atomforge-core

Create a simple yet powerful data transformation and analysis tool using the 'atomforge-core' Python package. Your task is to develop a command-line utility named 'DataForge' that allows users to easily manipulate, transform, and analyze datasets. This utility will be particularly useful for data scientists, analysts, and engineers who need quick insights and transformations without diving into complex programming scripts.

### Features:
- **Data Loading:** Users should be able to load various types of data files such as CSV, Excel, JSON, etc., directly into the tool.
- **Transformation Pipeline:** Implement a flexible pipeline system where users can apply a series of transformations on their data. These transformations might include filtering rows, aggregating columns, applying mathematical operations, etc.
- **Analysis Tools:** Provide basic statistical analysis tools like mean, median, mode, standard deviation, etc., directly within the utility.
- **Visualization Support:** Integrate basic visualization capabilities to allow users to quickly visualize their transformed data using plots and charts.
- **Export Options:** Allow users to export their transformed and analyzed data back into various formats like CSV, Excel, or even as a visual report.

### How 'atomforge-core' is Utilized:
- Use 'atomforge-core' to handle the core functionalities of data manipulation and transformation. Leverage its capabilities to streamline the process of building your pipeline system and integrating advanced data processing features.
- For the visualization part, consider using 'atomforge-core' alongside other popular Python visualization libraries like Matplotlib or Plotly if necessary.
- Ensure that your implementation showcases the flexibility and power of 'atomforge-core' in handling complex data workflows efficiently and effectively.

### Deliverables:
- A fully functional command-line utility named 'DataForge'.
- Documentation explaining how to use the utility, including examples of common tasks and workflows.
- A README file detailing the setup process, dependencies, and any special instructions needed to run the utility.
- Sample datasets and transformation pipelines to demonstrate the utility's capabilities.