Notebooks also tend to be set up in a cluster environment, allowing the data scientist to take advantage of computational resources beyond what is available on her laptop, and operate on the full data set without having to download a local copy. In that regard, they offer a number of advantages over any local scripts or tools. Notebooks are typically used by data scientists for quick exploration tasks. But why would one want to choose to use a notebook instead of a favorite IDE or command line? There are many limitations in the current browser-based notebook implementations, but what they do offer is an environment for exploration, collaboration, and visualization. Traditionally, notebooks have been used to document research and make results reproducible, simply by rerunning the notebook on source data.
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