Python Web Scraping Example

Python Web Scraping Example

December 10, 2021
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A Practical Introduction to Web Scraping in 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—and misinformation—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
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 you’ll create in this tutorial. Websites do this for two possible reasons:
The site has a good reason to protect its data. For instance, Google Maps doesn’t let you request too many results too quickly.
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.
Let’s start by grabbing all the HTML code from a single web page. You’ll use a page on Real Python that’s been set up for use with this tutorial.
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 quest module contains a function called urlopen() that can be used to open a URL within a program.
In IDLE’s interactive window, type the following to import urlopen():
>>>>>> from quest import urlopen
The web page that we’ll open is at the following URL:
>>>>>> url = ”
To open the web page, pass url to urlopen():
>>>>>> page = urlopen(url)
urlopen() returns an HTTPResponse object:
>>>>>> page
< object at 0x105fef820>
To extract the HTML from the page, first use the HTTPResponse object’s () method, which returns a sequence of bytes. Then use () to decode the bytes to a string using UTF-8:
>>>>>> html_bytes = ()
>>> html = (“utf-8”)
Now you can print the HTML to see the contents of the web page:
>>>>>> print(html)

Profile: Aphrodite

Name: Aphrodite

Favorite animal: Dove
Favorite color: Red
Hometown: Mount Olympus

Once you have the HTML as text, you can extract information from it in a couple of different ways.
A Primer on Regular Expressions
Regular expressions—or regexes for short—are patterns that can be used to search for text within a string. Python supports regular expressions through the standard library’s re module.
To work with regular expressions, the first thing you need to do is import the re module:
Regular expressions use special characters called metacharacters to denote different patterns. For instance, the asterisk character (*) stands for zero or more 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:
>>>>>> ndall(“ab*c”, “ac”)
[‘ac’]
The first argument of ndall() 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 an “a”, ends with a “c”, and has zero or more instances of “b” between the two. ndall() 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:
>>>>>> ndall(“ab*c”, “abcd”)
[‘abc’]
>>> ndall(“ab*c”, “acc”)
>>> ndall(“ab*c”, “abcac”)
[‘abc’, ‘ac’]
>>> ndall(“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:
>>>>>> ndall(“ab*c”, “ABC”)
>>> ndall(“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:
>>>>>> ndall(“a. c”, “abc”)
>>> ndall(“a. c”, “abbc”)
>>> ndall(“a. c”, “ac”)
>>> ndall(“a. c”, “acc”)
[‘acc’]
The pattern. * inside a regular expression stands for any character repeated any number of times. For instance, “a. *c” can be used to find every substring that starts with “a” and ends with “c”, regardless of which letter—or letters—are in between:
>>>>>> ndall(“a. *c”, “abc”)
>>> ndall(“a. *c”, “abbc”)
[‘abbc’]
>>> ndall(“a. *c”, “ac”)
>>> ndall(“a. *c”, “acc”)
Often, you use () to search for a particular pattern inside a string. This function is somewhat more complicated than ndall() because it returns an object called a MatchObject that stores different groups of data. This is because there might be matches inside other matches, and () returns every possible result.
The details of the MatchObject are irrelevant here. For now, just know that calling () on a MatchObject will return the first and most inclusive result, which in most cases is just what you want:
>>>>>> match_results = (“ab*c”, “ABC”, re. IGNORECASE)
>>> ()
‘ABC’
There’s one more function in the re module that’s useful for parsing out text. (), which is short for substitute, allows you to replace 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 () 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 = (“<. *>“, “ELEPHANTS”, string)
>>> string
‘Everything is ELEPHANTS. ‘
Perhaps that wasn’t quite what you expected to happen.
() uses the regular expression “<. *>” to find and replace everything between the first < and 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 = (“<. *? >“, “ELEPHANTS”, string)
“Everything is ELEPHANTS if it’s in ELEPHANTS. ”
This time, () finds two matches, and , and substitutes the string “ELEPHANTS” for both matches.
Check Your Understanding
Expand the block below to check your understanding.
Write a program that grabs the full HTML from the following URL:
Then use () 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.
First, import the urlopen function from the quest module:
from quest import urlopen
Then open the URL and use the () method of the HTTPResponse object returned by urlopen() to read the page’s HTML:
url = ”
html_page = urlopen(url)
html_text = ()(“utf-8”)
() returns a byte string, so you use () 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 Aphrodite’s profile that 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 = (string) text_start_idx = string_start_idx + len(string) next_html_tag_offset = html_text[text_start_idx:]("<") text_end_idx = text_start_idx + next_html_tag_offset raw_text = html_text[text_start_idx: text_end_idx] clean_text = (" \r\n\t") print(clean_text) It looks like there’s a lot going on in this forloop, but it’s just a little bit of arithmetic to calculate the right indices for extracting the desired text. Let’s break it down: You use () 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 the character immediately after the colon by adding the length of the string to start_string_idx and 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 () 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: 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: $ python3 -m pip install beautifulsoup4 Run pip show to see the details of the package you just installed: $ python3 -m pip show beautifulsoup4 Name: beautifulsoup4 Version: 4. 9. 1 Summary: Screen-scraping library Home-page: Author: Leonard Richardson Author-email: License: MIT Location: c:\realpython\venv\lib\site-packages Requires: Required-by: In particular, notice that the latest version at the time of writing was 4. 1. Create a BeautifulSoup Object Type the following program into a new editor window: from bs4 import BeautifulSoup page = urlopen(url) html = ()("utf-8") soup = BeautifulSoup(html, "") This program does three things: Opens the URL using urlopen() from the quest 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 "", tells the object which parser to use behind the scenes. "" 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. For example, BeautifulSoup objects have a. get_text() method that can be used to extract all the text from the document and automatically remove any HTML tags. Type the following code into IDLE’s interactive window: >>>>>> print(t_text())
Profile: Dionysus
Name: Dionysus
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 string. replace() 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 () string method is sometimes easier than working with regular expressions.
However, sometimes 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:
>>>>>> nd_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.
Let’s explore this a little by first unpacking the Tag objects from the list:
>>>>>> image1, image2 = nd_all(“img”)
Each Tag object has a property that returns a string containing the HTML tag type:
You can access the HTML attributes of the Tag object by putting their name 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/”. 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/’
>>> image2[“src”]
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 property:<br /> >>>>>><br /> <title>Profile: Dionysus
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 as written in the document looks like this:<br /> <title >Profile: Dionysus
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 property of the Tag object:
‘Profile: Dionysus’
One of the more useful 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/, then you can provide the following additional argument to. find_all():
>>>>>> nd_all(“img”, src=”/static/”)
[

