Releases: thomasthaddeus/DataAnalysisToolkit
v1.2.3
What's Changed
- updating packaging metadata files by @thomasthaddeus in #18
- Thomasthaddeus patch 1 by @thomasthaddeus in #19
- Dev by @thomasthaddeus in #20
- update documentation deployment by @thomasthaddeus in #21
- Fix docs build by @thomasthaddeus in #22
- Dev branch should build documentation correctly now by @thomasthaddeus in #23
- Update bandit.yml by @thomasthaddeus in #24
- Update dev to match main by @thomasthaddeus in #25
- Merge pull request #25 from thomasthaddeus/main by @thomasthaddeus in #26
Full Changelog: v1.2.3...v1.2.3
v1.2.2
What's Changed
- Dev by @thomasthaddeus in #17
This patch fixes workflow run file so that it builds
Full Changelog: v1.2.1...v1.2.2
v1.2.1 Minor Updates
What's Changed
- Merge pull request #14 from thomasthaddeus/dev by @thomasthaddeus in #15
- Dev by @thomasthaddeus in #16
Full Changelog: v1.2.0...v1.2.1
v1.2.0
What's Changed
- Create dependabot.yml by @thomasthaddeus in #8
- updating dev branch by @thomasthaddeus in #9
- Dev1 by @thomasthaddeus in #10
- Dev1 by @thomasthaddeus in #11
- Dev by @thomasthaddeus in #12
- updating dev branch by @thomasthaddeus in #13
- v1.2.0 by @thomasthaddeus in #14
Full Changelog: v1.1.1...v1.2.0
This update fixes some of the connector issues with the data connection class.
Adding Documentation
Full Changelog: v1.1.0...v1.1.1
In this version the documentation for the package has been updated and a makethedocs site has been created
v1.1.0
Release Notes for DataAnalysisToolkit Version 1.1.0
We're thrilled to announce the release of DataAnalysisToolkit version 1.1.0! This significant update builds upon the robust foundation of version 1.0.0, introducing an array of new features and enhancements to further streamline and empower your data analysis workflows.
What's New in 1.1.0
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:
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 page. Interested in contributing? Check out our contribution guidelines. 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!
v1.0.1
What's Changed
- unifying by @thomasthaddeus in #4
- Merge pull request #4 from thomasthaddeus/main by @thomasthaddeus in #5
New Contributors
- @thomasthaddeus made their first contribution in #4
Full Changelog: v1.0.0...v1.0.1
v1.0.0
Release Notes for DataAnalysisToolkit Version 1.0.0
We are excited to announce the release of DataAnalysisToolkit 1.0.0. This major release marks a significant milestone in our journey to provide a comprehensive and user-friendly Python package for data analysis. This version includes a range of new features, enhancements, and bug fixes to improve your data analysis experience.
What's New
Major Features
-
Data Loading and Preprocessing
- Load data directly from CSV files.
- Preprocess data with functionalities such as handling missing values, dropping duplicates, and encoding categorical features.
-
Statistical Analysis
- Perform various statistical calculations including mean, median, mode, and trimmed mean.
-
Outlier Detection
- Implement outlier detection using the z-score method.
-
Data Visualization
- Integrate a Data Visualizer for generating plots like histograms, scatter plots, box plots, and more.
-
Feature Engineering
- Include a module for feature engineering, enabling the creation of new data features that can improve model performance.
-
Model Evaluation
- Provide tools for evaluating machine learning models, including functions to generate confusion matrices, precision, recall, and other metrics.
-
Report Generation
- Generate comprehensive HTML reports of your data analysis, including statistical summaries and visualizations.
-
Data Imputation
- Offer advanced data imputation techniques like mean, median, most frequent, and constant value imputations.
Enhancements
- Improved performance and efficiency in data processing.
- Enhanced data visualization capabilities with additional plot types.
- More robust data imputation options to handle various missing data scenarios.
Bug Fixes
- Fixed issues related to data loading in specific edge cases.
- Resolved minor bugs in data visualization functions.
- Addressed inconsistencies in statistical calculations.
Breaking Changes
- Some function signatures have been modified for better clarity and consistency. Please refer to the documentation for detailed information.
Documentation
- Updated documentation is available, providing comprehensive guides and examples for all the functionalities of the DataAnalysisToolkit.
Acknowledgments
Special thanks to our contributors and the community for their invaluable feedback and suggestions that have significantly shaped this release.
Installation
To install this version, run:
pip install dataanalysistoolkit==1.0.0
We are committed to continually improving DataAnalysisToolkit and we welcome any feedback or suggestions for future releases. Thank you for your support!
DataAnalysisToolkit Team
Alpha Release
DataAnalyzer v0.1
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:
pip install dataanalyzer
Please see the README 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 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!