NumPy: Save a given array to a text file and load it

NumPy: Basic Exercise-37 with Solution

Write a NumPy program to save a given array to a text file and load it.

Sample Solution :

Python Code :

# Importing the NumPy library with an alias 'np'
import numpy as np

# Importing the 'os' module for operating system-dependent functionality
import os

# Creating a NumPy array 'x' with integers from 0 to 11 and reshaping it to a 4x3 matrix using np.arange() and .reshape()
x = np.arange(12).reshape(4, 3)

# Printing a message indicating the original array 'x'
print("Original array:")
print(x)

# Creating a string 'header' containing column names 'col1 col2 col3'
header = 'col1 col2 col3'

# Saving the array 'x' into a text file named 'temp.txt' using np.savetxt()
# Specifying the format as "%d" (integer) and including a header with column names
np.savetxt('temp.txt', x, fmt="%d", header=header)

# Printing a message indicating the content of the text file after loading
print("After loading, content of the text file:")

# Loading the data from 'temp.txt' into the variable 'result' using np.loadtxt()
result = np.loadtxt('temp.txt')

# Printing the loaded data 'result' from the text file
print(result) 

Sample Output:

Original array:
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]
After loading, content of the text file:
[[  0.   1.   2.]
 [  3.   4.   5.]
 [  6.   7.   8.]
 [  9.  10.  11.]]                         

Explanation:

In the above code -

The ‘x = np.arange(12).reshape(4, 3)’ statement creates a 1D array with elements ranging from 0 to 11, then reshapes it into a 2D array 'x' of shape (4, 3).

‘header = 'col1 col2 col3' statement creates a string 'header' that will be used as the header for the text file.

‘np.savetxt('temp.txt', x, fmt="%d", header=header)’ statement saves the 2D NumPy array 'x' to a text file named 'temp.txt' using the integer format ("%d") for the elements, and adds the specified header to the file.

‘result = np.loadtxt('temp.txt')’ statement loads the contents of the 'temp.txt' file into a NumPy array named 'result'.

Finally print(result) prints the loaded 'result' array.

Pictorial Presentation:

Python-Numpy Code Editor:

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