Sunil Ray — October 22, 2015
Data Mining Data Science Intermediate Libraries Programming Python Text Unstructured Data

Overview

  • Learn web scraping in Python using the BeautifulSoup library
  • Web Scraping is a useful technique to convert unstructured data on the web to structured data
  • BeautifulSoup is an efficient library available in Python to perform web scraping other than urllib
  • A basic knowledge of HTML and HTML tags is necessary to do web scraping in Python

Introduction

The need and importance of extracting data from the web is becoming increasingly loud and clear. Every few weeks, I find myself in a situation where we need to extract data from the web to build a machine learning model.

For example, last week we were thinking of creating an index of hotness and sentiment about various data science courses available on the internet. This would not only require finding new courses, but also scraping the web for their reviews and then summarizing them in a few metrics!

This is one of the problems / products whose efficacy depends more on web scraping and information extraction (data collection) than the techniques used to summarize the data.

Note: We have also created a free course for this article – Introduction to Web Scraping using Python. This structured format will help you learn better.

Ways to extract information from web

There are several ways to extract information from the web. Use of APIs being probably the best way to extract data from a website. Almost all large websites like Twitter, Facebook, Google, Twitter, StackOverflow provide APIs to access their data in a more structured manner. If you can get what you need through an API, it is almost always preferred approach over web scraping. This is because if you are getting access to structured data from the provider, why would you want to create an engine to extract the same information.

Sadly, not all websites provide an API. Some do it because they do not want the readers to extract huge information in a structured way, while others don’t provide APIs due to lack of technical knowledge. What do you do in these cases? Well, we need to scrape the website to fetch the information.

There might be a few other ways like RSS feeds, but they are limited in their use and hence I am not including them in the discussion here.

web scraping, beautifulsoup, python

 

What is Web Scraping?

Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).

You can perform web scraping in various ways, including use of Google Docs to almost every programming language. I would resort to Python because of its ease and rich ecosystem. It has a library known as ‘BeautifulSoup’ which assists this task. In this article, I’ll show you the easiest way to learn web scraping using python programming.

For those of you, who need a non-programming way to extract information out of web pages, you can also look at import.io . It provides a GUI driven interface to perform all basic web scraping operations. The hackers can continue to read this article!

 

Libraries required for web scraping

As we know, Python is an open source programming language. You may find many libraries to perform one function. Hence, it is necessary to find the best to use library. I prefer BeautifulSoup (Python library), since it is easy and intuitive to work on. Precisely, I’ll use two Python modules for scraping data:

  • Urllib2: It is a Python module which can be used for fetching URLs. It defines functions and classes to help with URL actions (basic and digest authentication, redirections, cookies, etc). For more detail refer to the documentation page. Note: urllib2 is the name of the library included in Python 2. You can use the urllib.request library included with Python 3, instead. The urllib.request library works the same way urllib.request works in Python 2. Because it is already included you don’t need to install it.
  • BeautifulSoup: It is an incredible tool for pulling out information from a webpage. You can use it to extract tables, lists, paragraph and you can also put filters to extract information from web pages. In this article, we will use latest version BeautifulSoup 4. You can look at the installation instruction in its documentation page.

BeautifulSoup does not fetch the web page for us. That’s why, I use urllib2 in combination with the BeautifulSoup library.

Python has several other options for HTML scraping in addition to BeatifulSoup. Here are some others:

 

Basics – Get familiar with HTML (Tags)

While performing web scarping, we deal with html tags. Thus, we must have good understanding of them. If you already know basics of HTML, you can skip this section. Below is the basic syntax of HTML:html, html tagsThis syntax has various tags as elaborated below:

  1. <!DOCTYPE html> : HTML documents must start with a type declaration
  2. HTML document is contained between <html> and </html>
  3. The visible part of the HTML document is between <body> and </body>
  4. HTML headings are defined with the <h1> to <h6> tags
  5. HTML paragraphs are defined with the <p> tag

Other useful HTML tags are:

  1. HTML links are defined with the <a> tag, “<a href=“http://www.test.com”>This is a link for test.com</a>”
  2. HTML tables are defined with<Table>, row as <tr> and rows are divided into data as <td>
    html table
  3. HTML list starts with <ul> (unordered) and <ol> (ordered). Each item of list starts with <li>

If you are new to this HTML tags, I would also recommend you to refer HTML tutorial from W3schools. This will give you a clear understanding about HTML tags.

