Web Scraper Url List

Jul 15, 2020 Web Scraping is an automat i c way to retrieve unstructured data from website and store them in a structured format. For example, if you want to analyse what kind of face mask can sell better in Singapore, you may want to scrape all the face mask information on E-Commerce website like Lazada.

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  4. How To Scrape Multiple Pages Of A Website Using A Python Web ...
  5. Scrape Data From A List Of URLs By Creating A Simple Scraper ...
  1. I am using Python 3.5 and trying to scrape a list of urls (from the same website), code as follows: import urllib.request from bs4 import BeautifulSoup urllist = 'URL1', 'URL2','URL.
  2. Sep 14, 2020 Web Scraping projects can get quite complex. For example, you might be trying to extract data from multiple different URLs from the same website. Today, we will go over how to set up a web scraper to extract data from multiple different URLs. A Free and Powerful Web Scraper. First up, you will need the right web scraper to tackle this task.
  3. Import bulk URL list. To import a bulk list of URL from file, Go to Scraper - Manage Inputs and then click on button ‘Import URLs’. It will show the ‘Import URLs’ pop-up which allows you to add URLs from a CSV file. This list of URLs can be added to your current list of URLs.
  4. Web scrapers gather website data in the same way a human would: They go to a web page of the website, get the relevant data, and move on to the next web page — only much faster. Every website has a different structure. These are a few important things to think about when building a web scraper.

Scrape Data from a List of URLs by Creating a Simple Scraper

Thursday, August 10, 2017 10:13 AM

Web scraping can be done by creating a web crawler in Python. Before coding a Python-based crawler, you need to look into source and get to know the structure of the target website. And of course you need to learn Python. It will be much easier if you already know how to code. But for a tech noob, it’s very difficult to learn everything from scratch. So we create our app Octoparse to help people who know little to nothing about coding to easily scrape any web data.

In this tutorial we will learn how create the simplest and easiest web scraper to scrape a list of URLs, without any coding at all. This method is best suited to beginners like some of you. (We will assume that Octoparse is already installed on your computer. If that’s not the case, download here)

This tutorial will walk you through these steps:

1. Create a “Loop Item” in the workflow

After setting up basic information for your task, drag a “Loop Item” and drop it into the workflow designer.

2. Add a list of URLs into the created “Loop Item”

After create a “Loop Item” in the workflow designer, add a list of URLs in “Loop Item” to create a pattern for navigating each webpage.

· Select “List of URLs” Loop mode under advanced options of “Loop Item” step

· Copy and paste all the URLs into “List of URLs” text box

· Click “OK” and then save the configuration


1) All the URLs should share the similar layout

2) Add no more than 20,000 URLs

3) You will need to manual copy and paste the URLs into “List of URLs” text box.

4) After entering all the URLs, “Go To Webpage” action will be automatically created in “Loop Item”.

When the webpage is completely loaded, click the data point on the webpage to extract data you need.

· Click the data you need and select “Extract Data”(“Extract Data” action will be automatically created.)

4. Run the scraper set up

The scraper is now created. Run the task with either “Local Extraction” or “Cloud Extraction”.

In this tutorial we run the scraper with “Cloud Extraction”.

· Click “Next” and then Click “Cloud Extraction” to run the scraper on the cloud platform


1) You are able to close the app or your computer when running the scraper with “Cloud Extraction”.

Just sit back and relax. Then come back for the data. No need to worry about Internet interruption or hardware limitation.

2) You can also run the scraper with “Local Extraction” (on your local machine).

· Click “View Data” to check the data extracted

· Choose “Export Current Page” or “Export All” to export data extracted


1) Octoparse supports exporting data in Excel(2003), Excel(2007), CSV, or having data delivered to your database.

2) You can also create Octoparse APIs and access data. See more at http://advancedapi.octoparse.com/help

Now we've learned how to scrape data from a list of URLs by creating a simple scraper without any coding! Very easy right? Try it for yourself!

Demo data extracted below:

(I also attach the demo task and demo task exported in excel. Find them here)

Now check out similar case studies:

· URLs - Advanced Mode

Web Scraper Url List

· Scrape data from multiple web pages

· Speed up Cloud Extraction (1)

· Speed up Cloud Extraction (2)

Once you’ve put together enough web scrapers, you start to feel like you can do it in your sleep. I’ve probably built hundreds of scrapers over the years for my own projects, as well as for clients and students in my web scraping course.

