How to read numeric data from a CSV file into a NumPy array?

NumPy: I/O Operations Exercise-1 with Solution

Write a NumPy program that reads data from a CSV file into a NumPy array. The CSV file contains numeric data.

Content of  data.csv file:
10,12,95
11,13,90
12,34,85
13,21,96
14,55,95

Sample Solution:

Python Code:

import numpy as np
import csv

# Define the path to the CSV file
csv_file_path = 'data.csv'

# Initialize an empty list to hold the numeric data
data_list = []

# Open and read the CSV file
with open(csv_file_path, newline='') as csvfile:
    reader = csv.reader(csvfile, delimiter=',')
    for row in reader:
        # Extract only the numeric elements from the row
        numeric_row = []
        for element in row:
            try:
                numeric_value = float(element)
                numeric_row.append(numeric_value)
            except ValueError:
                # Skip non-numeric elements
                continue
        if numeric_row:
            data_list.append(numeric_row)

# Convert the list of lists to a NumPy array
data_array = np.array(data_list, dtype=object)

# Print the resulting NumPy array
print(data_array)

Output:

[[10.0 12.0 95.0]
 [11.0 13.0 90.0]
 [12.0 34.0 85.0]
 [13.0 21.0 96.0]
 [14.0 55.0 95.0]]

Explanation:

• Import NumPy and CSV Libraries: Import the NumPy and CSV libraries to handle arrays and reading CSV files.
• Define CSV File Path: Specify the path to the CSV file containing the numeric data.
• Initialize Empty List: Create an empty list to store the valid numeric values from the CSV file.
• Open and Read CSV File: Use csv.reader() to read the contents of the CSV file.
• Extract Numeric Elements: Iterate through each row, attempting to convert each element to a float. Append valid numeric values to a temporary list for each row.
• Append Non-Empty Rows: If the temporary list contains numeric values, append it to the main list.
• Convert List to NumPy Array: Convert the list of numeric rows to a NumPy array using np.array(), specifying dtype=object to handle potential varying lengths of rows.
• Print NumPy Array: Output the resulting NumPy array to verify the extracted numeric data.

Python-Numpy Code Editor:

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