## Pick Your Path
Not sure where to start? Pick the path that matches what you're trying to do:
| I want to... | Start here |
|:---|:---|
| **Parse HTML** I already have | [Querying elements](parsing/selection.md) — CSS, XPath, and text-based selection |
| **Quickly scrape a page** and prototype | Pick a [fetcher](fetching/choosing.md) and test right away, or launch the [interactive shell](cli/interactive-shell.md) |
| **Build a crawler** that scales | [Spiders](spiders/getting-started.md) — concurrent, multi-session crawls with pause/resume |
| **Scrape without writing code** | [CLI extract commands](cli/extract-commands.md) or hook up the [MCP server](ai/mcp-server.md) to your favourite AI tool |
| **Migrate** from another library | [From BeautifulSoup](tutorials/migrating_from_beautifulsoup.md) or [Scrapy comparison](spiders/architecture.md#comparison-with-scrapy) |
---
We will start by quickly reviewing the parsing capabilities. Then we will fetch websites using custom browsers, make requests, and parse the responses.
Here's an HTML document generated by ChatGPT that we will be using as an example throughout this page:
```html
Complex Web Page
Products
Product 1
This is product 1
$10.99
In stock: 5
Product 2
This is product 2
$20.99
In stock: 3
Product 3
This is product 3
$15.99
Out of stock
Customer Reviews
Good value for money.
Jane Smith
```
Starting with loading raw HTML above like this
```python
from scrapling.parser import Selector
page = Selector(html_doc)
page # Complex Web Page</tit...'>
```
Get all text content on the page recursively
```python
page.get_all_text(ignore_tags=('script', 'style'))
# 'Complex Web Page\nHome\nAbout\nContact\nProducts\nProduct 1\nThis is product 1\n$10.99\nIn stock: 5\nProduct 2\nThis is product 2\n$20.99\nIn stock: 3\nProduct 3\nThis is product 3\n$15.99\nOut of stock\nCustomer Reviews\nGreat product!\nJohn Doe\nGood value for money.\nJane Smith'
```
## Finding elements
If there's an element you want to find on the page, you will find it! Your creativity level is the only limitation!
Finding the first HTML `section` element
```python
section_element = page.find('section')
# <data='<section id="products" schema='{"jsonabl...' parent='<main><section id="products" schema='{"j...'>
```
Find all `section` elements
```python
section_elements = page.find_all('section')
# [<data='<section id="products" schema='{"jsonabl...' parent='<main><section id="products" schema='{"j...'>, <data='<section id="reviews"><h2>Customer Revie...' parent='<main><section id="products" schema='{"j...'>]
```
Find all `section` elements whose `id` attribute value is `products`.
```python
section_elements = page.find_all('section', {'id':"products"})
# Same as
section_elements = page.find_all('section', id="products")
# [<data='<section id="products" schema='{"jsonabl...' parent='<main><section id="products" schema='{"j...'>]
```
Find all `section` elements whose `id` attribute value contains `product`.
