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chompjsvsparsel

MIT 4 1 175
15.5 thousand (month) Jul 30 2007 1.2.3(2 months ago)
1,067 8 36 BSD
1.9.0(22 days ago) Jul 26 2019 1.5 million (month)

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.

parsel is a library for parsing HTML and XML using selectors, similar to beautifulsoup. It is built on top of the lxml library and allows for easy extraction of data from HTML and XML files using selectors, similar to how you would use CSS selectors in web development. It is a light-weight library which is specifically designed for web scraping and parsing, so it is more efficient and faster than beautifulsoup in some use cases.

Some of the key features of parsel include:

  • CSS selector & XPath selector support:
    Two most common html parsing path languages are both supported in parsel. This allows selecting attributes, tags, text and complex matching rules that use regular expressions or XPath functions.
  • Modifying data:
    parsel allows you to modify the contents of an element, remove elements or add new elements to a document.
  • Support for both HTML and XML:
    parsel supports both HTML and XML documents and you can use the same selectors for both formats.

It is easy to use and less verbose than beautifulsoup, so it's quite popular among the developers who are working with Web scraping projects and parse data from large volume of web pages.

Highlights


css-selectorsxpath-selectors

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'])
from parsel import Selector

# this is our HTML page:
html = """
<head>
  <title>Hello World!</title>
</head>
<body>
  <div id="product">
    <h1>Product Title</h1>
    <p>paragraph 1</p>
    <p>paragraph2</p>
    <span class="price">$10</span>
  </div>
</body>
"""

selector = Selector(html)

# we can use CSS selectors:
selector.css("#product .price::text").get()
"$10"

# or XPath:
selector.xpath('//span[@class="price"]').get()
"$10"

# or get all matching elements:
print(selector.css("#product p::text").getall())
["paragraph 1", "paragraph2"]

# parsel also comes with utility methods like regular expression parsing:
selector.xpath('//span[@class="price"]').re("\d+")
["10"]

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