File Handling Using NumPy

NumPy provides built-in functions to save arrays to disk and load them back later, letting you persist data between sessions without needing to regenerate or recalculate it every time.

Saving and Loading Binary .npy Files

NumPy's native binary format (.npy) is the fastest and most space-efficient way to store an array, preserving its exact shape and data type.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

np.save("my_array.npy", arr)          # save to disk
loaded = np.load("my_array.npy")      # load back from disk
print(loaded)   # [1 2 3 4 5]

Saving Multiple Arrays Together

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

np.savez("arrays.npz", first=a, second=b)

data = np.load("arrays.npz")
print(data["first"])    # [1 2 3]
print(data["second"])   # [4 5 6]

Working with Text and CSV Files

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

np.savetxt("data.csv", arr, delimiter=",")
loaded = np.loadtxt("data.csv", delimiter=",")
print(loaded)

Coming Up Next

To close out this module, the final topic covers array creation shortcuts and logic functions for comparing and filtering array data.