Save NumPy Structured array to a file

NumPy: Structured Arrays Exercise-15 with Solution

Saving Structured Arrays to a File:

Write a NumPy program to save a structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float) to a file using NumPy's save function.

Sample Solution:

Python Code:

import numpy as np

# Define the data type for the structured array
dtype = [('name', 'U10'), ('age', 'i4'), ('height', 'f4')]

# Create the structured array with sample data
structured_array = np.array([
    ('Lehi Piero', 25, 5.5),
    ('Albin Achan', 30, 5.8),
    ('Zerach Hava', 35, 6.1),
    ('Edmund Tereza', 40, 5.9),
    ('Laura Felinus', 28, 5.7)
], dtype=dtype)

# Save the structured array to a file
# The file will be saved as 'structured_array.npy'
np.save('structured_array.npy', structured_array)

# Print a message indicating the array has been saved
print("Structured array saved to 'structured_array.npy'")

Output:

Structured array saved to 'structured_array.npy'

Explanation:

• Import Libraries:
• Imported numpy as "np" for array creation, manipulation, and saving to files.
• Define Data Type:
• Define the data type for the structured array using a list of tuples. Each tuple specifies a field name and its corresponding data type. The data types are:
• 'U10' for a string of up to 10 characters.
• 'i4' for a 4-byte integer.
• 'f4' for a 4-byte float.
• Create Structured Array:
• Created the structured array using np.array(), providing sample data for five individuals. Each individual is represented as a tuple with values for 'name', 'age', and 'height'.
• Save Structured Array:
• Use "np.save()" to save the structured array to a file named 'structured_array.npy'. The array is saved in NumPy's native binary format.
• Print a message indicating that the structured array has been saved to the file 'structured_array.npy'.

Python-Numpy Code Editor:

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