 

Scraping a web page using BeautifulSoup

Here, I am scraping data from a Wikipedia page. Our final goal is to extract list of state, union territory capitals in India. And some basic detail like establishment, former capital and others form this wikipedia page. Let’s learn with doing this project step wise step:

  1. Import necessary libraries:
#import the library used to query a website
import urllib2 #if you are using python3+ version, import urllib.request
#specify the url
wiki = "https://en.wikipedia.org/wiki/List_of_state_and_union_territory_capitals_in_India"
#Query the website and return the html to the variable 'page'
page = urllib2.urlopen(wiki) #For python 3 use urllib.request.urlopen(wiki)
#import the Beautiful soup functions to parse the data returned from the website
from bs4 import BeautifulSoup
#Parse the html in the 'page' variable, and store it in Beautiful Soup format
soup = BeautifulSoup(page)
  1. Use function “prettify” to look at nested structure of HTML page
    beautifulsoup, prettifyAbove, you can see that structure of the HTML tags. This will help you to know about different available tags and how can you play with these to extract information.

 

  1. Work with HTML tags
  1. soup.<tag>: Return content between opening and closing tag including tag.
    In[30]:soup.title
    Out[30]:<title>List of state and union territory capitals in India - Wikipedia, the free encyclopedia</title>
  2. soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u'List of state and union territory capitals in India - Wikipedia, the free encyclopedia'
    
  3. Find all the links within page’s <a> tags::  We know that, we can tag a link using tag “<a>”. So, we should go with option soup.a and it should return the links available in the web page. Let’s do it.
    In [40]:soup.a 
    Out[40]:<a id="top"></a>

    Above, you can see that, we have only one output. Now to extract all the links within <a>, we will use “find_all().
    beautifulsoup, find_all

    Above, it is showing all links including titles, links and other information.  Now to show only links, we need to iterate over each a tag and then return the link using attribute “href” with get.

beautifulsoup, find_all

 

 

  1. Find the right table: As we are seeking a table to extract information about state capitals, we should identify the right table first. Let’s write the command to extract information within all table tags.
    all_tables=soup.find_all('table')
    

    Now to identify the right table, we will use attribute “class” of table and use it to filter the right table. In chrome, you can check the class name by right click on the required table of web page –> Inspect element –> Copy the class name OR go through the output of above command find the class name of right table.

    right_table=soup.find('table', class_='wikitable sortable plainrowheaders')
    right_table
    beautifulsoup, find_all, tableAbove, we are able to identify right table.
  2. Extract the information to DataFrame: Here, we need to iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading <th>)
    table, dataframeAbove, you can notice that second element of <tr> is within tag <th> not <td> so we need to take care for this. Now to access value of each element, we will use “find(text=True)” option with each element.  Let’s look at the code:
#Generate lists
A=[]
B=[]
C=[]
D=[]
E=[]
F=[]
G=[]
for row in right_table.findAll("tr"):
    cells = row.findAll('td')
    states=row.findAll('th') #To store second column data
    if len(cells)==6: #Only extract table body not heading
        A.append(cells[0].find(text=True))
        B.append(states[0].find(text=True))
        C.append(cells[1].find(text=True))
        D.append(cells[2].find(text=True))
        E.append(cells[3].find(text=True))
        F.append(cells[4].find(text=True))
        G.append(cells[5].find(text=True))
#import pandas to convert list to data frame
import pandas as pd
df=pd.DataFrame(A,columns=['Number'])
df['State/UT']=B
df['Admin_Capital']=C
df['Legislative_Capital']=D
df['Judiciary_Capital']=E
df['Year_Capital']=F
df['Former_Capital']=G
df

Finally, we have data in dataframe:
python, dataframe
Similarly, you can perform various other types of web scraping using “BeautifulSoup“. This will reduce your manual efforts to collect data from web pages. You can also look at the other attributes like .parent, .contents, .descendants and .next_sibling, .prev_sibling and various attributes to navigate using tag name. These will help you to scrap the web pages effectively.-

 

But, why can’t I just use Regular Expressions?

