Category: How to extract all urls from a website using python

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how to extract all urls from a website using python

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Asked 2 years, 8 months ago. Active 2 years, 8 months ago. Viewed 3k times.

How to Scrape/Extract All Links From Any Web Page Easily

Appreciate your help! Tom Levy Tom Levy 41 1 1 silver badge 5 5 bronze badges. Active Oldest Votes. Is this a valid reason for using single-letter variables or would you still recommend request? It is probably not a big deal for this particular code snippet, but overall I would make a habit of making descriptive variable names unless you are doing a quick prototype.

Python Code : Get all the links from a website

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I need the urls end with ". It worked for several other reports different Slug IDs couple weeks ago, but not anymore. Don't know why. It retrieved back with 43 links. None of them is the url I am looking for. I know there are some urls that I need when I do "Inspect".

Such as:. Learn more. Asked yesterday. Active yesterday. Viewed 12 times. Also I tried to use beautifulSoap find all the hrefs from the website.

How To Extract Data From A Website Using Python

Such as Could anyone help me? I am really a new guy in Python. Thanks so much! New contributor. Active Oldest Votes. JingMT is a new contributor. Be nice, and check out our Code of Conduct.In this tutorial, I want to demonstrate how easy it is to build a simple URL crawler in Python that you can use to map websites. While this program is relatively simple, it can provide a great introduction to the fundamentals of web scraping and automation. We will be focusing on recursively extracting links from web pages, but the same ideas can be applied to a myriad of other solutions.

The first thing we should do is import all the necessary libraries. We will be using BeautifulSouprequestsand urllib for web scraping. Next, we need to select a URL to start crawling from. It is a safe sandbox that you can crawl without getting in trouble. Next, we are going to need to create a new deque object so that we can easily add newly found links and remove them once we are finished processing them.

Pre-populate the deque with your url variable:. We also want to keep track of local same domain as the targetforeign different domain as the targetand broken URLs:. We then need to get the base URL of the webpage so that we can easily differentiate local and foreign addresses:. Since I want to limit my crawler to local addresses only, I add the following to add new URLs to our queue:.

You could possibly get into trouble for scraping websites without permission. Use at your own risk! And that should be it. You have just created a simple tool to crawl a website and map all URLs found! Feel free to build upon and improve this code. For example, you could modify the program to search web pages for email addresses or phone numbers as you crawl them.

You could even extend functionality by adding command line arguments to provide the option to define output files, limit searches to depth, and much more. Learn about how to create command-line interfaces to accept arguments here. Thanks for reading! If you liked this tutorial and want more content like this, be sure to smash that follow button.

Also be sure to check out my websiteTwitterLinkedInand Github. If this article was helpful, tweet it. Learn to code for free. Get started. Stay safe, friends. Learn to code from home. Use our free 2, hour curriculum.Extracting all links of a web page is a common task among web scrapers, it is useful to build advanced scrapers that crawl every page of a certain website to extract data, it can also be used for SEO diagnostics process or even information gathering phase for penetration testers.

Let's install the dependencies:. Open up a new Python file and follow along, let's import the modules we need:. We are going to use colorama just for using different colors when printing, to distinguish between internal and external links:. We gonna need two global variables, one for all internal links of the website and the other for all the external links:. This will make sure that a proper scheme protocol, e.

how to extract all urls from a website using python

Now let's build a function to return all the valid URLs of a web page:. First, I initialized the urls set variable, I've used Python sets here because we don't want redundant links. Second, I've extracted the domain name from the URL, we gonna need it to check whether the link we grabbed is external or internal.

Let's get all HTML a tags anchor tags that contains all the links of the web page :. So we get the href attribute and check if there is something there.

Otherwise, we just continue to the next link. Since not all links are absolute, we gonna need to join relative URLs with its domain name e.

Let's finish up the function:. All we did here is checking:. The above function will only grab the links of one specific page, what if we want to extract all links of the entire website? Let's do this:. This function crawls the website, which means it gets all the links of the first page and then call itself recursively to follow all the links extracted previously.

However, this can cause some issues, the program will get stuck on large websites that got many links such as google. Alright, let's test this, make sure you use this on a website you're authorized to, otherwise I'm not responsible for any harm you make.Web Scraping is a process of data extracting from web sites.

