Metadata-Version: 2.4
Name: dftest
Version: 0.5.0
Summary: A library for testing and analyzing data integrity.
Author: Atai Ambus, Ran Gilad-Bachrach
License-Expression: MIT
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: dill~=0.4.0
Requires-Dist: matplotlib~=3.9
Requires-Dist: numpy<3,>=1.24
Requires-Dist: pandas<4,>=2.0
Requires-Dist: seaborn~=0.13.0
Requires-Dist: termcolor<4,>=2.4

# dftest

Real data is messy. If it's because of changes in standards, human error, or
just because the real data points are messy, when taking real data from
insitutions that were usually not keeping out around for the purposes of data
science, we tend to encounter inconsistent formatting, missing values,
duplicates, inconsistent typing, and other issues.

dftest (inspired by pytest) is a project aims to give data scientists tools to
detect problematic data which may lead to unexpected results, and loctae these
rows and columns which may need to be removed or require additional cleaning.

Full documentation at [README.ipynb](README.ipynb). Some of the outputs in the
notebooks are partial or incorrect - specifically, graphs don't display
correctly and the save action shows errors. This does not reflect actual
behaviour and will be fixed in the coming days.