Now, if you know regular expressions, you might be thinking that you can write code using regular expression which can do the same thing for you. I definitely had this question. In my experience with BeautifulSoup and Regular expressions to do same thing I found out:

  • Code written in BeautifulSoup is usually more robust than the one written using regular expressions. Codes written with regular expressions need to be altered with any changes in pages. Even BeautifulSoup needs that in some cases, it is just that BeautifulSoup is relatively better.
  • Regular expressions are much faster than BeautifulSoup, usually by a factor of 100 in giving the same outcome.

So, it boils down to speed vs. robustness of the code and there is no universal winner here. If the information you are looking for can be extracted with simple regex statements, you should go ahead and use them. For almost any complex work, I usually recommend BeautifulSoup more than regex.

 

End Note

In this article, we looked at web scraping methods using “BeautifulSoup” and “urllib2” in Python. We also looked at the basics of HTML and perform the web scraping step by step while solving a challenge. I’d recommend you to practice this and use it for collecting data from web pages.

Did you find this article helpful? Please share your opinions / thoughts in the comments section below.

Note: We have also created a free course for this article – Introduction to Web Scraping using Python. This structured format will help you learn better.

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About the Author

Sunil Ray
Sunil Ray

I am a Business Analytics and Intelligence professional with deep experience in the Indian Insurance industry. I have worked for various multi-national Insurance companies in last 7 years.

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37 thoughts on "Beginner’s guide to Web Scraping in Python using BeautifulSoup"

