DataQualityReport

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.

Usage

Installation

pip install dataqualityreport

Quick Start

from dataqualityreport import dqr_table
dqr_table(my_df) # supply your own dataframe: my_df

Example Table

Learn More

View the tutorial.

Developing / Contributing

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

Code Style and Standards

# Includes mypy, pydocstring, etc.
make lint

# automatically run on every `git commit`
poetry run pre-commit install

# Tests
make unittest