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function load_dataset

Maturity: 32

Loads a CSV dataset from a specified file path using pandas and returns it as a DataFrame with error handling for file not found and general exceptions.

File:
/tf/active/vicechatdev/vice_ai/smartstat_scripts/e1ecec5f-4ea5-49c5-b4f5-d051ce851294/project_1/analysis.py
Lines:
10 - 20
Complexity:
simple

Purpose

This function provides a robust way to load CSV datasets with built-in error handling. It attempts to read a CSV file using pandas, prints status messages to inform the user of success or failure, and gracefully handles common errors like missing files. It's designed for data analysis workflows where CSV data needs to be loaded with user-friendly error reporting.

Source Code

def load_dataset(file_path):
    try:
        data = pd.read_csv(file_path)
        print("Dataset loaded successfully.")
        return data
    except FileNotFoundError:
        print(f"Error: The file '{file_path}' was not found.")
        return None
    except Exception as e:
        print(f"An error occurred while loading the dataset: {e}")
        return None

Parameters

Name Type Default Kind
file_path - - positional_or_keyword

Parameter Details

file_path: String representing the path to the CSV file to be loaded. Can be an absolute path (e.g., '/home/user/data.csv') or relative path (e.g., 'data/dataset.csv'). The file must be in CSV format readable by pandas.read_csv().

Return Value

Returns a pandas DataFrame containing the loaded dataset if successful. Returns None if the file is not found or if any other exception occurs during loading. The DataFrame structure depends on the CSV file's contents (columns, rows, data types).

Dependencies

  • pandas

Required Imports

import pandas as pd

Usage Example

import pandas as pd

# Load a dataset from a CSV file
df = load_dataset('data/sales_data.csv')

if df is not None:
    print(f"Loaded {len(df)} rows")
    print(df.head())
else:
    print("Failed to load dataset")

# Example with absolute path
df = load_dataset('/home/user/datasets/customer_data.csv')

Best Practices

  • Always check if the returned value is None before attempting to use the DataFrame
  • Ensure the file path is correct and the file exists before calling this function
  • The function prints messages to stdout; consider redirecting or capturing output in production environments
  • For large CSV files, consider using pandas read_csv parameters like chunksize or usecols for memory efficiency
  • The function uses default pandas.read_csv() parameters; for CSV files with special formatting (custom delimiters, encoding, etc.), consider extending this function or using pd.read_csv() directly with appropriate parameters
  • Consider adding logging instead of print statements for production use

Tags

data-loading csv pandas file-io error-handling data-processing dataset data-import

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