The Basic Charts extension for CKAN provides the ability to display data from DataStore resources as interactive charts. This extension adds several chart types to the resource view options within CKAN, visualizing data in accessible formats. Utilizing the Flot Charts JavaScript library, it offers compatibility across a wide range of browsers, including older versions like IE6+. Key Features: Line Chart Visualization: Creates line charts to display trends and relationships between data points over a specified axis, suitable for analyzing changes over time or continuous variables.

The Data Visualization Libraries Software market is experiencing robust growth, driven by the increasing need for businesses to effectively analyze and present complex data. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key factors. The rise of big data and the subsequent demand for intuitive data interpretation are primary drivers. Businesses across all sectors—from large enterprises leveraging sophisticated analytics to SMEs seeking efficient reporting tools—are increasingly reliant on data visualization libraries to gain actionable insights. Furthermore, the shift towards cloud-based solutions offers scalability, accessibility, and cost-effectiveness, accelerating market adoption. Technological advancements, including the development of interactive dashboards and advanced visualization techniques such as augmented reality and virtual reality integration, are also contributing to market growth. While the on-premises segment continues to hold a significant share, the cloud-based segment is experiencing faster growth due to its flexibility and ease of deployment. Competition within the market is intense, with established players like Syncfusion, Google, and Highsoft AS alongside emerging players like Chart.js and ApexCharts vying for market share through innovation and strategic partnerships. Geographical distribution reveals strong growth in North America and Europe, driven by early adoption and robust digital infrastructure, while Asia-Pacific is emerging as a significant market with high growth potential due to rapid technological advancements and increasing digitization across various sectors. Despite the positive outlook, certain restraints exist. The complexity of some libraries may pose a challenge for users with limited technical expertise. Security concerns related to data handling and integration with existing systems also pose a hurdle for some businesses. Furthermore, the market is subject to fluctuations in technology trends and the emergence of alternative data analysis methods. However, continuous innovation, improved user interfaces, and the increasing availability of training and support resources are expected to mitigate these challenges and further propel market growth in the forecast period. The segmentation of the market by application (large enterprises, SMEs) and type (cloud-based, on-premises) provides a nuanced understanding of market dynamics and allows for targeted strategies by vendors. Future growth is anticipated to be driven by the continued integration of data visualization libraries within business intelligence (BI) tools and the increasing adoption of these libraries in diverse applications such as healthcare, finance, and manufacturing.

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!