Browse other questions tagged python html web-scraping page-numbering or ask your own question. Web parsing with python beautifulsoup producing inconsistent result. Scraping Multiple Page using Python and BeautifulSoup - Website url does not work. Hot Network Questions. Web Scraping “Web scraping (web harvesting or web data extraction) is a computer software technique of extracting information from websites.” HTML parsing is easy in Python, especially with help of the BeautifulSoup library. In this post we will scrape a website (our own) to extract all URL’s.

1. Python & Web Scraping Projects for $10 - $30. You need to write three scrapers: one using Beautiful Soup, one using Scrapy, one using Selenium. All of them should scrap the same information from the domain. The goal is to gather the information.
2. Beautiful Soup Is a Valuable Web Scraping Tool Beautiful Soup is a powerful Python screen scraper that gives you control over how your data comes through during scraping. It's a valuable business tool, as it can give you access to competitor's web data like pricing, market trends, and more.
3. BeautifulSoup version 4 is a famous Python library for web scraping. In addition, there was BeautifulSoup version 3, and support for it will be dropped on or after December 31, 2020. People had better learn newer versions. Below is the definition from BeautifulSoup Documentation.

Web Scraping means navigating structured elements on a website, and deeply going to next layers. Incoming big data will be retrieved and formated in desired styles. We apply Python BeautifulSoup to a simple example for scraping with step-by-step tutorials.

All codes here are not complicated, so you can easily understand even though you are still students in school. To benefit your learning, we will provide you download link to a zip file thus you can get all source codes for future usage.

Estimated reading time: 10 minutes

EXPLORE THIS ARTICLE
TABLE OF CONTENTS

BONUS
Source Code Download

We have released it under the MIT license, so feel free to use it in your own project or your school homework.

Download Guideline

• Prepare Python environment for Windows by clicking Python Downloads, or search a Python setup pack for Linux.
• The pack of Windows version also contains pip install for you to obtain more Python libraries in the future.

THE BASICS
Python Web Scraping

Web pages are written using HTML to be structured texts. Therefore, people can obtain organized information of websites by scraping. Because Python is good at that, we will introduce its library BeautifulSoup in the article.

What is BeautifulSoup?

BeautifulSoup version 4 is a famous Python library for web scraping. In addition, there was BeautifulSoup version 3, and support for it will be dropped on or after December 31, 2020. People had better learn newer versions. Below is the definition from BeautifulSoup Documentation.

BeautifulSoup Installation

If you already download and setup Python and its tool pip in Windows or Linux, you can install BeautifulSoup 4 package bs4 by pip install command lines, and then check the result by pip show.

Scraping An Example Website

We should choose an example website to start. Focusing only on the content about laptop computer, thus the portion of an Indian shopping website, Snapdeal Laptops, will be suitable to our target.

There are two layers, top layer for a product list and bottom layer for details in specification. For safety, we suggest a process of saving pages and then retrieve data from them. Therefore, the page contents along with images are downloaded. Below is the running process.

Next, you will learn how to scaping web pages by BeautifulSoup. The result will be saved as JSON format in a file named result.json.

STEP 1
Finding How Web Pages Link

The previous section let us know what type of data we will meet, so we need to inspect these HTML structured texts to find entry points for BeautifulSoup scraping to start.

Finding The Relationship

Probably, the website you want to crawl has more layers than that in this example. As there is no general rule for various web pages, it is better to get a entry point that BeautifulSoup can start from by finding how web pages link.

Below lists the JSON-styled data related to a specific laptop computer. Except that the fields under 'highlight' is found from layer 2, other fields come from layer 1. For example, 'title' indicates the product name with brief specification, and 'img_url' can be used to download product pictures.

What to Inspect in Layer 1

From the view of HTML document, let us continue inspecting Layer 1 in file laptop.html. In other words, by going through HTML structured text, BeautifulSoup can locate the key feature of class='product-tuple-image' for scraping items like <a pogId=, <a href=, <source srcset=, and <img title= to be pogid, href, img_url, and title, respectively. Where href directs to url in Layer 2 for that product.

Similarly, with another feature of class='product-tuple-description', BeautifulSoup can continue scraping <span product-desc-price=, <span product-price=, and <div product-discount= to retrieve JSON fields price, price_discount, and discount. So far, all items in Layer 1 have been discovered.

