Python Using Beautifulsoup
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
Here is some simple content for this page.
Parsing a page with BeautifulSoupAs you can see above, we now have downloaded an HTML can use the BeautifulSoup library to parse this document, and extract the text from the p first have to import the library, and create an instance of the BeautifulSoup class to parse our document:from bs4 import BeautifulSoup
soup = BeautifulSoup(ntent, ”)We can now print out the HTML content of the page, formatted nicely, using the prettify method on the BeautifulSoup object.
This step isn’t strictly necessary, and we won’t always bother with it, but it can be helpful to look at prettified HTML to make the structure of the and where tags are nested easier to all the tags are nested, we can move through the structure one level at a time. We can first select all the elements at the top level of the page using the children property of soup. Note that children returns a list generator, so we need to call the list function on it:list(ildren)
[‘html’, ‘n’,
Here is some simple content for this page.
]The above tells us that there are two tags at the top level of the page — the initial tag, and the tag. There is a newline character (n) in the list as well. Let’s see what the type of each element in the list is:[type(item) for item in list(ildren)]
[ctype, vigableString, ]As we can see, all of the items are BeautifulSoup objects:The first is a Doctype object, which contains information about the type of the second is a NavigableString, which represents text found in the HTML final item is a Tag object, which contains other nested most important object type, and the one we’ll deal with most often, is the Tag Tag object allows us to navigate through an HTML document, and extract other tags and text. You can learn more about the various BeautifulSoup objects can now select the html tag and its children by taking the third item in the list:html = list(ildren)[2]Each item in the list returned by the children property is also a BeautifulSoup object, so we can also call the children method on, we can find the children inside the html tag:list(ildren)
[‘n’,
Here is some simple content for this page.
, ‘n’]As we can see above, there are two tags here, head, and body. We want to extract the text inside the p tag, so we’ll dive into the body:body = list(ildren)[3]Now, we can get the p tag by finding the children of the body tag:list(ildren)
[‘n’,
Here is some simple content for this page.
, ‘n’]We can now isolate the p tag:p = list(ildren)[1]Once we’ve isolated the tag, we can use the get_text method to extract all of the text inside the t_text()
‘Here is some simple content for this page. ‘Finding all instances of a tag at onceWhat we did above was useful for figuring out how to navigate a page, but it took a lot of commands to do something fairly simple. If we want to extract a single tag, we can instead use the find_all method, which will find all the instances of a tag on a = BeautifulSoup(ntent, ”)
nd_all(‘p’)
[
Here is some simple content for this page.
]Note that find_all returns a list, so we’ll have to loop through, or use list indexing, it to extract nd_all(‘p’)[0]. get_text()
‘Here is some simple content for this page. ‘f you instead only want to find the first instance of a tag, you can use the find method, which will return a single BeautifulSoup (‘p’)
Here is some simple content for this page.
Searching for tags by class and idWe introduced classes and ids earlier, but it probably wasn’t clear why they were asses and ids are used by CSS to determine which HTML elements to apply certain styles to. But when we’re scraping, we can also use them to specify the elements we want to illustrate this principle, we’ll work with the following page:
First paragraph.
Second paragraph.
First outer paragraph.
Second outer paragraph.
We can access the above document at the URL. Let’s first download the page and create a BeautifulSoup object:page = (“)
soup = BeautifulSoup(ntent, ”)
soup
Now, we can use the find_all method to search for items by class or by id. In the below example, we’ll search for any p tag that has the class nd_all(‘p’, class_=’outer-text’)
[
First outer paragraph.
,
Second outer paragraph.
