learn-python/contrib/numpy/saving_numpy_arrays_to_files.md

Saving NumPy Arrays to Files

• Saving arrays in NumPy is important due to its efficiency in storage and speed, maintaining data integrity and precision, and offering convenience and interoperability.
• NumPy provides several methods to save arrays efficiently, either in binary or text formats.
• The primary methods are save, savez, and savetxt.

1. numpy.save():

The np.save function saves a single NumPy array to a binary file with a .npy extension. This format is efficient and preserves the array's data type and shape.

Syntax :

numpy.save(file, arr, allow_pickle=True, fix_imports=True)

• file : Name of the file.
• arr : Array to be saved.
• allow_pickle : This is an Optional parameter, Allows saving object arrays using Python pickles.(By Default True)
• fix_imports : This is an Optional parameter, Fixes issues for Python 2 to Python 3 compatibility.(By Default True)

Example :

import numpy as np

arr = np.array([1,2,3,4,5])
np.save("example.npy",arr) #saves arr into example.npy file in binary format

Inorder to load the array from example.npy

arr1 = np.load("example.npy")
print(arr1)

Output :

[1,2,3,4,5]

2. numpy.savez():

The np.savez function saves multiple NumPy arrays into a single file with a .npz extension. Each array is stored with a unique name.

Syntax :

numpy.savez(file, *args, **kwds)

• file : Name of the file.
• args : Arrays to be saved.( If arrays are unnamed, they are stored with default names like arr_0, arr_1, etc.)
• kwds : Named arrays to be saved.

Example :

import numpy as np

arr1 = np.array([1,2,3,4,5])
arr2 = np.array(['a','b','c','d'])
arr3 = np.array([1.2,3.4,5])
np.savez('example.npz', a1=arr1, a2=arr2, a3 = arr3) #saves arrays in npz format

Inorder to load the array from example.npz

arr = np.load('example.npz')
print(arr['a1'])
print(arr['a2'])
print(arr['a3'])

Output :

[1 2 3 4 5]
['a' 'b' 'c' 'd']
[1.2 3.4 5. ]

3. np.savetxt()

The np.savetxt function saves a NumPy array to a text file, such as .txt or .csv. This format is human-readable and can be used for interoperability with other tools.

Syntax :

numpy.savetxt(fname, X, delimiter=' ', newline='\n', header='', footer='', encoding=None)

• fname : Name of the file.
• X : Array to be saved.
• delimiter : It is a Optional parameter,This is a character or string that is used to separate columns.(By Default it is " ")
• newline : It is a Optional parameter, Character for seperating lines.(By Default it is "\n")
• header : It is a Optional parameter, String that is written at beginning of the file.
• footer : It is a Optional parameter, String that is written at ending of the file.
• encoding : It is a Optional parameter, Encoding of the output file. (By Default it is None)

Example :

import numpy as np

arr = np.array([1.1,2.2,3,4.4,5])
np.savetxt("example.txt",arr) #saves the array in example.txt

Inorder to load the array from example.txt

arr1 = np.loadtxt("example.txt")
print(arr1)

Output :

[1.1 2.2 3. 4.4 5. ]

By using these methods, you can efficiently save and load NumPy arrays in various formats suitable for your needs.