]
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 () to search through the document, you can directly access the particular tag 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.
BeautifulSoup 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 BeautifulSoup alone won’t get you very far.
Write a program that grabs the full HTML from the page at the URL Using Beautiful Soup, print out a list of all the links on the page by looking for HTML tags with the name a and retrieving the value taken on by the href attribute of each tag.
The final output should look like this:
You can expand the block below to see a solution:
First, import the urlopen function from the quest module and the BeautifulSoup class from the bs4 package:
Each link URL on the /profiles page is a relative URL, so create a base_url variable with the base URL of the website:
base_url = ”
You can build a full URL by concatenating base_url with a relative URL.
Now open the /profiles page with urlopen() and use () to get the HTML source:
html_page = urlopen(base_url + “/profiles”)
With the HTML source downloaded and decoded, you can create a new BeautifulSoup object to parse the HTML:
soup = BeautifulSoup(html_text, “”)
nd_all(“a”) returns a list of all links in the HTML source. You can loop over this list to print out all the links on the webpage:
for link in nd_all(“a”):
link_url = base_url + link[“href”]
print(link_url)
The relative URL for each link can be accessed through the “href” subscript. Concatenate this value with base_url to create the full link_url.
Interact With HTML Forms
The urllib module you’ve been working with so far in this tutorial is well suited for requesting the contents of a web page. Sometimes, though, you need to interact with a web page to obtain the content you need. For example, you might need to submit a form or click a button to display hidden content.
The Python standard library doesn’t provide a built-in means for working with web pages interactively, but many third-party packages are available from PyPI. Among these, MechanicalSoup is a popular and relatively straightforward package to use.
In essence, MechanicalSoup installs what’s known as a headless browser, which is a web browser with no graphical user interface. This browser is controlled programmatically via a Python program.
Install MechanicalSoup
You can install MechanicalSoup with pip in your terminal:
$ python3 -m pip install MechanicalSoup
You can now view some details about the package with pip show:
$ python3 -m pip show mechanicalsoup
Name: MechanicalSoup
Version: 0. 12. 0
Summary: A Python library for automating interaction with websites
Home-page: Author: UNKNOWN
Author-email: UNKNOWN
Requires: requests, beautifulsoup4, six, lxml
In particular, notice that the latest version at the time of writing was 0. 0. You’ll need to close and restart your IDLE session for MechanicalSoup to load and be recognized after it’s been installed.
Create a Browser Object
Type the following into IDLE’s interactive window:
>>>>>> import mechanicalsoup
>>> browser = owser()
Browser objects represent the headless web browser. You can use them to request a page from the Internet by passing a URL to their () method:
>>> page = (url)
page is a Response object that stores the response from requesting the URL from the browser:

The number 200 represents the status code returned by the request. A status code of 200 means that the request was successful. An unsuccessful request might show a status code of 404 if the URL doesn’t exist or 500 if there’s a server error when making the request.
MechanicalSoup uses Beautiful Soup to parse the HTML from the request. page has a attribute that represents a BeautifulSoup object:
>>>>>> type()

You can view the HTML by inspecting the attribute:
Log In

Please log in to access Mount Olympus:

Notice this page has a

on it with elements for a username and a password.
Submit a Form With MechanicalSoup
Open the /login page from the previous example in a browser and look at it yourself before moving on. Try typing in a random username and password combination. If you guess incorrectly, then the message “Wrong username or password! ” is displayed at the bottom of the page.
However, if you provide the correct login credentials (username zeus and password ThunderDude), then you’re redirected to the /profiles page.
In the next example, you’ll see how to use MechanicalSoup to fill out and submit this form using Python!
The important section of HTML code is the login form—that is, everything inside the

tags. The

on this page has the name attribute set to login. This form contains two elements, one named user and the other named pwd. The third element is the Submit button.
Now that you know the underlying structure of the login form, as well as the credentials needed to log in, let’s take a look at a program. that fills the form out and submits it.
In a new editor window, type in the following program:
import mechanicalsoup
# 1
browser = owser()
login_page = (url)
login_html =
# 2
form = (“form”)[0]
(“input”)[0][“value”] = “zeus”
(“input”)[1][“value”] = “ThunderDude”
# 3
profiles_page = (form, )
Save the file and press F5 to run it. You can confirm that you successfully logged in by typing the following into the interactive window:
”
Let’s break down the above example:
You create a Browser instance and use it to request the URL. You assign the HTML content of the page to the login_html variable using the property.
(“form”) returns a list of all

elements on the page. Since the page has only one

element, you can access the form by retrieving the element at index 0 of the list. The next two lines select the username and password inputs and set their value to “zeus” and “ThunderDude”, respectively.
You submit the form with (). Notice that you pass two arguments to this method, the form object and the URL of the login_page, which you access via
In the interactive window, you confirm that the submission successfully redirected to the /profiles page. If something had gone wrong, then the value of would still be “.
Now that we have the profiles_page variable set, let’s see how to programmatically obtain the URL for each link on the /profiles page.
To do this, you use () again, this time passing the string “a” to select all the anchor elements on the page:
>>>>>> links = (“a”)
Now you can iterate over each link and print the href attribute:
>>>>>> for link in links:… address = link[“href”]… text =… print(f”{text}: {address}”)…
Aphrodite: /profiles/aphrodite
Poseidon: /profiles/poseidon
Dionysus: /profiles/dionysus
The URLs contained in each href attribute are relative URLs, which aren’t very helpful if you want to navigate to them later using MechanicalSoup. If you happen to know the full URL, then you can assign the portion needed to construct a full URL.
In this case, the base URL is just. Then you can concatenate the base URL with the relative URLs found in the src attribute:
>>>>>> base_url = ”
>>> for link in links:… address = base_url + link[“href”]… print(f”{text}: {address}”)…
Aphrodite: Poseidon: Dionysus:
You can do a lot with just (), (), and (). That said, MechanicalSoup is capable of much more. To learn more about MechanicalSoup, check out the official docs.
Expand the block below to check your understanding
Use MechanicalSoup to provide the correct username (zeus) and password (ThunderDude) to the login form located at the URL Once the form is submitted, display the title of the current page to determine that you’ve been redirected to the /profiles page.
Your program should print the text All Profiles.
First, import the mechanicalsoup package and create a Broswer object:
Point the browser to the login page by passing the URL to () and grab the HTML with the attribute:
login_url = ”
login_page = (login_url)
login_html is a BeautifulSoup instance. Since the page has only a single form on it, you can access the form via Using (), select the username and password inputs and fill them with the username “zeus” and the password “ThunderDude”:
form =
Now that the form is filled out, you can submit it with ():
If you filled the form with the correct username and password, then profiles_page should actually point to the /profiles page. You can confirm this by printing the title of the page assigned to profiles_page:
print()
You should see the following text displayed:
All Profiles
If instead you see the text Log In or something else, then the form submission failed.
Interact With Websites in Real Time
Sometimes you want to be able to fetch real-time data from a website that offers continually updated information.
In the dark days before you learned Python programming, you had to sit in front of a browser, clicking the Refresh button to reload the page each time you wanted to check if updated content was available. But now you can automate this process using the () method of the MechanicalSoup Browser object.
Open your browser of choice and navigate to the URL. This /dice page simulates a roll of a six-sided die, updating the result each time you refresh the browser. Below, you’ll write a program that repeatedly scrapes the page for a new result.
The first thing you need to do is determine which element on the page contains the result of the die roll. Do this now by right-clicking anywhere on the page and selecting View page source. A little more than halfway down the HTML code is an

