Dataanalysistoolkit

**Full Changelog**: https://github.com/thomasthaddeus/DataAnalysisToolkit/compare/v1.1.0...v1.1.1

In this version the documentation for the package has been updated and a makethedocs site has been created

New Features
- **Enhanced Excel Connector**: Improved handling of complex Excel files, including better support for custom formats and large datasets.
- **Advanced SQL Connector**: Added functionality for more complex SQL queries, including support for stored procedures and transaction management in SQL databases.
- **Robust API Connector**: Enhanced API Connector with automatic rate-limit handling and support for additional authentication methods.
- **Data Integration Enhancements**: Introduced advanced data integration techniques in the Data Integrator, including time-series data alignment and multi-key merging capabilities.
- **Data Formatter Expansion**: Added new transformation functions to the Data Formatter for more complex data manipulation, including custom lambda expressions and regular expression-based transformations.

Improvements
- **Performance Optimization**: Further optimized for efficiency, particularly in handling large datasets and complex data transformations.
- **User Experience Enhancements**: Improved the interface and error messaging to make the toolkit more intuitive and user-friendly.
- **Expanded Documentation and Examples**: Updated documentation with more examples and use cases, including advanced scenarios and tips for best practices.

Bug Fixes
- Addressed various bugs and issues identified in version 1.0.0, enhancing stability and performance.

Upgrade Instructions
To upgrade to DataAnalysisToolkit version 1.1.0, run:

bash
pip install dataanalysistoolkit --upgrade


Getting Started
Check out the updated `tutorial_data_import.ipynb` for an in-depth guide on utilizing the new and improved features in version 1.1.0. This tutorial offers practical, real-world examples to help you get the most out of the toolkit.

Acknowledgements
Our heartfelt thanks go out to the community of developers, data scientists, and enthusiasts. Your insightful feedback, feature requests, and bug reports have been invaluable in shaping this release.

Need Help or Want to Contribute?
For support, bug reports, or feedback, please visit our [GitHub Issues](https://github.com/your-repo/dataanalysistoolkit/issues) page. Interested in contributing? Check out our [contribution guidelines](https://github.com/your-repo/dataanalysistoolkit/CONTRIBUTING.md). We welcome and appreciate your contributions to the DataAnalysisToolkit!

Thank you for your continued support, and we hope you enjoy the new features and improvements in version 1.1.0!

What's Changed
* unifying by thomasthaddeus in https://github.com/thomasthaddeus/DataAnalysisToolkit/pull/4
* Merge pull request 4 from thomasthaddeus/main by thomasthaddeus in https://github.com/thomasthaddeus/DataAnalysisToolkit/pull/5

New Contributors
* thomasthaddeus made their first contribution in https://github.com/thomasthaddeus/DataAnalysisToolkit/pull/4

**Full Changelog**: https://github.com/thomasthaddeus/DataAnalysisToolkit/compare/v1.0.0...v1.0.1

---

We are committed to continually improving DataAnalysisToolkit and we welcome any feedback or suggestions for future releases. Thank you for your support!

---

*DataAnalysisToolkit Team*

We're excited to announce the first release of DataAnalyzer!

DataAnalyzer is a Python-based tool designed to streamline various data analysis tasks. It provides the ability to load data from CSV files, perform statistical calculations, detect outliers, clean data, and visualize data.

New Features

- **Load data from CSV files**
- **Calculate statistics** such as mean, median, mode, and trimmed mean for a specified column
- **Detect outliers** in a specified column using the z-score method
- **Handle missing values** by either dropping or filling them
- **Drop duplicate rows** from the DataFrame
- **Encode categorical features** in the DataFrame
- **Split the data** into training and testing sets for machine learning tasks
- **Visualize data** by plotting a histogram for a specified column
- **Export data** to a new CSV file after processing

Installation

To install DataAnalyzer, you can use pip:

python
pip install dataanalyzer


Please see the [README](https://github.com/thomasthaddeus/DataAnalyzer/README.md) for more detailed usage instructions.

Feedback

We'd love to hear your feedback! If you have any suggestions or encounter any issues, please [open an issue](https://github.com/thomasthaddeus/DataAnalyzer/issues) on my GitHub page.

Future Plans

For the next release, we're planning to add more statistical calculation methods and enhance the data visualization capabilities.

Thanks to everyone who contributed to this release!