We will start by quickly reviewing the parsing capabilities. Then, we will fetch websites with custom browsers, make requests, and parse the response.

Here's an HTML document generated by ChatGPT that we will be using as an example throughout this page:
```html
<html>
  <head>
    <title>Complex Web Page</title>
    <style>
      .hidden { display: none; }
    </style>
  </head>
  <body>
    <header>
      <nav>
        <ul>
          <li> <a href="#home">Home</a> </li>
          <li> <a href="#about">About</a> </li>
          <li> <a href="#contact">Contact</a> </li>
        </ul>
      </nav>
    </header>
    <main>
      <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 id="reviews">
        <h2>Customer Reviews</h2>
        <div class="review-list">
          <div class="review" data-rating="5">
            <p class="review-text">Great product!</p>
            <span class="reviewer">John Doe</span>
          </div>
          <div class="review" data-rating="4">
            <p class="review-text">Good value for money.</p>
            <span class="reviewer">Jane Smith</span>
          </div>
        </div>
      </section>
    </main>
    <script id="page-data" type="application/json">
      {
        "lastUpdated": "2024-09-22T10:30:00Z",
        "totalProducts": 3
      }
    </script>
  </body>
</html>
```
Starting with loading raw HTML above like this
```python
from scrapling.parser import Selector
page = Selector(html_doc)
page  # <data='<html><head><title>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! 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_first('.product-list [data-id="1"]')
# <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_first("//*[@id='products']/div/article")
# <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')
['Products']
>>> page.css_first('[data-id="1"]').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 quote.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 get a website's response directly through HTTP requests or by fetching it from browsers.

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')
```

> 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 allows you to fake the TLS fingerprint for a specific version of a browser.
> 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 (previously known as `PlayWrightFetcher`) provides many options to fetch/load websites' pages through 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_first("#search a::attr(href)")
'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_first("#search a::attr(href)")
'https://github.com/D4Vinci/Scrapling'
```
It's built on top of [Playwright](https://playwright.dev/python/) and it's currently providing three 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.
- Stealthy Playwright with custom stealth mode explicitly written for it. It's not top-tier stealth mode, but it bypasses many online tests like [Sannysoft's](https://bot.sannysoft.com/). Check out the `StealthyFetcher` class below for more advanced stealth mode. 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.

> Note: All requests done by this fetcher are waiting by default for all JavaScript to be fully loaded and executed. In detail, it waits for the `load` and `domcontentloaded` load states to be reached; you can make it wait for the `networkidle` load state by passing `network_idle=True`, as you will see later.

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 custom version of a modified Firefox browser called [Camoufox](https://github.com/daijro/camoufox), bypassing most bot detections by default. Scrapling offers a faster custom version, includes extra tools, and features easy configurations to further increase undetectability.
```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
```
> Note: All requests done by this fetcher are waiting by default for all JavaScript to be fully loaded and executed. In detail, it waits for the `load` and `domcontentloaded` load states to be reached; you can make it wait for the `networkidle` load state by passing `network_idle=True`, as you will see later.

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.

---

That's Scrapling at a glance. If you want to learn more about it, continue to the next section.