A Practical Introduction to Web Scraping in Python

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Exercises Course: Introduction to Web Scraping With Python

Web scraping is the process of collecting and parsing raw data from the Web, and the Python community has come up with some pretty powerful web scraping tools.

The Internet hosts perhaps the greatest source of information on the planet. Many disciplines, such as data science, business intelligence, and investigative reporting, can benefit enormously from collecting and analyzing data from websites.

In this tutorial, you’ll learn how to:

• Parse website data using string methods and regular expressions
• Parse website data using an HTML parser
• Interact with forms and other website components

Note: This tutorial is adapted from the chapter “Interacting With the Web” in Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you’ll see occasional references to IDLE throughout this tutorial. However, you should have no problems running the example code from the editor and environment of your choice.

Source Code: Click here to download the free source code that you’ll use to collect and parse data from the Web.

Take the Quiz: Test your knowledge with our interactive “A Practical Introduction to Web Scraping in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

In this quiz, you'll test your understanding of web scraping in Python. Web scraping is a powerful tool for data collection and analysis. By working through this quiz, you'll revisit how to parse website data using string methods, regular expressions, and HTML parsers, as well as how to interact with forms and other website components.

Scrape and Parse Text From Websites

Collecting data from websites using an automated process is known as web scraping. Some websites explicitly forbid users from scraping their data with automated tools like the ones that you’ll create in this tutorial. Websites do this for two possible reasons:

1. The site has a good reason to protect its data. For instance, Google Maps doesn’t let you request too many results too quickly.
2. Making many repeated requests to a website’s server may use up bandwidth, slowing down the website for other users and potentially overloading the server such that the website stops responding entirely.

Before using your Python skills for web scraping, you should always check your target website’s acceptable use policy to see if accessing the website with automated tools is a violation of its terms of use. Legally, web scraping against the wishes of a website is very much a gray area.

Important: Please be aware that the following techniques may be illegal when used on websites that prohibit web scraping.

For this tutorial, you’ll use a page that’s hosted on Real Python’s server. The page that you’ll access has been set up for use with this tutorial.

Now that you’ve read the disclaimer, you can get to the fun stuff. In the next section, you’ll start grabbing all the HTML code from a single web page.

Build Your First Web Scraper

One useful package for web scraping that you can find in Python’s standard library is urllib , which contains tools for working with URLs. In particular, the urllib.request module contains a function called urlopen() that you can use to open a URL within a program.

In IDLE’s interactive window, type the following to import urlopen() :

>>> from urllib.request import urlopen 

The web page that you’ll open is at the following URL:

>>> url = "http://olympus.realpython.org/profiles/aphrodite" 

To open the web page, pass url to urlopen() :

>>> page = urlopen(url) 

urlopen() returns an HTTPResponse object:

>>> page 

To extract the HTML from the page, first use the HTTPResponse object’s .read() method, which returns a sequence of bytes. Then use .decode() to decode the bytes to a string using UTF-8:

>>> html_bytes = page.read() >>> html = html_bytes.decode("utf-8") 

Now you can print the HTML to see the contents of the web page:

>>> print(html)      
  
Name: Aphrodite  
Favorite animal: Dove  
Favorite color: Red  
Hometown: Mount Olympus    

The output that you’re seeing is the HTML code of the website, which your browser renders when you visit http://olympus.realpython.org/profiles/aphrodite :

With urllib , you accessed the website similarly to how you would in your browser. However, instead of rendering the content visually, you grabbed the source code as text. Now that you have the HTML as text, you can extract information from it in a couple of different ways.

Extract Text From HTML With String Methods

One way to extract information from a web page’s HTML is to use string methods. For instance, you can use .find() to search through the text of the HTML for the tags and extract the title of the web page.

To start, you’ll extract the title of the web page that you requested in the previous example. If you know the index of the first character of the title and the index of the first character of the closing tag, then you can use a string slice to extract the title.

Because .find() returns the index of the first occurrence of a substring, you can get the index of the opening tag by passing the string "" to .find() :

>>> title_index = html.find("") >>> title_index 14 

You don’t want the index of the tag, though. You want the index of the title itself. To get the index of the first letter in the title, you can add the length of the string "" to title_index :

>>> start_index = title_index + len("") >>> start_index 21 

Now get the index of the closing tag by passing the string "" to .find() :

>>> end_index = html.find("") >>> end_index 39 

Finally, you can extract the title by slicing the html string:

>>> title = html[start_index:end_index] >>> title 'Profile: Aphrodite' 

Real-world HTML can be much more complicated and far less predictable than the HTML on the Aphrodite profile page. Here’s another profile page with some messier HTML that you can scrape:

>>> url = "http://olympus.realpython.org/profiles/poseidon" 

Try extracting the title from this new URL using the same method as in the previous example:

>>> url = "http://olympus.realpython.org/profiles/poseidon" >>> page = urlopen(url) >>> html = page.read().decode("utf-8") >>> start_index = html.find("") >>> title = html[start_index:end_index] >>> title '\n\n tag.

