NumPy: Save a given array to a binary file
NumPy: Basic Exercise-35 with Solution
Write a NumPy program to save a given array to a binary file.
This problem involves writing a NumPy program to save a given array to a binary file. The task requires utilizing NumPy's built-in functions to efficiently store the array's data in a binary format, which allows for compact storage and quick loading. By saving the array to a binary file, the program ensures data persistence and facilitates efficient data handling for future use.
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 'a' with integers from 0 to 19 using np.arange()
a = np.arange(20)
# Saving the NumPy array 'a' as a file named 'temp_arra.npy' using np.save()
np.save('temp_arra.npy', a)
# Printing a message checking if the file 'temp_arra.npy' exists or not
print("Check if 'temp_arra.npy' exists or not?")
# Checking if the file 'temp_arra.npy' exists using os.path.exists()
if os.path.exists('temp_arra.npy'):
# Loading the data from 'temp_arra.npy' into 'x2' using np.load()
x2 = np.load('temp_arra.npy')
# Checking if the loaded array 'x2' is equal to the original array 'a' using np.array_equal()
print(np.array_equal(a, x2))
Output:
Check if 'temp_arra.npy' exists or not? True
Explanation:
The above code creates a NumPy array, saves it to a file, loads it back into another NumPy array, and checks if the original and loaded arrays are equal.
‘a = np.arange(20)’ creates a 1D array 'a' with elements ranging from 0 to 19.
np.save('temp_arra.npy', a): This statement saves the NumPy array 'a' to a binary file named 'temp_arra.npy' using NumPy's native file format.
if os.path.exists('temp_arra.npy'):: This statement checks if the file 'temp_arra.npy' exists in the current directory using the os.path.exists() function.
x2 = np.load('temp_arra.npy'): If the file exists, this line loads the contents of 'temp_arra.npy' back into a NumPy array named 'x2'.
Finally ‘print(np.array_equal(a, x2))’ uses the np.array_equal() function to check if the original array 'a' and the loaded array 'x2' are element-wise equal. If they are equal, it prints 'True', otherwise, it prints 'False'.
Python-Numpy Code Editor:
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