Update field value in NumPy Structured array
NumPy: Structured Arrays Exercise-3 with Solution
Modifying Field Values:
Write a NumPy program that updates the 'age' field for the first individual in the structured array created in exercise 1 to 30.
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)
print("Original Structured Array:")
print(structured_array)
# Update the 'age' field for the first individual to 30
# Access the first element and update the 'age' field
structured_array[0]['age'] = 30
# Print the updated structured array
print("\nUpdated Structured Array:")
print(structured_array)
Output:
Original Structured Array:
[('Lehi Piero', 25, 5.5) ('Albin Acha', 30, 5.8) ('Zerach Hav', 35, 6.1)
('Edmund Ter', 40, 5.9) ('Laura Feli', 28, 5.7)]
Updated Structured Array:
[('Lehi Piero', 30, 5.5) ('Albin Acha', 30, 5.8) ('Zerach Hav', 35, 6.1)
('Edmund Ter', 40, 5.9) ('Laura Feli', 28, 5.7)]
Explanation:
- Import Libraries:
- Imported numpy as "np" for array creation and manipulation.
- 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'.
- Update 'Age' Field:
- Access the first element of the structured array and update the 'age' field to 30.
- Print Updated Structured Array:
- Print the updated structured array to verify the changes.
Python-Numpy Code Editor: