chompjs can be used in web scrapping for turning JavaScript objects embedded in pages into valid Python dictionaries.
In web scraping this is particularly useful for parsing Javascript variables like:
python
import chompjs
js = """
var myObj = {
myMethod: function(params) {
// ...
},
myValue: 100
}
"""
chompjs.parse_js_object(js, json_params={'strict': False})
{'myMethod': 'function(params) {\n // ...\n }', 'myValue': 100}
In practice this can be used to extract hidden JSON data like data from <script id=__NEXT_DATA__> elements
from nextjs (and similar) websites. Unlike json.loads command chompjs can ingest json documents that contain
javascript natives like functions making it a super easy way to scrape hidden web data objects.
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
# basic use
import chompjs
js = """
var myObj = {
myMethod: function(params) {
// ...
},
myValue: 100
}
"""
chompjs.parse_js_object(js, json_params={'strict': False})
{'myMethod': 'function(params) {\n // ...\n }', 'myValue': 100}
# example how to use with hidden data parsing:
import httpx
import chompjs
from parsel import Selector
response = httpx.get("http://example.com")
hidden_script = Selector(response.text).css("script#__NEXT_DATA__::text").get()
data = chompjs.parse_js_object(hidden_script)
print(data['props'])
```
```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
```