NumPy is a powerful Python framework for numerical computing that supports massive, multi-dimensional arrays and matrices and offers a number of mathematical functions for modifying the arrays. It is an essential store for Python activities involving scientific computing, data analysis, and machine learning.

What is a Numpy array?

A NumPy array is a multi-dimensional data structure in Python used for numerical computations. It is similar to a list, but it allows for more efficient storage and manipulation of numerical data.

Saving Multiple Numpy Arrays

Saving multiple NumPy arrays into a single file can be necessary when you want to store related data together or when you need to share or distribute multiple arrays as a single unit. It helps in organizing your data and simplifies the process of loading and accessing the arrays later on. We will discuss some methods to do the same:

Creating Sample Arrays

Three different arrays array1,array2, and array3 are created.

Using np.savez() function

The numpy.savez function is used to save these arrays into a single .npz file called ‘multiple_arrays.npz’. The arrays are named inside the .npz file, and you can access them by their names when loading the file.

Loading the Arrays back

Output:

[1 2 3 4 5]
[10 20 30 40 50]
[0.1 0.2 0.3 0.4 0.5]

Using np.savez_compressed

We will save the three arrays into a single compressed file named ‘multiple_arrays_compressed.npz’

Loading the the arrays back

Output:

[1 2 3 4 5]
[10 20 30 40 50]
[0.1 0.2 0.3 0.4 0.5]

How saving multiple array is different from saving single array using np.save?

Saving multiple arrays using np.savez_compressed() allows you to store multiple arrays into a single compressed file, reducing disk space usage and improving efficiency during storage and transfer. In contrast, saving a single array using np.save() generates an uncompressed binary file, which may be less efficient for storing multiple arrays or when storage space is a concern. Additionally, np.savez_compressed() requires specifying keys for each array, while np.save() only needs the array and the file name.