tag that looks like this:
The text of the

tag might be different for you, but this is the page element you need for scraping the result.
Let’s start by writing a simple program that opens the /dice page, scrapes the result, and prints it to the console:
page = (“)
tag = (“#result”)[0]
result =
print(f”The result of your dice roll is: {result}”)
This example uses the BeautifulSoup object’s () method to find the element with id=result. The string “#result” that you pass to () uses the CSS ID selector # to indicate that result is an id value.
To periodically get a new result, you’ll need to create a loop that loads the page at each step. So everything below the line browser = owser() in the above code needs to go in the body of the loop.
For this example, let’s get four rolls of the dice at ten-second intervals. To do that, the last line of your code needs to tell Python to pause running for ten seconds. You can do this with sleep() from Python’s time module. sleep() takes a single argument that represents the amount of time to sleep in seconds.
Here’s an example that illustrates how sleep() works:
import time
print(“I’m about to wait for five seconds… “)
(5)
print(“Done waiting! “)
When you run this code, you’ll see that the “Done waiting! ” message isn’t displayed until 5 seconds have passed from when the first print() function was executed.
For the die roll example, you’ll need to pass the number 10 to sleep(). Here’s the updated program:
for i in range(4):
(10)
When you run the program, you’ll immediately see the first result printed to the console. After ten seconds, the second result is displayed, then the third, and finally the fourth. What happens after the fourth result is printed?
The program continues running for another ten seconds before it finally stops!
Well, of course it does—that’s what you told it to do! But it’s kind of a waste of time. You can stop it from doing this by using an if statement to run () for only the first three requests:
# Wait 10 seconds if this isn’t the last request
if i < 3: With techniques like this, you can scrape data from websites that periodically update their data. However, you should be aware that requesting a page multiple times in rapid succession can be seen as suspicious, or even malicious, use of a website. It’s even possible to crash a server with an excessive number of requests, so you can imagine that many websites are concerned about the volume of requests to their server! Always check the Terms of Use and be respectful when sending multiple requests to a website. Conclusion Although it’s possible to parse data from the Web using tools in Python’s standard library, there are many tools on PyPI that can help simplify the process. In this tutorial, you learned how to: Request a web page using Python’s built-in urllib module Parse HTML using Beautiful Soup Interact with web forms using MechanicalSoup Repeatedly request data from a website to check for updates Writing automated web scraping programs is fun, and the Internet has no shortage of content that can lead to all sorts of exciting projects. Just remember, not everyone wants you pulling data from their web servers. Always check a website’s Terms of Use before you start scraping, and be respectful about how you time your web requests so that you don’t flood a server with traffic. Additional Resources For more information on web scraping with Python, check out the following resources: Beautiful Soup: Build a Web Scraper With Python API Integration in Python Python & APIs: A Winning Combo for Reading Public Data