These sorts of problems can occur in countless unpredictable ways. You need a more reliable way to extract text from HTML.
Get to Know Regular Expressions
Regular expressions—or regexes for short—are patterns that you can use to search for text within a string. Python supports regular expressions through the standard library’s re module.
Note: Regular expressions aren’t particular to Python. They’re a general programming concept and are supported in many programming languages.
To work with regular expressions, the first thing that you need to do is import the re module:
>>> import re 
Regular expressions use special characters called metacharacters to denote different patterns. For instance, the asterisk character ( * ) stands for zero or more instances of whatever comes just before the asterisk.
In the following example, you use .findall() to find any text within a string that matches a given regular expression:
>>> re.findall("ab*c", "ac") ['ac'] 
The first argument of re.findall() is the regular expression that you want to match, and the second argument is the string to test. In the above example, you search for the pattern "ab*c" in the string "ac" .
The regular expression "ab*c" matches any part of the string that begins with "a" , ends with "c" , and has zero or more instances of "b" between the two. re.findall() returns a list of all matches. The string "ac" matches this pattern, so it’s returned in the list.
Here’s the same pattern applied to different strings:
>>> re.findall("ab*c", "abcd") ['abc'] >>> re.findall("ab*c", "acc") ['ac'] >>> re.findall("ab*c", "abcac") ['abc', 'ac'] >>> re.findall("ab*c", "abdc") [] 
Notice that if no match is found, then .findall() returns an empty list.
Pattern matching is case sensitive. If you want to match this pattern regardless of the case, then you can pass a third argument with the value re.IGNORECASE :
>>> re.findall("ab*c", "ABC") [] >>> re.findall("ab*c", "ABC", re.IGNORECASE) ['ABC'] 
You can use a period ( . ) to stand for any single character in a regular expression. For instance, you could find all the strings that contain the letters "a" and "c" separated by a single character as follows:
>>> re.findall("a.c", "abc") ['abc'] >>> re.findall("a.c", "abbc") [] >>> re.findall("a.c", "ac") [] >>> re.findall("a.c", "acc") ['acc'] 
The pattern .* inside a regular expression stands for any character repeated any number of times. For instance, you can use "a.*c" to find every substring that starts with "a" and ends with "c" , regardless of which letter—or letters—are in between:
>>> re.findall("a.*c", "abc") ['abc'] >>> re.findall("a.*c", "abbc") ['abbc'] >>> re.findall("a.*c", "ac") ['ac'] >>> re.findall("a.*c", "acc") ['acc'] 
Often, you use re.search() to search for a particular pattern inside a string. This function is somewhat more complicated than re.findall() because it returns an object called MatchObject that stores different groups of data. This is because there might be matches inside other matches, and re.search() returns every possible result.
The details of MatchObject are irrelevant here. For now, just know that calling .group() on MatchObject will return the first and most inclusive result, which in most cases is just what you want:
>>> match_results = re.search("ab*c", "ABC", re.IGNORECASE) >>> match_results.group() 'ABC' 
There’s one more function in the re module that’s useful for parsing out text. re.sub() , which is short for substitute, allows you to replace the text in a string that matches a regular expression with new text. It behaves sort of like the .replace() string method.
The arguments passed to re.sub() are the regular expression, followed by the replacement text, followed by the string. Here’s an example:
>>> string = "Everything is if it's in ." >>> string = re.sub("<.*>", "ELEPHANTS", string) >>> string 'Everything is ELEPHANTS.' 
Perhaps that wasn’t quite what you expected to happen.
re.sub() uses the regular expression "<.*>" to find and replace everything between the first < and the last >, which spans from the beginning of to the end of . This is because Python’s regular expressions are greedy, meaning they try to find the longest possible match when characters like * are used.
Alternatively, you can use the non-greedy matching pattern *? , which works the same way as * except that it matches the shortest possible string of text:
>>> string = "Everything is if it's in ." >>> string = re.sub("<.*?>", "ELEPHANTS", string) >>> string "Everything is ELEPHANTS if it's in ELEPHANTS." 
This time, re.sub() finds two matches, and , and substitutes the string "ELEPHANTS" for both matches.
Extract Text From HTML With Regular Expressions
Equipped with all this knowledge, now try to parse out the title from another profile page, which includes this rather carelessly written line of HTML:
TITLE >Profile: Dionysus/title / > 
The .find() method would have a difficult time dealing with the inconsistencies here, but with the clever use of regular expressions, you can handle this code quickly and efficiently:
# regex_soup.py import re from urllib.request import urlopen url = "http://olympus.realpython.org/profiles/dionysus" page = urlopen(url) html = page.read().decode("utf-8") pattern = ".*?" match_results = re.search(pattern, html, re.IGNORECASE) title = match_results.group() title = re.sub("<.*?>", "", title) # Remove HTML tags print(title) 
Take a closer look at the first regular expression in the pattern string by breaking it down into three parts:

  
matches the opening tag in html . The matches any text after .