Occasionally though, I find myself referencing documentation or re-reading old code looking for snippets I can reuse. One of the students in my course suggested I put together a “cheat sheet” of commonly used code snippets and patterns for easy reference.


I decided to publish it publicly as well – as an organized set of easy-to-reference notes – in case they’re helpful to others.

Web Scraper Url Listen

While it’s written primarily for people who are new to programming, I also hope that it’ll be helpful to those who already have a background in software or python, but who are looking to learn some web scraping fundamentals and concepts.

Table of Contents:

  1. Extracting Content from HTML
  2. Storing Your Data
  3. More Advanced Topics

Useful Libraries

For the most part, a scraping program deals with making HTTP requests and parsing HTML responses.

I always make sure I have requests and BeautifulSoup installed before I begin a new scraping project. From the command line:

Then, at the top of your .py file, make sure you’ve imported these libraries correctly.

Making Simple Requests

Make a simple GET request (just fetching a page)

Make a POST requests (usually used when sending information to the server like submitting a form)

Pass query arguments aka URL parameters (usually used when making a search query or paging through results)

Inspecting the Response

See what response code the server sent back (useful for detecting 4XX or 5XX errors)

Access the full response as text (get the HTML of the page in a big string)

Look for a specific substring of text within the response

Check the response’s Content Type (see if you got back HTML, JSON, XML, etc)

Extracting Content from HTML

Now that you’ve made your HTTP request and gotten some HTML content, it’s time to parse it so that you can extract the values you’re looking for.

Using Regular Expressions

Using Regular Expressions to look for HTML patterns is famously NOT recommended at all.

However, regular expressions are still useful for finding specific string patterns like prices, email addresses or phone numbers.

Run a regular expression on the response text to look for specific string patterns:

Using BeautifulSoup

BeautifulSoup is widely used due to its simple API and its powerful extraction capabilities. It has many different parser options that allow it to understand even the most poorly written HTML pages – and the default one works great.

Compared to libraries that offer similar functionality, it’s a pleasure to use. To get started, you’ll have to turn the HTML text that you got in the response into a nested, DOM-like structure that you can traverse and search

Look for all anchor tags on the page (useful if you’re building a crawler and need to find the next pages to visit)

Look for all tags with a specific class attribute (eg <li>...</li>)

Look for the tag with a specific ID attribute (eg: <div>...</div>)

Look for nested patterns of tags (useful for finding generic elements, but only within a specific section of the page)

Look for all tags matching CSS selectors (similar query to the last one, but might be easier to write for someone who knows CSS)

Get a list of strings representing the inner contents of a tag (this includes both the text nodes as well as the text representation of any other nested HTML tags within)

Return only the text contents within this tag, but ignore the text representation of other HTML tags (useful for stripping our pesky <span>, <strong>, <i>, or other inline tags that might show up sometimes)

Convert the text that are extracting from unicode to ascii if you’re having issues printing it to the console or writing it to files

Get the attribute of a tag (useful for grabbing the src attribute of an <img> tag or the href attribute of an <a> tag)

Putting several of these concepts together, here’s a common idiom: iterating over a bunch of container tags and pull out content from each of them

Using XPath Selectors

BeautifulSoup doesn’t currently support XPath selectors, and I’ve found them to be really terse and more of a pain than they’re worth. I haven’t found a pattern I couldn’t parse using the above methods.

If you’re really dedicated to using them for some reason, you can use the lxml library instead of BeautifulSoup, as described here.

Storing Your Data

Now that you’ve extracted your data from the page, it’s time to save it somewhere.

Note: The implication in these examples is that the scraper went out and collected all of the items, and then waited until the very end to iterate over all of them and write them to a spreadsheet or database.

I did this to simplify the code examples. In practice, you’d want to store the values you extract from each page as you go, so that you don’t lose all of your progress if you hit an exception towards the end of your scrape and have to go back and re-scrape every page.

Writing to a CSV

Probably the most basic thing you can do is write your extracted items to a CSV file. By default, each row that is passed to the csv.writer object to be written has to be a python list.

