Web Scraping: Scraping Table Data

Web Scraping is the most important concept of data collection. In Python, BeautifulSoupSelenium and **XPath **are the most important tools that can be used to accomplish the task of web scraping.

In this article, we will focus on BeautifulSoup and how to use it to scrape GDP data from Wikipedia page. The data we need on this site is in form of a table.

Definition of Concepts

Take a look at the following image then we can go ahead and define the components of an HTML table

Image for post

From the above image we can deduce the following:

The tag defines an HTML table.

An HTML table consists of one

Our interest is to inspect the elements of a given site (in this case the site we want to scrap — on the far right of Figure 1 shows the elements of the site). In most computers you visit the site and click **Ctrl+Shift+I **to inspect the page you wish to scrap.

Note: Elements of a web page are identified by using a class or id options on the tag. Ids are unique but classes are not. This means that a given class can identify more than one web element while one id identifies one and only one element.

Lets now see the image of the site we want to scrape

Image for post

Fig 3

From this Figure note the following:

  1. This is the Uniform Resource Locator (URL). We need this.
  2. Tag element for our object of interest. The object is defined by class and not idclass = “wikitable sortable jquery”.Note that the tag element contains 3 classes identifying one table (classes are separated by white space). Apart from general reference of a site element as we will use here, classes and ids are used as reference to support styling using languages like CSS.
  3. In the site there are 3 tables numbered 34 and 5 in the Figure above. For the sake of this article we will go through how to scrape table 3 and you can easily figure out of how to do 4 and 5.
  4. The button labelled 6 is very important when you are hovering through the page to identify the elements of your interest. Once your object of interest is highlighted the tag element will also be highlighted. e.g for our case label 2 is matching label 3.

Actual Scraping

Required packages: bs4, lxml, pandas and requests.

Once you have the said packages we can now go through the code.

In this snippet, we import necessary packages and parse HTML content of the site.

, and elements.


element defines a table header, and the element defines a table cell.

An HTML table may also include

element and one or more , element defines a table row, the , , , and elements.

#web-scraping #editors-pick #python #html

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Web Scraping: Scraping Table Data
Ray  Patel

Ray Patel


Cloud Based Web Scraping for Big Data Applications 

Have you ever wondered how companies started to maintain and store big data? Well, flash drives were only prevalent at the start of the millennium. But with the advancement of the internet and technology, the big data analytics industry is projected to reach $103 billion by 2027, according to** Statista**.

As the need to store big data and access instantly increases at an alarming rate, scraping and web crawling technologies are becoming more and more useful. Today, companies mainly use web scraping technology to regulate price, calculate the consumer satisfaction index, and assess its intelligence. Read on to find the uses of cloud-based web scraping for big data apps.

What is Web Scraping?

How Cloud-Based Web Scraping Benefits an Organisation?

#data-analytics #web-scraping #big-data #cloud based web scraping for big data applications #big data applications #cloud based web scraping

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How POST Requests with Python Make Web Scraping Easier

When scraping a website with Python, it’s common to use the

urllibor theRequestslibraries to sendGETrequests to the server in order to receive its information.

However, you’ll eventually need to send some information to the website yourself before receiving the data you want, maybe because it’s necessary to perform a log-in or to interact somehow with the page.

To execute such interactions, Selenium is a frequently used tool. However, it also comes with some downsides as it’s a bit slow and can also be quite unstable sometimes. The alternative is to send a

POSTrequest containing the information the website needs using the request library.

In fact, when compared to Requests, Selenium becomes a very slow approach since it does the entire work of actually opening your browser to navigate through the websites you’ll collect data from. Of course, depending on the problem, you’ll eventually need to use it, but for some other situations, a

POSTrequest may be your best option, which makes it an important tool for your web scraping toolbox.

In this article, we’ll see a brief introduction to the

POSTmethod and how it can be implemented to improve your web scraping routines.

#python #web-scraping #requests #web-scraping-with-python #data-science #data-collection #python-tutorials #data-scraping

Ray  Patel

Ray Patel


How to Deal With the Most Common Challenges in Web Scraping

For those who practice data extraction as an essential business tactic, we’ve revealed the most common web scraping challenges.


In the world of business, big data is key to competitors, customer preferences, and market trends. Therefore, web scraping is getting more and more popular. By using web scraping solutions, businesses get competitive advantages in the market. The reasons are many, but the most obvious are customer behavior research, price and product optimization, lead generation, and competitor monitoring. For those who practice data extraction as an essential business tactic, we’ve revealed the most common web scraping challenges.

#big data #data analytics #web scraping #data scraping #deal with the most common challenges in web scraping #scraper