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توضیحات

The best Python package for comparing two dataframes

ویژگی	مقدار
سیستم عامل	-
نام فایل	datadelta-0.0.2
نام	datadelta
نسخه کتابخانه	0.0.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Andrew Gibbs-Bravo
ایمیل نویسنده	andrewgbravo@gmail.com
آدرس صفحه اصلی	https://github.com/gibbsbravo/DataDelta
آدرس اینترنتی	https://pypi.org/project/datadelta/
مجوز	-

<div id="top"></div>  <div align="center"> <a href="https://github.com/gibbsbravo/DataDelta"> <img src="https://github.com/gibbsbravo/DataDelta/blob/master/images/DataDeltaLogo.png?raw=true" alt="Logo" width="200" height="200"> </a> The best Python package for comparing two dataframes <a href="https://github.com/gibbsbravo/DataDelta">Explore the docs »</a> </div>  <details> <summary>Table of Contents</summary> <ol> <li> <a href="#about-the-project">About The Project</a> </li> <li> <a href="#getting-started">Getting Started</a> <ul> <li><a href="#dependencies">Dependencies</a></li> <li><a href="#installation">Installation</a></li> </ul> </li> <li><a href="#usage-examples">Usage Examples</a></li> <li><a href="#contributing">Contributing</a></li> <li><a href="#example-html-report-output">Example HTML Report Output</a></li> <li><a href="#license">License</a></li> <li><a href="#contact">Contact</a></li> </ol> </details>  ## About The Project DataDelta is a very useful Python package for easily comparing two pandas dataframes for use in data analysis, data engineering, and tracking table changes across time. DataDelta generates a <a href="#example-html-report-output">report</a> as both a Python dict and HTML file that summarizes the key changes between two dataframes through completing a series of tests (that can also be selected individually). The Python report is intended for use as part of a DevOps / DataOps pipeline for testing to ensure table changes are expected. <a href="https://github.com/gibbsbravo/DataDelta/issues">Report Bug</a> · <a href="https://github.com/gibbsbravo/DataDelta/issues">Request Feature</a> (<a href="#top">back to top</a>)  ## Getting Started DataDelta is easy to install through pip or feel free to clone locally to make changes. ### Dependencies DataDelta has very few dependencies: - <a href='https://pandas.pydata.org/'>pandas</a>: _a fast, powerful, flexible and easy to use open source data analysis and manipulation tool_ - DataDelta is built on for comparing dataframes - <a href='https://numpy.org/'>numpy</a>: _The fundamental package for scientific computing with Python_ - used for transformations and calculations - <a href='https://jinja.palletsprojects.com/en/3.0.x/'>jinja2</a>: _a fast, expressive, extensible templating engine_ - used to generate the HTML report - <a href='https://docs.pytest.org/en/6.2.x/'>pytest</a> (optional): _a mature full-featured Python testing tool that helps you write better programs_ - used for testing ### Installation - Install using Pip through PyPI: ```sh pip install datadelta ``` OR - Clone the repo locally: ```sh git clone https://github.com/gibbsbravo/DataDelta.git ``` (<a href="#top">back to top</a>)  ## Usage Examples - Quick starter code to get summary dataframe changes report: ```sh import pandas as pd import datadelta as delta old_df = pd.read_csv('MainTestData_old_df.csv') # Add your old dataframe here new_df = pd.read_csv('MainTestData_new_df.csv') # Add your new dataframe here primary_key = 'A' # Set the primary key column_subset = None # Specify the subset of columns of interest or leave None to compare all columns # The consolidated_report dictionary will contain the summary changes consolidated_report, record_changes_comparison_df = delta.create_consolidated_report( old_df, new_df, primary_key, column_subset) # This will create a report named datadelta_html_report.html in the current working directory containing the summary changes delta.export_html_report(consolidated_report, record_changes_comparison_df, export_file_name='datadelta_html_report.html', overwrite_existing_file=False) ``` - Get dataframe summary: ```sh import pandas as pd import datadelta as delta new_df = pd.read_csv('MainTestData_new_df.csv') # Add your new dataframe here # Returns a report summarizing the key attributes and values of a dataframe summary_report = delta.get_df_summary( input_df=new_df, primary_key=primary_key, column_subset=column_subset, max_cols=15) ``` - Get record count changes report: ```sh old_df = pd.read_csv('MainTestData_old_df.csv') # Add your old dataframe here new_df = pd.read_csv('MainTestData_new_df.csv') # Add your new dataframe here primary_key = 'A' # Set the primary key column_subset = None # Specify the subset of columns of interest or leave None to compare all columns # Returns a report summarizing any changes to the number of records (and composition) between two dataframes record_count_change_report = delta.check_record_count( old_df, new_df, primary_key) ``` Other functions include: - check_column_names: Returns a report summarizing any changes to column names between two dataframes - check_datatypes: Returns a report summarizing any columns with different datatypes - check_chg_in_values: Returns a report summarizing any records with changes in values - get_records_in_both_tables: Returns the records found in both dataframes - get_record_changes_comparison_df: Returns a dataframe comparing any records with changes in values by column - export_html_report: Exports an html report of the differences between two dataframes (<a href="#top">back to top</a>)  ## Contributing Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again! 1. Fork the Project 2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) 3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the Branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request (<a href="#top">back to top</a>)  ## Example HTML Report Output ![Report Screenshot][report-screenshot] (<a href="#top">back to top</a>)  ## License Distributed under the GNU General Public License v3 (GPLV3) License. See `LICENSE.txt` for more information. (<a href="#top">back to top</a>)  ## Contact Andrew Gibbs-Bravo - andrewgbravo@gmail.com Project Link: [https://github.com/gibbsbravo/DataDelta](https://github.com/gibbsbravo/DataDelta) (<a href="#top">back to top</a>)   [contributors-shield]: https://img.shields.io/github/contributors/gibbsbravo/DataDelta.svg?style=for-the-badge [contributors-url]: https://github.com/gibbsbravo/DataDelta/graphs/contributors [forks-shield]: https://img.shields.io/github/forks/gibbsbravo/DataDelta.svg?style=for-the-badge [forks-url]: https://github.com/gibbsbravo/DataDelta/network/members [stars-shield]: https://img.shields.io/github/stars/gibbsbravo/DataDelta.svg?style=for-the-badge [stars-url]: https://github.com/gibbsbravo/DataDelta/stargazers [issues-shield]: https://img.shields.io/github/issues/gibbsbravo/DataDelta.svg?style=for-the-badge [issues-url]: https://github.com/gibbsbravo/DataDelta/issues [license-shield]: https://img.shields.io/github/license/gibbsbravo/DataDelta.svg?style=for-the-badge [license-url]: https://github.com/gibbsbravo/DataDelta/blob/master/LICENSE.txt [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555 [report-screenshot]: https://github.com/gibbsbravo/DataDelta/blob/master/images/DatasetComparisonReport.png?raw=true

نیازمندی

مقدار	نام
>=1.0.0	pandas
-	numpy
-	jinja2
-	pytest
-	check-manifest

نحوه نصب

نصب پکیج whl datadelta-0.0.2:

pip install datadelta-0.0.2.whl

نصب پکیج tar.gz datadelta-0.0.2:

pip install datadelta-0.0.2.tar.gz