{"id":23894,"date":"2022-08-19T12:52:06","date_gmt":"2022-08-19T07:22:06","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=23894"},"modified":"2026-07-13T12:40:54","modified_gmt":"2026-07-13T07:10:54","slug":"numpy-save","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/numpy-save\/","title":{"rendered":"NumPy save(): Save and Load Arrays with .npy Files"},"content":{"rendered":"

Quick answer:<\/strong> Use np.save(path, array) to write one NumPy array to a .npy file, np.load() to read it back, savez() for several named arrays, and savez_compressed() when archive compression is useful. Treat allow_pickle as a security decision when loading untrusted files.<\/p>\n

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Use save() for one array in a .npy file, savez() for multiple arrays, and load() with a deliberate allow_pickle policy when reading data.<\/figcaption><\/figure>\n

numpy.save()<\/code><\/strong> saves one NumPy array to a binary .npy<\/code> file. Use it when you want to preserve an array’s dtype and shape and load the array back later with numpy.load()<\/code>.<\/p>\n

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np.save(\"array.npy\", array)\nloaded = np.load(\"array.npy\")<\/code><\/pre>\n<\/div>\n
If you need to save multiple arrays, use np.savez()<\/code> or np.savez_compressed()<\/code>. If you need a human-readable text or CSV file, use np.savetxt()<\/code>.<\/p>\n
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