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numpy-ts supports the same binary and text file formats as NumPy. The API is split across two entry points: numpy-ts/node for filesystem access, and numpy-ts (or numpy-ts/core) for in-memory serialization suitable for browsers.

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Loading .npy files

The .npy format stores a single array with its dtype, shape, and byte order metadata.
In Node.js, you can use load and save from numpy-ts/node for convenient file I/O without manual serialization.

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Working with NPZ archives (multiple arrays)

The .npz format bundles multiple named arrays into a single ZIP archive.

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CSV loading with parseTxt

Load delimited text files (CSV, TSV, etc.) into arrays.

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Flexible text loading with genfromtxt

genfromtxt is like parseTxt but handles missing and irregular data more gracefully.

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Extracting data with fromregex

Pull numeric data from unstructured text using regular expressions.

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NumPy interoperability

Files saved by numpy-ts can be loaded by Python NumPy, and vice versa. This makes it easy to share data between TypeScript and Python workflows.
numpy-ts only supports numeric dtypes. Object arrays, structured arrays, string arrays, and pickle-based .npy files from Python cannot be loaded.