Nos ¨²ltimos anos, notamos um aumento constante na popularidade de notebooks anal¨ªticos. S?o aplica??es inspiradas em Mathematica que combinam texto, visualiza??o e c¨®digo em um documento computacional vivo. Os notebooks s?o amplamente usados por nossos times para prototipagem e explora??o em an¨¢lise de dados e aprendizado de m¨¢quina. Colocamos o Jupyter em Adote nessa edi??o do Radar para mostrar que ele emergiu como padr?o atual para notebooks Python. Contudo, recomendamos cautela para usar notebooks Jupyter em produ??o.
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 ¡ª along with the emergence of Python as the programming language of choice for practitioners in this field ¡ª has focused particular attention on Python notebooks, of which 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 ¡ª along with the emergence of Python as the programming language of choice for practitioners in this field ¡ª has focused particular attention on Python notebooks, of which seems to be gaining the most traction among ThoughtWorks teams.
