Reproducible Data Analysis in Jupyter, Part 4/10: Working with Data and GitHub скачать в хорошем качестве

Reproducible Data Analysis in Jupyter, Part 4/10: Working with Data and GitHub

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 fourth video, we refactor our data download code to make it more useful in the long-run.