Tutorial: Web Scraping with Python Using Beautiful Soup

Published: March 30, 2021 Learn how to scrape the web with Python! The internet is an absolutely massive source of data — data that we can access using web scraping and Python! In fact, web scraping is often the only way we can access data. There is a lot of information out there that isn’t available in convenient CSV exports or easy-to-connect APIs. And websites themselves are often valuable sources of data — consider, for example, the kinds of analysis you could do if you could download every post on a web access those sorts of on-page datasets, we’ll have to use web scraping. Don’t worry if you’re still a total beginner! In this tutorial we’re going to cover how to do web scraping with Python from scratch, starting with some answers to frequently-asked, we’ll work through an actual web scraping project, focusing on weather ‘ll work together to scrape weather data from the web to support a weather before we start writing any Python, we’ve got to cover the basics! If you’re already familiar with the concept of web scraping, feel free to scroll past these questions and jump right into the tutorial! The Fundamentals of Web Scraping:What is Web Scraping in Python? Some websites offer data sets that are downloadable in CSV format, or accessible via an Application Programming Interface (API). But many websites with useful data don’t offer these convenient nsider, for example, the National Weather Service’s website. It contains up-to-date weather forecasts for every location in the US, but that weather data isn’t accessible as a CSV or via API. It has to be viewed on the NWS site:If we wanted to analyze this data, or download it for use in some other app, we wouldn’t want to painstakingly copy-paste everything. Web scraping is a technique that lets us use programming to do the heavy lifting. We’ll write some code that looks at the NWS site, grabs just the data we want to work with, and outputs it in the format we this tutorial, we’ll show you how to perform web scraping using Python 3 and the Beautiful Soup library. We’ll be scraping weather forecasts from the National Weather Service, and then analyzing them using the Pandas to be clear, lots of programming languages can be used to scrape the web! We also teach web scraping in R, for example. For this tutorial, though, we’ll be sticking with Does Web Scraping Work? When we scrape the web, we write code that sends a request to the server that’s hosting the page we specified. The server will return the source code — HTML, mostly — for the page (or pages) we far, we’re essentially doing the same thing a web browser does — sending a server request with a specific URL and asking the server to return the code for that unlike a web browser, our web scraping code won’t interpret the page’s source code and display the page visually. Instead, we’ll write some custom code that filters through the page’s source code looking for specific elements we’ve specified, and extracting whatever content we’ve instructed it to example, if we wanted to get all of the data from inside a table that was displayed on a web page, our code would be written to go through these steps in sequence:1Request the content (source code) of a specific URL from the server2Download the content that is returned3Identify the elements of the page that are part of the table we want4Extract and (if necessary) reformat those elements into a dataset we can analyze or use in whatever way we that all sounds very complicated, don’t worry! Python and Beautiful Soup have built-in features designed to make this relatively straightforward. One thing that’s important to note: from a server’s perspective, requesting a page via web scraping is the same as loading it in a web browser. When we use code to submit these requests, we might be “loading” pages much faster than a regular user, and thus quickly eating up the website owner’s server Use Python for Web Scraping? As previously mentioned, it’s possible to do web scraping with many programming ever, one of the most popular approaches is to use Python and the Beautiful Soup library, as we’ll do in this tutorial. Learning to do this with Python will mean that there are lots of tutorials, how-to videos, and bits of example code out there to help you deepen your knowledge once you’ve mastered the Beautiful Soup Web Scraping Legal? Unfortunately, there’s not a cut-and-dry answer here. Some websites explicitly allow web scraping. Others explicitly forbid it. Many websites don’t offer any clear guidance one way or the scraping any website, we should look for a terms and conditions page to see if there are explicit rules about scraping. If there are, we should follow them. If there are not, then it becomes more of a judgement member, though, that web scraping consumes server resources for the host website. If we’re just scraping one page once, that isn’t going to cause a problem. But if our code is scraping 1, 000 pages once every ten minutes, that could quickly get expensive for the website, in addition to following any and all explicit rules about web scraping posted on the site, it’s also a good idea to follow these best practices:Web Scraping Best Practices:Never scrape more frequently than you need nsider caching the content you scrape so that it’s only downloaded pauses into your code using functions like () to keep from overwhelming servers with too many requests too our case for this tutorial, the NWS’s data is public domain and its terms do not forbid web scraping, so we’re in the clear to to scrape the web with Python, right in your browser! Our interactive APIs and Web Scraping in Python skill path will help you learn the skills you need to unlock new worlds of data with Python. (No credit card required! ) The Components of a Web PageBefore we start writing code, we need to understand a little bit about the structure of a web page. We’ll use the site’s structure to write code that gets us the data we want to scrape, so understanding that structure is an important first step for any web scraping we visit a web page, our web browser makes a request to a web server. This request is called a GET request, since we’re getting files from the server. The server then sends back files that tell our browser how to render the page for us. These files will typically include:HTML — the main content of the — used to add styling to make the page look — Javascript files add interactivity to web — image formats, such as JPG and PNG, allow web pages to show our browser receives all the files, it renders the page and displays it to ’s a lot that happens behind the scenes to render a page nicely, but we don’t need to worry about most of it when we’re web scraping. When we perform web scraping, we’re interested in the main content of the web page, so we look primarily at the MLHyperText Markup Language (HTML) is the language that web pages are created in. HTML isn’t a programming language, like Python, though. It’s a markup language that tells a browser how to display content. HTML has many functions that are similar to what you might find in a word processor like Microsoft Word — it can make text bold, create paragraphs, and so you’re already familiar with HTML, feel free to jump to the next section of this tutorial. Otherwise, let’s take a quick tour through HTML so we know enough to scrape consists of elements called tags. The most basic tag is the tag. This tag tells the web browser that everything inside of it is HTML. We can make a simple HTML document just using this tag:We haven’t added any content to our page yet, so if we viewed our HTML document in a web browser, we wouldn’t see anything:Right inside an html tag, we can put two other tags: the head tag, and the body main content of the web page goes into the body tag. The head tag contains data about the title of the page, and other information that generally isn’t useful in web scraping:We still haven’t added any content to our page (that goes inside the body tag), so if we open this HTML file in a browser, we still won’t see anything:You may have noticed above that we put the head and body tags inside the html tag. In HTML, tags are nested, and can go inside other ’ll now add our first content to the page, inside a p tag. The p tag defines a paragraph, and any text inside the tag is shown as a separate paragraph:

