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When you write a scientific paper, one of the most common tasks is to analyze the obtained results and design beautiful graphs explaining them. Currently, in the research community, Python’s ecosystem is the most popular for achieving these goals. It provides web-based interactive computational environments (e.g., Jupyter Notebook/Lab) to write code and describe the results, and pandas and matplotlib libraries to analyze data and produce graphs correspondingly. Unfortunately due to the rich functionality, it is hard to start using them effectively in your everyday research activities when you initiate your path as a researcher. In this article, I would like to share some tips and tricks on how to employ the matplotlib library to produce nice graphs for research papers.
I have been using VSCode almost for all activities related to software development. The last activity I preferred to do using an external tool was data analysis. However, recently I have switched even these activities to VSCode. In this article, I share my experience and provide hints on VSCode configuration to facilitate this task.