process_csv_data.py

"""
CSV to RDF Conversion

This script reads laptop data from a CSV file, converts it into RDF triples,
and serializes the RDF graph to Turtle format.

"""

import pandas as pd
from rdflib import Graph, URIRef, Literal, Namespace, RDF, XSD

def csv_to_rdf(csv_file, output_file):
    """
    Convert laptop data from CSV to RDF and save it to a file.

    Parameters:
    - csv_file (str): Path to the CSV file containing laptop data.
    - output_file (str): Path to the output RDF file in Turtle format.

    """
    # Create a namespace for the project
    ex = Namespace("http://example.org/")

    # Read the CSV data
    df = pd.read_csv(csv_file)

    # Create an RDF graph
    g = Graph()

    # Define the RDF type for laptops
    laptop_type = ex.Laptop

    # Iterate through the rows in the CSV and create RDF triples
    for index, row in df.iterrows():
        laptop_uri = ex[f"Laptop_{row['id']}"]
        g.add((laptop_uri, RDF.type, laptop_type))
        
        # Add other properties as triples
        for column, value in row.items():
            if pd.notna(value):  # Skip NaN values
                property_uri = ex[column]

                # Handle numeric values with appropriate datatypes
                if column in ['weight', 'screen_size', 'graphic_card_size', 'clock_speed']:
                    value_literal = Literal(value, datatype=XSD.float)
                else:
                    value_literal = Literal(value)

                g.add((laptop_uri, property_uri, value_literal))

    # Serialize the RDF graph to Turtle format and save it to a file
    g.serialize(output_file, format="turtle")

    print(f"RDF data has been saved to {output_file}")

# Example usage
if __name__ == "__main__":
    csv_path = "laptops.csv"
    rdf_output_path = "data.ttl"
    csv_to_rdf(csv_path, rdf_output_path)