ruiavsralger
Ruia is an async web scraping micro-framework, written with asyncio and aiohttp, aims to make crawling url as convenient as possible.
Ruia is inspired by scrapy however instead of Twisted it's based entirely on asyncio and aiohttp.
It also supports various features like cookies, headers, and proxy, which makes it very useful in dealing with complex web scraping tasks.
ralger is a small web scraping framework for R based on rvest and xml2.
It's goal to simplify basic web scraping and it provides a convenient and easy to use API.
It offers functions for retrieving pages, parsing HTML using CSS selectors, automatic table parsing and auto link, title, image and paragraph extraction.
Example Use
#!/usr/bin/env python
"""
Target: https://news.ycombinator.com/
pip install aiofiles
"""
import aiofiles
from ruia import AttrField, Item, Spider, TextField
class HackerNewsItem(Item):
target_item = TextField(css_select="tr.athing")
title = TextField(css_select="a.storylink")
url = AttrField(css_select="a.storylink", attr="href")
async def clean_title(self, value):
return value.strip()
class HackerNewsSpider(Spider):
start_urls = [
"https://news.ycombinator.com/news?p=1",
"https://news.ycombinator.com/news?p=2",
]
concurrency = 10
# aiohttp_kwargs = {"proxy": "http://0.0.0.0:1087"}
async def parse(self, response):
async for item in HackerNewsItem.get_items(html=await response.text()):
yield item
async def process_item(self, item: HackerNewsItem):
async with aiofiles.open("./hacker_news.txt", "a") as f:
self.logger.info(item)
await f.write(str(item.title) + "\n")
if __name__ == "__main__":
HackerNewsSpider.start(middleware=None)
library("ralger")
url <- "http://www.shanghairanking.com/rankings/arwu/2021"
# retrieve HTML and select elements using CSS selectors:
best_uni <- scrap(link = url, node = "a span", clean = TRUE)
head(best_uni, 5)
#> [1] "Harvard University"
#> [2] "Stanford University"
#> [3] "University of Cambridge"
#> [4] "Massachusetts Institute of Technology (MIT)"
#> [5] "University of California, Berkeley"
# ralger can also parse HTML attributes
attributes <- attribute_scrap(
link = "https://ropensci.org/",
node = "a", # the a tag
attr = "class" # getting the class attribute
)
head(attributes, 10) # NA values are a tags without a class attribute
#> [1] "navbar-brand logo" "nav-link" NA
#> [4] NA NA "nav-link"
#> [7] NA "nav-link" NA
#> [10] NA
#
# ralger can automatically scrape tables:
data <- table_scrap(link ="https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW")
head(data)
#> # A tibble: 6 × 4
#> Rank Title `Lifetime Gross` Year
#> <int> <chr> <chr> <int>
#> 1 1 Avatar $2,847,397,339 2009
#> 2 2 Avengers: Endgame $2,797,501,328 2019
#> 3 3 Titanic $2,201,647,264 1997
#> 4 4 Star Wars: Episode VII - The Force Awakens $2,069,521,700 2015
#> 5 5 Avengers: Infinity War $2,048,359,754 2018
#> 6 6 Spider-Man: No Way Home $1,901,216,740 2021