Data visualization, i.e., the graphical representation of data, is a vital skill for every scientist to develop – aiding with the interpretation of data and providing an accessible way to communicate these data with others. In the scientific world, data visualization is used to produce eye-catching figures to share results with peers and the wider community. While these visualizations are achievable using no coding, they can be restricted by the dataset size, plotting style and overall cost of the software. Learning to code solves many of these issues and while the learning curve remains a barrier to use, programming is becoming a must-have skill in many fields. Python is one of the world’s most popular programming languages and is at the forefront of data analysis and visualization, producing clear, engaging and reproducible figures in all manner of styles. As biological datasets increase in size and number, the reproducibility and flexibility of Python result in an invaluable tool for scientific data visualization. This article will introduce the use of Python for data visualization and provide some guidance on how to get started.