Best web scraping Tools for Data Extraction in 2023. Do you really know data scraping and its Tools?

Do you also have difficulties and losses in picking up a data scraping tool? Data scraping is no longer a new phrase nowadays; if you don’t know what they mean, let me do a quick intro for you. Data scraping, web scraping or data extraction all mean using bots to extract data or content from a website into a usable format for further uses. A data scraping tool is important because it helps people to obtain a large amount of information in a timely manner.

In the world we live in today, companies compete against each other with massive information collected from a multitude of users — whether it be their consumer behaviors, content shared on social media or celebrities following. People collect information before making decisions, such as going over the reviews to decide whether to buy the stuff. Therefore, at least you should have some web scraping knowledge in order for further use or to be successful.

Although we live in the generation of big data, many businesses and industries are still vulnerable in the data realm. One of the main reasons is due to the minimal understanding of data technology or their lack. Thus, it is necessary to make good use of data scraping tools. In today, data scraping tools or web scraping software is an essential key to the establishment of a data-driven business strategy. You can use Python, Selenium, and PHP to scrape the websites if you know the coding language. As a bonus, it is great if you are proficient in programming. However, don’t be anxious if you don’t know any coding language at all. Let me introduce some web scraping tools to facilitate effortless scraping.

Nowadays, there are more and more data scraping tools being created in the marketplace. Some tools like Octoparse, provide scraping templates and services which are a great bonus for companies lacking data scraping skill sets. On the other hand, some of the web scraping tools require you to have some programming skills in order to configure advanced scraping, for example, Apify. Thus, it really depends on what you want to scrape and what results you want to achieve. If you have no idea about how to get started with data scraping tools, follow me and start from the very beginning with basic steps.

Scraper python. Scrapy at a glance

Scrapy (/ˈskreɪpaɪ/) is an application framework for crawling web sites and extracting structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival.

Even though Scrapy was originally designed for, it can also be used to extract data using APIs (such as) or as a general purpose web crawler.

Walk-through of an example spider

In order to show you what Scrapy brings to the table, we’ll walk you through an example of a Scrapy Spider using the simplest way to run a spider.

Here’s the code for a spider that scrapes famous quotes from website, following the pagination:

Put this in a text file, name it to something like quotes_spider.py and run the spider using the

When this finishes you will have in the quotes.jsonl file a list of the quotes in JSON Lines format, containing text and author, looking like this:

{ "author" : "Jane Austen" , "text" : " \u201c The person, be it gentleman or lady, who has not pleasure in a good novel, must be intolerably stupid. \u201d " } { "author" : "Steve Martin" , "text" : " \u201c A day without sunshine is like, you know, night. \u201d " } { "author" : "Garrison Keillor" , "text" : " \u201c Anyone who thinks sitting in church can make you a Christian must also think that sitting in a garage can make you a car.

What just happened?

When you ran the command scrapy runspider quotes_spider.py , Scrapy looked for a Spider definition inside it and ran it through its crawler engine.

start_urls attribute (in this case, only the URL for quotes in humor category) and called the default callback method parse , passing the response object as an argument. In the parse callback, we loop through the quote elements using a CSS Selector, yield a Python dict with the extracted quote text and author, look for a link to the next page and schedule another request using the same parse method as callback.

Here you notice one of the main advantages about Scrapy: requests are. This means that Scrapy doesn’t need to wait for a request to be finished and processed, it can send another request or do other things in the meantime. This also means that other requests can keep going even if some request fails or an error happens while handling it.

While this enables you to do very fast crawls (sending multiple concurrent requests at the same time, in a fault-tolerant way) Scrapy also gives you control over the politeness of the crawl through. You can do things like setting a download delay between each request, limiting amount of concurrent requests per domain or per IP, and eventhat tries to figure out these automatically.

Note

This is usingto generate the JSON file, you can easily change the export format (XML or CSV, for example) or the storage backend (FTP or Amazon S3 , for example). You can also write anto store the items in a database.

Web scraping test. Web Scraping Tools

This is the most popular web scraping method where a business deploys an already made software for all their web scraping use cases.

If you want to access and gather data at scale, you need good web scraping tools that can surpass IP blocking, cloaking, and ReCaptcha. There are popular tools such as Scrapy, Beautiful Soup, Scrapebox, Scrapy Proxy Middleware, Octoparse, Parsehub, and Apify.

These tools help you with your web scraping task at scale and can surpass different obstacles to help you achieve your goals.

Selenium is a popular open-source web automation framework used for automated browser testing . This framework helps you write Selenium test scripts that can be used to automate testing of websites and web applications, then execute them in different browsers on multiple platforms using any programming language of your choice. However, it can be adapted to solve dynamic web scraping problems, as we will demonstrate in the blog on how you can do web scraping using JavaScript and Selenium.