```python
section_elements = page.find_all('section', {'id*':"product"})
```
Find all `h3` elements whose text content matches this regex `Product \d`
```python
page.find_all('h3', re.compile(r'Product \d'))
# [<data='<h3>Product 1</h3>' parent='<article class="product" data-id="1"><h3...'>, <data='<h3>Product 2</h3>' parent='<article class="product" data-id="2"><h3...'>, <data='<h3>Product 3</h3>' parent='<article class="product" data-id="3"><h3...'>]
```
Find all `h3` and `h2` elements whose text content matches the regex `Product` only
```python
page.find_all(['h3', 'h2'], re.compile(r'Product'))
# [<data='<h3>Product 1</h3>' parent='<article class="product" data-id="1"><h3...'>, <data='<h3>Product 2</h3>' parent='<article class="product" data-id="2"><h3...'>, <data='<h3>Product 3</h3>' parent='<article class="product" data-id="3"><h3...'>, <data='<h2>Products</h2>' parent='<section id="products" schema='{"jsonabl...'>]
```
Find all elements whose text content matches exactly `Products` (Whitespaces are not taken into consideration)
```python
page.find_by_text('Products', first_match=False)
# [<data='<h2>Products</h2>' parent='<section id="products" schema='{"jsonabl...'>]
```
Or find all elements whose text content matches regex `Product \d`
```python
page.find_by_regex(r'Product \d', first_match=False)
# [<data='<h3>Product 1</h3>' parent='<article class="product" data-id="1"><h3...'>, <data='<h3>Product 2</h3>' parent='<article class="product" data-id="2"><h3...'>, <data='<h3>Product 3</h3>' parent='<article class="product" data-id="3"><h3...'>]
```
Find all elements that are similar to the element you want
```python
target_element = page.find_by_regex(r'Product \d', first_match=True)
# <data='<h3>Product 1</h3>' parent='<article class="product" data-id="1"><h3...'>
target_element.find_similar()
# [<data='<h3>Product 2</h3>' parent='<article class="product" data-id="2"><h3...'>, <data='<h3>Product 3</h3>' parent='<article class="product" data-id="3"><h3...'>]
```
Find the first element that matches a CSS selector
```python
page.css('.product-list [data-id="1"]')[0]
# <data='<article class="product" data-id="1"><h3...' parent='<div class="product-list"> <article clas...'>
```
Find all elements that match a CSS selector
```python
page.css('.product-list article')
# [<data='<article class="product" data-id="1"><h3...' parent='<div class="product-list"> <article clas...'>, <data='<article class="product" data-id="2"><h3...' parent='<div class="product-list"> <article clas...'>, <data='<article class="product" data-id="3"><h3...' parent='<div class="product-list"> <article clas...'>]
```
Find the first element that matches an XPath selector
```python
page.xpath("//*[@id='products']/div/article")[0]
# <data='<article class="product" data-id="1"><h3...' parent='<div class="product-list"> <article clas...'>
```
Find all elements that match an XPath selector
```python
page.xpath("//*[@id='products']/div/article")
# [<data='<article class="product" data-id="1"><h3...' parent='<div class="product-list"> <article clas...'>, <data='<article class="product" data-id="2"><h3...' parent='<div class="product-list"> <article clas...'>, <data='<article class="product" data-id="3"><h3...' parent='<div class="product-list"> <article clas...'>]
```
With this, we just scratched the surface of these functions; more advanced options with these selection methods are shown later.
## Accessing elements' data
It's as simple as
```python
>>> section_element.tag
'section'
>>> print(section_element.attrib)
{'id': 'products', 'schema': '{"jsonable": "data"}'}
>>> section_element.attrib['schema'].json() # If an attribute value can be converted to json, then use `.json()` to convert it
{'jsonable': 'data'}
>>> section_element.text # Direct text content
''
>>> section_element.get_all_text() # All text content recursively
'Products\nProduct 1\nThis is product 1\n$10.99\nIn stock: 5\nProduct 2\nThis is product 2\n$20.99\nIn stock: 3\nProduct 3\nThis is product 3\n$15.99\nOut of stock'
>>> section_element.html_content # The HTML content of the element
'<section id="products" schema=\'{"jsonable": "data"}\'><h2>Products</h2>\n <div class="product-list">\n <article class="product" data-id="1"><h3>Product 1</h3>\n <p class="description">This is product 1</p>\n <span class="price">$10.99</span>\n <div class="hidden stock">In stock: 5</div>\n </article><article class="product" data-id="2"><h3>Product 2</h3>\n <p class="description">This is product 2</p>\n <span class="price">$20.99</span>\n <div class="hidden stock">In stock: 3</div>\n </article><article class="product" data-id="3"><h3>Product 3</h3>\n <p class="description">This is product 3</p>\n <span class="price">$15.99</span>\n <div class="hidden stock">Out of stock</div>\n </article></div>\n </section>'