]In the below example, we’ll look for any tag that has the class nd_all(class_=”outer-text”)
,
]We can also search for elements by nd_all(id=”first”)
[
]Using CSS SelectorsWe can also search for items using CSS selectors. These selectors are how the CSS language allows developers to specify HTML tags to style. Here are some examples:p a — finds all a tags inside of a p p a — finds all a tags inside of a p tag inside of a body body — finds all body tags inside of an html — finds all p tags with a class of outer-text. p#first — finds all p tags with an id of — finds any p tags with a class of outer-text inside of a body can learn more about CSS selectors autifulSoup objects support searching a page via CSS selectors using the select method. We can use CSS selectors to find all the p tags in our page that are inside of a div like (“div p”)
,
]Note that the select method above returns a list of BeautifulSoup objects, just like find and wnloading weather dataWe now know enough to proceed with extracting information about the local weather from the National Weather Service website! The first step is to find the page we want to scrape. We’ll extract weather information about downtown San Francisco from this page. Specifically, let’s extract data about the extended we can see from the image, the page has information about the extended forecast for the next week, including time of day, temperature, and a brief description of the conditions. Exploring page structure with Chrome DevToolsThe first thing we’ll need to do is inspect the page using Chrome Devtools. If you’re using another browser, Firefox and Safari have can start the developer tools in Chrome by clicking View -> Developer -> Developer Tools. You should end up with a panel at the bottom of the browser like what you see below. Make sure the Elements panel is highlighted:Chrome Developer ToolsThe elements panel will show you all the HTML tags on the page, and let you navigate through them. It’s a really handy feature! By right clicking on the page near where it says “Extended Forecast”, then clicking “Inspect”, we’ll open up the tag that contains the text “Extended Forecast” in the elements panel:The extended forecast textWe can then scroll up in the elements panel to find the “outermost” element that contains all of the text that corresponds to the extended forecasts. In this case, it’s a div tag with the id seven-day-forecast:The div that contains the extended forecast we click around on the console, and explore the div, we’ll discover that each forecast item (like “Tonight”, “Thursday”, and “Thursday Night”) is contained in a div with the class to Start Scraping! We now know enough to download the page and start parsing it. In the below code, we will:Download the web page containing the a BeautifulSoup class to parse the the div with id seven-day-forecast, and assign to seven_dayInside seven_day, find each individual forecast item. Extract and print the first forecast = (“)
seven_day = (id=”seven-day-forecast”)
forecast_items = nd_all(class_=”tombstone-container”)
tonight = forecast_items[0]
print(ettify())
Tonight
Mostly Clear
Low: 49 °F
Extracting information from the pageAs we can see, inside the forecast item tonight is all the information we want. There are four pieces of information we can extract:The name of the forecast item — in this case, description of the conditions — this is stored in the title property of img. A short description of the conditions — in this case, Mostly temperature low — in this case, 49 ’ll extract the name of the forecast item, the short description, and the temperature first, since they’re all similar:period = (class_=”period-name”). get_text()
short_desc = (class_=”short-desc”). get_text()
temp = (class_=”temp”). get_text()
print(period)
print(short_desc)
print(temp)
Low: 49 °FNow, we can extract the title attribute from the img tag. To do this, we just treat the BeautifulSoup object like a dictionary, and pass in the attribute we want as a key:img = (“img”)
desc = img[‘title’]
print(desc)
Tonight: Mostly clear, with a low around 49. Extracting all the information from the pageNow that we know how to extract each individual piece of information, we can combine our knowledge with CSS selectors and list comprehensions to extract everything at the below code, we will:Select all items with the class period-name inside an item with the class tombstone-container in a list comprehension to call the get_text method on each BeautifulSoup riod_tags = (“. tombstone-container “)
