httpx is a fully featured HTTP client for Python 3, which provides sync and async APIs, and support for both HTTP/1.1 and HTTP/2. It is designed to be a replacement for the popular requests package, with the added benefit of being fully compatible with Python 3's async features.
One of the main features of httpx is its support for asynchronous programming. This means that it can send multiple requests at the same time, without blocking the execution of your program. This can lead to significant performance improvements, especially when working with many small requests, or when dealing with slow or unreliable network connections.
httpx also supports sending HTTP/2 requests, which allows for more efficient use of network resources and can result in faster page loads.
One of the strengths of httpx is the possibility of working on streaming mode for the response data. This means you can process the response as it comes in, instead of waiting for the entire response to be received. This is useful when working with large files, or when you need to process the data in real-time.
Additionally, httpx provides a number of other features that are common in modern HTTP clients, such as support for sending and receiving cookies, handling redirects, and working with multipart file uploads. It also include support for several well-known authentication modules like BasicAuth, DigestAuth, and BearerAuth.
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.
```python
import httpx
# Just like requests httpx can be used directly
response = httpx.get("http://webscraping.fyi/")
response.status_code
200
response.text
"text"
response.content
b"bytes"
# HTTP2 needs to be enabled explicitly and is recommended for web scraping:
response = httpx.get("http://webscraping.fyi/", http2=True)
# httpx can automatically convert json responses to Python dictionaries:
response = httpx.get("http://httpbin.org/json")
print(response.json())
{'slideshow': {'author': 'Yours Truly', 'date': 'date of publication', 'slides': [{'title': 'Wake up to WonderWidgets!', 'type': 'all'}, {'items': ['Why WonderWidgets are great', 'Who buys WonderWidgets'], 'title': 'Overview', 'type': 'all'}], 'title': 'Sample Slide Show'}}
# for POST request it can ingest Python's dictionaries as JSON:
response = requests.post("http://httpbin.org/post", json={"query": "hello world"})
# or form data:
response = requests.post("http://httpbin.org/post", data={"query": "hello world"})
# persistent client can be established using Client object
# this allows to set default values and automatically track cookies
from httpx import Client
c = Client(headers={"User-Agent": "webscraping.fyi"}, http2=True)
c.get('http://httpbin.org/cookies/set/foo/bar')
print(c.cookies['foo'])
'bar'
print(c.get('http://httpbin.org/cookies').json())
{'cookies': {'foo': 'bar'}}
# for asynchronous requests AsyncClient must be used:
import asyncio
from httpx import AsyncClient
async def example_use():
async with AsyncClient(headers={"User-Agent": "webscraping.fyi"}) as client:
response = await client.get("http://httpbing.org/get")
# to schedule multiple requests concurrently use asyncio gather or as_completed
three_concurrent_responses = await asyncio.gather(
client.get("http://httpbing.org/get"),
client.get("http://httpbing.org/get"),
client.get("http://httpbing.org/get"),
)
asyncio.run(example_use())
```
```r
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
#>
#> 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
```