Over the past couple of years, we've noticed a steady rise in the popularity of analytics notebooks. These are Mathematica-inspired applications that combine text, visualization and code in a living, computational document. Jupyter Notebooks are widely used by our teams for prototyping and exploration in analytics and machine learning. We've moved Jupyter to Adopt for this issue of the Radar to reflect that it has emerged as the current default for Python notebooks. However, we caution to use Jupyter Notebooks in production.
Over the last couple of years, we've noticed a steady rise in the popularity of analytics notebooks. These are Mathematica-inspired applications that combine text, visualization and code in a living, computational document. Increased interest in machine learning — along with the emergence of Python as the programming language of choice for practitioners in this field — has focused particular attention on Python notebooks, of which Jupyter seems to be gaining the most traction among ThoughtWorks teams. People seem to keep finding creative uses for Jupyter beyond a simple analytics tool. For example, see Jupyter for automated testing.
Over the last couple of years, we've noticed a steady rise in the popularity of analytics notebooks. These are Mathematica-inspired applications that combine text, visualization and code in a living, computational document. In a previous edition, we mentioned GorillaREPL, a Clojure variant of these. But increased interest in machine learning — along with the emergence of Python as the programming language of choice for practitioners in this field — has focused particular attention on Python notebooks, of which Jupyter seems to be gaining the most traction among ThoughtWorks teams.