  
.*? non-greedily matches all text after the opening , stopping at the first match for . differs from the first pattern only in its use of the / character, so it matches the closing tag in html .

The second regular expression, the string "<.*?>" , also uses the non-greedy .*? to match all the HTML tags in the title string. By replacing any matches with "" , re.sub() removes all the tags and returns only the text.
Note: Web scraping in Python or any other language can be tedious. No two websites are organized the same way, and HTML is often messy. Moreover, websites change over time. Web scrapers that work today aren’t guaranteed to work next year—or next week, for that matter!
Regular expressions are a powerful tool when used correctly. In this introduction, you’ve barely scratched the surface. For more about regular expressions and how to use them, check out the two-part series Regular Expressions: Regexes in Python.
Check Your Understanding
Expand the block below to check your understanding.
Exercise: Scrape Data From a Website Show/Hide
Write a program that grabs the full HTML from the following URL:
>>> url = "http://olympus.realpython.org/profiles/dionysus" 
Then use .find() to display the text following Name: and Favorite Color: (not including any leading spaces or trailing HTML tags that might appear on the same line).
You can expand the block below to see a solution.
Solution: Scrape Data From a Website Show/Hide
First, import the urlopen function from the urlib.request module:
from urllib.request import urlopen 
Then open the URL and use the .read() method of the HTTPResponse object returned by urlopen() to read the page’s HTML:
url = "http://olympus.realpython.org/profiles/dionysus" html_page = urlopen(url) html_text = html_page.read().decode("utf-8") 
The .read() method returns a byte string, so you use .decode() to decode the bytes using the UTF-8 encoding.
Now that you have the HTML source of the web page as a string assigned to the html_text variable, you can extract Dionysus’s name and favorite color from his profile. The structure of the HTML for Dionysus’s profile is the same as for Aphrodite’s profile, which you saw earlier.
You can get the name by finding the string "Name:" in the text and extracting everything that comes after the first occurence of the string and before the next HTML tag. That is, you need to extract everything after the colon ( : ) and before the first angle bracket ( < ). You can use the same technique to extract the favorite color.
The following for loop extracts this text for both the name and favorite color:
for string in ["Name: ", "Favorite Color:"]: string_start_idx = html_text.find(string) text_start_idx = string_start_idx + len(string) next_html_tag_offset = html_text[text_start_idx:].find(") text_end_idx = text_start_idx + next_html_tag_offset raw_text = html_text[text_start_idx : text_end_idx] clean_text = raw_text.strip(" \r\n\t") print(clean_text) 
It looks like there’s a lot going on in this for loop, but it’s just a little bit of arithmetic to calculate the right indices for extracting the desired text. Go ahead and break it down:

  
You use html_text.find() to find the starting index of the string, either "Name:" or "Favorite Color:" , and then assign the index to string_start_idx .
  
Since the text to extract starts just after the colon in "Name:" or "Favorite Color:" , you get the index of the character immediately after the colon by adding the length of the string to start_string_idx , and then assign the result to text_start_idx .
  
You calculate the ending index of the text to extract by determining the index of the first angle bracket ( < ) relative to text_start_idx and assign this value to next_html_tag_offset . Then you add that value to text_start_idx and assign the result to text_end_idx .
  
You extract the text by slicing html_text from text_start_idx to text_end_idx and assign this string to raw_text .
  
You remove any whitespace from the beginning and end of raw_text using .strip() and assign the result to clean_text .

At the end of the loop, you use print() to display the extracted text. The final output looks like this:
Dionysus Wine 
This solution is one of many that solves this problem, so if you got the same output with a different solution, then you did great!
When you’re ready, you can move on to the next section.
Use an HTML Parser for Web Scraping in Python
Although regular expressions are great for pattern matching in general, sometimes it’s easier to use an HTML parser that’s explicitly designed for parsing out HTML pages. There are many Python tools written for this purpose, but the Beautiful Soup library is a good one to start with.
Install Beautiful Soup
To install Beautiful Soup, you can run the following in your terminal:
$ python -m pip install beautifulsoup4 
With this command, you’re installing the latest version of Beautiful Soup into your global Python environment.
Create a BeautifulSoup Object
Type the following program into a new editor window:
# beauty_soup.py from bs4 import BeautifulSoup from urllib.request import urlopen url = "http://olympus.realpython.org/profiles/dionysus" page = urlopen(url) html = page.read().decode("utf-8") soup = BeautifulSoup(html, "html.parser") 
This program does three things:

  
Opens the URL http://olympus.realpython.org/profiles/dionysus by using urlopen() from the urllib.request module
  
Reads the HTML from the page as a string and assigns it to the html variable
  
Creates a BeautifulSoup object and assigns it to the soup variable

The BeautifulSoup object assigned to soup is created with two arguments. The first argument is the HTML to be parsed, and the second argument, the string "html.parser" , tells the object which parser to use behind the scenes. "html.parser" represents Python’s built-in HTML parser.
Use a BeautifulSoup Object
Save and run the above program. When it’s finished running, you can use the soup variable in the interactive window to parse the content of html in various ways.
Note: If you’re not using IDLE, then you can run your program with the -i flag to enter interactive mode. Something like python -i beauty_soup.py will first run your program and then leave you in a REPL where you can explore your objects.
For example, BeautifulSoup objects have a .get_text() method that you can use to extract all the text from the document and automatically remove any HTML tags.
Type the following code into IDLE’s interactive window or at the end of the code in your editor:
>>> print(soup.get_text()) Profile: Dionysus Name: Dionysus Hometown: Mount Olympus Favorite animal: Leopard Favorite Color: Wine 
There are a lot of blank lines in this output. These are the result of newline characters in the HTML document’s text. You can remove them with the .replace() string method if you need to.
Often, you need to get only specific text from an HTML document. Using Beautiful Soup first to extract the text and then using the .find() string method is sometimes easier than working with regular expressions.
However, other times the HTML tags themselves are the elements that point out the data you want to retrieve. For instance, perhaps you want to retrieve the URLs for all the images on the page. These links are contained in the src attribute of HTML tags.
In this case, you can use find_all() to return a list of all instances of that particular tag:
>>> soup.find_all("img") [, ] 
This returns a list of all tags in the HTML document. The objects in the list look like they might be strings representing the tags, but they’re actually instances of the Tag object provided by Beautiful Soup. Tag objects provide a simple interface for working with the information they contain.
You can explore this a little by first unpacking the Tag objects from the list:
>>> image1, image2 = soup.find_all("img") 
Each Tag object has a .name property that returns a string containing the HTML tag type:
>>> image1.name 'img' 
You can access the HTML attributes of the Tag object by putting their names between square brackets, just as if the attributes were keys in a dictionary.

For example, the tag has a single attribute, src , with the value "/static/dionysus.jpg" . Likewise, an HTML tag such as the link has two attributes, href and target .
To get the source of the images in the Dionysus profile page, you access the src attribute using the dictionary notation mentioned above:
>>> image1["src"] '/static/dionysus.jpg' >>> image2["src"] '/static/grapes.png' 
Certain tags in HTML documents can be accessed by properties of the Tag object. For example, to get the tag in a document, you can use the .title property:
>>> soup.title  
If you look at the source of the Dionysus profile by navigating to the profile page, right-clicking on the page, and selecting View page source, then you’ll notice that the tag is written in all caps with spaces:
 
Beautiful Soup automatically cleans up the tags for you by removing the extra space in the opening tag and the extraneous forward slash ( / ) in the closing tag.
You can also retrieve just the string between the title tags with the .string property of the Tag object:
>>> soup.title.string 'Profile: Dionysus' 
One of the features of Beautiful Soup is the ability to search for specific kinds of tags whose attributes match certain values. For example, if you want to find all the tags that have a src attribute equal to the value /static/dionysus.jpg , then you can provide the following additional argument to .find_all() :
>>> soup.find_all("img", src="/static/dionysus.jpg") [] 
This example is somewhat arbitrary, and the usefulness of this technique may not be apparent from the example. If you spend some time browsing various websites and viewing their page sources, then you’ll notice that many websites have extremely complicated HTML structures.
When scraping data from websites with Python, you’re often interested in particular parts of the page. By spending some time looking through the HTML document, you can identify tags with unique attributes that you can use to extract the data you need.
Then, instead of relying on complicated regular expressions or using .find() to search through the document, you can directly access the particular tag that you’re interested in and extract the data you need.
In some cases, you may find that Beautiful Soup doesn’t offer the functionality you need. The lxml library is somewhat trickier to get started with but offers far more flexibility than Beautiful Soup for parsing HTML documents. You may want to check it out once you’re comfortable using Beautiful Soup.
Note: HTML parsers like Beautiful Soup can save you a lot of time and effort when it comes to locating specific data in web pages. However, sometimes HTML is so poorly written and disorganized that even a sophisticated parser like Beautiful Soup can’t interpret the HTML tags properly.
In this case, you’re often left with using .find() and regular expression techniques to try to parse out the information that you need.
Beautiful Soup is great for scraping data from a website’s HTML, but it doesn’t provide any way to work with HTML forms. For example, if you need to search a website for some query and then scrape the results, then Beautiful Soup alone won’t get you very far.