Choosing Between Python and Tableau For Your Data Visualization (2024)

I’ve always wondered which is the best way to visualize my data for a better understanding — is the appearance that important? Or should I choose methods that give more insight? This always created conflicts in every work I do relating to Data Analysis. The understanding of data is one of the most important for starting with your analysis.

I use Python for almost all my work as it’s my favorite programming language for Data Analysis and Machine Learning. I learned how to use libraries for my Data Visualization — they worked really well and were easy to learn, but they take time to prepare. With Tableau, I learned about the efficiency the software provides because of its user-friendly system. The graphs in Tableau are more attractive for the Data Scientist and for the end-user that wants to understand the results, but they have limitations. So which one should I choose?

Python offers a variety of free data visualization libraries to Data Scientists such as Matplotlib, Seaborn, and Plotly. These libraries are the most popular among Python users. With Matplotlib, you can visualize any data and their available documentation offers a better user experience. This library offers simple visualizations for analysis such as distributions, bar charts, and regression. However, a big problem with Matplotlib is that their visualization is too basic presentation-wise that may be less attractive for the end-user. Matplotlib is said to be the base of the rest of the Python Data Visualization Libraries, and that's why we will also look over the other popular ones.

Seaborn is a library based on Matplotlib and it improved the functionality of Matplotlib by creating more attractive visualizations and requires less code to build them. With Seaborn, you can build visualizations with a variety of colors and shapes that will give a better visual experience to the end-user. Seaborn has available documentation too, however, it might be less informative compared to Matplotlib. For Matplotlib users, changing to Seaborn might be beneficial if they want to save time when coding and if they want to have better aesthetics in their visualizations.

Plotly is another Data Visualization Library that simplifies the creation of graphs. Same as Seaborn it requires less code compared to Matplotlib and has attractive aesthetics. Plotly works better when customizing graphs compared to Seaborn. This library offers interactivity of their graphs and allows you to explore different data points. Plotly also has available documentation to improve the user experience.

Tableau is an interactive data visualization software for Business Intelligence. The user has the possibility of interacting with the data: compare, filter, connect some variables with others, etc. In addition, the graphs and dashboards can be created with highly visual tools, which facilitates a quick understanding of the data. Tableau doesn’t require coding for the creation of graphs.

Tableau is easy to use, and while some prior data analysis knowledge can help, it is unnecessary. Tableau helps the user to create various visualizations, such as line charts, distribution charts, heat maps, and scatter charts. The disadvantages of Tableau are that it's not for free — their pricing is expensive compared to other BI software in the market. The main disadvantage about Tableau is that only allows you to do basic preprocessing of data that doesn’t work efficiently for data cleaning.

Which one to choose and when depends on the purpose of your analysis and the type of data. If the data requires more extensive cleaning the best solution would be to it through Python. Tableau has limitations in preprocessing of data. An alternative is to do data cleansing with Python and visualization with Tableau, but this might create conflicts when following a specific order of your work.

In Machine Learning the first step is to understand the data structure—this doesn't require attractive visualizations. Matplotlib will make you accomplish Machine Learning goals. When doing clustering, Seaborn is also a good alternative as there are more customizations.

Tableau is expensive, so for starters, it’s not the best alternative compared to the free libraries that Python offers or other BI software. But, Tableau works great for companies when reporting dashboards that are connected to their database. Big companies usually have the capability of paying for the licenses and give the ability to their employees to work with BI software. Tableau allows for more interactivity and is easier to make plots with than coding.

The most important when deciding which one to use is regarding the workflow. Python is the best when working with a variety of data that requires advanced analytics. In Machine learning, a good workflow will improve the results and will keep a better understanding of the work. For more attractive plotting, Python offers a variety of libraries to accomplish this. As said before, Tableau is a good option for companies which purpose is to interact with graphs and have attractive visualizations or a user that wants to accomplish the same — this is why it works perfectly for Business Intelligence. Every Data Scientist has their own taste regarding visualization, but they should focus on what provides a better result. Creativity will also improve your results when presenting them to others.

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