3 min read

Data today is the world’s most important resource. However, without properly visualizing your data to discover meaningful insights, it’s useless. Creating visualizations helps in getting a clearer and concise view of the data, making it more tangible for (non-technical) audiences. Python is the choice of programming language for developers these days. However, sometimes developers face issues performing data visualization with Python.

In this post, Tim Großmann, and Mario Döbler, the authors of the Data Visualization with Python course, discuss some of the best practices you should keep in mind while visualizing data with Python.

#1 Start looking and experimenting with examples

One of the most important ways to deeply understand and learn to use Python for data visualizations is to download example projects and play around with them. You should read their documentation and comments and change values, observing what influence it has. In many cases, they can even serve as a starting point to insert your own data. Think about how you could modify the given examples to visualize your own data.

#2 Start from scratch and build on it

Sometimes starting with an empty canvas is the best approach. Start with only the necessary components like your data and the import of your library of choice. This builds a nice flow and process that will enable you to debug problems with precision.

Once you have gone through the whole process of building a simple visualization, you will have a good understanding of where an error might occur and how to fix it. Starting from scratch sometimes shows you that simpler solutions will save you a lot of time while still communicating the essence of your idea.

#3 Make full use of documentation

There are libraries with plenty of documentation to answer every single question you have. Make sure to make best use of it, research their API, look at the given example, and search for open issues on their GitHub pages when encountering a problem. Especially the libraries covered in the course “Data Visualization with Python” not only has extensive documentation, but also an active community that is constantly creating new questions on StackOverflow which will help you to find solutions to your problems in no time.

#4 Use every opportunity you have with data to visualize it

Every time you encounter new data take a few minutes and think about what information might be interesting and visualize it. Think back to the last time you had to give a presentation about your findings and all you had was a table with numerical values in it. For you it was understandable, but your colleagues sat there and scratched their heads. Try to create some simple visualizations that would have impressed the entire team with your results. Only practice makes you perfect.

We hope that these tips will not only enable you to get better insights into your data but also gives you the tool to communicate results better. Don’t forget to checkout our course Data Visualization with Python to understand, explore, and effectively present data using the powerful data visualization techniques of Python.

About the authors

Tim Großmann is a CS student with interest in diverse topics ranging from AI to IoT. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of big data engineering. He’s highly involved in different Open Source projects and actively speaks at meetups and conferences about his projects and experiences.

Mario Döbler is a graduate student with a focus in deep learning and AI. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of deep learning. Currently, he dedicates himself to apply deep learning to medical data to make health care accessible to everyone.

Read Next

8 ways to improve your data visualizations

Seaborn v0.9.0 brings better data visualization with new relational plots, theme updates, and more

Getting started with Data Visualization in Tableau


Subscribe to the weekly Packt Hub newsletter. We'll send you the results of our AI Now Survey, featuring data and insights from across the tech landscape.

* indicates required