periods = [t_text() for pt in period_tags]
periods
[‘Tonight’,
‘Thursday’,
‘ThursdayNight’,
‘Friday’,
‘FridayNight’,
‘Saturday’,
‘SaturdayNight’,
‘Sunday’,
‘SundayNight’]As we can see above, our technique gets us each of the period names, in order. We can apply the same technique to get the other three fields:short_descs = [t_text() for sd in (“. tombstone-container “)]
temps = [t_text() for t in (“. tombstone-container “)]
descs = [d[“title”] for d in (“. tombstone-container img”)]print(short_descs)print(temps)print(descs)
[‘Mostly Clear’, ‘Sunny’, ‘Mostly Clear’, ‘Sunny’, ‘Slight ChanceRain’, ‘Rain Likely’, ‘Rain Likely’, ‘Rain Likely’, ‘Chance Rain’]
[‘Low: 49 °F’, ‘High: 63 °F’, ‘Low: 50 °F’, ‘High: 67 °F’, ‘Low: 57 °F’, ‘High: 64 °F’, ‘Low: 57 °F’, ‘High: 64 °F’, ‘Low: 55 °F’]
[‘Tonight: Mostly clear, with a low around 49. ‘, ‘Thursday: Sunny, with a high near 63. North wind 3 to 5 mph. ‘, ‘Thursday Night: Mostly clear, with a low around 50. Light and variable wind becoming east southeast 5 to 8 mph after midnight. ‘, ‘Friday: Sunny, with a high near 67. Southeast wind around 9 mph. ‘, ‘Friday Night: A 20 percent chance of rain after 11pm. Partly cloudy, with a low around 57. South southeast wind 13 to 15 mph, with gusts as high as 20 mph. New precipitation amounts of less than a tenth of an inch possible. ‘, ‘Saturday: Rain likely. Cloudy, with a high near 64. Chance of precipitation is 70%. New precipitation amounts between a quarter and half of an inch possible. ‘, ‘Saturday Night: Rain likely. Cloudy, with a low around 57. Chance of precipitation is 60%. ‘, ‘Sunday: Rain likely. ‘, ‘Sunday Night: A chance of rain. Mostly cloudy, with a low around 55. ‘]Combining our data into a Pandas DataframeWe can now combine the data into a Pandas DataFrame and analyze it. A DataFrame is an object that can store tabular data, making data analysis easy. If you want to learn more about Pandas, check out our free to start course order to do this, we’ll call the DataFrame class, and pass in each list of items that we have. We pass them in as part of a dictionary key will become a column in the DataFrame, and each list will become the values in the column:import pandas as pd
weather = Frame({
“period”: periods,
“short_desc”: short_descs,
“temp”: temps,
“desc”:descs})
weather
desc
period
short_desc
temp
0
Tonight: Mostly clear, with a low around 49. W…
1
Thursday: Sunny, with a high near 63. North wi…
Thursday
Sunny
High: 63 °F
2
Thursday Night: Mostly clear, with a low aroun…
ThursdayNight
Low: 50 °F
3
Friday: Sunny, with a high near 67. Southeast …
Friday
High: 67 °F
4
Friday Night: A 20 percent chance of rain afte…
FridayNight
Slight ChanceRain
Low: 57 °F
5
Saturday: Rain likely. Cloudy, with a high ne…
Saturday
Rain Likely
High: 64 °F
6
Saturday Night: Rain likely. Cloudy, with a l…
SaturdayNight
7
Sunday: Rain likely. Cloudy, with a high near…
Sunday
8
Sunday Night: A chance of rain. Mostly cloudy…
SundayNight
Chance Rain
Low: 55 °F
We can now do some analysis on the data. For example, we can use a regular expression and the method to pull out the numeric temperature values:temp_nums = weather[“temp”](“(? Pd+)”, expand=False)
weather[“temp_num”] = (‘int’)
temp_nums
0 49
1 63
2 50
3 67
4 57
5 64
6 57
7 64
8 55
Name: temp_num, dtype: objectWe could then find the mean of all the high and low temperatures:weather[“temp_num”]()
58. 444444444444443We could also only select the rows that happen at night:is_night = weather[“temp”](“Low”)
weather[“is_night”] = is_night
is_night
0 True
1 False
2 True
3 False
4 True
5 False
6 True
7 False
8 True
Name: temp, dtype: boolweather[is_night]
Name: temp, dtype: bool
temp_num
49
True
50
57
55
Next Steps For This Web Scraping ProjectIf you’ve made it this far, congratulations! You should now have a good understanding of how to scrape web pages and extract data. Of course, there’s still a lot more to learn! If you want to go further, a good next step would be to pick a site and try some web scraping on your own. Some good examples of data to scrape are:News articlesSports scoresWeather forecastsStock pricesOnline retailer pricesYou may also want to keep scraping the National Weather Service, and see what other data you can extract from the page, or about your own ternatively, if you want to take your web scraping skills to the next level, you can check out our interactive course, which covers both the basics of web scraping and using Python to connect to APIs. With those two skills under your belt, you’ll be able to collect lots of unique and interesting datasets from sites all over the web! Learn 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! )beginner, data mining, python, python tutorials, scraping, tutorial, Tutorials, web scraping
