For an article on A/B testing that I am preparing, I asked my partner Dr. Nina Zumel if she could do me a favor and write some code to produce the diagrams. She prepared an excellent parameterized diagram generator. However being the author of the book Practical Data Science with […]
I’d like to share a great new feature in the wvpy package (available at PyPi). This package is useful in converting Jupiter notebooks to/from python, and also in rendering many parameterized notebooks. The idea is to make Jupyter notebook easier to use in production. The latest feature is an extension […]
I am sharing a new short data science video: Parameterized Juypter Notebooks. It is an example from the wvpy package showing how to programmatically re-run the same notebook with many different inputs. If you are doing data science in Python, this may help you with your projects. link
I would like to share what I have found to be a very effective personal Jupyter workflow for data science development. DALL-E “An Effective Personal Jupyter Data Science Workflow” Jupyter (nee IPython) workbooks are JSON documents that allow a data scientist to mix: code, markdown, results, images, and graphs. They […]
One of the great conveniences of performing a data science style analysis using Jupyter is that Jupyter notebooks are literate containers that combine code, text, results, and graphs. This is also one of the pain points in working with Jupyter notebooks with partners or with source control. That is: Jupyter […]
I recently got back from Strata West 2017 (where I ran a very well received workshop on R and Spark). One thing that really stood out for me at the exhibition hall was Bokeh plus datashader from Continuum Analytics. I had the privilege of having Peter Wang himself demonstrate datashader […]