DataQualityReport (DQR) endeavors to quickly, both in machine and human attention time, make users aware of potential data quality issues to be addressed in Pandas Dataframes. It is a priority that generating diagnostics should be FAST (<1 minute), such that users are encouraged to use this utility without interuption to interactive development.
pip install dataqualityreport
from dataqualityreport import dqr_table
dqr_table(my_df) # supply your own dataframe: my_df
View the tutorial.
DataQualityReport uses Poetry for python library management.
All contributors must agree to the Contribution License Agreement.
# To create a venv with required dependencies
poetry install
This will use the existing poetry.lock file to know what dependencies (including versions) should be installed.
# run a command within the venv
poetry run ipython
# open a shell using the venv
poetry shell
A list of dependencies can be found in /pyproject.toml
# add a library using the CLI
poetry add matplotib
# To update the `poetry.lock` file based on new dependencies:
poetry update
# Includes mypy, pydocstring, etc.
make lint
# automatically run on every `git commit`
poetry run pre-commit install
# Tests
make unittest