Choosing a chart library for data analysis

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For a few months now I have been searching for a combination of different features that helps me to create data visuaization without much work and preferably through code. Through this journey I tried different libraries, frameworks and platforms to build them. My approach is from a developer point of view that has programming knowledge, however, I have had the chance to use tools that does not require programming at all. In this article, we will explore some of the best data visualization libraries and platforms available today.

The need for data visualization

In today’s data-driven world, the ability to visualize data is more important than ever. Data visualization allows us to understand complex data sets, identify trends, and communicate insights effectively. However, not all data visualization tools are created equal, regardless of the one you pick, they need to be able to:

• Handle large data sets: The tool should be able to handle large data sets without crashing or slowing down.
• Support multiple chart types: The tool should support a variety of chart types, such as bar charts, line charts, scatter plots, and heat maps.
• Integrate with other tools: The tool should be able to integrate with other data analysis and visualization tools, such as Python, R, and SQL.
• Create interactive visualizations: The tool should allow users to interact with the data, such as zooming in and out, filtering, and drilling down into specific data points.
• Be easy to use: The tool should be user-friendly and intuitive, allowing users to create visualizations quickly and easily.

In the following sections, we will explore some of the best data visualization libraries and platforms that I have experience with and I have used in the past to create data visualizations.

D3

In this series, d3 might be the one that requires the most programming knowledge, but it is also the one that gives you the most control over the chart being generated. D3.js is a JavaScript library for producing dynamic, interactive data visualizations in web browsers. It makes use of the widely implemented SVG, HTML5, and CSS standards. It also enables you to animate the charts and combine them with other libraries, such as React, Angular, and Vue.js.

However, it does require speding time learning the library and its API, which can be a bit overwhelming at first. But once you get the hang of it, it is a powerful tool that can help you create stunning visualizations. I have used it to create charts for a closed’source project that dealt with tranactions fraud detection, and it was a great experience. In order to sharp my skills with D3, I have published a open-source project.

To get deep into d3 structure and detailes the book “Interactive Data Visualization for the Web” by Scott Murray is a great resource in addition to the D3.js documentation (Murray, 2017). I do have to mention that the version of d3 in the book is outdated, so for the latest version of d3, you will have to check the documentation, but it is still a great source for the foundation of d3 and its mental model.

matplotlib

Matplot is the intersection between the power of the code and visualization, it gives you full control of the chart being generated, and it does require programming knolwedge in Python, it has its pros and cons, but for people that are used to python ecosystem this is the one visualization go to.

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