New Features:
1. **Correlation Matrix Analysis:**
- **Description**: This feature calculates the correlation matrix for numerical columns in your dataset.
- **Benefit**: It helps in understanding the relationship between different numerical variables. A correlation matrix is a table showing correlation coefficients between variables, which can be crucial for identifying patterns and dependencies in your data.
- **Output**: The correlation matrix is saved in a separate sheet named 'Correlation Matrix' in the generated Excel report.
2. **Basic Mathematical Statistics:**
- **Description**: This feature computes fundamental statistical measures such as mean, median, mode, and range for all numerical columns in your dataset.
- **Benefit**: Provides a quick overview of the central tendency and dispersion of your data, which is essential for initial data exploration and identifying anomalies.
- **Output**: These basic mathematical statistics are saved in a separate sheet named 'Basic Mathematics' in the generated Excel report.
Improvements:
- **Progress Bars**: Enhanced user experience with progress bars indicating the loading and report generation processes, ensuring users are informed of the process status.
- **Robust Data Handling**: Improved data loading methods to better handle different file types and encodings, ensuring compatibility with a wider range of datasets.
Bug Fixes:
- **Fixed Read-Only File System Errors**: Ensured that the output files are saved to directories with proper write permissions, reducing instances of write errors.
- **Enhanced Error Logging**: Improved error logging to provide more detailed information, helping users to identify and resolve issues more efficiently.
Documentation:
- **Expanded Docstrings**: Added detailed docstrings to methods and classes, improving code readability and helping developers understand the functionalities better.
- **Usage Examples**: Updated README with new usage examples demonstrating the new features.
Miscellaneous:
- **Code Refactoring**: Refactored the codebase for better readability and maintainability. Reduced the number of local variables in methods and ensured compliance with best practices.
- **Linting**: Achieved a higher pylint score by fixing various code quality issues, including line length, unused imports, and appropriate exception handling.
With these enhancements, SheetBuddy v2.0.0 offers more powerful and insightful data analysis capabilities, making it an even more valuable tool for data scientists and analysts.
**Full Changelog**: https://github.com/AshishRogannagari/SheetBuddy/compare/v1.o.o...v2.o.o
v1.o.o
Release Date: 06-23-2024
We are excited to announce the release of SheetBuddy v1.0.0! This major update brings a host of new features, improvements, and bug fixes to enhance your data analysis and reporting experience.
Major New Features
Excel Sheet Styling: Introducing advanced styling options for Excel sheets to make your data more presentable and easier to interpret.
Additional Functionalities: Added new capabilities to extend the scope of data analysis and reporting.
Improvements and Enhancements:
Improved Stability and Performance: We have made significant improvements to ensure that SheetBuddy runs more reliably and efficiently.
Enhanced Data Processing: Optimized data processing methods to handle larger datasets smoothly.
Bug Fixes:
General Bug Fixes: Fixed various issues from the previous version that were affecting the library's performance and usability.
Compatibility Fixes: Addressed compatibility issues with different versions of Python and Excel to ensure a seamless experience.
Breaking Changes:
API Changes: Some functions have been renamed or modified. Please refer to the migration guide for detailed instructions on updating your code.
Deprecated Methods:
Certain outdated methods have been removed in favor of more efficient alternatives.
Deprecations
Deprecated older methods of data analysis. Please use the new streamlined methods introduced in this version.
Deprecated specific styling functions that have been replaced with more versatile options.
Documentation Updates:
Updated the documentation to include detailed guides on using the new styling features and additional functionalities.
Added new examples and tutorials to help you get started quickly with the new features.
Examples and Use Cases:
Styled Excel Output:
import sheetbuddy as sb
data = sb.read_csv('data.csv')
styled_excel = sb.style_excel(data)
sb.save_excel(styled_excel, 'styled_output.xlsx')