semvec/pypi-packages huggingface dataset:
wheat-yield-prediction-toolkit 0.1.6
Wheat Yield Prediction Toolkit is a package that provides tools for predicting wheat yield at the county level in the main wheat-producing regions of the US using deep learning approaches. The package includes modules for data collection, preprocessing, modeling, and uncertainty analysis.
Requires Python: >=3.9

# Wheat Yield Prediction Toolkit

Are you interested in predicting winter wheat yield in your area? Look no further! The Wheat Yield Prediction Toolkit is a collection of tools for gathering and processing data from various sources to help you make informed predictions about winter wheat yield.

## Overview

The Wheat Yield Prediction Toolkit includes several modules for collecting data from different sources:



- USDA Quick Stats API for yield data
- Gee for remote sensing
- SoilGrids for soil data images
- NASA POWER provider for weather data

This README will guide you through the steps to set up and use the toolkit. Let's get started!
Search-Engines-Scraper-Tasos 0.6
Search google, bing, yahoo, and other search engines with python
Requires Python: >=3.7

# search_engines  
A Python library that queries Google, Bing, Yahoo and other search engines and collects the results from multiple search engine results pages.  
Please note that web-scraping may be against the TOS of some search engines, and may result in a temporary ban.

## Supported search engines  

_[Google](https://www.google.com)_  
_[Bing](https://www.bing.com)_  
_[Yahoo](https://search.yahoo.com)_  
_[Duckduckgo](https://duckduckgo.com)_  
_[Startpage](https://www.startpage.com)_  
_[Aol](https://search.aol.com)_  
_[Dogpile](https://www.dogpile.com)_  
_[Ask](https://uk.ask.com)_  
_[Mojeek](https://www.mojeek.com)_  
_[Brave](https://search.brave.com/)_  
_[Torch](http://xmh57jrzrnw6insl.onion/4a1f6b371c/search.cgi)_  

## Features  

 - Creates output files (html, csv, json).  
 - Supports search filters (url, title, text).  
 - HTTP and SOCKS proxy support.  
 - Collects dark web links with Torch.  
 - Easy to add new search engines. You can add a new engine by creating a new class in `search_engines/engines/` and add it to the  `search_engines_dict` dictionary in `search_engines/engines/__init__.py`. The new class should subclass `SearchEngine`, and override the following methods: `_selectors`, `_first_page`, `_next_page`. 
 - Python2 - Python3 compatible.  

## Requirements  

_Python 2.7 - 3.x_ with  
_[Requests](http://docs.python-requests.org/en/master/)_ and  
_[BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)_  

## Installation  

Run the setup file: `$ python setup.py install`.  
Done!  

## Usage  

As a library:  

```
from search_engines import Google

engine = Google()
results = engine.search("my query")
links = results.links()

print(links)
```

As a CLI script:  

```  
$ python search_engines_cli.py -e google,bing -q "my query" -o json,print
```

## Other versions  

 - [async-search-scraper](https://github.com/soxoj/async-search-scraper) A really cool asynchronous implementation, written by @soxoj