tableau vs seaborn: What’s better for you?

More often than not, for data science enthusiasts, a comparison of Tableau vs Seaborn or any visualization library from python or R is put forth to decide what works best in terms of making credible and visually appealing visualizations for their data. The fact that Tableau is a data visualization tool and Seaborn is a Python library makes the competition between Tableau vs Seaborn even more interesting.

The possibilities are endless on both the platforms and in this article, we’ll discuss certain aspects of Tableau and Seaborn to conclude what might be the best way to go for your data viz needs.

What is Tableau?

According to the Tableau Public website; Tableau Public is a free platform to publicly share and explore data visualizations online. With millions of inspiring data visualizations, or “vizzes” as we affectionately call them, anyone can see and understand vizzes about any public data topic under the sun, making data part of everyday life and supporting a community to grow and learn from each other.

In other simpler terms, Tableau public is a free platform where you can create visually appealing and accurate data representations using a wide range of visualizations available to communicate your data insights to all interested parties easily and with just a couple of clicks. It is very user-friendly and can be learned quickly with no prior knowledge whatsoever.

Find how to get started with Tableau here -> Tableau Public Tutorials

What is Seaborn?

Seaborn is a data visualization library built on top of the predominant and original Matplotlib library which helps users make beautiful data visualizations while allowing the visuals to be highly customizable and easily reproducible. Seaborn comes with a lot of inbuilt visuals that a user can choose from and can handle vast datasets easily.

According to Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, https://doi.org/10.21105/joss.03021, It provides a high-level interface to matplotlib and integrates closely with the pandas data structures. Functions in the seaborn library expose a declarative, dataset-oriented API that makes it easy to translate questions about data into graphics that can answer them. When given a dataset and a specification of the plot to make, seaborn automatically maps the data values to visual attributes such as color, size, or style, internally computes statistical transformations, and decorates the plot with informative axis labels and a legend.

This sums up the first and foremost difference between Tableau vs Seaborn i.e., one is an application and the other is a statistical data visualization library used with the Python Programming language.

Find more about Seaborn here: Seaborn Official Website

Tableau vs Seaborn

Tableau vs Seaborn : All you need to know

We’ll go over different segments and compare what may work best for your data. To clear this out at the beginning itself, both Tableau and Seaborn are amazing and it’s best to not assume one to be better than the other in a discussion of Tableau vs Seaborn but ideally, the discussion is to make a better choice based on the situation at hand.

  • Data: If you are working on data that is not very large and you need to dive deeper into your data using visualizations to understand it better, Seaborn is clearly a better choice in Tableau vs Seaborn. But if your data needs you to merge multiple datasets or establish connections or relationships between multiple datasets, it’s easier when done using Tableau.
  • Visualizations: If you are looking for data visualizations to be put into reports and change according to real-time changes, then Tableau serves this purpose better than Seaborn. Using Seaborn, one can make pair plots and other very statistically important visuals and dive deeper into the data whilst understanding the patterns. It may not be easy to make bubble charts using Seaborn but you sure can make a KDE plot to understand the probabilities of your data well.
  • Dashboards: Dashboards are easily made using Tableau and can be effectively put in place with multiple visualizations together to offer viewers a complete understanding of the data in a single place. Seaborn is not the ideal choice when it comes to making dashboards, while one could still try making a dashboard using Shiny in R programming, they don’t even measure up close to how beautiful they look with Tableau.
  • Filters: Filtering your data using Seaborn happens in a way where Pandas needs to be used with the code for visualization so that you can filter your data effectively. It is not easy and can make up lines and lines of code to apply a filter on one visual leaving out the part of the code that is important to make aesthetic visuals. In Tableau, one can apply one filter to multiple visuals at a time and can apply multiple filters easily too. If you constantly want to change the input for the visual, choose Tableau.

For more on how you can download Tableau -> Download Tableau Public here

Conclusion

Some other factors like Data Storytelling and real-time predictive analytics and speed can be factors where Tableau beats Seaborn. But if you want to include a couple of statistical visualizations and understand your data well with more analysis-friendly visuals, Seaborn wins. In this comparison of Tableau vs Seaborn, there cannot be a winner but I hope this article helps you see where you can use Tableau or Seaborn.

Using Tableau vs Seaborn is also dependent on the job role you hold. For someone who is specifically into Business Intelligence, they won’t find much of use in Seaborn. Someone who is a Data Scientist who deals with a lot of different tools in her analysis, wouldn’t want to go for a different application altogether for a handful of visualizations.

If you are someone who does not use Python, don’t worry. Using tableau needs no programming knowledge and you can get going with Seaborn level visualizations or even better using Tableau which takes no more than a couple of hours to learn and get started with.

For more such content, check out our website: Buggy Programmer

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An eternal learner, I believe Data is the panacea to the world's problems. I enjoy Data Science and all things related to data. Let's unravel this mystery about what Data Science really is, together. With over 33 certifications, I enjoy writing about Data Science to make it simpler for everyone to understand. Happy reading and do connect with me on my LinkedIn to know more!

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