Top 5 Python Libraries for Web Data Extraction and Scraping
Python is popular for being a high-level language and yet with a simple flow and readable coding style. With a wide range of applications including web development and machine learning, Python continues to hold the trust of several leading experts in the field of data collection, extraction, web data mining and web scraping given its extensive, feature-rich and well-documented libraries, and a strong support for Object Oriented Programming.
Developing data extractors and web scraping tools in Python using libraries like Beautiful Soup or Selenium is currently popular given its advanced functions and simplicity in use. Many of these libraries are easy to learn and implement with your original applications; since these packages can be applied on the latter in the API format to build the customized web scrapers. With these python libraries you can perform web mining and scraping on a variety of fields like scraping data from Twitter and Amazon with other Python tools.
Python scripts and libraries, including the ones mentioned here, are open source and come with an extensive documentation and community support which makes the interfacing and usability much easier. Here are 5 of the best Python packages for scraping and extracting data.
1. Beautiful Soup
There is a lot of content which you can get from the internet but not all of it is in a format you desire, before you can even make sense of it all. What you need here is a scraper paired with something (called a parser) which can extract the content you really need. Beautiful Soup is a package for parsing HTML and XML documents from the web. It creates parse trees and sits on HTML or XML parsers like html5lib and lxml to extract data from the HTML easily.
Running an HTML document through Beautiful Soup gives us the BeautifulSoup object which contains all the web data which it extracted in a nested data structure. The functions of the library perform automated tree traversal to find and extract data from the nested structure.
Of course, the raw page needs to be downloaded before parsing and that can be done easily using the Requests library. It offers hundreds of powerful functions such as find(), find_all(), get_text() and more for finding a desired attribute and text from the raw HTML data so that you can read the data you really need. These functions include automatically detected encoding to look beneath the encoding and get that hidden data to you; Beautiful Soup automatically converts incoming documents to Unicode and outgoing documents to UTF-8.
You can download and install BeautifulSoup for Python 3 or 2 using the pip command or the easy_install command in the Python shell
pip install beautifulsoup4
Also, you could upgrade your Python to Python 3 with the following command to avoid errors in installation:
Although many applications that boomed up because of this tool use Beautiful Soup 3, the latest version is the Beautiful Soup 4 (bs4) package, which is compatible with Python 2.7 and later versions. The bs4 version is a bit more simplified than BeautifulSoup 3; the default parser is changed to html.parser (with alternative parsers like html5lib), empty XML tags are detected automatically, and changes in the “BeautifulSoup” constructor to manually mention the webpage type XML or HTML.
Its functionality and simplicity make it one of the most useful packages for data extraction and web scraping in Python.
Selenium is a tool in Python that acts like a webdriver, opening browsers, performing clicks, filling forms, scrolling and more on a webpage. The Selenium framework is mostly used in automated testing of web applications but its functionality has found an application in automated web scraping. Using web drivers like ChromeDriver for Chrome, we can visit websites and links, and Selenium automates the process in Python in an isolated Python environment.
Using pip, you can install selenium like this:
pip install selenium
Selenium requires a driver to interface with the chosen browser such as ChromeDriver for Chrome and Safari Driver in Safari 10. You must install and add these before running Selenium examples.
With growth in Python and XML alike, the Lxml library of Python helps linking the two languages in reading, extracting and processing of XML and HTML pages. lxml provides a high-performance parsing for XML files with greater speed and quality compared to Beautiful Soup, but works similarly by creating and parsing tree structures of XML nodes. Interaction with such nodes helps in creating parent-child relationships and modules like the etree module (ElementTree API).
lxml also supports XML Path or XPath like Selenium, making it easier to parse complex XML web page structures. But you can merge the advanced functionality of Beautiful Soup with Lxml as they both support and are compatible with each other; Beautiful Soup uses it as a parser.
Similarly, you can use the “requests” library in Python to visit and scrape information from the websites; this combination is pretty common for web scraping.
Python provides data extraction tools like Lxml, which is a Python binding for two preexisting C libraries libxml2 and libxslt, with all the rich features and without the laborious memory management and segfaults. It uses Python Unicode for API, is well-documented and Pythonic in all the good ways. The key benefits of this library are its ease of use, speed in parsing large documents and pages, simplicity in functionality, and provides easy conversion of the data to Python data types, thus it can be easily merged with your application.
Install lxml for Python 2 and 3 with the pip command:
pip install lxml
You can run the command as admin on systems like Linux:
sudo pip install lxml
Scrapy is not just a Python library but it is an entire data scraping framework that provides spider-bots who can crawl several websites at once and extract their data. Using Scrapy, you can create your own spiders and host them on the Scrapy Cloud or as a Scrapy API. The spider is created using a set of commands and a target webpage, along with a parse method such as self.parse for HTTP requests.
Once the program for the spider is executed, it will make a request to the given webpage, get the HTML for the first URL from the supplied list of URLs, and parse it according to its parameters. Scrapy’s method for indexing and tagging is using CSS along with XPath to move in the HTML tree.
We can extract and store links that end with a certain extension (for example but not limited to: .jpg, .png for images, .pdf, .docx for documents) in addition to standard web content, and create a separate HTML document containing the extracted data. Additionally, the Requests library can download the media attachments or the data and perform further processing on it.
Using the APIs, you can extract data and use it as a general purpose web crawler. The crawling and extracting includes managing web page requests, follow various web links automatically, preserving user sessions, and handle output pipelines.
The first thing we’ll need to do to scrape a web page is to download the page. The requests library of Python helps us to do exactly that. The library will make a GET request to a web server, which will download the HTML contents of a given web page for us. There are several different types of requests we can make using requests, of which GET is just one.
Requests verifies SSL (Secure Sockets Layer for a secure connection) certificates for HTTPS requests, just like a web browser.
With the HTTP library of Requests, you can access webpages from the URL which is the first step in web extraction. You can then pull content in HTML format from the site as raw data. It acts like a simple-to-use API, so you can focus more on the cleaning and analysis part and leave the scraping to the library.
The “requests.get” function of the library sends an HTTP request to the URL of the desired webpage and the server website responds by returning the HTML content of the webpage.
The response is raw encoded HTML data of the page in string format and it is stored in a requests object or another location. Instead of the encoded data, if you want to see the actual text content of the webpage, you can use the “.text” property of the object which will decode the data and extract the text.
In addition to text data, you can also retrieve the header data, response values and JSON values. Similarly we can send such data and files to the server, for form filing or uploading documents, using the “.post” function. Thus, the Requests library can handle all types of HTTP requests to scrap and extract data from the webpages.
To further parse the data we use tools like BeautifulSoup using a desired HTML parser such as html5lib, lxml and html.parser.
To install Requests, simply run this simple command in your terminal of choice:
pip install requests
install the pip environment first if you don’t have it already.
Additionally, you can get all of these libraries from PyPi, the Python Package Index, and get important information to use each of them.
There are other powerful libraries such as Pandas, Scikit-learn, SciPy and NumPy all of which provide vital functions and features for storing, cleaning, analyzing and working with the data received from the data extraction tools. They are all also used for data wrangling and processing, which is important to receive accurate results in further calculations.