What to Inspect in Layer 2

Further, BeautifulSoup traverses the HTML file of Layer 2 such as 638317853217.html. As mentioned previously, that is for detailed specification of laptops. The task in Layer 2 can be done by scraping items inside the feature of class='highlightsTileContent '.

STEP 2
Scraping By Beautifulsoup

Before scraping, we got to introduce a popular Python library PyPI requests to get contents from websites. Then, BeautifulSoup will perform iteratedly in layers to convert all information into JSON-style data.

PyPI requests

PyPI requests is an elegant and simple HTTP library for Python. In the following paragraph, we leverage it to read text from web pages and save as HTML files. Of course, you can install it by issuing pip install requests in command box.

PyPI requests requests.get() not only get web pages, but also pull down binary data for pictures like that in download_image().

Python Scraping in Layer 1

Let us start from getLayer_1() in which each web page has been saved before BeautifulSoup parsing proceed.

Saving contents as backup is helpful for debuging. Once exceptions happens while BeautifulSoup scraping, it is hard for you to find again the exact content you need from massive web pages. In addition to that, too frequent data scraping may trigger prohibition of some websites.

For BeautifulSoup, the very first expression like soup = BeautifulSoup(page, 'html.parser') need to select one kind of parsers. The parser could be html.parser or html5lib, whose difference can be found in Differences between parsers.

Based on understanding about what the text structure is in STEP 1, we find prices from the class of product-tuple-description by using attrs={} with which BeautifulSoup anchors a lot of locations in this example. Apart from prices, pictures are done in the same way.

Layer 1 can discover data about titles, images, and prices. Importantly, you should notice that BeautifulSoup uses find_all() or find() to gather information for all pieces or one piece, like the usage in database.

For each page in Layer 2, 'highlight': getLayer_2() is called iteratedly to retrieve more. Finally, json.dump() save JSON-formated data as a file. Subsequently, let us go through steps for BeautifulSoup scraping in Layer 2 in next paragraph.

Python Scraping in Layer 2

Like the way in Layer 1, getLayer_2() find more product details by locating the class of highlightsTileContent. Then, in a loop, it store searched data in an array. You can check what is in this array in JSON style as discussed in STEP 1.

STEP 3
Handling Exception

BeautifulSoup scraping won’t be smooth continuously, because the input HTML elements may be partial missing. In that case, you have to take actions to avoid interrupt, and keep the entire procedure going on.

Why Will Exceptions Probably Occur?

In laptop.html, if every product has identical fields, there will be no exception. However, when any product has less fields than that of other normal products, it could happen like what the following Python scripts express.

Always, you have to deal with every kind of exception, so as to prevent the scrapying process from interrupt. Imaging that programs suddenly exit due to errors after crawling several thousands of pieces, the loss won’t be little.

What Exceptions to Handle

Here list only two conditions. However, you may encounter extra ones when scrapying more and more web pages.

Python Beautifulsoup Web Scraping Tutorial

Python Beautiful Soup Web Scraping For Data Science Projects

FINAL
Conclusion

We didn’t write detailed skills in BeautifulSoup Documentation, but show many opinions and directions about scraping in Python. Therefore, if you are not familiar with skills, please refer to online resources for practices.

Thank you for reading, and we have suggested more helpful articles here. If you want to share anything, please feel free to comment below. Good luck and happy coding!

Suggested Reading

• 4 Practices for Python File Upload to PHP Server
• Compare Python Dict Index and Other 5 Ops with List

APIs are not always available. Sometimes you have to scrape data from a webpage yourself. Luckily the modules Pandas and Beautifulsoup can help!

Related Course:Complete Python Programming Course & Exercises

Web scraping

Pandas has a neat concept known as a DataFrame. A DataFrame can hold data and be easily manipulated. We can combine Pandas with Beautifulsoup to quickly get data from a webpage.

If you find a table on the web like this:

We can convert it to JSON with:

And in a browser get the beautiful json output:

Converting to lists

Rows can be converted to Python lists.
We can convert it to a dataframe using just a few lines:

Pretty print pandas dataframe

You can convert it to an ascii table with the module tabulate.
This code will instantly convert the table on the web to an ascii table:
This will show in the terminal as: