Metadata-Version: 2.1
Name: datasoap
Version: 1.1.1
Summary: Supplementary library for pandas that processes dataframes derived from CSV files.
Home-page: https://github.com/Snake-Fingers/datasoap
License: MIT
Author: Mason Fryberger
Author-email: mason.fryberger@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: pandas
Project-URL: Documentation, https://datasoap.readthedocs.io/en/latest/
Project-URL: Repository, https://github.com/Snake-Fingers/datasoap
Description-Content-Type: text/markdown

# datasoap

## What is it?

datasoap is a supplementary library for pandas that processes dataframes derived from CSV files. The module checks cell data for correct numerical formatting and converts mismatched data to the correct data type (ex. str > float64).

## Main Features

- Strips unnecessary characters from numerical data fields in pandas dataframes to ensure consistent data formatting
- Provides before and after representations of dataframes to allow for comparison

## Repository

Source code is hosted on: [github.com/snake-fingers/data-soap](https://github.com/Snake-Fingers/datasoap)

## Dependencies

pandas - Python package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

## Installation

```
poetry add datasoap
```

## Documentation

User Docs can be found at [datasoap docs](https://datasoap.readthedocs.io/en/latest/)

## Background

datasoap originated from a Code Fellows 401 Python midterm project. The project team includes Alex Angelico, Grace Choi, Robert Carter, Mason Fryberger, and Jae Choi. After working with a few painful datasets using, we wanted to create a library that allows users to more efficiently manipulate clean datasets extracted from CSVs that may have inconsistent formatting.

## Future Features in Planning
- Additional functionality that re-names columns with the common unit of measure.
- Automation of identifying columns that require reformatting, removing the need for user input on instantiation
- Extend useablity to work with other virtual environments such as pip, and conda
