Data Science: Web Scraping Using Python
Web scraping
Web scraping is the practise of extracting content and data from a website using bots.
Consider the following scenario: Let’s say you’re looking for information on a website. Let’s take a look at India for a moment. So, what exactly do you do? You may, however, copy and paste the data from Wikipedia into your own document. But what if you need to get a huge volume of data from a website as soon as possible? To train a Machine Learning algorithm, for example, enormous volumes of data from a website? Copying and pasting will not work in this circumstance! That’s when Web Scraping will come in handy.
Web scraping, often known as web data extraction, is a data scraping technique that collects data from websites. Web scraping can be done manually by a software user, but it usually refers to automated activities carried out by a bot or web crawler.It’s a type of copying in which specific data is acquired and copied from the internet, usually into a central local database or spreadsheet for retrieval or analysis later.
The process of web scraping entails retrieving a web page and extracting information from it. Fetching is the process of downloading a webpage. As a result, web crawling is the most important part of web scraping, as it allows you to collect pages for further processing. After the data has been fetched, extraction can begin. A page’s content can be analysed, searched, reformatted, and the data put into a spreadsheet or a database.
What is Web Scraping used for?
Web scraping has a wide range of uses in a variety of industries. Let’s have a look at a few of them right now!
1. Price Monitoring
Companies can use web scraping to scrape product data for their own and competitor products to examine how it affects their pricing strategy. Companies can use this information to determine the best pricing for their items in order to maximise income.
2. Market Research
Companies can utilise web scraping for market research. Companies can benefit greatly from high-quality online scraped data gathered in huge volumes when assessing customer patterns and determining which route the firm should take in the future.
3. News Monitoring
Web scraping news sites can provide a company with detailed reports on current events. This is especially important for companies that are frequently in the news or rely on daily news for their day-to-day operations. After all, a single day’s news can make or kill a firm!
4. Email Marketing
Web scraping can also be used for email marketing. They can use web scraping to collect Email IDs from various sites and then send bulk promotional and marketing emails to everyone who owns these Email IDs.
Libraries Used for Web Scraping
Well, python has many different libraries which are available for different tasks to perform. For web scraping using python, it can be done by using the following libraries.
- Requests: It allows you to send HTTP/1.1 requests with ease and it doesn’t require manually add query strings to your URLs or to form-encode your POST data.
- BeautifulSoup : This is used to pull the data out of HTML and XML files for web scraping purposes. It creates a parse tree from page source code that can be used to extract data in a hierarchical and more readable manner.
- Pandas: Pandas are mainly used for data analysis. Pandas allow importing data from various file formats such as CSV, JSON, SQL, Microsoft Excel. Pandas allow various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.
- Selenium: Selenium is a web testing library. It is used to automate browser activities.
Steps For Web Scraping
1. Find the URL you want to scrape: For this example, I’m going to use data from the IMDb website for the most popular Web-Series with the shortest runtime period. This is the one I have here.
2. Inspect the page: In most cases, the data is nested in tags. So we examine the website to discover where the data we want to scrape is nested beneath which tag. To inspect the page, simply select the element, right-click it, and select “Inspect.” In the coding section, found tags will be more useful.
3. Find the data which is to be extracted: In my example, I’m going to extract data from nested “div” tags that contain the name of the web-series, the movie runtime (duration), genre, and IMDb ratings, by selecting the class tags from the inspect.
4. Write the code: I’ve used google colab for this work
To begin, one must import all of the necessary libraries into their file. If packages are not installed on the machine before importing, it is advised that they be installed first using the pip command.
Now enter the URL of the website, from which you want to extract the data. In our case, we’ve selected it in step 2.
BeautifulSoup’s Find and Find All methods are used to extract data from required tags and store it in variables. Create a dataframe with Pandas Libary in which the data is kept in a structured manner so that it can be exported into the desired file format. I’ve saved the data in.csv file here.
The output of all the extracted information is stored in the .csv file.
This is a very simple software to use and grasp the foundations of web scraping. By doing so, one can gain a basic understanding of web scraping and learn how to scrape data from the internet in a more efficient manner.
https://github.com/yashpatel2711/DataScience-Series/blob/main/18it106_web_scraping.ipynb
