Scrape A Website Python

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Beautiful Soup: Build a Web Scraper With Python

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

Learn
>Apply

It can be challenging to wrap your head around a long block of HTML code. To make it easier to read, you can use an HTML formatter to clean it up automatically. Good readability helps you better understand the structure of any code block. While it may or may not help improve the HTML formatting, it’s always worth a try.
The HTML you’ll encounter will sometimes be confusing. Luckily, the HTML of this job board has descriptive class names on the elements that you’re interested in:
class=”title is-5″ contains the title of the job posting.
class=”subtitle is-6 company” contains the name of the company that offers the position.
class=”location” contains the location where you’d be working.
In case you ever get lost in a large pile of HTML, remember that you can always go back to your browser and use the developer tools to further explore the HTML structure interactively.
By now, you’ve successfully harnessed the power and user-friendly design of Python’s requests library. With only a few lines of code, you managed to scrape static HTML content from the Web and make it available for further processing.
However, there are more challenging situations that you might encounter when you’re scraping websites. Before you learn how to pick the relevant information from the HTML that you just scraped, you’ll take a quick look at two of these more challenging situations.
Hidden Websites
Some pages contain information that’s hidden behind a login. That means you’ll need an account to be able to scrape anything from the page. The process to make an HTTP request from your Python script is different from how you access a page from your browser. Just because you can log in to the page through your browser doesn’t mean you’ll be able to scrape it with your Python script.
However, the requests library comes with the built-in capacity to handle authentication. With these techniques, you can log in to websites when making the HTTP request from your Python script and then scrape information that’s hidden behind a login. You won’t need to log in to access the job board information, which is why this tutorial won’t cover authentication.
Dynamic Websites
In this tutorial, you’ll learn how to scrape a static website. Static sites are straightforward to work with because the server sends you an HTML page that already contains all the page information in the response. You can parse that HTML response and immediately begin to pick out the relevant data.
On the other hand, with a dynamic website, the server might not send back any HTML at all. Instead, you could receive JavaScript code as a response. This code will look completely different from what you saw when you inspected the page with your browser’s developer tools.
What happens in the browser is not the same as what happens in your script. Your browser will diligently execute the JavaScript code it receives from a server and create the DOM and HTML for you locally. However, if you request a dynamic website in your Python script, then you won’t get the HTML page content.
When you use requests, you only receive what the server sends back. In the case of a dynamic website, you’ll end up with some JavaScript code instead of HTML. The only way to go from the JavaScript code you received to the content that you’re interested in is to execute the code, just like your browser does. The requests library can’t do that for you, but there are other solutions that can.
For example, requests-html is a project created by the author of the requests library that allows you to render JavaScript using syntax that’s similar to the syntax in requests. It also includes capabilities for parsing the data by using Beautiful Soup under the hood.
You won’t go deeper into scraping dynamically-generated content in this tutorial. For now, it’s enough to remember to look into one of the options mentioned above if you need to scrape a dynamic website.
Step 3: Parse HTML Code With Beautiful Soup
You’ve successfully scraped some HTML from the Internet, but when you look at it, it just seems like a huge mess. There are tons of HTML elements here and there, thousands of attributes scattered around—and wasn’t there some JavaScript mixed in as well? It’s time to parse this lengthy code response with the help of Python to make it more accessible and pick out the data you want.
Beautiful Soup is a Python library for parsing structured data. It allows you to interact with HTML in a similar way to how you interact with a web page using developer tools. The library exposes a couple of intuitive functions you can use to explore the HTML you received. To get started, use your terminal to install Beautiful Soup:
$ python -m pip install beautifulsoup4
Then, import the library in your Python script and create a Beautiful Soup object:
from bs4 import BeautifulSoup
soup = BeautifulSoup(ntent, “”)
When you add the two highlighted lines of code, you create a Beautiful Soup object that takes ntent, which is the HTML content you scraped earlier, as its input.
The second argument, “”, makes sure that you use the appropriate parser for HTML content.
Find Elements by ID
In an HTML web page, every element can have an id attribute assigned. As the name already suggests, that id attribute makes the element uniquely identifiable on the page. You can begin to parse your page by selecting a specific element by its ID.
Switch back to developer tools and identify the HTML object that contains all the job postings. Explore by hovering over parts of the page and using right-click to Inspect.
The element you’re looking for is a

with an id attribute that has the value “ResultsContainer”. It has some other attributes as well, but below is the gist of what you’re looking for:


Beautiful Soup allows you to find that specific HTML element by its ID:
results = (id=”ResultsContainer”)
For easier viewing, you can prettify any Beautiful Soup object when you print it out. If you call. prettify() on the results variable that you just assigned above, then you’ll see all the HTML contained within the

:
print(ettify())
When you use the element’s ID, you can pick out one element from among the rest of the HTML. Now you can work with only this specific part of the page’s HTML. It looks like the soup just got a little thinner! However, it’s still quite dense.
Find Elements by HTML Class Name
You’ve seen that every job posting is wrapped in a

element with the class card-content. Now you can work with your new object called results and select only the job postings in it. These are, after all, the parts of the HTML that you’re interested in! You can do this in one line of code:
job_elements = nd_all(“div”, class_=”card-content”)
Here, you call. find_all() on a Beautiful Soup object, which returns an iterable containing all the HTML for all the job listings displayed on that page.
Take a look at all of them:
for job_element in job_elements:
print(job_element, end=”\n”*2)
That’s already pretty neat, but there’s still a lot of HTML! You saw earlier that your page has descriptive class names on some elements. You can pick out those child elements from each job posting with ():
title_element = (“h2″, class_=”title”)
company_element = (“h3″, class_=”company”)
location_element = (“p”, class_=”location”)
print(title_element)
print(company_element)
print(location_element)
Each job_element is another BeautifulSoup() object. Therefore, you can use the same methods on it as you did on its parent element, results.
With this code snippet, you’re getting closer and closer to the data that you’re actually interested in. Still, there’s a lot going on with all those HTML tags and attributes floating around:
Next, you’ll learn how to narrow down this output to access only the text content you’re interested in.
Find Elements by Class Name and Text Content
Not all of the job listings are developer jobs. Instead of printing out all the jobs listed on the website, you’ll first filter them using keywords.
You know that job titles in the page are kept within

elements. To filter for only specific jobs, you can use the string argument:
python_jobs = nd_all(“h2″, string=”Python”)
This code finds all

elements where the contained string matches “Python” exactly. Note that you’re directly calling the method on your first results variable. If you go ahead and print() the output of the above code snippet to your console, then you might be disappointed because it’ll be empty:
>>>>>> print(python_jobs)
[]
There was a Python job in the search results, so why is it not showing up?
When you use string= as you did above, your program looks for that string exactly. Any differences in the spelling, capitalization, or whitespace will prevent the element from matching. In the next section, you’ll find a way to make your search string more general.
Pass a Function to a Beautiful Soup Method
In addition to strings, you can sometimes pass functions as arguments to Beautiful Soup methods. You can change the previous line of code to use a function instead:
python_jobs = nd_all(
“h2”, string=lambda text: “python” in ())
Now you’re passing an anonymous function to the string= argument. The lambda function looks at the text of each

element, converts it to lowercase, and checks whether the substring “python” is found anywhere. You can check whether you managed to identify all the Python jobs with this approach:
>>>>>> print(len(python_jobs))
10
Your program has found 10 matching job posts that include the word “python” in their job title!
Finding elements depending on their text content is a powerful way to filter your HTML response for specific information. Beautiful Soup allows you to use either exact strings or functions as arguments for filtering text in Beautiful Soup objects.
However, when you try to run your scraper to print out the information of the filtered Python jobs, you’ll run into an error:
AttributeError: ‘NoneType’ object has no attribute ‘text’
This message is a common error that you’ll run into a lot when you’re scraping information from the Internet. Inspect the HTML of an element in your python_jobs list. What does it look like? Where do you think the error is coming from?
Identify Error Conditions
When you look at a single element in python_jobs, you’ll see that it consists of only the

element that contains the job title:
When you revisit the code you used to select the items, you’ll see that that’s what you targeted. You filtered for only the

title elements of the job postings that contain the word “python”. As you can see, these elements don’t include the rest of the information about the job.
The error message you received earlier was related to this:
You tried to find the job title, the company name, and the job’s location in each element in python_jobs, but each element contains only the job title text.
Your diligent parsing library still looks for the other ones, too, and returns None because it can’t find them. Then, print() fails with the shown error message when you try to extract the attribute from one of these None objects.
The text you’re looking for is nested in sibling elements of the

