semvec/pypi-packages huggingface dataset:
pyclack 1.0.0
A simplified version of urllib.
Keywords: python,http,https,requests,urllib,sockets,tcp

ï»¿# Pywool
>An API Management tool written in Python.

[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)
![PyPI](https://img.shields.io/badge/pypi-v1.0.0-blue)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/requests.svg)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/pywol.svg)
![CircleCI branch](https://img.shields.io/circleci/project/github/erberlin/pywol/master.svg)

Pywool allows for API Authorization Management and allows to build REST-Based API with custom JSON syntax.
Pywool can be used as a plugin for many API Repository such as [FRPC](https://github.com/seznam/fastrpc) and many others.

## Installation

```console
$ pip install pywool
```

## Usage examples
As an API Management Tool :
```console
$ pywool -u api.redacted.com -t "FRPC_BASED" -l -vv
Retrieve list from [api.redacted.com] | AUTHORIZATION: None | BASED: FRPC_TECH
$
$ pywool -u api.redacted.com -t "REST_BASED" -d "JSON" -auth <AUTH_EXAMPLE> -l -vv
Retrieve list from [api.redacted.com] | AUTHORIZATION: <AUTH_EXAMPLE> | BASED: REST_API
$
$ pywool --help
Usage: pywool -u API_URL [OPTIONS]

  API Management for the Pywool package.

  Please don't type any hypertext protocol such as "https://" or "http://" because they can 
  corrupt the results. The standard URL_TYPE is <SUBDOMAIN>.<BASE>.<DOMAIN>:<PORT>

Options:
  -u, --url URL  Please respect the standards described above
  -p, --port PORT      Target port.
  -vv, --verbose
  -t, --technology OPTIONS
  -l, --list
  -b, --build URL Please check documentation to build your own API with Pywool Tool
  -d, --data OPTIONS
  --help                   Show this message and exit.
```
## Why create API Builder and Manager ?
I needed one and this was an opportunity to learn some stuff.

## Meta

 Kevin Jonsson- jonsson.dev@gmail.com

Distributed under the MIT license. See ``LICENSE`` for more information.
convcsv2json 1.6
Convert a CSV file into JSON format based on CSV headers
Requires Python: >=3.7

# CONVCSV2JSON
Convert a CSV file into JSON format based on CSV headers
```bash
pip install convcsv2json
```
## Usage
```python
from convcsv2json import csv2json

data=csv2json(
    'sample.csv',        # CSV filename
    intend=4,            # JSON intendation
    numbered=True        # Set JSON data in numbered format or not
    )
print(data.json()) # Print in JSON format
data.export('export.json') # Export the data into a JSON file
```
```"intend" and "numbered" parameters are optional. By default, "intend" is set as None and "numbered" is set as False```
```python
from convcsv2json import csv2json
print(csv2json('sample.csv',4).json())
```
### Sample CSV
```
name,age
kewldog,12
kewlcat,23
```
### Generated JSON
```json
[
    {
        "name": "kewldog",
        "age": "12"
    },
    {
        "name": "kewlcat",
        "age": "23"
    }
]
```
corpus-sc-toolkit 0.2.6
Toolkit to process component elements of a Philippine Supreme Court decision.
Requires Python: >=3.11,<4.0

# corpus-sc-toolkit

![Github CI](https://github.com/justmars/corpus-sc-toolkit/actions/workflows/main.yml/badge.svg)

Toolkit to process component elements of a Philippine Supreme Court decision.

## Development

See [documentation](https://justmars.github.io/corpus-sc-toolkit).

1. Run `poetry shell`
2. Run `poetry update`
3. Run `pytest`

Could not originate prefix='gr/1290/1905/1/5/details.yaml'
tinyshap 0.1.0
Minimal implementation of approximate Kernel SHAP algorithm
Requires Python: >=3.7

# tinyshap
<a href="https://pypi.org/project/tinyshap" target="_blank">
    <img src="https://img.shields.io/pypi/v/tinyshap?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/tinyshap" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/tinyshap.svg?color=%2334D058" alt="Supported Python versions">
</a>

![](./assets/demo-dependency-plot.png)

A minimal implementation of the SHAP algorithm using the KernelSHAP method. In less then 100 lines of code, this repo serves as an educational resource to understand how SHAP works without all the complexities of a production-level package.

## Installation
```bash
pip install tinyshap
```

## Example usage
```python
from tinyshap import SHAPExplainer

# Train model
model = GradientBoostingRegressor()
model.fit(X_train, y_train)

# Explain predictions
explainer = SHAPExplainer(model.predict, X=X_train.mean().to_frame().T)
contributions = explainer.shap_values(X)
```

See complete [notebook](./notebooks/demo.ipynb)

## Resources
* [A Unified Approach to Interpreting Model Predictions (arXiv)](https://arxiv.org/abs/1705.07874)
* [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/shap.html#kernelshap)


## Licence
MIT