Beautiful Soup: Build a Web Scraper With 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: Web Scraping With Beautiful Soup and Python
The incredible amount of data on the Internet is a rich resource for any field of research or personal interest. To effectively harvest that data, you’ll need to become skilled at web scraping. The Python libraries requests and Beautiful Soup are powerful tools for the job. If you like to learn with hands-on examples and have a basic understanding of Python and HTML, then this tutorial is for you.
In this tutorial, you’ll learn how to:
Inspect the HTML structure of your target site with your browser’s developer tools
Decipher data encoded in URLs
Use requests and Beautiful Soup for scraping and parsing data from the Web
Step through a web scraping pipeline from start to finish
Build a script that fetches job offers from the Web and displays relevant information in your console
Working through this project will give you the knowledge of the process and tools you need to scrape any static website out there on the World Wide Web. You can download the project source code by clicking on the link below:
Let’s get started!
What Is Web Scraping?
Web scraping is the process of gathering information from the Internet. Even copying and pasting the lyrics of your favorite song is a form of web scraping! However, the words “web scraping” usually refer to a process that involves automation. Some websites don’t like it when automatic scrapers gather their data, while others don’t mind.
If you’re scraping a page respectfully for educational purposes, then you’re unlikely to have any problems. Still, it’s a good idea to do some research on your own and make sure that you’re not violating any Terms of Service before you start a large-scale project.
Reasons for Web Scraping
Say you’re a surfer, both online and in real life, and you’re looking for employment. However, you’re not looking for just any job. With a surfer’s mindset, you’re waiting for the perfect opportunity to roll your way!
There’s a job site that offers precisely the kinds of jobs you want. Unfortunately, a new position only pops up once in a blue moon, and the site doesn’t provide an email notification service. You think about checking up on it every day, but that doesn’t sound like the most fun and productive way to spend your time.
Thankfully, the world offers other ways to apply that surfer’s mindset! Instead of looking at the job site every day, you can use Python to help automate your job search’s repetitive parts. Automated web scraping can be a solution to speed up the data collection process. You write your code once, and it will get the information you want many times and from many pages.
In contrast, when you try to get the information you want manually, you might spend a lot of time clicking, scrolling, and searching, especially if you need large amounts of data from websites that are regularly updated with new content. Manual web scraping can take a lot of time and repetition.
There’s so much information on the Web, and new information is constantly added. You’ll probably be interested in at least some of that data, and much of it is just out there for the taking. Whether you’re actually on the job hunt or you want to download all the lyrics of your favorite artist, automated web scraping can help you accomplish your goals.
Challenges of Web Scraping
The Web has grown organically out of many sources. It combines many different technologies, styles, and personalities, and it continues to grow to this day. In other words, the Web is a hot mess! Because of this, you’ll run into some challenges when scraping the Web:
Variety: Every website is different. While you’ll encounter general structures that repeat themselves, each website is unique and will need personal treatment if you want to extract the relevant information.
