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In recent years, Jupyter notebooks have grown in popularity in severaldomains of software engineering, such as data science, machine learning, andcomputer science education. Their popularity has to do with their rich featuresfor presenting and visualizing data, however, recent studies show thatnotebooks also share a lot of drawbacks: high number of code clones, lowreproducibility, etc.  In this work, we carry out a comparison between Python code written inJupyter Notebooks and in traditional Python scripts. We compare the code fromtwo perspectives: structural and stylistic. In the first part of the analysis,we report the difference in the number of lines, the usage of functions, aswell as various complexity metrics. In the second part, we show the differencein the number of stylistic issues and provide an extensive overview of the 15most frequent stylistic issues in the studied mediums. Overall, we demonstratethat notebooks are characterized by the lower code complexity, however, theircode could be perceived as more entangled than in the scripts. As for thestyle, notebooks tend to have 1.4 times more stylistic issues, but at the sametime, some of them are caused by specific coding practices in notebooks andshould be considered as false positives. With this research, we want to pavethe way to studying specific problems of notebooks that should be addressed bythe development of notebook-specific tools, and provide various insights thatcan be useful in this regard.

A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts