Reproducible Data Analysis in Jupyter, Part 7/10: Refactoring for Speed скачать в хорошем качестве

Reproducible Data Analysis in Jupyter, Part 7/10: Refactoring for Speed

Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested code. This series of videos presents a case study in how I personally approach reproducible data analysis within the Jupyter notebook. For more information and resources relating to these videos, see http://jakevdp.github.io/blog/2017/03... In this seventh video, we refactor a slow piece of our code, ensuring that it passes our previous unit tests.