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My First Heading

Here is a 2000+ word expert guide on how to select values between two nodes in BeautifulSoup and Python:


Web scraping is a useful technique for extracting data from websites. However, the data we want to extract is not always conveniently located in one place. Often, the target data is nested within the HTML structure of the page.

In this comprehensive guide, we will explore techniques for extracting data located between two known nodes or elements in the HTML document using the popular BeautifulSoup library in Python.

Whether you‘re an experienced web scraper looking to expand your skillset or a beginner wanting to learn how to extract nested data, this guide has you covered. By the end, you‘ll have expert-level knowledge of using BeautifulSoup‘s find_all() and find_next_siblings() methods to precisely target the data you need.


Before diving in, let‘s briefly go over what background knowledge will be helpful:

  • Proficiency in Python
  • Basic understanding of HTML structure
  • Experience using BeautifulSoup for basic web scraping tasks
  • Familiarity with concepts like elements, tags, and attributes

If you already have experience using BeautifulSoup and need a refresher on targeting nested data, feel free to skip ahead. Otherwise, let‘s start from the beginning.

A Quick BeautifulSoup Refresher

BeautifulSoup is a Python library that makes it easy to navigate, search, and extract data from HTML and XML documents. It represents the document as a parse tree that we can traverse to find the elements we want and extract their data.

Here‘s a quick example to showcase BeautifulSoup‘s basic usage:

from bs4 import BeautifulSoup

html = """

<p>My first paragraph.</p>

soup = BeautifulSoup(html, ‘html.parser‘)

h1 = soup.find(‘h1‘)

p = soup.find(‘p‘)  

This script would print out:

My First Heading 
My first paragraph.

We first instantiate a BeautifulSoup object with the HTML document string – this creates the parse tree. We can then use methods like find() to navigate to specific elements and extract their text or other attributes.

With this basic usage in mind, let‘s now see how to leverage BeautifulSoup to target nested data.

Using find_all() to Locate Nodes

In web scraping, we often need to extract data located between two known tags or landmarks on the page. For example, we may want to grab all the paragraphs between two heading tags.

BeautifulSoup‘s find_all() method allows us to conveniently locate multiple elements in the document. By passing in the name of the tag we want to find, it will return a list of all matching elements.

Let‘s look at an example:

html = """
<h2>Heading 1</h2>
<p>Paragraph 1</p> 
<p>Paragraph 2</p>
<h2>Heading 2</h2>
<p>Paragraph 3</p>
<p>Paragraph 4</p>

soup = BeautifulSoup(html, ‘html.parser‘)

headings = soup.find_all(‘h2‘)

This would print out:

[<h2>Heading 1</h2>, <h2>Heading 2</h2>]

We can see that find_all() has returned a list containing the two


Later, we will loop through these heading nodes to extract the paragraphs in between them. But first, let‘s look at how we can grab those paragraphs using find_next_siblings().

Traversing Siblings with find_next_siblings()

Once we‘ve located landmark nodes like headings, we can use find_next_siblings() to traverse the parse tree and selectively grab the target nodes that come after.

find_next_siblings() will return a generator that we can iterate through, checking each sibling element one after the other until we hit the next heading.

Here‘s an example:

html = """ 
<h2>Heading 1</h2>
<p>Paragraph 1</p>
<p>Paragraph 2</p> 
<h2>Heading 2</h2>
<p>Paragraph 3</p> 
<p>Paragraph 4</p>

soup = BeautifulSoup(html, ‘html.parser‘)
first_h2 = soup.find(‘h2‘)

for p in first_h2.find_next_siblings(‘p‘):

This would print out:

Paragraph 1
Paragraph 2

By passing ‘p‘ to find_next_siblings(), we iterate through only the paragraph siblings until the next heading is encountered.

With find_all() and find_next_siblings() in our toolkit, we‘re now ready to put together a script to extract data between landmarks.

Putting It All Together

Let‘s say we want to extract all paragraphs between

tags, storing them in a dictionary with the heading text as the key.

Here is what the code would look like:

html = """
<h2>Heading 1</h2>
<p>Paragraph 1</p> 
<p>Paragraph 2</p>
<h2>Heading 2</h2>
<p>Paragraph 3</p>
<p>Paragraph 4</p>  

soup = BeautifulSoup(html, ‘html.parser‘)

data = {}
for h2 in soup.find_all(‘h2‘):
    values = [] 
    for p in h2.find_next_siblings(‘p‘): 
    data[h2.text] = values


This script would print out:

{‘Heading 1‘: [‘Paragraph 1‘, ‘Paragraph 2‘], 
 ‘Heading 2‘: [‘Paragraph 3‘, ‘Paragraph 4‘]}

Here‘s what it‘s doing step-by-step:

  1. Find all

    elements using find_all() and loop through them.

  2. For each

    , iterate through its paragraph siblings using find_next_siblings(‘p‘).

  3. Append the paragraph text to a values list.

  4. Once a new heading is reached, store the values list under the heading key in the data dictionary.

  5. Print the final extracted data!

And there we have it – a clean way to grab values located between two types of nodes. The key is combining find_all() and find_next_siblings() to selectively target the data we want.

You can also filter by other attributes besides the tag name. For example, find_nextsiblings(class=‘intro‘) would return all siblings with a class ‘intro‘.

Advanced Usage

While the basic approach outlined above works great, there are some additional tips and tricks to make your between-node scraping scripts even more robust:

Handle missing elements gracefully: Not all pages will have perfectly consistent markup. Use exception handling and checks for None values when accessing attributes to avoid crashes.

Extract other attributes besides text: In addition to the .text attribute, you can extract values from attributes like href, src, etc.

Search by CSS class: Pass a CSS selector into find_all() and find_next_siblings() instead of the tag name.

Use a CSS selector library: For more complex queries, use a library like SoupSieve or lxml which support full CSS selectors.

Add delays: Don‘t hammer large sites too aggressively. Add delays of 1-5 seconds between requests using time.sleep().

Generalize the landmarks: Rather than hardcoding specific tags like

, accept a list of landmark tags to search between.

Pagination handling: For sites split across multiple pages, adapt the logic to extract data from each page.

There are all sorts of additional optimizations you can make depending on the specific sites you are scraping. The key is to start simple and then iterate to make your script more robust.

Going Beyond Two Nodes

While the examples above focus on extracting data between two types of landmarks, you can extend this approach to work with multiple node types.

For example, say you wanted to grab comments between


tags, as well as between

and tags.

Here‘s one way to achieve that:

for h2 in soup.find_all(‘h2‘):
   # Extract between h2 and p

for p in soup.find_all(‘p‘):
   # Extract between p and img

The key is you will likely need nested loops – iterate through the first landmark type, then loop through the next landmarks within that.

With some creative thinking and nesting of loops, you can likely access any data located between multiple landmark tags on the page.


Extracting data between HTML nodes is a common need when web scraping. In this guide, you learned how to:

  • Use find_all() to locate landmark tags
  • Iterate through siblings with find_next_siblings()
  • Append extracted text between landmarks
  • Store the extracted data in a structured format like a dictionary

These techniques allow you to precisely target nested data within a page‘s HTML structure.

To recap, the key skills covered include:

  • Locating nodes with find_all()
  • Iterating siblings with find_next_siblings()
  • Nesting loops and conditionals to handle complex pages
  • Building robust scrapers that can handle imperfect HTML

Whether you‘re an experienced pro or new to web scraping, I hope this guide provides a comprehensive overview of targeting values between nodes with BeautifulSoup. Master these techniques and you‘ll be able to extract nested data like a pro.

Happy scraping!

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