datachecker

A Data checker which uses pandera to perform the bulk checks. This wrapper can be used to check contents of a dataframe against a pre defined schema either defined directly within your python script, or saved as a supported configuration file.

Getting started

To start using this project, first make sure your system meets its requirements.

It's suggested that you install this package and its requirements within a virtual environment.

Requirements

• Python 3.10+ installed

Contributors have some additional requirements - please see our contributing guidance.

Installing the package

Whilst in the root folder, in a terminal, you can install the package and its Python dependencies using:

pip install git+https://github.com/ONSdigital/datachecker.git

Pre-Defined Checks

These checks can be included in the lists for individual columns in your schema, depending on the data type.

Data Type	Check Name	Parameter	Check Definition
integer / double	Minimum value	min_val	Checks that all values are above or equal to the minimum value
integer / double	Maximum value	max_val	Checks that all values are below or equal to the maximum value
character	Minimum length	min_length	Checks that all strings have length are above or equal to the minimum length
character	Maximum length	max_length	Checks that all strings have length below or equal to the maximum length
character	allowed strings	allowed_strings	Validates that entries match a set of permitted values, list or regex can be used. (Optional and can use forbidden strings instead)
any	Missing values check	allow_na	Checks for missing or NA values in the column.
double	Minimum decimal places	min_decimal	Checks that all values have more or equal amounts of decimal places
double	Maximum decimal places	max_decimal	Checks that all values have less or equal amounts of decimal places
character	forbidden strings	forbidden_strings	Validates that entries do not contain a set of forbidden values, list can be used. (Optional and can use allowed strings instead. Does not support regex to use regex we recommend using allowed_characters. A TypeError message will be provided with further details)
date / datetime	Minimum Date	min_date	Checks that all dates are after the minimum date using the format “YYYY-MM-DD”
date / datetime	Maximum Date	max_date	Checks that all dates are before the maximum date using the format “YYYY-MM-DD”
date/ datetime	Minimum Datetime	min_datetime	Checks that all dates are after the minimum datetime. Accepted formats: Y, YM, YMD, YMDH, YMDHM and YMDHMS
date/ datetime	Maximum Datetime	max_datetime	Checks that all dates are before the maximum datetime. Accepted formats: Y, YM, YMD, YMDH, YMDHM and YMDHMS

Custom Checks

The ability to add custom checks is supported through pandera using lambda functions. Custom checks cannot be defined in the main schema and must instead be defined as its own dictionary in your python script. Then when creating your DataValidator object, simply pass this as an additional argument and your custom check will be applied across the entire dataframe.

Note: You will get a log entry per column for this check, even for columns that are not contained in your custom check.

my_custom_checks = {
    "my_custom_check_name" : lambda df: (df["column_1"] < 100) & (df["column_2"].isna())
}

new_validator = DataValidator(
    schema = schema, 
    data=df,
    file = "output_report.yaml",
    format="yaml",
    custom_checks = my_custom_checks)

new_validator.validate()

Install for contributors/developers

To install the contributing requirements, use:

python -m pip install -U pip setuptools
pip install -e .[dev]
pre-commit install

This installs an editable version of the package. This means that when you update the package code you do not have to reinstall it for the changes to take effect. This saves a lot of time when you test your code.

Remember to update the setup and requirement files inline with any changes to your package.

Creating the documentation locally

first pip instal the developer requirements, then run mkdocs serve to host the documentation on your local environment

pip install -e .[docs]
mkdocs serve

Project structure layout

The cookiecutter template generated for each project will follow this folder structure:

.
├── datachecker/
│   └── datachecker/
│       ├── checks_loaders_and_exporters/
│       │   ├── __init__.py
│       │   └── checks.py
│       │   └── schema_loader.py
│       │   └── validator_exporter.py
│       │   └── validator_template.html
│       ├── __init__.py
│       ├── main.yml
└── ...

Bumping project version

bump-my-version is used to streamline the process for creating new versions and releases. To view the possible version bumps use bump-my-version show-bump and the three possible options will be presented. Then to bump the version use bump-my-version bump <increment> where <increment> is replaced with either major, minor or patch.

A git tag is also created and can be pushed using the folowing code (pushing the v1.0.0 release tag!) git push origin v1.0.0

Licence

Unless stated otherwise, the codebase is released under the MIT License. This covers both the codebase and any sample code in the documentation. The documentation is © Crown copyright and available under the terms of the Open Government 3.0 licence.

Contributing

If you want to help us build and improve datachecker, please take a look at our contributing guidelines.

Acknowledgements

This project structure is based on the govcookiecutter template project.