Web scraping is the art of extracting data from a website in an automated and well-structured form. There could be different formats for scraping data like excel, CSV, and many more. Some practical use cases of web scraping are market research, price monitoring, price intelligence, market research, and lead generation. Web scraping is an instrumental technique to make the best use of publicly available data and make smarter decisions. So it’s great for everyone to know at least the basics of web scraping to benefit from it.

This article will cover web scraping basics by playing around with Python’s framework called Beautiful Soup. We will be using Google Colab as our coding environment.

Well, to start with, web scraping is the process of extracting web data. Why do you need web data? You need web data because you base all your decisions related to business strategy on web data. Whether it is price intelligence, sentiment analysis, or lead generation, you need data to arrive at your strategy. Nov 23, 2020 Web scraping (or data scraping) is a technique used to collect content and data from the internet. This data is usually saved in a local file so that it can be manipulated and analyzed as needed. If you’ve ever copied and pasted content from a website into an Excel spreadsheet, this is essentially what web scraping is, but on a very small scale.

Is Web Scraping Easy

1. Oct 01, 2015 Web Scraping is almost a new profession – there tons of freelancers making their living off extracting web content and data. Having built your own “kit” of different tools any beginning coder can become quickly a professional full-blown Web Scraper. I hope this Web Scraping Tutorial will guide you safely through this journey.
2. 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.
3. In this video, you will learn how to login into websites with python and scrape their data. See the website scraped in this video and the code we created her.

Steps Involved in Web Scraping

1. First of all, we need to identify the webpage we want to scrape and send an HTTP request to that URL. In response, the server returns the HTML content of the webpage. For this task, we will be using a third-party HTTP library to handle python-requests.
2. Once we are successful in accessing the HTML content, the major task comes to the parsing of data. We can not process data simply through string processing since most of the HTML data is nested. That’s where the parser comes in, making a nested tree structure of the HTML data. One of the most advanced HTML parser libraries is html5lib.
3. Next comes the tree traversal, which involves navigating and searching the parse tree. For this purpose, we will be using Beautiful Soup(a third-party python library). This Python library is used for pulling data out of HTML and XML files.

Now we have seen how the process of web scraping works. Let’s get started with coding,

Step1: Installing Third-Party Libraries

In most cases, Colab comes with already installed third-party packages. But still, if your import statements are not working, you can get this issue resolved by installing few packages by the following commands,

Step2: Accessing the HTML Content From the Webpage

It will display the output of the form,

Let’s try to understand this piece of code,

1. In the first line of code, we are importing the requests library.
2. Then we are specifying the URL of the webpage we want to scrape.
3. In the third line of code, we send the HTTP request to the specified URL and save the server’s response in an object called r.
4. Finally print(r.content) returns the raw HTML content of the webpage.

Step3: Parsing the HTML Content

Output:

It gives a very long output; some of the screenshots are attached below.

One of the greatest things about Beautiful Soup is that it is built on the HTML parsing libraries like html5lib, html.parse, lxml etc that allows Beautiful Soap’s object and specify the parser library to be created simultaneously.

In the code above, we have created the Beautiful Soup object by passing two arguments:

Incardex. r.content: Raw HTML content.

html5lib: Specifies the HTML parser we want to use.

Finally, soup.prettify() is printed, giving the parse tree visual representation from the raw HTML content.

Step4: Searching and navigating the parse tree

Now it’s time to extract some of the useful data from the HTML content. The soup objects contain the data in the form of the nested structure, which could be further programmatically extracted. In our case, we are scraping a webpage consisting of some quotes. So we will create a program that solves these quotes. The code is given below,

Before moving further, it is recommended to go through the HTML content of the webpage, which we printed using soup.prettify() method and try to find a pattern to navigate to the quotes.

Now I will explain how we get this done in the above code,

If we navigate through the quotes, we will find that all the quotes are inside a div container whose id is ‘all_quotes.’ So we find that div element (termed as table in the code) using find() method:

The first argument in this function is that the HTML tag needed to be searched. The second argument is a dictionary type element to specify the additional attributes associated with that tag. find() method returns the first matching element. One may try table.prettify() to get a better feeling of what this piece of code does.

If we focus on the table element, the div container contains each quote whose class is quote. So we will loop through each div container whose class is quote.

