semvec/pypi-packages huggingface dataset:
untidy 0.0.1a4
Python package for creating messy data.
License: MIT
Requires Python: >=3.8

# Untidy
A Python library for uncleaning your dataset.

[![Check
status](https://github.com/dainstudios/untidy/actions/workflows/check.yml/badge.svg)](https://github.com/dainstudios/untidy/actions/workflows/check.yml)

## Overview
Have you ever wondered how to introduce specific problems to your clean data? Now you can apply our out-of-the-box solution to untidy your data according to your needs.

The solution can be used primarily for educational purposes, where clean example data is made more realistic.

Real world data is often poised with missing values, datetime issues, data type mismatches, string encoding problems.

You can introduce the following problems to your data:
* Adding missing values
* Adding outliers
* Changing the encoding of strings
* Changing the data type of numeric columns to strings
* Adding duplicate rows
* Adding duplicate columns
* Adding extra characters to strings

The package is designed to work with `pandas` DataFrames.

```
from untidy import untidyfy
messy_df = untidyfy(clean_df, 
                    corruption_level=4, # how much mess you want (0-10)
                    nans=True,
                    outliers=True,
                    text_noise=True,
                    mess_with_numbers=True,
                    mess_with_string_encodings=True,
                    duplicate_rows=True,
                    duplicate_columns=True)
```

## Installation
Can be installed via directly via pip or by downloading the `untidy-{release-version}.tar.gz` file under release section. Run the command

```commandline
pip install `untidy-{release-version}.tar.gz`
```
 
* * *

<p align="center">
  <img src="https://github.com/dainstudios/untidy/blob/main/resources/dain-logo.svg" alt="DAIN logo" width="250"/>
</p>
django-internationalflavor 0.4.3
Complementing django-localflavor with fields that are applicable to multiple countries
License: MIT

django-internationalflavor
==========================
.. image:: https://travis-ci.com/ralphje/django-internationalflavor.svg?branch=master
   :target: https://travis-ci.com/ralphje/django-internationalflavor?branch=master

.. image:: http://codecov.io/github/ralphje/django-internationalflavor/coverage.svg?branch=master
   :target: http://codecov.io/github/ralphje/django-internationalflavor?branch=master

.. image:: https://landscape.io/github/ralphje/django-internationalflavor/master/landscape.svg
   :target: https://landscape.io/github/ralphje/django-internationalflavor/master

.. image:: https://img.shields.io/pypi/v/django-internationalflavor.svg
   :target: https://pypi.python.org/pypi/django-internationalflavor

.. image:: https://readthedocs.org/projects/django-internationalflavor/badge/?version=latest&style=flat
   :target: https://readthedocs.org/projects/django-internationalflavor/?badge=latest

``django-internationalflavor`` is born to complement the ``django-localflavor`` package. While localflavor is
awesome when you are making a localized app, the reality is that you often need to accommodate for users from multiple
countries. While Django has great support for internationalization and localization, there is no package that helps you
store data from all over the world. This package aims to fill the gap and provides fields that are designed for use in
almost every country, while enforcing consistent data types.

Please note, this package is still in development. Most importantly, it lacks tests.

Documentation and source
------------------------
The documentation of this project is available at http://django-internationalflavor.readthedocs.org/en/latest/

The source code lives at https://github.com/ralphje/django-internationalflavor

Installation
------------

    pip install django-internationalflavor