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How To Use Data Scraper

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A Beginner’s Guide to learn web scraping with python! – Edureka

Last updated on Sep 24, 2021 641. 9K Views Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything… Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything and everything about Computers. 1 / 2 Blog from Web Scraping Web Scraping with PythonImagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics: Why is Web Scraping Used? What Is Web Scraping? Is Web Scraping Legal? Why is Python Good For Web Scraping? How Do You Scrape Data From A Website? Libraries used for Web Scraping Web Scraping Example: Scraping Flipkart Website Why is Web Scraping Used? Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping: Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc. ) from websites, which are analyzed and used to carry out Surveys or for R&D. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the is Web Scraping? Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. Is Web Scraping Legal? Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “” file. You can find this file by appending “/” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “” file, the URL is in-depth Knowledge of Python along with its Diverse Applications Why is Python Good for Web Scraping? Here is the list of features of Python which makes it more suitable for web scraping. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help Do You Scrape Data From A Website? When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data and extracts it. To extract data using web scraping with python, you need to follow these basic steps: Find the URL that you want to scrape Inspecting the Page Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format Now let us see how to extract data from the Flipkart website using Python, Deep Learning, NLP, Artificial Intelligence, Machine Learning with these AI and ML courses a PG Diploma certification program by NIT braries used for Web Scraping As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries: Selenium: Selenium is a web testing library. It is used to automate browser activities. BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily. Pandas: Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format. Subscribe to our YouTube channel to get new updates..! Web Scraping Example: Scraping Flipkart WebsitePre-requisites: Python 2. x or Python 3. x with Selenium, BeautifulSoup, pandas libraries installed Google-chrome browser Ubuntu Operating SystemLet’s get started! Step 1: Find the URL that you want to scrapeFor this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is 2: Inspecting the PageThe data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect” you click on the “Inspect” tab, you will see a “Browser Inspector Box” 3: Find the data you want to extractLet’s extract the Price, Name, and Rating which is in the “div” tag respectively. Learn Python in 42 hours! Step 4: Write the codeFirst, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit with extension. I am going to name my file “web-s”. Here’s the command:gedit, let’s write our code in this file. First, let us import all the necessary libraries:from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pdTo configure webdriver to use Chrome browser, we have to set the path to chromedriverdriver = (“/usr/lib/chromium-browser/chromedriver”)Refer the below code to open the URL: products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
(“)
Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in

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tags. So, I will find the div tags with those respective class-names, extract the data and store the data in a variable. Refer the code below:content = ge_source
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
()
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.
Web Scraping 101: 10 Myths that Everyone Should Know | Octoparse

Web Scraping 101: 10 Myths that Everyone Should Know | Octoparse

1. Web Scraping is illegal
Many people have false impressions about web scraping. It is because there are people don’t respect the great work on the internet and use it by stealing the content. Web scraping isn’t illegal by itself, yet the problem comes when people use it without the site owner’s permission and disregard of the ToS (Terms of Service). According to the report, 2% of online revenues can be lost due to the misuse of content through web scraping. Even though web scraping doesn’t have a clear law and terms to address its application, it’s encompassed with legal regulations. For example:
Violation of the Computer Fraud and Abuse Act (CFAA)
Violation of the Digital Millennium Copyright Act (DMCA)
Trespass to Chattel
Misappropriation
Copy right infringement
Breach of contract
Photo by Amel Majanovic on Unsplash
2. Web scraping and web crawling are the same
Web scraping involves specific data extraction on a targeted webpage, for instance, extract data about sales leads, real estate listing and product pricing. In contrast, web crawling is what search engines do. It scans and indexes the whole website along with its internal links. “Crawler” navigates through the web pages without a specific goal.
3. You can scrape any website
It is often the case that people ask for scraping things like email addresses, Facebook posts, or LinkedIn information. According to an article titled “Is web crawling legal? ” it is important to note the rules before conduct web scraping:
Private data that requires username and passcodes can not be scrapped.
Compliance with the ToS (Terms of Service) which explicitly prohibits the action of web scraping.
Don’t copy data that is copyrighted.
One person can be prosecuted under several laws. For example, one scraped some confidential information and sold it to a third party disregarding the desist letter sent by the site owner. This person can be prosecuted under the law of Trespass to Chattel, Violation of the Digital Millennium Copyright Act (DMCA), Violation of the Computer Fraud and Abuse Act (CFAA) and Misappropriation.
It doesn’t mean that you can’t scrape social media channels like Twitter, Facebook, Instagram, and YouTube. They are friendly to scraping services that follow the provisions of the file. For Facebook, you need to get its written permission before conducting the behavior of automated data collection.
4. You need to know how to code
A web scraping tool (data extraction tool) is very useful regarding non-tech professionals like marketers, statisticians, financial consultant, bitcoin investors, researchers, journalists, etc. Octoparse launched a one of a kind feature – web scraping templates that are preformatted scrapers that cover over 14 categories on over 30 websites including Facebook, Twitter, Amazon, eBay, Instagram and more. All you have to do is to enter the keywords/URLs at the parameter without any complex task configuration. Web scraping with Python is time-consuming. On the other side, a web scraping template is efficient and convenient to capture the data you need.
5. You can use scraped data for anything
It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. However, it is not legal if you scrape confidential information for profit. For example, scraping private contact information without permission, and sell them to a 3rd party for profit is illegal. Besides, repackaging scraped content as your own without citing the source is not ethical as well. You should follow the idea of no spamming, no plagiarism, or any fraudulent use of data is prohibited according to the law.
Check Below Video: 10 Myths About Web Scraping!
6. A web scraper is versatile
Maybe you’ve experienced particular websites that change their layouts or structure once in a while. Don’t get frustrated when you come across such websites that your scraper fails to read for the second time. There are many reasons. It isn’t necessarily triggered by identifying you as a suspicious bot. It also may be caused by different geo-locations or machine access. In these cases, it is normal for a web scraper to fail to parse the website before we set the adjustment.
Read this article: How to Scrape Websites Without Being Blocked in 5 Mins?
7. You can scrape at a fast speed
You may have seen scraper ads saying how speedy their crawlers are. It does sound good as they tell you they can collect data in seconds. However, you are the lawbreaker who will be prosecuted if damages are caused. It is because a scalable data request at a fast speed will overload a web server which might lead to a server crash. In this case, the person is responsible for the damage under the law of “trespass to chattels” law (Dryer and Stockton 2013). If you are not sure whether the website is scrapable or not, please ask the web scraping service provider. Octoparse is a responsible web scraping service provider who places clients’ satisfaction in the first place. It is crucial for Octoparse to help our clients get the problem solved and to be successful.
8. API and Web scraping are the same
API is like a channel to send your data request to a web server and get desired data. API will return the data in JSON format over the HTTP protocol. For example, Facebook API, Twitter API, and Instagram API. However, it doesn’t mean you can get any data you ask for. Web scraping can visualize the process as it allows you to interact with the websites. Octoparse has web scraping templates. It is even more convenient for non-tech professionals to extract data by filling out the parameters with keywords/URLs.
9. The scraped data only works for our business after being cleaned and analyzed
Many data integration platforms can help visualize and analyze the data. In comparison, it looks like data scraping doesn’t have a direct impact on business decision making. Web scraping indeed extracts raw data of the webpage that needs to be processed to gain insights like sentiment analysis. However, some raw data can be extremely valuable in the hands of gold miners.
With Octoparse Google Search web scraping template to search for an organic search result, you can extract information including the titles and meta descriptions about your competitors to determine your SEO strategies; For retail industries, web scraping can be used to monitor product pricing and distributions. For example, Amazon may crawl Flipkart and Walmart under the “Electronic” catalog to assess the performance of electronic items.
10. Web scraping can only be used in business
Web scraping is widely used in various fields besides lead generation, price monitoring, price tracking, market analysis for business. Students can also leverage a Google scholar web scraping template to conduct paper research. Realtors are able to conduct housing research and predict the housing market. You will be able to find Youtube influencers or Twitter evangelists to promote your brand or your own news aggregation that covers the only topics you want by scraping news media and RSS feeds.
Source:
Dryer, A. J., and Stockton, J. 2013. “Internet ‘Data Scraping’: A Primer for Counseling Clients, ” New York Law Journal. Retrieved from
How to Use Google Sheets for Web Scraping & Campaign Building