>>> print(section_element.prettify()) # The prettified version
'''
<section id="products" schema='{"jsonable": "data"}'><h2>Products</h2>
<div class="product-list">
<article class="product" data-id="1"><h3>Product 1</h3>
<p class="description">This is product 1</p>
<span class="price">$10.99</span>
<div class="hidden stock">In stock: 5</div>
</article><article class="product" data-id="2"><h3>Product 2</h3>
<p class="description">This is product 2</p>
<span class="price">$20.99</span>
<div class="hidden stock">In stock: 3</div>
</article><article class="product" data-id="3"><h3>Product 3</h3>
<p class="description">This is product 3</p>
<span class="price">$15.99</span>
<div class="hidden stock">Out of stock</div>
</article>
</div>
</section>
'''
>>> section_element.path # All the ancestors in the DOM tree of this element
[<data='<main><section id="products" schema='{"j...' parent='<body> <header><nav><ul><li> <a href="#h...'>,
<data='<body> <header><nav><ul><li> <a href="#h...' parent='<html><head><title>Complex Web Page</tit...'>,
<data='<html><head><title>Complex Web Page</tit...'>]
>>> section_element.generate_css_selector
'#products'
>>> section_element.generate_full_css_selector
'body > main > #products > #products'
>>> section_element.generate_xpath_selector
"//*[@id='products']"
>>> section_element.generate_full_xpath_selector
"//body/main/*[@id='products']"
```
## Navigation
Using the elements we found above
```python
>>> section_element.parent
<data='<main><section id="products" schema='{"j...' parent='<body> <header><nav><ul><li> <a href="#h...'>
>>> section_element.parent.tag
'main'
>>> section_element.parent.parent.tag
'body'
>>> section_element.children
[<data='<h2>Products</h2>' parent='<section id="products" schema='{"jsonabl...'>,
<data='<div class="product-list"> <article clas...' parent='<section id="products" schema='{"jsonabl...'>]
>>> section_element.siblings
[<data='<section id="reviews"><h2>Customer Revie...' parent='<main><section id="products" schema='{"j...'>]
>>> section_element.next # gets the next element, the same logic applies to `quote.previous`.
<data='<section id="reviews"><h2>Customer Revie...' parent='<main><section id="products" schema='{"j...'>
>>> section_element.children.css('h2::text').getall()
['Products']
>>> page.css('[data-id="1"]')[0].has_class('product')
True
```
If your case needs more than the element's parent, you can iterate over the whole ancestors' tree of any element, like the one below
```python
for ancestor in section_element.iterancestors():
# do something with it...
```
You can search for a specific ancestor of an element that satisfies a function; all you need to do is pass a function that takes a `Selector` object as an argument and returns `True` if the condition is satisfied or `False` otherwise, like below:
```python
>>> section_element.find_ancestor(lambda ancestor: ancestor.css('nav'))
<data='<body> <header><nav><ul><li> <a href="#h...' parent='<html><head><title>Complex Web Page</tit...'>
```
## Fetching websites
Instead of passing the raw HTML to Scrapling, you can retrieve a website's response directly via HTTP requests or by fetching it in a browser.
A fetcher is made for every use case.
### HTTP Requests
For simple HTTP requests, there's a `Fetcher` class that can be imported and used as below:
```python
from scrapling.fetchers import Fetcher
page = Fetcher.get('https://scrapling.requestcatcher.com/get', impersonate="chrome")
```
With that out of the way, here's how to do all HTTP methods:
```python
>>> from scrapling.fetchers import Fetcher
>>> page = Fetcher.get('https://scrapling.requestcatcher.com/get', stealthy_headers=True, follow_redirects=True)
>>> page = Fetcher.post('https://scrapling.requestcatcher.com/post', data={'key': 'value'}, proxy='http://username:password@localhost:8030')
>>> page = Fetcher.put('https://scrapling.requestcatcher.com/put', data={'key': 'value'})
>>> page = Fetcher.delete('https://scrapling.requestcatcher.com/delete')
```
For Async requests, you will replace the import like below:
```python
>>> from scrapling.fetchers import AsyncFetcher
>>> page = await AsyncFetcher.get('https://scrapling.requestcatcher.com/get', stealthy_headers=True, follow_redirects=True)
>>> page = await AsyncFetcher.post('https://scrapling.requestcatcher.com/post', data={'key': 'value'}, proxy='http://username:password@localhost:8030')
>>> page = await AsyncFetcher.put('https://scrapling.requestcatcher.com/put', data={'key': 'value'})
>>> page = await AsyncFetcher.delete('https://scrapling.requestcatcher.com/delete')
```
!!! note "Notes:"
1. You have the `stealthy_headers` argument, which, when enabled, makes requests to generate real browser headers and use them, including a referer header, as if this request came from a Google search of this domain. It's enabled by default.
