library of functions for managing and improving data quality in Datasets
Project description
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
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
pip install data-quality-kit
from data_quality_quick.validate_formats import check_type_format
Functionalities
- df_is_empty: Check if a DataFrame is empty.
- check_nulls: Checks for null values in a specified column of a DataFrame.
- check_type_format: Check if all non-null entries in a specified column of a DataFrame are of the specified data type.
- check_no_duplicates: Checks for duplicate values in the specified primary key column of a DataFrame.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
data-quality-kit-0.1.0.tar.gz
(7.8 kB
view hashes)
Built Distribution
Close
Hashes for data_quality_kit-0.1.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 458b3d40baa4859cfcf08489691901aef35e781daf67ac3861dcdd913c4d0d97 |
|
MD5 | cf0606d98edff122f9ce4d25b4b40acf |
|
BLAKE2b-256 | 15bf78e7089b637d500a53f1cf34965234d94bb0c931a2413c7ed847ec38bef9 |