BeautifulSoup parses HTML into a tree-like structure called the DOM (Document Object Model). Each HTML tag becomes a node in this tree.

Developers can traverse parents, children, and siblings easily. This structure allows precise data extraction. Parsing makes unstructured HTML readable and searchable.

BeautifulSoup extracts elements using methods like find() and find_all(). These methods search HTML tags based on tag name, class, id, or attributes.

Once found, text and attributes can be accessed easily. This enables structured scraping of headings, links, tables, and content. It simplifies data extraction from complex pages.

Requests fetches the HTML content from a URL, while BeautifulSoup parses it.

The response text from requests is passed into BeautifulSoup. This combination is the most common scraping workflow.

Requests handles HTTP communication; BeautifulSoup handles parsing. Together they form the backbone of Python web scraping.

BeautifulSoup is ideal for static content, while Selenium handles JavaScript-heavy sites.

This is one of the most common interview questions.

Both extract text, but get_text() is more reliable.

Choosing the right parser improves performance.

Web scraping is the process of extracting data from websites automatically. It involves fetching web pages and parsing their content.

Python libraries like BeautifulSoup make scraping easy. Scraped data is used for analysis, research, and automation. Interviews often test this basic concept.

BeautifulSoup is installed using pip install beautifulsoup4. It works with Python 3.

The library requires a parser such as html.parser or lxml. Installation is simple and quick. This is a common beginner question.

A parser interprets HTML or XML content. BeautifulSoup supports multiple parsers. Parsers convert raw HTML into a tree structure.

Choosing a good parser improves speed and accuracy. html.parser and lxml are most common.

Links are extracted by finding all <a> tags. The href attribute contains the URL.

BeautifulSoup accesses attributes using dictionary syntax. This is a very common scraping task. Interviewers often ask this.

Attributes are accessed using tag[‘attribute’] or tag.get(‘attribute’). This allows retrieval of URLs, class names, ids, and more.

Attribute extraction is essential for scraping structured data. It is safer to use get() to avoid errors. This is a common interview concept.

No, BeautifulSoup cannot execute JavaScript. It only parses static HTML received from the server. For JavaScript-rendered pages, tools like Selenium or Playwright are used.

This limitation is frequently asked in interviews. Understanding this avoids scraping mistakes.

You should check if the tag exists before accessing it. Using if tag: prevents errors.

The get() method is safer for attributes. Proper checks improve script stability. Handling missing data is a practical skill.

prettify() formats HTML in a readable way. It adds indentation and line breaks.

It is useful for debugging and understanding page structure. It does not change data extraction. This method helps beginners visualize HTML.

Tables are scraped by locating <table>, <tr>, <th>, and <td> tags. Rows and columns are extracted in loops.

Data is often stored in lists or DataFrames. This is a very common interview use case. Tables are widely scraped.

Web scraping legality depends on website policies and local laws. Many sites specify rules in robots.txt.

Scraping public data is often allowed. Ethical scraping avoids overloading servers. Interviewers expect awareness of this.

robots.txt tells bots which pages can be accessed. It helps protect sensitive routes. Scrapers should respect robots.txt rules. Ignoring it may lead to IP bans. This is an important ethical consideration.

BeautifulSoup is easy to learn and use. It handles messy HTML gracefully.

It integrates well with requests and pandas. It is beginner-friendly yet powerful. This makes it a top choice for scraping tasks.