CSV to DataFrame Converter in Python

CSV to Table Function in Python

Here we will develop a Python function that reads a CSV file and creates a table (dataframe) using the pandas library. We will also include all necessary imports, documentation blocks, input validation, commentary, and a usage example.

Necessary Imports

import pandas as pd

Function Definition

def csv_to_table(file_path):
    """
    Reads a CSV file and creates a pandas DataFrame.
    
    Parameters:
    file_path (str): The path to the CSV file.
    
    Returns:
    pd.DataFrame: A pandas DataFrame containing the data from the CSV file.
    
    Raises:
    FileNotFoundError: If the specified file does not exist.
    ValueError: If the file content is not in the expected format or is empty.
    """
    # Input validation: ensure file_path is a string
    if not isinstance(file_path, str):
        raise TypeError("file_path must be a string")
    
    try:
        # Use pandas to read the CSV file
        df = pd.read_csv(file_path)
    except FileNotFoundError:
        raise FileNotFoundError(f"No such file or directory: '{file_path}'")
    except pd.errors.EmptyDataError:
        raise ValueError("The CSV file is empty")
    except pd.errors.ParserError:
        raise ValueError("Error parsing the CSV file. Ensure it is in proper CSV format")

    # Check if the resulting dataframe is empty
    if df.empty:
        raise ValueError("The resulting DataFrame is empty")
    
    return df

Code Usage Example

To illustrate how to use the csv_to_table function, we'll provide a practical example.

# Assuming 'example.csv' is a valid CSV file located in the current directory.
try:
    df = csv_to_table('example.csv')
    print(df)  # Display the created DataFrame
except (FileNotFoundError, ValueError, TypeError) as e:
    print(f"Error: {e}")

Commentary

1. Necessary Imports:

   • Importing pandas as pd for handling CSV files and creating DataFrames.

2. Function Definition:

   • The csv_to_table function reads a CSV file and returns a DataFrame.
   • Includes robust input validation to ensure the file path is a string.
   • Handles potential exceptions such as FileNotFoundError, parser errors, and empty data.
   • Comments describe each significant step, making the code more understandable and maintainable.

3. Code Usage Example:

   • Demonstrates how to use the function with a sample CSV file.
   • Includes error handling to gracefully inform the user of errors in the file reading process.

This function and accompanying usage example should meet the needs of a professional data scientist, adhering to best practices in software engineering. For further learning and advanced techniques, consider exploring advanced courses on the Enterprise DNA Platform.