Learn various ways of performing Input/Output (I/O) operations in NumPy.

Creating NPY and NPZ files in NumPy

Generally, NumPy arrays are saved in .npy or .npz file format.

The .npy file is a simple format for saving NumPy arrays to disk with the full information about them. It stores all information about a NumPy array including its dtype and shape so that the array can be reconstructed on any machine despite the machine’s architecture. On the other hand, several arrays can be contained into a single file in uncompressed .npz format.

The numpy.save() function is used to save the array in .npy format, whereas numpy.savez() is used to save the array in .npz format.

import numpy as np

# Creating two 1-D numpy arrays
a = np.arange(start=1, stop=5, step=1)
b = np.arange(start=6, stop=10, step=1)

# Printing the arrays
print("a: ", a)
print("b: ", b)
print("\n")

# Saving the a to .npy format
np.save('a.npy', a)

# Saving the arrays a and b  to .npz format
np.savez('ab.npz', a, b)

a: [1 2 3 4]
b: [6 7 8 9]

Likewise, the numpy.load() function is used to load the array into the Python code. While loading the .npz file, the individual array can be accessed by passing “arr_0, arr_1, ….. arr_n”, a as a key to the loaded object.

# Loading saved data
a_loaded = np.load('a.npy')
ab_loaded = np.load('ab.npz')

# Printing the loaded data
print("a_loaded: ", a_loaded)
print("First array in ab_loaded: ", ab_loaded['arr_0'])
print("Second array in ab_loaded: ", ab_loaded['arr_1'])

a_loaded: [1 2 3 4]
First array in the ab_loaded: [1 2 3 4]
Second array in the ab_loaded: [6 7 8 9]

TXT Files in NumPy

NumPy arrays can also be stored in plain .txt file formats. The numpy.savetxt() and numpy.loadtxt() methods are used to save and load arrays respectively.

import numpy as np

# Creating a 1-D numpy arrays
a = np.arange(start=1, stop=5, step=1)

# Saving array as a txt file
np.savetxt("a.txt", a)

# Loading array from txt file
loaded_arr = np.loadtxt("a.txt")

print("Loaded Array: ", loaded_arr)

Loaded Array: [1. 2. 3. 4.]

This is how you can perform Input/Output operations in NumPy!

End of Course

With this, we have come to the end of our NumPy for Scientific Computing with Python Course. We hope that this course helped you as a stepping stone towards your Data Science journey with Python. If you have any questions or feedback, please feel free to let us know in the comment section.

Also, now that you are able to code in Python, you can enroll in a domain-specific course by going through all of our data science courses.

(As a reminder, we are constantly updating our courses so make sure to check in on a future date to find more resources introduced in this course.)

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