semvec/pypi-packages huggingface dataset:
pysendyit 0.1.2
Un-Official Sendyit Python wrapper
License: MIT
Requires Python: >=3.6
Keywords: pysendyit sendy wrapper api

# Unofficial Sendy python API Wrapper
The wrapper provides convenient access to the [Sendy Logistics API](https://www.sendyit.com/) from applications written in server-side python.

![Testing](https://github.com/0x6f736f646f/sendyit-python/workflows/Testing/badge.svg)
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[![Build Status](https://travis-ci.com/0x6f736f646f/sendyit-python.svg?branch=master)](https://travis-ci.com/0x6f736f646f/sendyit-python)
[![Maintainability](https://api.codeclimate.com/v1/badges/6c6702b7007a11eb203f/maintainability)](https://codeclimate.com/github/0x6f736f646f/sendyit-python/maintainability)

## Installation
```sh
pip install pysendyit
```

## Usage
In order to run the demo, export the the following values to your environment. They can be found/generated at the API Dashboard.
```bash
export API_USERNAME="{your-api-username}"
export API_KEY="{your-api-key}"
export BASE_URL="{your-base-url}"
```

```python
from pysendyit.pysendyit import Sendy
import os

# Creating an instance of the Sendy Class
sendy = Sendy(api_username=os.getenv('API_USERNAME'), api_key=os.getenv('API_KEY'), base_url=os.getenv('BASE_URL'))

# Printing response
print(sendy.track_or_cancel_delivery(command="track", order_no="AA2395374", request_token_id="request_token_id"))

```

## Contributing

Please read [CONTRIBUTING.md](https://gist.github.com/PurpleBooth/b24679402957c63ec426) for details on our code of conduct, and the process for submitting pull requests to us.

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments

* Sendy Logistics
Basicensemblelearningargumentation 0.0.1
A basic argumentation process where the outcomes from different models can argue for generating a final output.
License: MIT
Requires Python: >=3.8

<h1>BasicEnsembleArgumentation</h1>

<p>The BasicEnsembleArgumentation library provides methods to generate reliable results. A data set is classified by several models (classifiers). Different models produce different outputs and the produced outputs are combined by an argumentation approach. The argumentation approach produces a final output through a decision making process. The whole process follows an ensemble architecture. </p>  
<h1>User Manual</h1>

<p>Install Basicenssembleargumentation: </p>

<code>pip install Basicenssembleargumentation</code>

<p>Import the library: </p>

<code>from EnsArg import argumentation</code>

<p>Import necessary pacakages: </p>

Change Log
============

0.0.1 (24/05/2020)
-------------------
- First Release
Basicensembleargumentation 0.0.1
A basic argumentation process where the outcomes from different models can argue for generating a final output.
License: MIT
Requires Python: >=3.8

<h1>BasicEnsembleArgumentation</h1>

<p>The BasicEnsembleArgumentation library provides methods to generate reliable results. A data set is classified by several models (classifiers). Different models produce different outputs and the produced outputs are combined by an argumentation approach. The argumentation approach produces a final output through a decision making process. The whole process follows an ensemble architecture. </p>  
<h1>User Manual</h1>

<p>Install Basicenssembleargumentation: </p>

<code>pip install Basicenssembleargumentation</code>

<p>Import the library: </p>

<code>from EnsArg import argumentation</code>

<p>Import necessary pacakages: </p>

Change Log
============

0.0.1 (24/05/2020)
-------------------
- First Release