In order for the spreadsheet to make sense and have consistent columns, you need to make sure all of the items that you’ve extracted have their properties in the same order. This isn’t usually a problem if the lists are created consistently.

If you’re extracting lots of properties about each item, sometimes it’s more useful to store the item as a python dict instead of having to remember the order of columns within a row. The csv module has a handy DictWriter that keeps track of which column is for writing which dict key.

Writing to a SQLite Database

You can also use a simple SQL insert if you’d prefer to store your data in a database for later querying and retrieval.

More Advanced Topics

These aren’t really things you’ll need if you’re building a simple, small scale scraper for 90% of websites. But they’re useful tricks to keep up your sleeve.

Javascript Heavy Websites

Web Scraper Url Listing

Contrary to popular belief, you do not need any special tools to scrape websites that load their content via Javascript. In order for the information to get from their server and show up on a page in your browser, that information had to have been returned in an HTTP response somewhere.

It usually means that you won’t be making an HTTP request to the page’s URL that you see at the top of your browser window, but instead you’ll need to find the URL of the AJAX request that’s going on in the background to fetch the data from the server and load it into the page.

There’s not really an easy code snippet I can show here, but if you open the Chrome or Firefox Developer Tools, you can load the page, go to the “Network” tab and then look through the all of the requests that are being sent in the background to find the one that’s returning the data you’re looking for. Start by filtering the requests to only XHR or JS to make this easier.

Once you find the AJAX request that returns the data you’re hoping to scrape, then you can make your scraper send requests to this URL, instead of to the parent page’s URL. If you’re lucky, the response will be encoded with JSON which is even easier to parse than HTML.

Content Inside Iframes

This is another topic that causes a lot of hand wringing for no reason. Sometimes the page you’re trying to scrape doesn’t actually contain the data in its HTML, but instead it loads the data inside an iframe.

Again, it’s just a matter of making the request to the right URL to get the data back that you want. Make a request to the outer page, find the iframe, and then make another HTTP request to the iframe’s src attribute.

Sessions and Cookies

While HTTP is stateless, sometimes you want to use cookies to identify yourself consistently across requests to the site you’re scraping.

The most common example of this is needing to login to a site in order to access protected pages. Without the correct cookies sent, a request to the URL will likely be redirected to a login form or presented with an error response.

However, once you successfully login, a session cookie is set that identifies who you are to the website. As long as future requests send this cookie along, the site knows who you are and what you have access to.

Delays and Backing Off

If you want to be polite and not overwhelm the target site you’re scraping, you can introduce an intentional delay or lag in your scraper to slow it down

Some also recommend adding a backoff that’s proportional to how long the site took to respond to your request. That way if the site gets overwhelmed and starts to slow down, your code will automatically back off.

Spoofing the User Agent

By default, the requests library sets the User-Agent header on each request to something like “python-requests/2.12.4”. You might want to change it to identify your web scraper, perhaps providing a contact email address so that an admin from the target website can reach out if they see you in their logs.

More commonly, this is used to make it appear that the request is coming from a normal web browser, and not a web scraping program.

Using Proxy Servers

Even if you spoof your User Agent, the site you are scraping can still see your IP address, since they have to know where to send the response.

Web Scraper - The #1 web Scraping Extension

If you’d like to obfuscate where the request is coming from, you can use a proxy server in between you and the target site. The scraped site will see the request coming from that server instead of your actual scraping machine.

If you’d like to make your requests appear to be spread out across many IP addresses, then you’ll need access to many different proxy servers. You can keep track of them in a list and then have your scraping program simply go down the list, picking off the next one for each new request, so that the proxy servers get even rotation.

Setting Timeouts

If you’re experiencing slow connections and would prefer that your scraper moved on to something else, you can specify a timeout on your requests.

How To Scrape Multiple Pages Of A Website Using A Python Web ...

Handling Network Errors

Just as you should never trust user input in web applications, you shouldn’t trust the network to behave well on large web scraping projects. Eventually you’ll hit closed connections, SSL errors or other intermittent failures.

Learn More

Scrape Data From A List Of URLs By Creating A Simple Scraper ...

If you’d like to learn more about web scraping, I currently have an ebook and online course that I offer, as well as a free sandbox website that’s designed to be easy for beginners to scrape.

You can also subscribe to my blog to get emailed when I release new articles.