Mohammad
Mohammad says: October 23, 2015 at 6:21 am
Hi Sunil, Thanks for such a great article. I am new in data science area but you people make me confident to be a good analyst. Carry on. Reply
YM
YM says: November 03, 2015 at 11:50 pm
Thank you for the article! I am taking an online course and was looking all over the web to understand Beautiful Soup. I find your article most helpful. Reply
Mohammad
Mohammad says: November 07, 2015 at 1:04 pm
Hi, I tried to complete this work with Python 2.7.10, Pycharm and ipython notebook. All my efforts failed for soup =BeautifulSoup(page) ipython error : IncompleteRead python 2.7 and Pycharm error : TypeError: 'module' object is not callable What to do ? Please help Reply
Diddy
Diddy says: November 16, 2015 at 10:59 pm
thank you! Reply
Naveen
Naveen says: November 18, 2015 at 2:53 pm
Hi Mohammad try the below code ,it should work soup =BeautifulSoup.BeautifulSoup(page). Regards, Naveen Reply
Rakesh
Rakesh says: November 19, 2015 at 4:54 pm
Hi Sunil, Nice explanation it helped me understand more about data scraping through python. Just a little update I don't know whether it's some version issue or something else. You 've mentioned "find_all" in the script, which when I ran has thrown an error, after exploring more on the web I found "findAll" (underscore removed and A in caps) which worked for me. Thanks Reply
SG
SG says: November 23, 2015 at 4:17 pm
Excellent Article. Very concise, thorough and simple to understand. I would greatly appreciate other examples of grabbing data from a website and displaying results in a dataframe ( table )... If it's not too much of an inconvenience, could you provide example similar to above for obtaining best gasoline / petroleum prices in a particular region or at least point us to some good reference material? I just have to figure out how to get pandas installed on my windows 8.1 system. I installed portable python, which is basically running python from a folder. Guess I'll have to download pandas into that folder similar to how I did BeautifulSoup4. Thanks again... Reply
Datahut
Datahut says: May 05, 2016 at 5:18 am
Thanku for this informative blog post. I liked it so much. Reply
Arun
Arun says: June 09, 2016 at 9:39 pm
Nice article , thanks for the effort Reply
Surendra Varma
Surendra Varma says: August 06, 2016 at 5:34 pm
Very good article, easy to understand. Reply
Utsav Maniar
Utsav Maniar says: August 16, 2016 at 1:17 pm
Thank you for the great article. Can you please make or suggest some tutorial on how to use API to extract data from websites like twitter and perform sentiment analysis? Reply
Baseer
Baseer says: August 17, 2016 at 3:59 am
Now it's my habit to learn a one small thing from AV, Indeed thanks for great to learn in this article Reply
Pavan Kumar
Pavan Kumar says: September 09, 2016 at 1:04 am
That's a very precise and neat article,Learnt basic knowledge about beautiful soup Reply
Shabbir
Shabbir says: December 07, 2016 at 1:01 am
Nice Article!! Beautifulsoup vs regex anology: web content is like money in a digital vault. Beautiful soup is a clean and process driven way of opening the vault door. Whereas, regex way is like breaking that door with a hammer!! Both are good. I somehow prefer the hammer way. Reply
abiya
abiya says: December 08, 2016 at 10:40 am
Great list of plugins friend. The Tables still serve a purpose. Although in the era of responsive sites it is a must have to be able to display tables responsively. League Table looks great! Definitely look into this and it has come at the right time as I am working on a site to list statistics and a table like this will work perfectly. Reply
Shanthi
Shanthi says: December 13, 2016 at 5:41 am
Really nice exercise! Thank you Reply
Jerome Dixon
Jerome Dixon says: January 04, 2017 at 4:24 am
Very good article. I would add a note about Selenium. I like to use Selenium and Beautiful Soup together though they overlap in functionality. Selenium can click through webpage, submit passwords, and extract data but Beautiful Soup much easier to use...together they work very well for multiple use cases. Reply
Vikash
Vikash says: February 06, 2017 at 7:13 am
Simply awesome! Great help! Thank you so much. Reply
Christos
Christos says: March 20, 2017 at 10:07 am
Excellent, to the point article! Reply
Ramakant sharma
Ramakant sharma says: March 29, 2017 at 7:27 am
simply awesome....big thanks ..keep posting Reply
ben taylor
ben taylor says: April 03, 2017 at 10:27 pm
Excellent article Sunil. It was simple enough for someone like me who has very basic html knowledge to implement. Reply
Zuri
Zuri says: April 11, 2017 at 3:37 pm
This was a great article! As a beginner to web scraping you explained everything very well. Thanks for sharing! Reply
udaykumarp
udaykumarp says: May 05, 2017 at 5:58 am
great one Reply
yogesh
yogesh says: May 24, 2017 at 7:19 pm
very nice article for beginner Reply
Avi
Avi says: June 07, 2017 at 3:19 pm
A good one Reply
saba
saba says: June 08, 2017 at 4:42 pm
you need to install Beautifulsoup package index Reply
Nikhil
Nikhil says: June 08, 2017 at 10:37 pm
Nice post Reply
Abdoul
Abdoul says: July 30, 2017 at 4:50 am
It's a big great article, thnk you. Reply
Shubham Agarwal
Shubham Agarwal says: September 10, 2017 at 2:40 pm
It turn out to be a great help for me Thank you : ) Reply
sivanagamahesh
sivanagamahesh says: December 14, 2017 at 4:38 pm
Hi, Thank you so much for posting this. I really appreciate your work. Keep it up. Great work! Reply
Ágata
Ágata says: March 05, 2018 at 11:21 pm
Thank you! It was really clear and helpful! Craving to learn more here =) Reply
Anjaneya
Anjaneya says: April 07, 2018 at 12:35 pm
Really helpful...Thanks much!!! Reply
Krishna sharma
Krishna sharma says: April 07, 2018 at 4:28 pm
Thanks a ton guys!!! Reply
luv jain
luv jain says: April 21, 2018 at 5:08 pm
Can anyone help me to find a particular paragraph with a heading, from multiple web pages having same heading available? Reply
Aarti Popshetwar
Aarti Popshetwar says: May 25, 2018 at 5:58 pm
which text editor you have to used for this whole code execution like import, and other Reply
Aishwarya Singh
Aishwarya Singh says: May 25, 2018 at 6:56 pm
Hi, Jupyter notebook is used in this article. Reply
Aishwarya Singh
Aishwarya Singh says: May 25, 2018 at 7:34 pm
Hi, Yes, you can use beautifulSoup to get this done. First, you have to understand Document Object Model (DOM). Find the source code of the page by right clicking on the webpage and select source code. Here you could look what is the id or class of heading you want to parse. Later you can parse it using the following code. soup = BeautifulSoup('

') soup.find_all("h2", class_="CLASSNAME") Reply

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