Let us see what is data mining and data scraping firstly and then discuss the Python libraries for Data Mining and Data Scraping
There are many python packages/libraries used in data science some of Python libraries for Data Mining and Data Scraping.
Data Mining
Data mining extracts insights from raw data by analyzing trends and anomalies. You can get this type of data from various sources. It can scrape web pages for data mining, mostly through online surveys, cookies, and public records collected by third parties and organizations.
How does data mining work?
There is no right or wrong way to extract data. As long as you pool your data sources and produce honest results, you’re doing data mining right.
Data mining does not focus on why or where you get your data, as long as it is legitimate and reliable. Data acquisition is the first of the five steps in data mining. And The Data scientists still need the right place to store and work with their data, as they divide it into relevant categories before visualizing it.
Actual data mining is the process of mining data for insights. You can do this using Excel spreadsheets or mathematical models to extract better information using coding languages like Python, SQL and R.
What is data mining used for?
While web scraping is primarily used for recycling, data mining is primarily focused on generating value from data. Most projects that require data mining fall into data science rather than technical projects.
Data mining can be used for online marketing by collecting data from third parties or mining your own business data for insights. Data mining also has scientific and technical applications. For example, meteorologists generate large amounts of weather data to accurately predict the weather.
Data Scraping
Data scraping, also known as web scraping, is the process of importing information from a website into a spreadsheet or local file saved on your computer. This is one of the most effective ways to get data from the web and, in some cases, funnel that data to another website.
Popular uses of data scraping include:
- Web content research/business intelligence
- Pricing for travel booking sites/price comparison sites
- Find sales opportunities/conduct market research by crawling public data sources (eg Yale and Twitter)
- Sending product data from an e-commerce site to another online seller (for example, Google Shopping)
- And that list is just scratching the surface. Data scraping has a large number of applications: it is useful in any situation where data needs to be moved from one place to another.
The basics of data scraping are relatively easy to master. Let’s see how to set up a simple data extraction action using Excel.
Web scraping is the method of collecting data from the desired web pages and is also known as data collection and extraction. Using Hypertext Transfer Protocol, extraction tools and applications access the World Wide Web, collect valuable data, and extract it according to their needs. Information is stored in a central database or downloaded to your hard drive for later use.
Web scraping is the practice of extracting data directly from a website. In general, web scraping has three main requirements; A destination website, a web scraping tool, and a database to store the collected data.
With web scraping, you are not limited to official data sources. Instead, you can use all publicly available data on websites and online platforms. If you browse a website and write its content manually, you are doing web scraping.
However, manual web scraping consumes a lot of time and energy. Not to mention that the front end of a website rarely contains all of the publicly available data.
Web scraping is used for many purposes including financial and academic studies. A corporation or organization can use these strategies to collect data on its competitors and improve sales. Also, they play a vital role in generating online leads and attracting lots of customers.
How does web scraping work?
With all the data available online, you would need a huge amount of money to start doing anything with it, and human web scraping just isn’t enough.
That’s where specialized web scraping tools come into play. They automatically read the underlying HTML code of the website. Although, some advanced scrapers can include CSS and JavaScript elements.
It then reads and duplicates any encrypted or restricted data. A good web scraping tool can replicate the public content of an entire website. You can also tell your web scraping tool to collect specific types of data from the export to an Excel or CVS spreadsheet.
Now Let’s Discuss Python Libraries for Data Mining and Data Scraping
SQLAlchemy
SQLAlchemy is a set of Python database tools that help you efficiently access data stores. Presents the most applied patterns for high-performance database access. SQLAlchemy ORM and SQLAlchemy Core are the two main components of SQLAlchemy. By covering Python database functions and APIs, SQLAlchemy adds a core level of abstraction. It also distributes SQL statements and schemas to users. SQLAlchemy ORM is a stand-alone object relational mapper. SQLAlchemy allows developers to control their database and automate redundant activities.
Key features of SQLAlchemy
Core and ORM are two separate components of SQLAlchemy. Core is a complete set of SQL abstraction tools, while Object Relational Mapper is an optional package that extends Core.
SQLAlchemy is an accurate, high-performance library that has been deployed in millions of environments and is fully tested.
SQLAlchemy components can be used independently of each other. Connection pooling, SQL statement collection, and transaction services are separate components extended by multiple add-on points.
Benefits of using SQLAlchemy
- SQLAlchemy has enterprise-grade APIs, which makes your code more robust and flexible.
- The flexible structure of SQLAlchemy makes it easy to write complex queries.
Disadvantages of using SQLAlchemy
The idea of a unit of work is still very unusual.
It has a complex API with a long learning curve.
Scrapy
If you work with data extraction where the data is retrieved from the screen (displays the data), then scrappy is a valid Python package for you. Scrapy allows you to improve screen scraping and web crawling. Data scientists use Scrapy for data mining and automated testing. Scrapy is an open source tool for extracting data from web pages, used by many IT professionals around the world. Scrappy is developed in Python and is cross-platform, running on Linux, Windows, BSD, and Mac OS X. Because of Scrapy’s superior interoperability, many software professionals prefer Python for data scraping and analysis purposes.
Scrapy Key Features
- Built-in functionality to collect and extract data from HTML/XML sources.
- Support for creating feed exports in various formats (JSON, CSV, XML) and storing them on multiple backends.
- Extensibility is well supported, allowing you to connect your functionality through well-defined signals and APIs.
Benefits of using Scrapy
- Scrappy is an ideal solution for large-scale projects due to its architecture and features. This simplifies project migration, which is valuable for large projects.
- Scrapy is very efficient in terms of speed as it is asynchronous and designed specifically for web scraping.
Disadvantages of using Scrapy
Scrappy does not support Javascript based websites.
The installation process varies depending on the operating system.
Scrapy requires Python 2.7 or later.
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BeautifulSoup
BeautifulSoup is a Python data mining and extraction library that extracts data from HTML and XML sources. It allows data scientists to develop web crawlers that crawl websites. BeautifulSoup can retrieve data and structure it in the desired format. The extracted HTML data contains a large amount of encoded web data that cannot be interpreted by users. Its most recent version, BS4 (BeautifulSoup 4), organizes messy web data into easy-to-understand XML structures, enabling data analysis. BeautifulSoup automatically recognizes the encoding and easily interprets HTML documents, which contain special characters. We can search through the parsed document and find what we are looking for in it.
Key features of BeautifulSoup
- The beautiful Soup Parse Tree provides some simple techniques and Pythonic idioms for browsing, searching, and manipulating.
- Pretty Soup automatically converts incoming documents to Unicode and outgoing documents to UTF-8.
- BeautifulSoup is based on well-known Python parsers like lxml and html5lib, allowing us to interface with various parsing algorithms.
Benefits of using Beautiful Soup
- Easy to learn and understand for beginners.
- It comes with extensive documentation.
- As we use this library, it has strong community support for troubleshooting.
Disadvantages of using BeautifulSoup
- BeautifulSoup is slow, but with multi-threading, a lot can be done faster. This works at a disadvantage since the coder must be adept at multithreading.
- BeautifulSoup has a beautiful atmosphere. However, it makes it difficult to use proxies, which prevents complex projects from using the libraries.