elements your filter returned. Beautiful Soup can help you to select sibling, child, and parent elements of each Beautiful Soup object.
Access Parent Elements
One way to get access to all the information you need is to step up in the hierarchy of the DOM starting from the

elements that you identified. Take another look at the HTML of a single job posting. Find the

element that contains the job title as well as its closest parent element that contains all the information that you’re interested in:
The

element with the card-content class contains all the information you want. It’s a third-level parent of the

title element that you found using your filter.
With this information in mind, you can now use the elements in python_jobs and fetch their great-grandparent elements instead to get access to all the information you want:
python_job_elements = [
for h2_element in python_jobs]
You added a list comprehension that operates on each of the

title elements in python_jobs that you got by filtering with the lambda expression. You’re selecting the parent element of the parent element of the parent element of each

title element. That’s three generations up!
When you were looking at the HTML of a single job posting, you identified that this specific parent element with the class name card-content contains all the information you need.
Now you can adapt the code in your for loop to iterate over the parent elements instead:
for job_element in python_job_elements:
# — snip —
When you run your script another time, you’ll see that your code once again has access to all the relevant information. That’s because you’re now looping over the

elements instead of just the

title elements.
Using the attribute that each Beautiful Soup object comes with gives you an intuitive way of stepping through your DOM structure and addressing the elements you need. You can also access child elements and sibling elements in a similar manner. Read up on navigating the tree for more information.
Keep Practicing
If you’ve written the code alongside this tutorial, then you can run your script as is, and you’ll see the fake job information pop up in your terminal. Your next step is to tackle a real-life job board! To keep practicing your new skills, revisit the web scraping process using any or all of the following sites:
PythonJobs
Remote(dot)co
Indeed
The linked websites return their search results as static HTML responses, similar to the Fake Python job board. Therefore, you can scrape them using only requests and Beautiful Soup.
Start going through this tutorial again from the top using one of these other sites. You’ll see that each website’s structure is different and that you’ll need to rebuild the code in a slightly different way to fetch the data you want. Tackling this challenge is a great way to practice the concepts that you just learned. While it might make you sweat every so often, your coding skills will be stronger for it!
During your second attempt, you can also explore additional features of Beautiful Soup. Use the documentation as your guidebook and inspiration. Extra practice will help you become more proficient at web scraping using Python, requests, and Beautiful Soup.
To wrap up your journey into web scraping, you could then give your code a final makeover and create a command-line interface (CLI) app that scrapes one of the job boards and filters the results by a keyword that you can input on each execution. Your CLI tool could allow you to search for specific types of jobs or jobs in particular locations.
If you’re interested in learning how to adapt your script as a command-line interface, then check out How to Build Command-Line Interfaces in Python With argparse.
Conclusion
The requests library gives you a user-friendly way to fetch static HTML from the Internet using Python. You can then parse the HTML with another package called Beautiful Soup. Both packages are trusted and helpful companions for your web scraping adventures. You’ll find that Beautiful Soup will cater to most of your parsing needs, including navigation and advanced searching.
In this tutorial, you learned how to scrape data from the Web using Python, requests, and Beautiful Soup. You built a script that fetches job postings from the Internet and went through the complete web scraping process from start to finish.
You learned how to:
Decipher the data encoded in URLs
Download the page’s HTML content using Python’s requests library
Parse the downloaded HTML with Beautiful Soup to extract relevant information
With this broad pipeline in mind and two powerful libraries in your tool kit, you can go out and see what other websites you can scrape. Have fun, and always remember to be respectful and use your programming skills responsibly.
You can download the source code for the sample script that you built in this tutorial by clicking the link below:
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
5 Steps To Build a Faster Web Crawler | Better Programming

