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Web Scraping is an essential and valuable skill for Data Scientists and Engineers. Data is the most significant unit in any data science project. It is crucial to gather the relevant data to start any data science project. Web scraping tools automate the process of data extraction from a web source. For example, you may be working on a retail store analytics application as your Data Science project. Data extraction of the competitors’ websites will be an essential part of the project. Web scraping tool will let you collect data from 10 or more such online sources in a few minutes.
You need to select the correct web scraping tool for your project. Let us take a look at the top five such tools available in the market.
PyScrappy is a popular web scraping tool you may use to scrape data from several sources. It is a Python package offering quick and flexible web scraping options.
You can use the tool to carry out automatic data scraping. The tool requires a few input parameters from the user to carry out the rest of the scraping. GitHub hosts the source code of the tool. Some of the interesting features and benefits of the tool are:
• Feed exports in various formats like JSON, CSV, and XML
• Automatic data extraction from the web sources
• Cross-platform application framework
• Asynchronous scheduling and processing of the requests
• In-built support for data extraction and selection
4. Common Crawl
Common Crawl is a web scraping tool by Common Crawl Foundation, a registered not-for-profit organization. The idea behind creating the tool is to provide an opportunity to every user across the globe to explore and gain data insights. You can use the tool without having any second thoughts on the financial aspects since it is open-source.
The tool can be effective in beginner-level or advanced Data Science projects to gather web data and gain valuable insights. Following are the key features of Common Crawl:
• Raw web page data available and accessible as open datasets
• Open datasets of text extractions
• Compatibility with non-code based usage cases
• Useful resources for trainers/educators teaching data analytics
3. Content Grabber
Content Grabber is a web scraping tool with capabilities in store for individuals as well as corporations. It can easily extract the data from any web sources and export it in your desired database format, such as MySQL, SQL Server, or others. You can also extract the data like spreadsheets, CSV, or XML files.
Many of the other web scraping tools lack the extraction capabilities from dynamic websites. However, such inefficiencies and issues are not present in Content Grabber. The tool can process AJAX websites with utmost efficiency and can also manage website logins.
Some of the features and benefits of Content Grabber are:
• Seamless integration with third-party analytics applications
• Scripting capabilities
• Agent logging and agent debugger
• Effective error handling
• Integrated version control
ParseHub is one of the most effective and flexible web scraping tools available in the market. You can use the tool in your data science projects to automatically extract data from multiple web sources. It enables the users to build web scrapers without writing extensive codes.
Key features and capabilities of ParseHub are:
• Text clean-up and HTML before exporting the data
• Easy to use and interactive user interface
• Automatic collection and storage of the data sets on the servers
• Automated IP rotation
• Accessible on multiple frameworks and platforms, such as Mac OS, Linux, and Windows
• Different options to choose from while exporting the data like JSON or Excel
• Data extraction from maps and tables
Crawly is an open-source tool to perform web crawling and web scraping. It is fast and flexible to fit well with any data science projects and applications irrespective of their complexity levels. The tool uses Diffbot’s automated article extract API to extract and convert the web data in a structured format. Concise data extraction makes it easier to store the data in the databases for further analysis and visualization.
Crawly can extract image/video games, text, comments, titles, and a lot more from web sources. The output can then come in the form of CSV, JSON, or Excel files.
You may be a student or a newbie in the field of Data Science working on your initial projects. Crawly is a simple-to-use tool not requiring exceptionally high tech expertise.
The key features and benefits of the tool are:
• Structured data extraction
• Data export in a variety of formats
• Easy to use interface
Pricing of the tools can be one of the main concerns for selecting the most suitable tool for your data science project. The table presents an easy comparison of the five tools as per their respective prices.
|PyScrappy||Common Crawl||Content Grabber||ParseHub||Crawly|
|Open-source||Open-source||· Two pricing models – Licensed-version or monthly subscription
· Server, professional, and premium are three versions at $69, $149, and $299 monthly charges respectively.
|· 14-day free trial with limited support and data retention, 200 pages per run allowance
· Standard and professional versions at $149 and $499 per month respectively
Five top web scraping tools viz PyScrappy, Common Crawl, Content Grabber, ParseHub, and Crawly come with an interesting feature set. Each of these tools also has a few shortcomings. Common Crawl, for instance, does not offer support for live data and dynamic websites yet. The feature is available in Content Grabber but the prices of the tool are way higher than the other alternatives. Similarly, troubleshooting can be a complex process to carry out in ParseHub. PyScrappy can be time-consuming with dynamic websites. The same can be the case with Crawly. You shall analyze the web scraping needs for your Data Science project and then map those requirements with the features of the tools available. The selection of the best match out of all can provide superior performance and efficiency with the fulfillment of the requirements.