Python Bs4 Web Scraping

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Web Scraping for Beginners with : Python | Scrapy| BS4

Web Scraping with Pandas and Beautifulsoup. APIs are not always available. Sometimes you have to scrape data from a webpage yourself. Luckily the modules Pandas and Beautifulsoup can help! Related Course: Complete Python Programming Course & Exercises. Pandas has a neat concept known as a DataFrame. Find and Findall are two functions in the BeautifulSoup Python library used for web scraping and Parsing the HTML page.

Learn how to extract data from websites using : Python | Scrapy and BeautifulSoup

Ultimate Guide to Web Scraping with Python Part 1: Requests and BeautifulSoup. Part one of this series focuses on requesting and wrangling HTML using two of the most popular Python libraries for web scraping: requests and BeautifulSoup. After the 2016 election I became much more interested in media bias and the manipulation of individuals. Scraping eBay step by step. In this section, we will walk you through the step by step process of scraping eBay for products and their prices. 1.Selecting the required information. The very first task in web scraping is to identify the target web page. It is the web page from which you need to extract all the required information. Many data analysis, big data, and machine learning projects require scraping websites to gather the data that you’ll be working with. The Python programming language is widely used in the data science community, and therefore has an ecosystem of modules and tools that you can use in your own projects.

Description

Web scraping is the process of automatically downloading a web page’s data and extracting specific information from it.
The extracted information can be stored in a database or as various file types.

Basic Scraping Rules:

  • Always check a website’s Terms and Conditions before you scrape it to avoid legal issues.
  • Do not request data from a website too aggressively (spamming) with your program as this may break the website.
  • The layout of a website may change from time to time ,so make sure your code adapts to it when it does.

Popular web scraping tools include BeautifulSoup and Scrapy.

BeautifulSoup is a python library for pulling data (parsing) out of HTML and XML files.
Scrapy is a free open source application framework used for crawling web sites and extracting structured data

which can be used for a variety of things like data mining,research ,information process or historical archival.

Web scraping software tools may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.
Scraping a web page involves fetching it and extracting from it. Fetching is the downloading of a page (which a browser does when you view the page). to fetch pages for later processing. Once fetched, then extraction can take place. The content of a page may be parsed, searched, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page, to make use of it for another purpose somewhere else. An example would be to find and copy names and phone numbers, or companies and their URLs, to a list (contact scraping).
Web scraping is used for contact scraping, and as a component of applications used for web indexing, web mining and data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.
Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. . A web scraper is an Application Programming Interface (API) to extract data from a web site. Companies like Amazon AWS and Google provide web scraping tools, services and public data available free of cost to end users.

Who this course is for:

  • Beginners to web scraping
  • Data Analyst
  • Data Scientist
  • Database Administrators
  • Internet researchers
  • Entrepreneurs


What you’ll learn

  • Prototype web scraping script with python interactive shell
  • Build a web scraping script with BeautifulSoup and Python
  • Create a Scrapy spider to crawl website and scrape data


Python Bs4 Web Scraping Tutorial

By Lenin Mishra

In this blog you will learn to perform Web Scraping using the Beautiful Soup and Requests in Python 3.

How will you learn?

You are going to scrape a Wikipedia table in order to fetch all the information, filter it(if necessary) and store them in a csv.

Contents of this article

Benefits of Web Scraping

21st century is the age of Data. Every organization depends on minute analysis of various data sources in order to grow their business.

Python

With web scraping, one can accumulate tons of relevant data from various sources with a lot of ease, therefore, skipping on the manual effort. Real Estate Listings, Job listings, price tracking on ecommerce websites, stock market trends and many more - Web Scraping has become a go to tool for each of these objectives and much more.

Web

Beautiful Soup vs Selenium vs Scrapy

Python Bs4 Web Scraping Pdf

When it comes to using Python for web scraping, there are 3 libraries that developers consider for their scraping pipeline.They are Beautiful Soup, Selenium or Scrapy.

Each of these libraries has its pro and cons of its own. One should shoose the library that is best suited for their requirement.

The pros and cons of each of these libraries are described below.

Importance of DOM in Web Scraping

In order to scrape the necessary content, it is imperative that you understand HTML DOM properly.

Python Bs4 Web Scraping Software

The HTML DOM is an Object Model for HTML. It defines:

  • HTML elements as objects
  • Properties for all HTML elements
  • Methods for all HTML elements
  • Events for all HTML elements

When a web page is loaded, the browser creates a Document Object Model of the page.

An HTML page consists of different tags - head,body, div, img, table etc. We are interested in scraping the table tag of an HTML.

Let’s dig deeper into the componenets of a table tag in HTML.

The above HTML code will generate the following table.

Observe the following:-

  1. The entire table is defined within <table> tag.
  2. Header resides in <thead> tag.
  3. Data resides in <tbody> tag.
  4. Each table row is defined within a <tr> tag.
  5. Each table header is defined with a <th> tag.
  6. Each table data/cell is defined with a <td> tag.

Now using the above information, we can scrape our wikipedia tables.

Examples

Parsing HTML Table with Beautiful Soup

The first step involves scraping an entire Wikipedia page and then identifying the table that we would like to store as csv.

For this article, we will scrape all the Tropical Cyclones of January, 2020.

Step 1 - Make a GET request to the Wikipedia page and fetch all the content.

The wiki_page_text variable contains the content from the page.

Step 2

We will pass the content through Beautiful Soup. This should give us a BeautifulSoup object, which represents the document as a nested data structure.

Let’s experiment with the soup variable which is a BeautifulSoup object.

This way you can interact with various elements of HTML using the Beautiful Soup object.

Let’s find our table that we want to scrape.

We are interested in the table with the caption Tropical cyclones formed in January 2020. Let’s read that particular table.

We should be able to see the HTML for just the Tropical Cyclones formed in 1 January, 2020 table in our required_table variable.

Parsing headers from Wikipedia table

Let’s move on to parsing the headers of the table.

As we had discussed earlier, each table header is defined with a th tag. So, we could just look up all the th elements within the required_table.

Our variable headers is now a list containing all the header names.

Parsing rows of data from Wikipedia table

Let’s now parse the rows containing the data. As discussed above, Each table data/cell is defined with a td tag and the entire row resides within a tr tag.

Now, we want to store every row as a list, so that it can be easily converted to a csv file. For this purpose, we will parse the tr tags and loop through each tr tag to find the td tag.

Now our variable rows contains all the rows of the tables in a list format.

Web Scraping With Python Beautifulsoup

Converting data into CSV

Now that we have both our headers and the data rows, the only task that remains is to convert them to a CSV file.

Conclusion

Beautiful Soup has a lot of useful functionality to parse HTML data. It is user-friendly and has a well explained documentation. In fact, Beautiful Soup could help you with most of your parsing of the static websites.

In this article, you have learned how to scrape Wikipedia tables using Python, requests, and Beautiful Soup. You learned how to:

  1. Inspect the DOM structure of Wikipedia tools.
  2. Download the page HTML content using Python requests library with a GET request.
  3. Parse the downloaded HTML with Beautiful Soup to extract relevant information.

Bs4 Python Tutorial

To learn more about Python HTTP Methods, check out our blog.