Nos últimos anos, notamos um aumento constante na popularidade de notebooks analíticos. São aplicações inspiradas em Mathematica que combinam texto, visualização e código em um documento computacional vivo. Os notebooks Jupyter são amplamente usados por nossos times para prototipagem e exploração em análise de dados e aprendizado de máquina. Colocamos o Jupyter em Adote nessa edição do Radar para mostrar que ele emergiu como padrão atual para notebooks Python. Contudo, recomendamos cautela para usar notebooks Jupyter em produção.
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.
