semvec/pypi-packages huggingface dataset:
criteria-etl 1.0a5
A library for Criteria related data wrangling.
Requires Python: >=3.7

# Criteria-ETL

This repository contains general tools for Extracting, Transforming and Loading data in Criteria projects.

## Structure of the package

### `transformers` module

- `columns_base`: scikit-learn compatible classes which transform `pandas.DataFrame`'s along the columns axis.
- `rows_base`: scikit-learn compatible classes which transform `pandas.DataFrame`'s along the rows axis.

### `utils` module

- `common_func`: miscellaneous functions which might be useful in any project.
- `config`: placeholder for setting-up global variables of the project, used for loading data.
- `dataload`: functions used for loading data. It imports global variables from `config` sub-module.
- `display`: functions used for displaying tables and plotting.
- `expansion_func`: functions designed for handling statistical sources which present an **expansion factor**. 

### `impute` module


- `model_based`: scikit-learn compatible classes for imputing observations based on predictive models.


## Tests

To run the test it is required to install ```pytest``` in your system. You can do it wiht

```bash
pip install pytest
```

Then, from the root directory you can run

```bash
pytest
```

And it will run every test in the ```tests``` directory.

## Issues and Pull Request conventions

Issues are the way each user can report bugs, request new features, ask for help, and so on.

When creating a new issue, please add at least one of this four possible tags: _bug, documentation, enhancement, or help wanted_.

Pull request should reference an Issue in case the motivation for that PR was that Issue.

Any member of the team has access to code reviews and issues. To be notified via e-mail, please click the watch
button in the top-right corner of the repository page.

## How to Contribute

Code guidelines are taken from [PEP 8](https://www.python.org/dev/peps/pep-0008/) and docstring format used is [Numpy's](https://numpydoc.readthedocs.io/en/latest/format.html).