5 Steps To Build a Faster Web Crawler | Better Programming

Make your Python scraper up to 100 times fasterPhoto from Web Developer Ninja on raping a large amount of data requires you to have a very fast web scraper. If you want to scrape 10 million items and your scraper gets 50 items per minute, you’ll be waiting for 130 days for that scraper to finish. That’s way too long! This guide provides a structured approach to building a super-fast web let me take you from this:Scraper with average rate lower than 50 items/minTo this:Scraper with average rate greater than 2, 000 items/minIf you’re scraping in Python and want to go fast, there is only one library to use: Scrapy. This is a fantastic web scraping framework if you’re going to do any substantial scraping. BeautifulSoup, Requests, and Selenium are just too slow for large projects. If you aren’t familiar with Scrapy, I would recommend learning it and then revisiting this article later are now two things we need to do before we start scraping:Change your user agent. Your user agent tells servers who is accessing their website. By default, Scrapy tells servers that a bot is crawling their site. If you don’t change this setting, you are going to get banned in minutes. To change it, google “User agents” and set one of them equal to the USER_AGENT variable in It is also possible to rotate your user agent. However, I’ve found this unnecessary. If you want to do this, just google how — I believe the setup is relatively and set up proxies for your crawler. When choosing proxies, you should consider the pricing structure of the proxies. Do you pay per GB of bandwidth? Do you pay per proxy? Do you pay per thread? For large projects, I always pay per thread and use StormProxies. For smaller projects, I’d recommend SmartProxy. They charge by GB of bandwidth and provide unlimited threads. Next, you want to set up your proxies, which can be done by creating a new middleware in the file as shown below. This will set the proxies for all spiders in your project. You then need to add the middleware to your file. Alternatively, you can set proxies for each spider individually. There is a short video on how to do this below:Middleware for proxies (all spiders)Setting proxies for each spider individuallyWork smarter, not section is about the three scraping techniques that are going to make a huge difference to your and conquerIf you are using a single large spider, split it into many smaller ones. You do this so that you can make use of Scrapyd (more details in Step 4). Scrapyd allows you to run many spiders simultaneously (with Scrapy, you can only run one spider at a time). Each smaller spider will crawl part of what the large spider crawled. These mini-spiders should not overlap in the content they crawl, as this will waste time. If you split one spider into ten smaller ones, your scraping process is going to be around ten times faster (provided there are no other bottlenecks — see Step 5). Minimize the number of requests sentSending requests and waiting for responses is the slowest part of using a scraper. If you can reduce the number of requests sent, your scraper will be much faster. For example, if you are scraping prices and titles from an e-commerce site, then you don’t need to visit each item’s page. You can get all the data you need from the results page. If you have 30 items per page, then using this technique will make your scraper 30 times faster (it only has to send one request now instead of 30). Always be on the lookout for ways to reduce your number of requests. Below is a list of things you can try. If you can think of any others, please leave a ways to reduce requests:Increase the number of results on the results page (e. g. from ten to 100) filters before scraping (e. price filters) a general spider — not a items to the database in batchesAnother cause of a slow scraper is that people tend to scrape their data and then immediately add that data to their database. This is slow for two reasons. Firstly, processing in batches is always going to be faster than adding item by item. Secondly, with batching, you can make use of the many tools Python has to offer for batch uploading to databases. For example, the pandas library can be used to put your data into a dataframe and then upload that data to a SQL database. That is much faster! If you are interested in learning more, I highly recommend you read this article on batch uploading to SQL NCURRENT_REQUESTS:“The maximum number of concurrent (i. e. simultaneous) requests that will be performed by the Scrapy downloader. ” — Scrapy’s documentationThis is the number of simultaneous requests that your spider will send. You will want to experiment a little with different values and see which gives you the best scrape rate. A good place to start is 50. If you get a lot of timeout errors, then you have set this too high. Reduce by 10% and try again. 2. DOWNLOAD_TIMEOUT:“The amount of time (in secs) that the downloader will wait before timing out. ” — Scrapy’s docsThis is how long the spider will wait for the response after sending a request before retrying. Set this too low and you will get endless timeout errors. Set this too high and your spider will be waiting around instead of retrying a request, wasting time and slowing you down. Start at 100 seconds and experiment to find the optimal value. 3. DOWNLOAD_DELAY:“The amount of time (in secs) that the downloader should wait before downloading consecutive pages from the same website. ” — Scrapy’s docsThis is how long your spider will wait between downloading responses. For maximum speed, set this to zero. If you get response codes 400, 403, or 502 consistently, then you are scraping too fast. Increase the download delay slightly and try again (a good starting point is 0. 5). According to its documentation, Scrapyd is an application for deploying and running Scrapy rapyd allows you to run multiple spiders simultaneously. This will enable us to improve the overall speed of the scraping process significantly. If you want spider deployment that’s free and easy to set up, use Scrapyd. The Scrapy Cluster docs include a number of alternatives, but I would still recommend nefits of using Scrapyd:If your project contains one or more large spiders, split them up (as mentioned in Step 2 of this guide) spiders that don’t need to be split up can be run as they setup of Scrapyd can appear intimidating if you only read the docs, but the video below makes it very easy to understand and implement:Note: It is possible to run all the spiders in a project with a single command. It takes a small amount of work to set up, but for projects with ten or more spiders, I’d recommend doing it. Learn how on Stack have followed this guide and your scraper is running at a respectable speed. There is now one final step to take your crawler from respectable to lightning-fast: Dealing with bottlenecks! Photo from is a bottleneck? It is the limiting factor for the speed of your process. If addressed, it will give your process a significant speed boost up to the next aling with bottlenecks is an iterative process that goes like this:You have a bottleneck slowing down your find out what the bottleneck address the bottleneck, and your process becomes have a new is the process you are going to be repeating and repeating (and repeating) until you’ve squeezed every last bit of speed from your is a table containing some common scraping of common scraping bottlenecks and solutions (by the author)Well done. You’ve learned the ins and outs of building a rapid web scraper in Python. I hope you found this article useful and would love to hear any ideas you have. What projects are you working on? What do you like about coding/scraping? What’s your highest ever items/min rate? Thanks for reading. As always, if you have any questions, just leave a comment.
What Is Web Scraping And How Does It Work? | Zyte.com

