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Jupyter notebooks has emerged as a standard tool for data scienceprogramming. Programs in Jupyter notebooks are different from typical programsas they are constructed by a collection of code snippets interleaved with textand visualisation. This allows interactive exploration and snippets may beexecuted in different order which may give rise to different results due toside-effects between snippets. Previous studies have shown the presence ofconsiderable code duplication -- code clones -- in sources of traditionalprograms, in both so-called systems programming languages and so-calledscripting languages. In this paper we present the first large-scale study ofcode cloning in Jupyter notebooks. We analyse a corpus of 2.7 million Jupyternotebooks hosted on GitHJub, representing 37 million individual snippets and227 million lines of code. We study clones at the level of individual snippets,and study the extent to which snippets are recurring across multiple notebooks.We study both identical clones and approximate clones and conduct a small-scaleocular inspection of the most common clones. We find that code cloning iscommon in Jupyter notebooks -- more than 70% of all code snippets are exactcopies of other snippets (with possible differences in white spaces), andaround 50% of all notebooks do not have any unique snippet, but consists solelyof snippets that are also found elsewhere. In notebooks written in Python, atleast 80% of all snippets are approximate clones and the prevalence of codecloning is higher in Python than in other languages. We further find thatclones between different repositories are far more common than clones withinthe same repository. However, the most common individual repository from whicha Jupyter notebook contains clones is the repository in which itself resides.

Jupyter Notebooks on GitHub: Characteristics and Code Clones