Selenium has three major components:

Selenium IDE : It is a browser plugin – a faster, easier way to create, execute, and debug your Selenium scripts.
Selenium WebDriver: It is a set of portable APIs that help you write automated tests in any language that runs on top of your browser.
Selenium Grid: It automates the process of distributing and scaling tests across multiple browsers, operating systems, and platforms.

Web scraper cloud. A CLOUD BASED WEB SCRAPER

Introduction

In this article, we will guide you through the process of building a web scraper and setting it up to run autonomously on the cloud. It's important to understand what web scraping is before we delve into deployment. According to Wikipedia, web scraping is the process of extracting data from websites. There are various reasons one might want to extract data from a website, such as for analytical purposes or personal use. The use case will depend on the specific needs and goals of the individual or organization. If you're interested in learning more about web scraping, you can check out the Wikipedia article linked here:. It provides a comprehensive overview of the topic.

There are several techniques for web scraping that can be implemented using a variety of programming languages. In this article, we will be using the Python programming language. Don't worry if you're not familiar with Python, as we will be explaining each step in detail. If you do have a basic understanding of Python syntax, this should be a fairly easy process.

Our web scraper will be tasked with extracting news articles from a specific news website. The main reason for creating an autonomous web scraper is to extract data that is constantly being updated, such as news articles. This allows us to easily gather and analyze the latest information from a particular site. So, let's get started and build our web scraper!

Disclaimer: before scraping any website be sure to read their user terms and conditions. Some sites may take legal action if you don't follow usage guidelines.

Platforms and services

In this section, we will provide an overview of the platforms and services we will be using to create a cloud-based web scraper as an example. We will briefly explain the purpose and function of each platform or service to give you a better understanding of how they will be used in the process.

IBM cloud platform: this will be our cloud platform of choice, for the reason being that you can access several services without having to provide credit card information. For our example we'll get to work with :
- Cloud functions service: this service will allow us to execute our web scraper on the cloud.
- Cloudant: a non-relational, distributed database service. We'll use this to store the data we scrape.
Docker container platform: this platform we'll allow us to containerize our web scraper in a well defined environment with all necessary dependencies. This action allows our web scraper to work on any given platform that supports docker containers. In our example our docker container will be used by the ibm cloud functions service .
Github: we'll use Github for version control and also to link to our docker container. Linking our docker container to a github repository containing our web scraper will automatically initiate a new build for our docker container image. The new image will carry all changes made to the repository's content.
Cloud phantomjs platform: this platform will help render the web pages from the HTTP requests we'll make on the cloud. Once a page is rendered the response is returned as HTML.
Rapid API platform: this platform will help manage our API calls to the cloud phantomjs platform and also provide an interface that shows execution statistics.

Источник: https://lajfhak.ru-land.com/stati/2023-top-10-best-web-scraping-tools-data-extraction-categories-data-extraction-tools

Data scraping. How To Scrape Data from a Website

Web scraping has existed for a long time and, in its good form, it is a key underpinning of the internet.

Web scraping can be used in a variety of situations, especially when information is needed in bulk and there is no API to access it from.

Considering the amount of information available on the internet today, access to these information have been relatively easier these past few years due to the availability of broadband internet and internet connection reaching even rural areas. The main problem has been collecting data in bulk, organizing it and analyzing it.

Web scraping automatically extracts data and presents it in a format you can easily make sense of.

We’ll be making use of Python programming language for data scraping because:

it has the largest community support for scraping, data science and the likes
it is easy to learn

Python comes pre-installed on Mac OS X but for Windows user, you’ll have to install it via the official website. We’ll be making use ofPython2.

A prerequisite for scraping data from websites is basic knowledge of HTML. HTML is the standard markup language for creating web pages and it is used to define structures of content on web pages. You have to understand how these contents are structured to be able to know where and how to get them.
You can take a quick HTML introduction course on W3Schools if you need a refresher.

Many languages have libraries and pieces of software already written that will help you with the task of web scraping. However, with Python, scraping is relatively as easy as writingif…elsestatements.

Let’s take a look at the libraries we’ll be using for scraping:

Jupyter Notebook — The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. I’ll be sharing the code used in this tutorial via a Jupyter Notebook hosted on Github!
requests — this library makes dealing with HTTP requests simple. Why do you need to deal with HTTP requests? Because they are what your web browser sends in the background when it wants to retrieve a page from the internet.
BeautifulSoup — This is a Python library that handles extracting the relevant data from HTML and XML. You can read more on the installation and how to use BeautifulSoup here .

