When writing articles for publication in academic journals, the layout and style of the figures are expected to conform to a predefined format. This ensures consistency across all of that publication’s articles and that any included figures are high quality when printed.
Python is widely used within the scientific community and provides a great way to create scientific plots. However, when we use matplotlib, one of the most popular plotting libraries within Python, the default plots are poor and need adjusting to ensure they meet the requirements.
Changing the styles of matplotlib figures can be time-consuming, which is where the scienceplots library comes in handy. With just a few lines of code, we can instantly transform the way our figure looks without spending too much time working out how to change different parts of our figures.
The scienceplots library allows users to create simple, informative plots similar to those found in academic journals and research papers. Not only that, it also sets the required DPI to 600 (for some styles), which is often a requirement by publications to ensure high-quality printed figures.
The scienceplots library contains numerous styles, including support for multiple languages, including Chinese and Japanese. You can explore the full range of styles within the scienceplots library at the link below.
Within this article, we will explore how we can transform some basic and common data visualisations into something that can be included in a scientific publication.
Before creating plots with the scienceplots library, you need to ensure that you have LaTeX installed on your computer. LaTeX is a typesetting system that is designed for the creation of technical and…