semvec/pypi-packages huggingface dataset:
SplashScreen 9.0.0
SplashScreen For Python Editor
License: MIT
Keywords: Não Utilize

# NÃ£o Utilizi Esta ApplicaÃ§Ã£o  Pois Ela SerÃ¡ Utiliza Em Uma Aplicativo


# Esta AplicaÃ§Ã£o Ã© Para Um Editor Py em Desenvolvimento

__Criado Por JosÃ© Alexsandro__




[Desenvolvido Pela Star Comics](https://starcomicstar.weebly.com/)

[Portifolio_Do_Autor](https://alexsandrodev.netlify.app/)
TEDCrawler 1.4.0
TEDCrawler is a Python library for taking data from TEDTalks. TEDTalks does not have an open api (at least until the day of publication of this package).
License: MIT
Keywords: ted TEDTalks Talks dataset

# TEDCrawler

TEDCrawler is a Python library for taking data from TEDTalks. TEDTalks does not have an open api (at least until the day of publication of this package).

![](https://i.ibb.co/wstJn9y/ted-talks-logo.png)
## Installation

Use the package manager [pip](https://pypi.org/project/TEDCrawler/) to install TEDCrawler.

```bash
pip install TEDCrawler
```

## Usage
To get all TED Talks data in a respective language
```python
from TEDCrawler import TEDCrawler

tc = TEDCrawler()
tc.download('pt-br','csv')
```

To get data from specified ted talks
```python
from TEDCrawler import TEDCrawler

tc = TEDCrawler()
links = [
'https://www.ted.com/talks/susan_lupack_the_race_to_decode_a_mysterious_language?language=pt-br', 
'https://www.ted.com/talks/ali_kashani_a_friendly_autonomous_robot_that_delivers_your_food?language=pt-br']
tc.crawLink(links)
```

To get data from specified ted talks by keyword
```python
from TEDCrawler import TEDCrawler

tc = TEDCrawler()
tc.findByWord('leadership', 'pt-br', 'csv')
```


## License
[MIT](https://choosealicense.com/licenses/mit/)
ms-cv 0.1.1
A correlation vector implementation in python
License: MIT

# ms_cv - Correlation Vector

[![GitHub Workflow - Build](https://img.shields.io/github/workflow/status/OpenXbox/ms_cv/build?label=build)](https://github.com/OpenXbox/ms_cv/actions?query=workflow%3Abuild)
[![PyPi](https://img.shields.io/pypi/v/ms_cv.svg)](https://pypi.python.org/pypi/ms_cv)

A correlation vector implementation in python, based on Microsoft's implementation.

## Usage

```py
import ms_cv

cll_vec = ms_cv.CorrelationVector()
print('Initial cll vector: {}'.format(cll_vec.get_value()))
print('Next iteration: {}'.format(cll_vec.increment()))
```

## Installation

```sh
pip install ms_cv
```

## Requirements

* None

## Compatibility

* Python 3

## License

MIT LICENSE

## Authors

`ms_cv` was written by `OpenXbox <noreply@openxbox.org>`.

Based on the implementation by Microsoft:

<https://github.com/search?q=org%3Amicrosoft+correlationVector>

<https://github.com/Microsoft/Telemetry-Client-for-Android/blob/master/AndroidCll/src/main/java/com/microsoft/cll/android/CorrelationVector.java>
wlnupdates 1.0.6
A simple API wrapper for wlnupdates
License: MIT
Requires Python: >= 3.7

<!-- Source: https://github.com/MattIPv4/template/blob/master/README.md -->

<!-- Title -->
<h1 align="center" id="wlupdatesapipy">
    wlnupdates-api.py
</h1>

<!-- Tag line --> 
<h3 align="center">A simple API wrapper for wlnupdates to get json data based on ID.</h3>


----

<!-- Content -->
## Installation

Install via pip (recommended)

```Shell
python3 -m pip install wlnupdates-api
```
## Wrapper
All Wrapper functions only accepts ID as param
Wrapper param | Value | Description
------------ | ------------- | -------------
ID | String **(optional)** | ID for the novel/series you're requesting


```Python
from wlnupdates-api import Wrapper

data = Wrapper()

print(data.get_series_data('3')

```
## Search
title_search param | Value | Description
------------ | ------------- | -------------
name | String **(required)** | Name for the novel/series you're requesting
similarityNum | Float **(optional)** | This param will output any lists with a value above the number you entered.
fullList | Boolean **(optional)** | If true, will output clean list with match(similarity num, name) and sid(series ID)

### Usage for Search
```Python
from wlnupdates-api import Search

data = Search()

print(data.title_search(name, similarityNum=0, fullList=False))
```


## Contributing

Contributions are always welcome!\
Take a look at any existing issues on this repository for starting places to help contribute towards, or simply create your own new contribution to the project.

When you are ready, simply create a pull request for your contribution and we will review it whenever we can!


* If you have an issue, please create a GitHub issue here to report it, include as much detail as you can.