What Is Web Scraping And How Does It Work? | Zyte.com

In today’s competitive world everybody is looking for ways to innovate and make use of new technologies. Web scraping (also called web data extraction or data scraping) provides a solution for those who want to get access to structured web data in an automated fashion. Web scraping is useful if the public website you want to get data from doesn’t have an API, or it does but provides only limited access to the data.
In this article, we are going to shed some light on web scraping, here’s what you will learn:
What is web scraping? The basics of web scrapingWhat is the web scraping process? What is web scraping used for? The best resources to learn more about web scraping
What is web scraping?
Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.
In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.
If you’ve ever copy and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.
Web scraping is popular
And it should not be surprising because web scraping provides something really valuable that nothing else can: it gives you structured web data from any public website.
More than a modern convenience, the true power of data web scraping lies in its ability to build and power some of the world’s most revolutionary business applications. ‘Transformative’ doesn’t even begin to describe the way some companies use web scraped data to enhance their operations, informing executive decisions all the way down to individual customer service experiences.
The basics of web scraping
It’s extremely simple, in truth, and works by way of two parts: a web crawler and a web scraper. The web crawler is the horse, and the scraper is the chariot. The crawler leads the scraper, as if by hand, through the internet, where it extracts the data requested. Learn the difference between web crawling & web scraping and how they work.
The crawler
A web crawler, which we generally call a “spider, ” is an artificial intelligence that browses the internet to index and search for content by following links and exploring, like a person with too much time on their hands. In many projects, you first “crawl” the web or one specific website to discover URLs which then you pass on to your scraper.
The scraper
A web scraper is a specialized tool designed to accurately and quickly extract data from a web page. Web scrapers vary widely in design and complexity, depending on the project. An important part of every scraper is the data locators (or selectors) that are used to find the data that you want to extract from the HTML file – usually, XPath, CSS selectors, regex, or a combination of them is applied.
The web data scraping process
If you do it yourself
This is what a general DIY web scraping process looks like:
Identify the target websiteCollect URLs of the pages where you want to extract data fromMake a request to these URLs to get the HTML of the pageUse locators to find the data in the HTMLSave the data in a JSON or CSV file or some other structured format
Simple enough, right? It is! If you just have a small project. But unfortunately, there are quite a few challenges you need to tackle if you need data at scale. For example, maintaining the scraper if the website layout changes, managing proxies, executing javascript, or working around antibots. These are all deeply technical problems that can eat up a lot of resources. There are multiple open-source web data scraping tools that you can use but they all have their limitations. That’s part of the reason many businesses choose to outsource their web data projects.
If you outsource it
1. Our team gathers your requirements regarding your project.
2. Our veteran team of web data scraping experts writes the scraper(s) and sets up the infrastructure to collect your data and structure it based on your requirements.
3. Finally, we deliver the data in your desired format and desired frequency.
Ultimately, the flexibility and scalability of web scraping ensure your project parameters, no matter how specific, can be met with ease. Fashion retailers inform their designers with upcoming trends based on web scraped insights, investors time their stock positions, and marketing teams overwhelm the competition with deep insights, all thanks to the burgeoning adoption of web scraping as an intrinsic part of everyday business.
What is web scraping used for?
Price intelligence
In our experience, price intelligence is the biggest use case for web scraping. Extracting product and pricing information from e-commerce websites, then turning it into intelligence is an important part of modern e-commerce companies that want to make better pricing/marketing decisions based on data.
How web pricing data and price intelligence can be useful:
