semvec/pypi-packages huggingface dataset:
mkdocstrings-python-generator 1.0.0rc1
Autogenerates a project's mkdocs reference documentation via mkdocstrings-python
License: MIT
Requires Python: <4.0,>=3.10

# mkdocstrings-python-generator

mkdocstrings-python-generator is a mkdocs plugin for generating markdown pages automatically from python source code.

It is intended to fill a gap which is currently left to each user of mkdoctstings-python.  Namely: generating markdown
files for each python file.

_Note despite the name there is no affiliation between mkdocstrings and mkdocstrings-python-generator. Please try to 
determine which plugin is to blame before posting issues here or [there](https://github.com/mkdocstrings/python)._


## Features

Its advantages over the [mkdocstrings-python recipe](https://mkdocstrings.github.io/recipes/#automatic-code-reference-pages) are:

 - ✅ Easier to use (no writing code for yourself)
 - ✅ Well formatted nav out of the box. Package names with underscores are not title case 📦
 - ✅ Compatibility with both explicit nav defined in mkdocs.yaml and implicit nav with no definition in mkdocs.yaml
 - ✅ Supports `__init__.py` files as [section indexes](https://squidfunk.github.io/mkdocs-material/setup/setting-up-navigation/#section-index-pages) if supported by the theme.
 - ✅ Edit URI compatible with both `edit_uri` and `edit_uri_template`

## Minimal Example

See [Configuration](../configuration/) for more detail


```yaml
# Configure mkdocstrings-python
- mkdocstrings:
    handlers:
      python:
        options:
          show_submodules: false

# Configure mkdocstrings-python-generator
- mkdocstrings-python-generator:
    source_dirs:
      # Path to your source directory relative to mkdocs.yaml directory.
      - base: src/
```
nlptextprep 0.0.3
One step Python package for NLP friendly data preprocessing.
License: MIT

.. These are examples of badges you might want to add to your README:
   please update the URLs accordingly

    .. image:: https://api.cirrus-ci.com/github/<USER>/nlptextprep.svg?branch=main
        :alt: Built Status
        :target: https://cirrus-ci.com/github/<USER>/nlptextprep
    .. image:: https://readthedocs.org/projects/nlptextprep/badge/?version=latest
        :alt: ReadTheDocs
        :target: https://nlptextprep.readthedocs.io/en/stable/
    .. image:: https://img.shields.io/coveralls/github/<USER>/nlptextprep/main.svg
        :alt: Coveralls
        :target: https://coveralls.io/r/<USER>/nlptextprep
    .. image:: https://img.shields.io/pypi/v/nlptextprep.svg
        :alt: PyPI-Server
        :target: https://pypi.org/project/nlptextprep/
    .. image:: https://img.shields.io/conda/vn/conda-forge/nlptextprep.svg
        :alt: Conda-Forge
        :target: https://anaconda.org/conda-forge/nlptextprep
    .. image:: https://pepy.tech/badge/nlptextprep/month
        :alt: Monthly Downloads
        :target: https://pepy.tech/project/nlptextprep
    .. image:: https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter
        :alt: Twitter
        :target: https://twitter.com/nlptextprep

.. image:: https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold
    :alt: Project generated with PyScaffold
    :target: https://pyscaffold.org/

|

===========
nlptextprep
===========


    One Step Python Package to preprocess text data for NLP tasks. This package is designed to clean, transform, and standardize text data, making it an ideal choice for applications in natural language processing, text analysis, and data cleaning.


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

.. code-block:: bash

   pip install nlptextprep

Usage
-----

.. code-block:: python

   from nlptextprep import preprocess_text

   text = "This is a sample text containing a #hashtag, 100+ @mention in 70% , (4 - 2)/7 and line breaks in https://botpenguin.com .\nCheck it out!"
   cleaned_text = preprocess_text(text)
   print(cleaned_text)


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.6. For details and usage
information on PyScaffold see https://pyscaffold.org/.