Load data from CSV file into NumPy Structured array

NumPy: Structured Arrays Exercise-14 with Solution

Loading Data from a CSV File:

Write a NumPy program that loads data from a CSV file into a structured array. Assume the CSV file has columns corresponding to 'name', 'age', and 'height'.

Sample Solution:

Python Code:

import numpy as np

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

# Load data from the CSV file into a structured array
# Assume the CSV file is named 'data.csv' and is in the same directory
structured_array = np.genfromtxt('data.csv', delimiter=',', dtype=dtype, names=True)

# Print the structured array loaded from the CSV file
print("Structured Array loaded from 'data.csv':")
print(structured_array)

Output:

Structured Array loaded from 'data.csv':
[('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.
• Load Data from CSV File:
• Used "np.genfromtxt()" to load data from the CSV file named 'data.csv' into a structured array. The parameters used are:
• 'data.csv': Name of the CSV file.
• delimiter=',': Specifies that the file is comma-separated.
• dtype=dtype: Specifies the data type for the structured array.
• names=True: Indicates that the first row of the CSV file contains the column names.
• Finally print the structured array loaded from the CSV file to verify the operation.

Python-Numpy Code Editor:

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