– can be leveraged for batch processing and standardized analyses. Here I highlight some of the more advanced features of ipython notebook with particular focus on recently added features. ![]() ![]() Ipython notebook particularly shines for creating narrative reports – a form of literate programming which is an excellent workflow for data analysis.Ī narrative report mixes code, plots, and a text narrative that highlight results, non-results, thoughts and concerns. A first draft of a narrative report might sound like stream-of-consciousness beat poetry meets data analysis. A bit of editing tightens the narrative and serves to aggregate and summarize one’s thoughts – map-reduce for the brain. The report can then be used for self-archival and sharing insights with other team members. It can also be used to support open science. It can be viewed statically via the ipython Notebook viewer.It can be shared as a self-contained file and run on another computer that has the ipython notebook server installed – e.g.it can be exported to static formats, like PDF, via ipython nbconvert.An .ipynb file can be shared, stored, viewed, and converted in a number of ways: Matlab offers this possibility with cell-mode publishing, but the ipython notebook is leaps and bounds above Matlab’s report generation. The notebook interface is particularly well-adapted for narrative reports, as it transparently mixes code, plots, printouts, text, Markdown, and LaTeX.Īn IPython notebook – which includes code, text, and the results of computations – is essentially a JSON-formatted file with an.
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