Durability: Websites constantly change. Say you’ve built a shiny new web scraper that automatically cherry-picks what you want from your resource of interest. The first time you run your script, it works flawlessly. But when you run the same script only a short while later, you run into a discouraging and lengthy stack of tracebacks!
Unstable scripts are a realistic scenario, as many websites are in active development. Once the site’s structure has changed, your scraper might not be able to navigate the sitemap correctly or find the relevant information. The good news is that many changes to websites are small and incremental, so you’ll likely be able to update your scraper with only minimal adjustments.
However, keep in mind that because the Internet is dynamic, the scrapers you’ll build will probably require constant maintenance. You can set up continuous integration to run scraping tests periodically to ensure that your main script doesn’t break without your knowledge.
An Alternative to Web Scraping: APIs
Some website providers offer application programming interfaces (APIs) that allow you to access their data in a predefined manner. With APIs, you can avoid parsing HTML. Instead, you can access the data directly using formats like JSON and XML. HTML is primarily a way to present content to users visually.
When you use an API, the process is generally more stable than gathering the data through web scraping. That’s because developers create APIs to be consumed by programs rather than by human eyes.
The front-end presentation of a site might change often, but such a change in the website’s design doesn’t affect its API structure. The structure of an API is usually more permanent, which means it’s a more reliable source of the site’s data.
However, APIs can change as well. The challenges of both variety and durability apply to APIs just as they do to websites. Additionally, it’s much harder to inspect the structure of an API by yourself if the provided documentation lacks quality.
The approach and tools you need to gather information using APIs are outside the scope of this tutorial. To learn more about it, check out API Integration in Python.
Scrape the Fake Python Job Site
In this tutorial, you’ll build a web scraper that fetches Python software developer job listings from the Fake Python Jobs site. It’s an example site with fake job postings that you can freely scrape to train your skills. Your web scraper will parse the HTML on the site to pick out the relevant information and filter that content for specific words.
You can scrape any site on the Internet that you can look at, but the difficulty of doing so depends on the site. This tutorial offers you an introduction to web scraping to help you understand the overall process. Then, you can apply this same process for every website you’ll want to scrape.
Throughout the tutorial, you’ll also encounter a few exercise blocks. You can click to expand them and challenge yourself by completing the tasks described there.
Step 1: Inspect Your Data Source
Before you write any Python code, you need to get to know the website that you want to scrape. That should be your first step for any web scraping project you want to tackle. You’ll need to understand the site structure to extract the information that’s relevant for you. Start by opening the site you want to scrape with your favorite browser.
Explore the Website
Click through the site and interact with it just like any typical job searcher would. For example, you can scroll through the main page of the website:
You can see many job postings in a card format, and each of them has two buttons. If you click Apply, then you’ll see a new page that contains more detailed descriptions of the selected job. You might also notice that the URL in your browser’s address bar changes when you interact with the website.
Decipher the Information in URLs
A programmer can encode a lot of information in a URL. Your web scraping journey will be much easier if you first become familiar with how URLs work and what they’re made of. For example, you might find yourself on a details page that has the following URL:
You can deconstruct the above URL into two main parts:
The base URL represents the path to the search functionality of the website. In the example above, the base URL is The specific site location that ends with is the path to the job description’s unique resource.
Any job posted on this website will use the same base URL. However, the unique resources’ location will be different depending on what specific job posting you’re viewing.
URLs can hold more information than just the location of a file. Some websites use query parameters to encode values that you submit when performing a search. You can think of them as query strings that you send to the database to retrieve specific records.
You’ll find query parameters at the end of a URL. For example, if you go to Indeed and search for “software developer” in “Australia” through their search bar, you’ll see that the URL changes to include these values as query parameters:
The query parameters in this URL are? q=software+developer&l=Australia. Query parameters consist of three parts:
Start: The beginning of the query parameters is denoted by a question mark (? ).
Information: The pieces of information constituting one query parameter are encoded in key-value pairs, where related keys and values are joined together by an equals sign (key=value).