How to Use Google Sheets for Web Scraping & Campaign Building

We’ve all been in a situation where we had to extract data from a website at some working on a new account or campaign, you might not have the data or the information available for the creation of the ads, for an ideal world, we would have been provided with all of the content, landing pages, and relevant information we need, in an easy-to-import format such as a CSV, Excel spreadsheet, or Google Sheet. (Or at the very least, provided what we need as tabbed data that can be imported into one of the aforementioned formats. )But that’s not always the way it lacking the tools for web scraping — or the coding knowledge to use something like Python to help with the task — may have had to resort to the tedious job of manually copying and pasting possibly hundreds or thousands of a recent job, my team was asked to:AdvertisementContinue Reading BelowGo to the client’s wnload more than 150 new products spread across 15 different and paste the product name and landing page URL for each product into a, you can imagine how lengthy the task would have been if we’d done just that and manually executed the only is it time-consuming, but with someone manually going through that many items and pages and physically having to copy and paste the data product by product, the chances of making a mistake or two are quite would then require even more time to review the document and make sure it was has to be a better news: There is! Let me show you how we did is IMPORTXML? Enter Google Sheets. I’d like you to meet the IMPORTXML cording to Google’s support page, IMPORTXML “imports data from any of various structured data types including XML, HTML, CSV, TSV, and RSS and ATOM XML feeds. ”AdvertisementContinue Reading BelowEssentially, IMPORTXML is a function allows you to scrape structured data from webpages — no coding knowledge example, it’s quick and easy to extract data such as page titles, descriptions, or links, but also more complex Can IMPORTXML Help Scrape Elements of a Webpage? The function itself is pretty simple and only requires two values:The URL of the webpage we intend to extract or scrape the information the XPath of the element in which the data is stands for XML Path Language and can be used to navigate through elements and attributes in an XML example, to extract the page title from, we would use:=IMPORTXML(“, “//title”)This will return the value: Moon landing –, if we are looking for the page description, try this:=IMPORTXML(“, ”//meta[@name=’description’]/@content”)Here is a shortlist of some of the most common and useful XPath queries:Page title: //titlePage meta description: //meta[@name=’description’]/@contentPage H1: //h1Page links: //@hrefSee IMPORTXML in ActionSince discovering IMPORTXML in Google Sheets, it has truly become one of our secret weapons in the automation of many of our daily tasks, from campaign and ads creation to content research, and reover, the function combined with other formulas and add-ons can be used for more advanced tasks that otherwise would require sophisticated solutions and development, such as tools built in in this instance, we will look at IMPORTXML in its most basic form: scraping data from a web ’s have a look at a practical agine that we’ve been asked to create a campaign for Search Engine would like us to advertise the last 30 articles that have been published under the PPC section of the vertisementContinue Reading BelowA pretty simple task, you might say. Unfortunately, the editors are not able to send us the data and have kindly asked us to refer to the website to source the information required to set up the mentioned at the beginning of our article, one way to do this would be to open two browser windows — one with the website, and the other with Google Sheets or Excel. We would then start copying and pasting the information over, article by article, and link by using IMPORTXML in Google Sheets, we can achieve the same output with little to no risk of making mistakes, in a fraction of the ’s 1: Start with a Fresh Google SheetFirst, we open a new, blank Google Sheets document:Step 2: Add the Content You Need to ScrapeAdd the URL of the page (or pages) we want to scrape the information vertisementContinue Reading BelowIn our case, we start with 3: Find the XPathWe find the XPath of the element we want to import the content of into our data our example, let’s start with the titles of the latest 30 to Chrome. Once hovering over the title of one of the articles, right-click and select will open the Chrome Dev Tools window:Make sure that the article title is still selected and highlighted, then right-click again and choose Copy > Copy vertisementContinue Reading BelowStep 4: Extract the Data Into Google SheetsBack in your Google Sheets document, introduce the IMPORTXML function as follows:=IMPORTXML(B1, ”//*[starts-with(@id, ‘title’)]”)A couple of things to note:First, in our formula, we have replaced the URL of the page with the reference to the cell where the URL is stored (B1), when copying the XPath from Chrome, this will always be enclosed in double-quotes. (//*[@id=”title_1″])However, in order to make sure it doesn’t break the formula, the double quotes sign will need to be changed to the single quote sign. (//*[@id=’title_1’])Note that in this instance, because the page ID title changes for each article (title_1, title_2, etc), we must slightly modify the query and use “starts-with” in order to capture all elements on the page with an ID that contains ‘title. ’Here is what that looks on the Google Sheets document:And in just a few moments, this is what the results look like after the query has been loaded the data onto the spreadsheet:As you can see, the list returns all articles that are featured on the page that we have just scraped (including my previous piece about automation and how to use Ad Customizers to Improve Google Ads campaign performance). AdvertisementContinue Reading BelowYou can apply this to scraping any other piece of information need to set up your ad campaign, as ’s add the landing page URLs, the featured snippet of each article, and the name of the author into our Sheets the landing page URLs, we need to tweak the query to specify that we are after the HREF element attached to the article erefore, our query will look like this:=IMPORTXML(B1, ”//*[starts-with(@id, ‘title’)]/@href”)Now, append ‘/@href’ to the end of the! Straight away, we have the URLs of the landing pages:You can do the same for the featured snippets and author names:TroubleshootingOne thing to beware of is that in order to be able to fully expand and fill in the spreadsheet with all data returned by the query, the column in which the data is populated must have enough cells free and no other data in the vertisementContinue Reading BelowThis works in a similar way to when we use an ARRAYFORMULA, for the formula to expand there must be no other data in the same nclusionAnd there you have a fully automated, error-free, way to scrape data from (potentially) any webpage, whether you need the content and product descriptions, or ecommerce data such as product price or shipping a time when information and data can be the advantage required to deliver better than average results, the ability to scrape web pages and structured content in an easy and quick way can be priceless. Besides, as we have seen above, IMPORTXML can help to cut execution times and reduce the chances of making ditionally, the function is not just a great tool that can be exclusively used for PPC tasks, but instead can be really useful across many different projects that require web scraping, including SEO and content Resources:10 Google Sheets Add-Ons That Make SEO Work EasierHow to Build a Link Analysis Dashboard with the Google Query Function in Google Sheets [Free Template]PPC 101: A Complete Guide to PPC Marketing BasicsAdvertisementContinue Reading BelowImage CreditsAll screenshots taken by author, August 2021

Frequently Asked Questions about how to use data scraper

How do you use a data scraper?

To extract data using web scraping with python, you need to follow these basic steps:Find the URL that you want to scrape.Inspecting the Page.Find the data you want to extract.Write the code.Run the code and extract the data.Store the data in the required format.Sep 24, 2021

Is it legal to scrape data?

It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. However, it is not legal if you scrape confidential information for profit. For example, scraping private contact information without permission, and sell them to a 3rd party for profit is illegal.Aug 16, 2021

How do I use Google data scraper?

Here’s how.Step 1: Start with a Fresh Google Sheet. First, we open a new, blank Google Sheets document:Step 2: Add the Content You Need to Scrape. Add the URL of the page (or pages) we want to scrape the information from. … Step 3: Find the XPath. … Step 4: Extract the Data Into Google Sheets.Aug 4, 2021

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