The extracted data can be content, urls, contact information, etc, which we can store in a local file or database. This process can be done manually by code called scrapper or by an automated software implemented using a bot or web crawler. The web scraping is not always legal. Some sites has dis-allow the scraping in the 'robots. Some popular sites provide APIs to access their data in a structured way. But not all websites. So, we need a web scraper for data extraction, data mining and store in a structured way.

Python is the most popular programming language for web scraping. It provides many libraries that can handle web crawler related process smoothly. In this article, we are using urllib library. For using this, we only need to import this like following in Python3. In Python, there are several libraries to parse data from web resources.

The lxml is one of them that has strong performance in parsing very large files. We can easily install this using pip tool. These are the various web scraping examples using Python urllib library. To get started, find the URL you want to extract the data from.

Suppose, we have taken twitter search URL. Next, open it with urllib. The above code returns the content of the specified URL. The parsed.In this article, we are going to learn how to extract data from a website using Python. We can write programs using languages such as Python to perform web scraping automatically.

In order to understand how to write a web scraper using Python, we first need to understand the basic structure of a website. We have already written an article about it here on our website.

Take a quick look at it once before proceeding here to get a sense of it. The way to scrape a webpage is to find specific HTML elements and extract its contents. So, to write a website scraper, you need to have good understanding of HTML elements and its syntax. Assuming you have good understanding on these per-requisites, we will now proceed to learn how to extract data from website using Python. The first step in writing a web scraper using Python is to fetch the web page from web server to our local computer.

One can achieve this by making use of a readily available Python package called urllib. We can install the Python package urllib using Python package manager pip. We just need to issue the following command to install urllib on our computer:.

Once we have urllib Python package installed, we can start using it to fetch the web page to scrape its data. For the sake of this tutorial, we are going to extract data from a web page from Wikipedia on comet found here:.

This wikipedia article contains a variety of HTML elements such as texts, images, tables, headings etc. We can extract each of these elements separately using Python. The URL of this web page is passed as the parameter to this request. As a result of this, the wikipedia server will respond back with the HTML content of this web page. However, as a web scraper we are mostly interested only in human readable content and not so much on meta content.

We achieve this in the next line of the program by calling the read function of urllib package. The above line of Python code will give us only those HTML elements which contain human readable contents. At this point in our program we have extracted all the relevant HTML elements that we would be interested in. It is now time to extract individual data elements of the web page. Using this library, we will be able to extract out the exact HTML element we are interested in.

We can install Python Beautifulsoup package into our local development system by issuing the command:. For example, if we want to extract the first paragraph of the wikipedia comet article, we can do so using the code:. Above code will extract all the paragraphs present in the article and assign it to the variable pAll.

Now pAll contains a list of all paragraphs, so each individual paragraphs can be accessed through indexing. So in order to access the first paragraph, we issue the command:.Update your browser to view this website correctly. Update my browser now. It's a great library, easy to use but at the same time a bit slow when processing a lot of documents. I added a performance test at the end to compare each alternative. First of, below is the source code to extracts links using BeautifulSoup.

how to extract all urls from a website using python

We will use LXML as the parser implementation for BeautifulSoup because according to the documentation it's the fastest. Just for the performance test, I added a slightly modified code below which doesn't use the a tag filter.

Will there be any difference in execution time? Specifically for our URL extraction case, the code isn't even complicated but strips away all the overhead. This is great in case you need a Python-only implementation. During my research I found Selectolax. It's a super fast HTML parser. Under the hood, it uses the Modest engine to do the parsing. As a final alternative, the following code snippet uses a regular expression to parse HTML tags. At the same time, this can be a big plus because it will use less memory and regular expressions are very fast.

In any case - use with caution. I did run each extraction method 1, times on this file and used the average runtime for the result. I didn't expect the differences in execution time to be so big for each method.

It matters a great deal which of them you use. Interestingly doing the manual filtering with BeautifulSoup is faster than using the a tag filter, something I wouldn't have expected.

While the Regex implementation is the fastest, Selectolax is not far off and provides a complete DOM parser. Your browser is out-of-date! Close Home About. BeautifulSoup First of, below is the source code to extracts links using BeautifulSoup. Method Execution time per iteration BeautifulSoup