| Name | dataquality-rules JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | This library is used for Data Quality |
| upload_time | 2024-12-18 15:24:30 |
| maintainer | None |
| docs_url | None |
| author | Abhishek Kumar |
| requires_python | >=3.6 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# Data Quality
This project provides a Data Quality Rule (DQR) enabler class for validating and reporting data quality metrics using Apache Spark and Jinja2. It allows users to perform various checks on a DataFrame, such as checking for null values, duplicates, uniqueness, range constraints, and values within a specific list. The results can be saved as an HTML report for easy review and sharing.
# Features
* Schema Validation : Compare the DataFrame's schema with an expected schema.
* Null Value Check : Identify the percentage of null values in specified columns.
* Duplicate Check : Find duplicate rows based on one or more columns.
* Uniqueness Check : Measure the uniqueness of values in specified columns.
* Range Check : Ensure column values fall within a defined range.
* Value Set Check : Verify if column values exist within a predefined list.
* HTML Report Generation : Automatically generate an HTML report summarizing all checks with visual tables.
# Installation
You can install the library using pip:
```bash
pip install dataquality_rules
```
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