semvec/pypi-packages huggingface dataset:
scikit-res 0.1.1
Very Basic package to store results of ML models
Requires Python: ==3.10.12

![image](./.assets/img.png)

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)  ![Python](https://img.shields.io/badge/python-3.10.x-green.svg) ![Repo Size](https://img.shields.io/github/repo-size/Sulstice/global-chem)  [![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) [![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/) ![Coverage](./.assets/cov.svg) ![CI](https://github.com/AlexandreGazagnes/skres/actions/workflows/ci.yaml/badge.svg)

# Scikit-res : Scikit-learn + results

## About

### Context

Very Basic package to store results of ML models
Grid search results are hard to exploit. This package aims to store them in a more convenient way.


## Installation

### Clone the repository

Please clone the repository using the following command :

* for https :
```bash
git clone https://github.com/AlexandreGazagnes/skres.git
```
* for ssh :
```bash
git clone git@github.com:AlexandreGazagnes/skres.git
```

### Install the dependencies

The project uses [Poetry](https://python-poetry.org/) to manage its dependencies. Please install it using the following command :

```bash
pip install poetry
```

Then, please install the dependencies using the following command :

```bash
poetry install
```

Alternatively, you can install the dependencies using the following command :

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -r .utils/requirements-dev.txt
```

## Usage

```python
from skres import SkRes
grid_search = GridSearchCV(...)
grid_search.fit(X_train, y_train)
skres = SkRes(grid_search)
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License

[GPLv3](LICENSE)
joserfc-wrapper 0.2.3
joserfc-wrapper is a library for easy use of JWT and automatic management of signature keys.
Requires Python: >=3.10,<4.0

### The `joserfc-wrapper` library simplifies the use of JWT and automates the management of signature keys.

#### Install
`pip install joserfc-wrapper`

#### Reason

The main purpose of this wrapper is to simplify the management of signature keys for generating JWT tokens using the [joserfc]((https://github.com/authlib/joserfc)) library and adhering to RFC standards. It offers two options for managing signature keys: securely storing generated keys in [HashiCorp Vault](https://github.com/hvac/hvac) or storing them on the filesystem. Additionally, it facilitates the use of JWT tokens in projects.

#### Need a custom solution for storing keys? We've got you covered.

If necessary, a custom object can be created to manage signing keys, including storing them in a database. However, this custom class must be a subclass of the parent [AbstractKeyStorage](https://github.com/heximcz/joserfc-wrapper/blob/main/joserfc_wrapper/AbstractKeyStorage.py) abstract class to implement the necessary methods.

#### Using a Virtual Environment for `joserfc_wrapper`
We recommend running joserfc_wrapper in a virtual environment for the following reasons:

- Project Isolation: A virtual environment isolates this project's dependencies from the rest of your system. This allows you to work with specific library versions required for joserfc_wrapper without the risk of conflicts with other projects.

- Virtual environments make it easy to manage and track project dependencies, which is crucial for ensuring reproducibility and consistency in development.

- Using a virtual environment ensures seamless integration and deployment, guaranteeing consistent operation across all development and production environments.

To create a virtual environment for joserfc_wrapper, follow these steps:
```bash
python -m venv .venv
source .venv/bin/activate
pip install joserfc_wrapper
```
By following these guidelines, you can ensure a smooth and problem-free experience when working with `joserfc_wrapper`.

#### [Documentation](https://github.com/heximcz/joserfc-wrapper/blob/main/docs/index.md)

### License

[joserfc](https://github.com/authlib/joserfc?tab=readme-ov-file#license) (BSD-3)

[joserfc_wrapper](./LICENSE) (MIT)

#### Contributions to the development of this library are welcome, ideally in the form of a pull request.
Feluda 0.0.6
feluda
Keywords: excel,excelreader,excelwriter,python,excel reader and writer,read excel data using python,write data in excel using python


# Feluda Package

This is a tutorial project for publishing your own python library on PyPi.

## Requirments

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install all the requirements.
Just follow the commands below:

Upgrade Setuptoos:
```bash
python -m pip install --upgrade twine setuptools
```

Install Wheel:
```bash
pip install wheel
```

Make a build for your Library/Package:
```bash
python setup.py sdist bdist_wheel
```

Install twine:
```bash
pip install twine
```

Upload Your Library:
```bash
twine upload dist/*
```

If the above command fail to upload:
```bash
python -m twine upload dist/*
```



If you want to upload through Git repo,
Add this code after import statements
```python
here = os.path.abspath(os.path.dirname(__file__))

with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh:
    long_description = "\n" + fh.read()
```
After add this code you have to add an Readme.md file and the just follow the commands above.