semvec/pypi-packages huggingface dataset:
email-fetcher 1.0.0
Enchanting open-source E-mail finding library for scraping lovers
License: MIT

# EmailFetcher

EmailFetcher is a Python library for getting valid email address from website. Find emails from website easily in just few line of codes.

âš warning: This library created only for  educational purpose and author is not responsible for any unethical activity.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install EmailFetcher.

```bash
pip install EmailFetcher
```
## Features
- avoid style tag E-mails
- accept list of multiple page source or single page source as string
- Advance regex and xpath used for image fetch accuracy
- Filter shared and example emails.


## Usage

```python
from EmailFetcher.getemail import email
import requests

website = "https://pritam-raj.github.io"

page_source = requests.get(website).text

email(page_source,website)
```
## Fetching email from multiple website 
```python
from concurrent.futures import as_completed
from requests_futures.sessions import FuturesSession

websites = ["https://www.nycautorepair.nyc/","https://expressoil.com"]

page_sources = []

with FuturesSession() as session:
    futures = [session.get(url,allow_redirects=True) for url in websites]
    for future in as_completed(futures):
        res = future.result()
        page_sources.append(res.text)

email(page_sources)
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License
[MIT](https://choosealicense.com/licenses/mit/)
emailfetcher 1.0.0
Enchanting open-source E-mail finding library for scraping lovers
License: MIT

# EmailFetcher

EmailFetcher is a Python library for getting valid email address from website. Find emails from website easily in just few line of codes.

âš warning: This library created only for  educational purpose and author is not responsible for any unethical activity.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install EmailFetcher.

```bash
pip install EmailFetcher
```
## Features
- avoid style tag E-mails
- accept list of multiple page source or single page source as string
- Advance regex and xpath used for image fetch accuracy
- Filter shared and example emails.


## Usage

```python
from EmailFetcher.getemail import email
import requests

website = "https://pritam-raj.github.io"

page_source = requests.get(website).text

email(page_source,website)
```
## Fetching email from multiple website 
```python
from concurrent.futures import as_completed
from requests_futures.sessions import FuturesSession

websites = ["https://www.nycautorepair.nyc/","https://expressoil.com"]

page_sources = []

with FuturesSession() as session:
    futures = [session.get(url,allow_redirects=True) for url in websites]
    for future in as_completed(futures):
        res = future.result()
        page_sources.append(res.text)

email(page_sources)
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License
[MIT](https://choosealicense.com/licenses/mit/)
ProbotDB 1.1
ProBot embed messages feature based database, :shitoh:
License: MIT
Keywords: probotdb,probot,db

# ProBot.DB
### ProBot embed messages feature based database, lol.

Store your data in ProBot database easily, using the embed messages feature, for free :).

## Installation

pip install ProBotDB

## Usage
Now all you need is a server ID with ProBot, and your auth token from probot.io.
```py
from ProbotDB import ProBotDB

database = ProBotDB('auth token', 'server ID', 'embed name')

database.set('foo', 'bar')
database.get('foo') # bar
database.push('foo', 'ok')
database.delete('foo')
database.clear()
```
databricks-clay 0.0.1
Databricks Deployment Utils
License: MIT

# Documentation

https://databricks-clay.readthedocs.io/en/latest/

# Development Setup

Create virual environment and install dependencies for local development:

```
python3.7 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -r dev_requirements.txt
```

Exporting variables doesn't make for a great development experience so I recommend using the enviroment manager tools of your editor and for testing create a ./pytest.ini that looks like this:

```
[pytest]
env =
    VARIABLE=value
    VARIABLE2=value2
```

**REMINDER: do NOT commit any files that contain security tokens**

Git ignore already contains an exclusion for pytest.ini


# Build

Build python wheel:
```
python setup.py sdist bdist_wheel
```

There is a CI build configured for this repo that builds on main origin on a private Azure DevOps service.

# Test

Dependencies for testing:
```
pip install --editable .
```

Run tests:
```
pytest
```

Test Coverage:
```
pytest --cov=databricks-clay --cov-report=html
```

View the report in a browser:
```
./htmlcov/index.html
```
gibson-dataset 0.5.0
Gibson dataset
License: MIT

# Gibson dataset
[![pypi](https://img.shields.io/pypi/v/gibson-dataset.svg)](https://pypi.org/project/gibsion-dataset/)
![](https://travis-ci.com/micheleantonazzi/gibson-dataset.svg?branch=main)

This python library is a tool to acquire and organize data using Gibson as simulation environment.

## CUDA support
This library use Nvidia GPU to accelerate the operations over the data acquired using Gibson.
To do this, [CuPy](https://cupy.dev/) framework is used: it offers an interface highly compatible than NumPy,
but its functionalities are accelerated with CUDA.
To use CuPy's features you must have a Nvidia GPU and the CUDA Toolkit correctly installed.

**NB: you can configure gibson-dataset to work with NumPy or CuPy without any effort.
If you don't a Nvidia device correctly configured, NumPy is used as default framework.**
probot.db 1.0
ProBot embed messages feature based database, :shitoh:
License: MIT
Keywords: probotdb,probot,db

# ProBot.DB
### ProBot embed messages feature based database, lol.

Store your data in ProBot database easily, using the embed messages feature, for free :).

## Installation

pip install probot.db

## Usage
Now all you need is a server ID with ProBot, and your auth token from probot.io.
```py
import ProbotDB 

database = ProBotDB('auth token', 'server ID', 'embed name')

database.set('foo', 'bar')
database.get('foo') # bar
database.push('foo', 'ok')
database.delete('foo')
database.clear()
```