Here the findAll() method is very useful that is similar to find() method as far as arguments are concerned, but the major difference is that it returns a list of all matching elements.

Dell vostro 3550 screen resolution. We are iterating through each quote using a variable called row.

Let’s analyze one sample of HTML row content for better understanding:

Now consider the following piece of code:

Here we are creating a dictionary to save all the information about a quote. Dot notation is used to access the nested structure. To access the text inside the HTML element, we use .text:

Further, we can also add, remove, modify and access tag’s attributes. We have done this by treating the tag as a dictionary:

Then we have appended all the quotes to the list called quotes.

Finally we will generate a CSV file, which will be used to save our data.

We have named our file inspirational_qoutes.csv and saved all the quotes in it to be used in the future also. Here is how our inspirational_quotes.csv file looks like,

In the output above, we have only shown three rows, but there are 33 rows in reality. So this means that we have extracted a considerable amount of data from the webpage by just giving a simple try.

Note: In some cases, web scraping is considered illegal, which can cause the blockage of your IP address permanently by the website. So you need to be careful and scrape only those websites and webpages which allow it.

Why Use Web Scraping?

Some of the real-world scenarios in which web scraping could be of massive use are,

Lead Generation

One of the critical sales activities for most businesses is its lead generation. According to a Hubspot report, generating traffic and leads was the number one priority of 61% of inbound marketers. Web scraping can play a role in it by enabling marketers to access the structured lead lists all over the internet.

Market Research

Doing the right market research is the most important element of every running business, and therefore it requires highly accurate information. Market analysis is being fueled by high volume, high quality, and highly insightful web scraping, which can be of different sizes and shapes. This data can be a very useful tool for performing business intelligence. Ps4 controller remote play. The main focus of the market research is on the following business aspects:

• It can be used to analyze market trends.
• It can help us to predict the market pricing.
• It allows optimizing entry points according to customer needs.
• It can be very helpful in monitoring the competitors.

Create Listings

Web scraping can be a very handy and fruitful technique for creating the listings according to the business types, for example, real estates and eCommerce stores. A web scraping tool can help the business browse thousands of listings of the competitor’s products on their store and gather all the necessary information like pricing, product details, variants, and reviews. It can be done in just a few hours, which can further help create one’s own listings, thus focusing more on customer demands.

Compare Information

Web scraping helps various businesses gather and compare information and provide that data in a meaningful way. Let’s consider price comparison websites that extract reviews, features, and all the essential details from various other websites. These details can be compiled and tailored for easy access. So a list can be generated from different retailers when the buyer searches for a particular product. Hence the web scraping will make the decision-making process a lot easier for the consumer by showing various product analytics according to consumer demand.

Aggregate Information

Web scraping can help aggregate the information and display it in an organized form to the user. Let’s consider the case of news aggregators. Web scraping will be used in the following ways,

1. Using web scraping, one can collect the most accurate and relevant articles.
2. It can help in collecting links for useful videos and articles.
3. Build timelines according to the news.
4. Capture trends according to the readers of the news.

So in this article, we had an in-depth analysis of how web scraping works considering a practical use case. We have also done a very simple exercise on creating a simple web scraper in Python. Now you can scrape any other websites of your choice. Furthermore, we have also seen some real-world scenarios in which web scraping can play a significant role. We hope that you enjoyed the article and everything was clear, interesting and understandable.

If you are looking for amazing proxy services for your web scraping projects, don’t forget to look at ProxyScraperesidential and premium proxies.

The worldwide web is a treasure trove of data. The availability of big data, lightning-fast development of data analytics software and increasingly inexpensive computing power have further heightened the importance of's easier said than done. Content is constantly being fed on the internet, on a regular basis. This leads to a lot of clutter when you’re looking for data relevant to your needs. That’s when web scraping comes in to help you scrape the web for useful data depending on your requirements and preference.

Below are the basic things you need to know about how to gather information online using web scraping and how to use IP proxies efficiently.

What is Web Scraping?

Web scraping or web harvesting is a technique used to extract requirement relevant and large amounts of data from websites. This information can be stored locally on your computer in the form of spreadsheets. This can be very insightful for a business to plan its marketing strategy as per the analysis of the data obtained.