Separator: Every URL can have multiple query parameters, separated by an ampersand symbol (&).
Equipped with this information, you can pick apart the URL’s query parameters into two key-value pairs:
q=software+developer selects the type of job.
l=Australia selects the location of the job.
Try to change the search parameters and observe how that affects your URL. Go ahead and enter new values in the search bar up top:
Change these values to observe the changes in the URL.
Next, try to change the values directly in your URL. See what happens when you paste the following URL into your browser’s address bar:
If you change and submit the values in the website’s search box, then it’ll be directly reflected in the URL’s query parameters and vice versa. If you change either of them, then you’ll see different results on the website.
As you can see, exploring the URLs of a site can give you insight into how to retrieve data from the website’s server.
Head back to Fake Python Jobs and continue exploring it. This site is a purely static website that doesn’t operate on top of a database, which is why you won’t have to work with query parameters in this scraping tutorial.
Inspect the Site Using Developer Tools
Next, you’ll want to learn more about how the data is structured for display. You’ll need to understand the page structure to pick what you want from the HTML response that you’ll collect in one of the upcoming steps.
Developer tools can help you understand the structure of a website. All modern browsers come with developer tools installed. In this section, you’ll see how to work with the developer tools in Chrome. The process will be very similar to other modern browsers.
In Chrome on macOS, you can open up the developer tools through the menu by selecting View → Developer → Developer Tools. On Windows and Linux, you can access them by clicking the top-right menu button (⋮) and selecting More Tools → Developer Tools. You can also access your developer tools by right-clicking on the page and selecting the Inspect option or using a keyboard shortcut:
Mac: Cmd+Alt+I
Windows/Linux: Ctrl+Shift+I
Developer tools allow you to interactively explore the site’s document object model (DOM) to better understand your source. To dig into your page’s DOM, select the Elements tab in developer tools. You’ll see a structure with clickable HTML elements. You can expand, collapse, and even edit elements right in your browser:
The HTML on the right represents the structure of the page you can see on the left.
You can think of the text displayed in your browser as the HTML structure of that page. If you’re interested, then you can read more about the difference between the DOM and HTML on CSS-TRICKS.
When you right-click elements on the page, you can select Inspect to zoom to their location in the DOM. You can also hover over the HTML text on your right and see the corresponding elements light up on the page.
Click to expand the exercise block for a specific task to practice using your developer tools:
Find a single job posting. What HTML element is it wrapped in, and what other HTML elements does it contain?
Play around and explore! The more you get to know the page you’re working with, the easier it will be to scrape it. However, don’t get too overwhelmed with all that HTML text. You’ll use the power of programming to step through this maze and cherry-pick the information that’s relevant to you.
Step 2: Scrape HTML Content From a Page
Now that you have an idea of what you’re working with, it’s time to start using Python. First, you’ll want to get the site’s HTML code into your Python script so that you can interact with it. For this task, you’ll use Python’s requests library.
Create a virtual environment for your project before you install any external package. Activate your new virtual environment, then type the following command in your terminal to install the external requests library:
$ python -m pip install requests
Then open up a new file in your favorite text editor. All you need to retrieve the HTML are a few lines of code:
import requests
URL = ”
page = (URL)
print()
This code issues an HTTP GET request to the given URL. It retrieves the HTML data that the server sends back and stores that data in a Python object.
If you print the attribute of page, then you’ll notice that it looks just like the HTML that you inspected earlier with your browser’s developer tools. You successfully fetched the static site content from the Internet! You now have access to the site’s HTML from within your Python script.
Static Websites
The website that you’re scraping in this tutorial serves static HTML content. In this scenario, the server that hosts the site sends back HTML documents that already contain all the data that you’ll get to see as a user.
When you inspected the page with developer tools earlier on, you discovered that a job posting consists of the following long and messy-looking HTML:
Senior Python Developer
Payne, Roberts and Davis
Stewartbury, AA