Dynamic pricingRevenue optimizationCompetitor monitoringProduct trend monitoringBrand and MAP compliance
Market research
Market research is critical – and should be driven by the most accurate information available. High quality, high volume, and highly insightful web scraped data of every shape and size is fueling market analysis and business intelligence across the globe.
Market trend analysisMarket pricingOptimizing point of entryResearch & developmentCompetitor monitoring
Alternative data for finance
Unearth alpha and radically create value with web data tailored specifically for investors. The decision-making process has never been as informed, nor data as insightful – and the world’s leading firms are increasingly consuming web scraped data, given its incredible strategic value.
Extracting Insights from SEC FilingsEstimating Company FundamentalsPublic Sentiment IntegrationsNews Monitoring
Real estate
The digital transformation of real estate in the past twenty years threatens to disrupt traditional firms and create powerful new players in the industry. By incorporating web scraped product data into everyday business, agents and brokerages can protect against top-down online competition and make informed decisions within the market.
Appraising Property ValueMonitoring Vacancy RatesEstimating Rental YieldsUnderstanding Market Direction
News & content monitoring
Modern media can create outstanding value or an existential threat to your business – in a single news cycle. If you’re a company that depends on timely news analyses, or a company that frequently appears in the news, web scraping news data is the ultimate solution for monitoring, aggregating, and parsing the most critical stories from your industry.
Investment Decision MakingOnline Public Sentiment AnalysisCompetitor MonitoringPolitical CampaignsSentiment Analysis
Lead generation
Lead generation is a crucial marketing/sales activity for all businesses. In the 2020 Hubspot report, 61% of inbound marketers said generating traffic and leads was their number 1 challenge. Fortunately, web data extraction can be used to get access to structured lead lists from the web.
Brand monitoring
In today’s highly competitive market, it’s a top priority to protect your online reputation. Whether you sell your products online and have a strict pricing policy that you need to enforce or just want to know how people perceive your products online, brand monitoring with web scraping can give you this kind of information.
Business automation
In some situations, it can be cumbersome to get access to your data. Maybe you need to extract data from a website that is your own or your partner’s in a structured way. But there’s no easy internal way to do it and it makes sense to create a scraper and simply grab that data. As opposed to trying to work your way through complicated internal systems.
MAP monitoring
Minimum advertised price (MAP) monitoring is the standard practice to make sure a brand’s online prices are aligned with their pricing policy. With tons of resellers and distributors, it’s impossible to monitor the prices manually. That’s why web scraping comes in handy because you can keep an eye on your products’ prices without lifting a finger.
Learn more about web scraping
Here at Zyte (formerly Scrapinghub), we have been in the web scraping industry for 12 years. With our data extraction services and automatic web scraper, Zyte Automatic Extraction, we have helped extract web data for more than 1, 000 clients ranging from Government agencies and Fortune 100 companies to early-stage startups and individuals. During this time we gained a tremendous amount of experience and expertise in web data extraction.
Here are some of our best resources if you want to deepen your web scraping knowledge:
What are the elements of a web scraping project? Web scraping toolsHow to architect a web scraping solutionIs web scraping legal? Web scraping best practices

Frequently Asked Questions about scrape a website python

What is the fastest way to scrape a website in Python?

Setup. If you’re scraping in Python and want to go fast, there is only one library to use: Scrapy. This is a fantastic web scraping framework if you’re going to do any substantial scraping. BeautifulSoup, Requests, and Selenium are just too slow for large projects.Aug 29, 2020

How do you scrape a website?

This is what a general DIY web scraping process looks like:Identify the target website.Collect URLs of the pages where you want to extract data from.Make a request to these URLs to get the HTML of the page.Use locators to find the data in the HTML.Save the data in a JSON or CSV file or some other structured format.

Can you scrape every website?

So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.

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