chompjsvspyquery

MIT 5 1 218

47.0 thousand (month) Jul 30 2007 1.4.0(2025-08-04 21:07:54 ago)

2,381 5 55 NOASSERTION

Dec 05 2008 2.0 million (month) 2.0.1(2024-08-30 08:12:22 ago)

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.

PyQuery is a Python library for working with XML and HTML documents. It is similar to BeautifulSoup and is often used as a drop-in replacement for it.

PyQuery is inspired by javascript's jQuery and uses similar API allowing selecting of HTML nodes through CSS selectors. This makes it easy for developers who are already familiar with jQuery to use PyQuery in Python.

Unlike jQuery, PyQuery doesn't support XPath selectors and relies entirely on CSS selectors though offers similar HTML parsing features like selection of HTML elements, their attributes and text as well as html tree modification.

PyQuery also comes with a http client (through requests) so it can load and parse web URLs by itself.

Highlights

css-selectors

Example Use

```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']) ```

```python from pyquery import PyQuery as pq # this is our HTML page: html = """ Hello World!

Product Title

paragraph 1

paragraph2

$10

""" doc = pq(html) # we can use CSS selectors: print(doc('#product .price').text()) "$10" # it's also possible to modify HTML tree in various ways: # insert text into selected element: print(doc('h1').append('discounted')) "

Product Titlediscounted

" # or remove elements doc('p').remove() print(doc('#product').html()) """

Product Titlediscounted

$10 """ # pyquery can also retrieve web documents using requests: doc = pq(url='http://httpbin.org/html', headers={"User-Agent": "webscraping.fyi"}) print(doc('h1').html()) ```