Here’s a paragraph of text!

Here’s a second paragraph of text!

Rendered in a browser, that HTML file will look like this: Here’s a paragraph of text! Here’s a second paragraph of text! Tags have commonly used names that depend on their position in relation to other tags:child — a child is a tag inside another tag. So the two p tags above are both children of the body — a parent is the tag another tag is inside. Above, the html tag is the parent of the body biling — a sibiling is a tag that is nested inside the same parent as another tag. For example, head and body are siblings, since they’re both inside html. Both p tags are siblings, since they’re both inside can also add properties to HTML tags that change their behavior. Below, we’ll add some extra text and hyperlinks using the a tag.

Here’s a paragraph of text! Python

Here’s how this will look:In the above example, we added two a tags. a tags are links, and tell the browser to render a link to another web page. The href property of the tag determines where the link goes. a and p are extremely common html tags. Here are a few others:div — indicates a division, or area, of the page. b — bolds any text inside. i — italicizes any text — creates a — creates an input a full list of tags, look we move into actual web scraping, let’s learn about the class and id properties. These special properties give HTML elements names, and make them easier to interact with when we’re element can have multiple classes, and a class can be shared between elements. Each element can only have one id, and an id can only be used once on a page. Classes and ids are optional, and not all elements will have can add classes and ids to our example:

Here’s a paragraph of text! Learn Data Science Online

Here’s a second paragraph of text! Python

Here’s how this will look:As you can see, adding classes and ids doesn’t change how the tags are rendered at requests libraryNow that we understand the structure of a web page, it’s time to get into the fun part: scraping the content we want! The first thing we’ll need to do to scrape a web page is to download the page. We can download pages using the Python requests requests library will make a GET request to a web server, which will download the HTML contents of a given web page for us. There are several different types of requests we can make using requests, of which GET is just one. If you want to learn more, check out our API ’s try downloading a simple sample website, ll need to first import the requests library, and then download the page using the method:import requests
page = (“)
page
After running our request, we get a Response object. This object has a status_code property, which indicates if the page was downloaded successfully:A status_code of 200 means that the page downloaded successfully. We won’t fully dive into status codes here, but a status code starting with a 2 generally indicates success, and a code starting with a 4 or a 5 indicates an can print out the HTML content of the page using the content ntent

A simple example page