Next, we need to get the BeautifulSoup library using Python’s package management tool known aspip.

In the terminal (or command prompt for Windows users), type:

pip install bs4 requests

Some rules to take note of before scraping:

You should check a website’s Terms and Conditions before you scrape it. Be careful to read the statements about the legal use of data. Usually, the data you scrape should not be used for commercial purposes.
Do not request data from the website too aggressively with your program (also known as spamming), as this may break the website. Make sure your program behaves in a reasonable manner (i.e. acts like a human). One request for one webpage per second is good practice.
The layout of a website may change from time to time, so make sure to revisit the site and rewrite your code as needed.

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Scraping bot. Method 1: Using Selenium

We need to install ato automate using selenium, our task is to create a bot that will be continuously scraping the google news website and display all the headlines every 10mins.

Stepwise implementation:

Step 1:

The next step is to open the required website.

# path of the chromedriver we have just downloaded

PATH=r"D:\chromedriver"

driver=webdriver.Chrome(PATH)# to open the browser

# url of google news website

url=' https://news.google.com/topstories?hl=en-IN&gl=IN&ceid=IN:en '

# to open the url in the browser

driver.get(url)

Output:

Step 3: Extracting the news title from the webpage, to extract a specific part of the page, we need its XPath, which can be accessed by right-clicking on the required element and selecting Inspect in the dropdown bar.

After clicking Inspect a window appears. From there, we have to copy the elements full XPath to access it:

Note: You might not always get the exact element that you want by inspecting (depends on the structure of the website), so you may have to surf the HTML code for a while to get the exact element you want. And now, just copy that path and paste that into your code. After running all these lines of code, you will get the title of the first heading printed on your terminal.

Python3

# Xpath you just copied

news_path='/html/body/c-wiz/div/

div/main/c-wiz/div1>/div

# to get that element

link=driver.find_element_by_xpath(news_path)

# to read the text from that element

print(link.text)

Output:

‘Attack on Afghan territory’: Taliban on US airstrike that killed 2 ISIS-K men

Step 4: Now, the target is to get the X_Paths of all the headlines present.

One way is that we can copy all the XPaths of all the headlines (about 6 headlines will be there in google news every time) and we can fetch all those, but that method is not suited if there are a large number of things to be scrapped. So, the elegant way is to find the pattern of the XPaths of the titles which will make our tasks way easier and efficient. Below are the XPaths of all the headlines on the website, and let’s figure out the pattern.

/html/body/c-wiz/div/div/div/div/main/c-wiz/div/div/div/div/article/h3/a

So, by seeing these XPath’s, we can see that only the 5th div is changing (bolded ones). So based upon this, we can generate the XPaths of all the headlines. We will get all the titles from the page by accessing them with their XPath. So to extract all these, we have the code as

Now, the code is almost complete, the last thing we have to do is that the code should get headlines for every 10 mins. So we will run a while loop and sleep for 10 mins after getting all the headlines.

Open source web crawler. WebCollector

WebCollector is an open source web crawler framework based on Java.It provides some simple interfaces for crawling the Web,you can setup a multi-threaded web crawler in less than 5 minutes.

Wu GQ, Hu J, Li L, Xu ZH, Liu PC, Hu XG, Wu XD. Online Web news extraction via tag path feature fusion. Ruan Jian Xue Bao/Journal of Software, 2016,27(3):714-735 (in Chinese). http://www.jos.org.cn/1000-9825/4868.htm

HomePage

Installation

Using Maven

Without Maven

WebCollector jars are available on the HomePage .

webcollector-version-bin.zip contains core jars.

Example Index

Annotation versions are named withDemoAnnotatedxxxxxx.java.

Basic

DemoAutoNewsCrawler.java | DemoAnnotatedAutoNewsCrawler.java
DemoManualNewsCrawler.java | DemoAnnotatedManualNewsCrawler.java
DemoExceptionCrawler.java

CrawlDatum and MetaData

DemoMetaCrawler.java
DemoAnnotatedMatchTypeCrawler.java
DemoAnnotatedDepthCrawler.java
DemoBingCrawler.java | DemoAnnotatedBingCrawler.java
DemoAnnotatedDepthCrawler.java

Http Request and Javascript

DemoRedirectCrawler.java | DemoAnnotatedRedirectCrawler.java
DemoPostCrawler.java
DemoRandomProxyCrawler.java
AbuyunDynamicProxyRequester.java
DemoSeleniumCrawler.java

NextFilter

DemoNextFilter.java
DemoHashSetNextFilter.java

Quickstart

Lets crawl some news from github news.This demo prints out the titles and contents extracted from news of github news.

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Best web scraping Tools for Data Extraction in 2023. Do you really know data scraping and its Tools?