![]() It can also be used to support open science. 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. A first draft of a narrative report might sound like stream-of-consciousness beat poetry meets data analysis. ![]() 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. Here I highlight some of the more advanced features of ipython notebook with particular focus on recently added features. – can be leveraged for batch processing and standardized analyses. I covered ipython notebook a couple of years ago, back when it was a relatively new tool, but it’s become a lot more powerful as it has matured and its ecosystem has grown. It has become an excellent tool for running and documenting exploratory analyses, while the rest of the Python ecosystem – IDEs, IPython console, debugging tools, etc. You can try out an interactive ipython notebook session on. It’s not unlike Mathematica, Maple, or RMarkdown. The ipython Notebook interface, which runs in the browser, allows one to write and run interactive notebooks which combine code, documentation – including Markdown and LaTeX equations – and interaction seemlessly. Coming from a Matlab background, it’s natural to search for something Matlab-like to replace it – an IDE with integrated editor, code execution, plotting, benchmarking, file management, etc.Īn increasingly attractive alternative is the IPython Notebook. By far the most popular post on this blog is a review of several Python integrated development environments (IDEs) geared toward science.
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