semvec/pypi-packages huggingface dataset:
acore-df 0.1.1
Azerothcore DataFrame.
License: MIT
Requires Python: >=3.8


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Welcome to ``acore_df`` Documentation
==============================================================================
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    :target: https://acore-df.readthedocs.io/en/latest/

这个项目是为了解决在修改 Azerothcore 世界数据库, 会存在很多 ID code 到人类友好的名字的转换问题. 在以前, 为了增加代码的可维护性, 我需要定义 ID 的 Enum, 然后用 ID 取数据库中查找其他的 attribute. 而这样做很难保证数据在不断更新过程中的一致性. 于是我设计了一个新的方案. 新方案的核心是将 Google Sheet 中编写的数据作为 Ground Truth, 然后用 Python 自动生成所有的 Enum, SqlAlchemy ORM Class, Data Class 的定义的 Python 模块, 然后将数据 load 到一个 Sqlite 数据库中. 所有的 IO 都会走本地的 Sqlite 数据库, 这样会获得非常高的性能.


.. _install:

Install
------------------------------------------------------------------------------

``acore_df`` is released on PyPI, so all you need is to:

.. code-block:: console

    $ pip install acore-df

To upgrade to latest version:

.. code-block:: console

    $ pip install --upgrade acore-df
lib-ml-group3 0.3.11
A library for pre-processing data used in machine learning models
License: MIT
Requires Python: <3.13,>=3.9

# lib-ml

`lib-ml` is a Python library designed for preprocessing text data, especially tailored for machine learning applications. The library offers robust tools for tokenization, sequence padding, and label encoding, ensuring that text data is optimally prepared for model training and analysis. The library is available on PyPI and can be easily integrated into your projects. New releases are automatically created and pushed to PyPI with every push to main.

##  Repository Structure

```sh
└── lib-ml/
    ├── .github
    │   └── workflows
    ├── LICENSE
    ├── README.md
    ├── dist
    │   ├── lib_ml_group3-0.2.0-py3-none-any.whl
    │   └── lib_ml_group3-0.2.0.tar.gz
    ├── lib_ml_group3
    │   ├── __init__.py
    │   ├── load_model.py
    │   └── preprocessing.py
    ├── poetry.lock
    └── pyproject.toml
```

## Installation

Install `lib-ml` from PyPI:

```bash
pip install lib-ml-group3
```

## Features

`lib-ml` includes the following features:
- **Data Tokenization**: Convert text into sequences of tokens or characters.
- **Sequence Padding**: Pad sequences to a uniform length to ensure consistency among data inputs.
- **Label Encoding**: Encode labels in a way that is suitable for machine learning models.
- **Persistence**: Save and load preprocessed data using Python's pickle module for easy reusability.

## Usage

Here is a quick example of how to use `lib-ml` for text data preprocessing:

```python
from lib_ml.preprocessing import preprocess_data

# Preprocess the data and save it to disk
preprocess_data()
```

The `preprocess_data()` function reads data from specified input directories, processes the text and labels, and saves the tokenized and encoded outputs to designated output directories.

## License

`lib-ml` is open source software [licensed as MIT](LICENSE).

## Support

If you have any questions or issues with `lib-ml`, please open an issue on the project repository, and we will get back to you as soon as possible.