2. The `impersonate` argument lets you fake the TLS fingerprint for a specific browser version.
3. There's also the `http3` argument, which, when enabled, makes the fetcher use HTTP/3 for requests, which makes your requests more authentic
This is just the tip of the iceberg with this fetcher; check out the rest from [here](fetching/static.md)
### Dynamic loading
We have you covered if you deal with dynamic websites like most today!
The `DynamicFetcher` class (formerly `PlayWrightFetcher`) offers many options for fetching and loading web pages using Chromium-based browsers.
```python
>>> from scrapling.fetchers import DynamicFetcher
>>> page = DynamicFetcher.fetch('https://www.google.com/search?q=%22Scrapling%22', disable_resources=True) # Vanilla Playwright option
>>> page.css("#search a::attr(href)").get()
'https://github.com/D4Vinci/Scrapling'
>>> # The async version of fetch
>>> page = await DynamicFetcher.async_fetch('https://www.google.com/search?q=%22Scrapling%22', disable_resources=True)
>>> page.css("#search a::attr(href)").get()
'https://github.com/D4Vinci/Scrapling'
```
It's built on top of [Playwright](https://playwright.dev/python/), and it's currently providing two main run options that can be mixed as you want:
- Vanilla Playwright without any modifications other than the ones you chose. It uses the Chromium browser.
- Real browsers like your Chrome browser by passing the `real_chrome` argument or the CDP URL of your browser to be controlled by the Fetcher, and most of the options can be enabled on it.
Again, this is just the tip of the iceberg with this fetcher. Check out the rest from [here](fetching/dynamic.md) for all details and the complete list of arguments.
### Dynamic anti-protection loading
We also have you covered if you deal with dynamic websites with annoying anti-protections!
The `StealthyFetcher` class uses a stealthy version of the `DynamicFetcher` explained above.
Some of the things it does:
1. It easily bypasses all types of Cloudflare's Turnstile/Interstitial automatically.
2. It bypasses CDP runtime leaks and WebRTC leaks.
3. It isolates JS execution, removes many Playwright fingerprints, and stops detection through some of the known behaviors that bots do.
4. It generates canvas noise to prevent fingerprinting through canvas.
5. It automatically patches known methods to detect running in headless mode and provides an option to defeat timezone mismatch attacks.
6. It makes requests look as if they came from Google's search page of the requested website.
7. and other anti-protection options...
```python
>>> from scrapling.fetchers import StealthyFetcher
>>> page = StealthyFetcher.fetch('https://www.browserscan.net/bot-detection') # Running headless by default
>>> page.status == 200
True
>>> page = StealthyFetcher.fetch('https://nopecha.com/demo/cloudflare', solve_cloudflare=True) # Solve Cloudflare captcha automatically if presented
>>> page.status == 200
True
>>> page = StealthyFetcher.fetch('https://www.browserscan.net/bot-detection', humanize=True, os_randomize=True) # and the rest of arguments...
>>> # The async version of fetch
>>> page = await StealthyFetcher.async_fetch('https://www.browserscan.net/bot-detection')
>>> page.status == 200
True
```
Again, this is just the tip of the iceberg with this fetcher. Check out the rest from [here](fetching/stealthy.md) for all details and the complete list of arguments.
---
That's Scrapling at a glance. If you want to learn more, continue to the next section.