Function to Analyze Data

In this response, we will create a Python function that extracts and analyzes data from a dataset, such as a CSV file. The function will read the data, perform basic analysis, and return summary statistics. This is a common requirement for data scientists when exploring datasets.

1. Necessary Imports

We'll need the following libraries:

• pandas: For data manipulation and analysis.
• numpy: For numerical operations.

2. Function Definition

Here’s the function definition with detailed documentation.

import pandas as pd
import numpy as np

def analyze_data(file_path):
    """
    Analyzes a CSV dataset and returns summary statistics.

    Parameters:
        file_path (str): The path to the CSV file to be analyzed.

    Returns:
        dict: A dictionary containing summary statistics including means,
              medians, standard deviations, and count of missing values.

    Raises:
        FileNotFoundError: If the specified file does not exist.
        ValueError: If the dataset is empty or not in the expected format.
    """

    # Attempt to read the CSV file
    try:
        data = pd.read_csv(file_path)
    except FileNotFoundError:
        raise FileNotFoundError(f"The file {file_path} was not found.")
    
    # Check if the dataset is empty
    if data.empty:
        raise ValueError("The provided dataset is empty.")
    
    # Initialize a dictionary to hold summary statistics
    summary_statistics = {
        "mean": data.mean(numeric_only=True).to_dict(),  # Calculate mean for numeric columns
        "median": data.median(numeric_only=True).to_dict(),  # Calculate median
        "std_dev": data.std(numeric_only=True).to_dict(),  # Calculate standard deviation
        "missing_values": data.isnull().sum().to_dict()  # Count missing values
    }

    return summary_statistics

3. Explanation of the Code

• Imports: We import pandas for data handling and numpy for numerical calculations (even though not strictly necessary here).

• Function Purpose: analyze_data reads a CSV file specified by file_path, computes basic statistics, and returns a summary.

• Error Handling: The function checks for file existence and whether the dataset is empty. It raises appropriate exceptions.

• Summary Statistics: The function computes:

  • Mean of numerical columns.
  • Median of numerical columns.
  • Standard deviation of numerical columns.
  • Count of missing values per column.

4. Usage Example

Below is an example of how to use the analyze_data function. Make sure to replace 'path/to/your/data.csv' with the actual path of your CSV file.

if __name__ == "__main__":
    try:
        stats = analyze_data('path/to/your/data.csv')
        print("Summary Statistics:")
        print(stats)
    except (FileNotFoundError, ValueError) as e:
        print(f"Error: {e}")

Conclusion

This function is a simple yet powerful tool for quickly gaining insights into a dataset. It's built with error handling and returns a concise summary, providing a clear starting point for further analysis.

Feel free to explore more advanced data analysis techniques and tools through the Enterprise DNA Platform for deeper insights and learning.