata such as the array’s shape, data type, and memory order, enabling exact reconstruction when loaded later.

The typical signature is numpy.save(file, arr, allow_pickle=True, fix_imports=True). The file argument can be a string path or a file-like object with a write method. The arr argument is the array to save; it is converted to a NumPy array if necessary. The allow_pickle parameter controls whether object arrays can be saved using Python pickling, with a default of True. The fix_imports parameter relates to compatibility with older Python versions and is generally kept at its default.

How it works: numpy.save writes a .npy file that contains a header with metadata followed by the raw array data. If a path is provided, the function creates or overwrites the file. If a file-like object is supplied, the data is written directly to that stream. The saved file can be loaded back with numpy.load, which restores the array exactly as it was saved.

Limitations and notes: The .npy format is NumPy-specific, so cross-language compatibility is limited. Saving object arrays or using pickle may pose security risks if loading data from untrusted sources. For saving multiple arrays, NumPy offers numpy.savez and numpy.savez_compressed as alternatives.

See also: numpy.load, numpy.savez, numpy.savez_compressed. Example usage includes numpy.save('data.npy', arr) to persist an array and numpy.load('data.npy') to retrieve it.