Web scraping has provided businesses real-time access to data from the world wide web. So if you’re an e-commerce company and you are looking for data, having a web scraping application will help you download hundreds of pages of useful data on competitor websites, without having to deal with the pain of doing it manually.

Why Beneficial?

Web scraping kills the manual monotony of data extraction and overcomes the hurdles of the process. For example, there are websites that have data that you cannot copy and paste. This is where web scraping comes into play by helping you extract any kind of data that you want.

You can also convert and save it in the format of your choice. When you extract web data with the help of a web scraping tool, you should be able to save the data in a format such as CSV. You can then retrieve, analyze, and use the data the way you want.

Web scraping simplifies the process of extracting data, speeds up the process by automating it, and provides easy access to the extracted data by providing it in a CSV format. There are many other benefits of web scraping, such as using it for lead generation, market research, brand monitoring, anti-counterfeiting activities, machine learning using large data sets, and so on.

However, when scraping the web at any reasonable scale, using proxies is strongly recommended.

In order to scale your web scraping project, it is important to understand proxy management, since it’s the core of scaling any data extraction project.

What Are Proxies?

An IP address typically looks like this: 289.9.879.15. This combination of numbers is basically a label attached to your device while you’re using the internet. It helps locate your device.

Web Scraping For Beginners Youtube

A proxy is a third-party server that allows you to route your request through their servers and use their IP address in the process. When using a proxy, the website you are making the request to no longer sees your IP address but the IP address of the proxy, giving you the ability to scrape the web with higher safety.

Benefits of using a proxy:

1. Allows you to mine a website with much more reliability, thereby reducing the chances of your spider getting banned or blocked.
2. Enables you to make your request from a specific geographical region or device (mobile IPs for example) which helps you to see region-specific content that the website displays. This is very useful when scraping product data from online retailers.
3. Using a proxy pool allows you to make a higher volume of requests to a target website without being banned.
4. Saves you from IP bans that some websites impose. For example, requests from AWS servers are very commonly blocked by websites as it holds a track record of overloading websites with large volumes of requests using AWS servers.
5. Enables you to make unlimited concurrent sessions on the same or different websites.

What are the proxy options?

If you go by the fundamentals of proxies, there are 3 main types of IPs to choose from. Each category has its own set of pros and cons and can be well-suited for a specific purpose.

1. Datacenter IPs

This is the most common type of proxy IP. They are the IPs of servers housed in data centers. These are extremely cheap to buy. If you have the right proxy management solution, it can be a solid base to build a very robust web crawling solution for your business.

Web Scraping For Beginners For Beginners

2. Residential IPs

These are the IPs of private residences, enabling you to route your request through a residential network. They are harder to get, hence much more expensive. They can be financially cumbersome when you can achieve similar results with cheaper datacenter IPs. With proxy servers, the scraping software can mask the IP address with residential IP proxies, enabling the software to access all the websites which might not have been available without a proxy.

Web Scraping For Beginners Pdf

3. Mobile IPs

These are the IPs of private mobile devices. It is extremely expensive since it’s very difficult to obtain IPs of mobile devices. It is not recommended unless you’re looking to scrape the results shown to mobile users. This is legally even more complicated because most of the time, the device owner isn't aware that you are using their GSM network for web scraping.

With proper proxy management, datacenter IPs give similar results as residential or mobile IPs without the legal concerns and at a fraction of the cost.

AI in Web Scraping

Many research studies suggest that artificial intelligence (AI) can be the answer to the challenges and roadblocks of web scraping. Researchers from the Massachusetts Institute of Technology recently released a paper on an artificial intelligence system that can extract information from sources on the web and learn how to do it on its own. This study has also introduced a mechanism of extracting structured data from unstructured sources automatically, thereby establishing a link between human analytical ability and AI-powered mechanism.

This could probably be the future to fill the gap of lack of human resources or eventually make it an entirely AI dominated process.

Web scraping has been enabling innovation and establishing groundbreaking results from data-driven business strategies. However, it comes with its unique set of challenges which can hinder the possibilities and as a result makes it more difficult to achieve desired results.

In just the last decade, humans have created more information than the entire history of the human race put together. This calls for more innovations like artificial intelligence to structure this highly unstructured data landscape, and open up a larger landscape of possibilities.