# Data-Quality-Kit
## Functional Description
A library of functions for managing and improving data quality in Datasets
## Owner
For any bugs or questions, please reach out to [Dante Pedrozo](mailto:dante.victor.33@gmail.com)
## Branching Methodology
This project follows a Git Flow simplified branching methodology
- **Master Branch**: production code
- **Develop Branch**: main integration branch for ongoing development. Features and fixes are merged into this branch before reaching master
- **Feature Branch**: created from develop branch to work on new features
## Prerequisites
This project uses:
- Language: Python 3.10
- Libraries:
- pandas
- pytest
- assertpy
## How to use it
Install the library
```bash
pip install data-quality-kit
```
```
from data_quality_quick.validate_formats import check_type_format
```
## Functionalities
- **Completeness**
- **assert_that_dataframe_is_empty**: Check if a DataFrame is empty.
- **Validity**
- **assert_that_there_are_not_nulls**: Checks for null values in a specified column of a DataFrame.
- **Consistency**
- **assert_that_there_are_not_duplicates**: Checks for duplicate values in the specified primary key column of a DataFrame.
- **assert_that_columns_values_match** : Check if all values in column2 of df2 are present in column1 of df1.
- **Accuracy**
- **assert_that_type_value**: Check if all non-null entries in a specified column of a DataFrame are of the specified data type.
- **assert_that_values_in_catalog**: Checks whether all values in the specified column of a DataFrame are present
in a catalog (list of values).
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"description": "# Data-Quality-Kit\n\n## Functional Description\nA library of functions for managing and improving data quality in Datasets\n\n## Owner\nFor any bugs or questions, please reach out to [Dante Pedrozo](mailto:dante.victor.33@gmail.com)\n\n## Branching Methodology\nThis project follows a Git Flow simplified branching methodology\n- **Master Branch**: production code\n- **Develop Branch**: main integration branch for ongoing development. Features and fixes are merged into this branch before reaching master\n- **Feature Branch**: created from develop branch to work on new features\n\n## Prerequisites\nThis project uses:\n- Language: Python 3.10\n- Libraries: \n - pandas\n - pytest\n - assertpy\n\n## How to use it\nInstall the library\n\n```bash\npip install data-quality-kit\n```\n```\nfrom data_quality_quick.validate_formats import check_type_format\n```\n\n## Functionalities\n\n- **Completeness**\n - **assert_that_dataframe_is_empty**: Check if a DataFrame is empty.\n- **Validity**\n - **assert_that_there_are_not_nulls**: Checks for null values in a specified column of a DataFrame.\n- **Consistency**\n - **assert_that_there_are_not_duplicates**: Checks for duplicate values in the specified primary key column of a DataFrame.\n - **assert_that_columns_values_match** : Check if all values in column2 of df2 are present in column1 of df1.\n- **Accuracy**\n - **assert_that_type_value**: Check if all non-null entries in a specified column of a DataFrame are of the specified data type.\n - **assert_that_values_in_catalog**: Checks whether all values in the specified column of a DataFrame are present\n in a catalog (list of values).\n\n",
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