chompjsvsfeedparser
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:
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.
feedparser is a Python module for downloading and parsing syndicated feeds. It can handle RSS 0.90, Netscape RSS 0.91, Userland RSS 0.91, RSS 0.92, RSS 0.93, RSS 0.94, RSS 1.0, RSS 2.0, Atom 0.3, Atom 1.0, and CDF feeds. It also parses several popular extension modules, including Dublin Core and Apple’s iTunes extensions.
To use Universal Feed Parser, you will need Python 3.6 or later. Universal Feed Parser is not meant to run standalone; it is a module for you to use as part of a larger Python program.
feedparser can be used to scrape data feeds as it can download them and parse the XML structured data.
Example Use
# 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'])
import feedparser
# the feed can be loaded from a remote URL
data = feedparser.parse('http://feedparser.org/docs/examples/atom10.xml')
# local path
data = feedparser.parse('/home/user/data.xml')
# or raw string
data = feedparser.parse('<xml>...</xml>')
# the result dataset is a nested python dictionary containing feed data:
data['feed']['title']