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Python toolkit for evaluating and visualizing the data quality of excel spreadsheets, csv files or other tabular data \n\n![Alt text](DQToolkit.png?raw=true \"DQToolkit Visual\")\n\nPurpose of the project\n======================\n\nDataQualityToolkit is a Python powered library for the evaluation and visualization of the data \nquality of data provided in excel spreadsheets, csv files or other tabular data fetched from the web\n\n\nGeneral Info\n=========================\n\n\nAuthor: Open Risk, http://www.openriskmanagement.com\n\nLicense: Apache 2.0\n\nDocumentation: Open Risk Manual, http://www.openriskmanual.org/wiki/Data_Quality\n\nTraining: Open Risk Academy, https://www.openriskacademy.com/login/index.php\n\nDevelopment website: https://github.com/open-risk/DataQualityToolkit\n\nDiscussion: https://www.openriskcommons.org/\n\nFunctionality\n=============\n\nNB: The 0.2 release is (still) a heavily (pre-)alpha version. \n\nYou can use DataQualityToolkit to:\n- Automatically produce validation reports and visualizations given an existing set of validation rules\n- Add to the validation rules\n- There is an assumption that the spreadsheets are formatted in standard columnar format with all worksheets starting at the same header row\n- There are many assumptions about the structure of wikitables (www source case)\n\nFile structure\n==============\n\n* datasets/ Contains datasets useful for getting started with the DataQualityToolkit\n* examples/ Contains examples\n* DQToolkit.py Main objects\n\nUsage\n=====\n\nLook at the examples directory on how to produce the visuals include in this README file\n\nDependencies\n============\n\n-  DataQualityToolkit is written in Python and depends on the standard numerical and data processing Python libraries (Numpy, Scipy, Pandas)\n-  The Visualization API depends on 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