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Einblick reimagines the modern data science workflow in a collaborative data science canvas, rather than a linear notebook. Working in a canvas environment offers many advantages including live collaboration, an expansive visual interface, and a progressive computation engine. In this article, we’ll highlight one of the key ways we’re saving data scientists time–our operators. We’ll go through a couple of our core operators, why Python is such a crucial part of our software solution, and how we augmented our offerings with a user operator interface. The latter allows users to customize and use their own operators, which can be used in any Einblick canvas, and shared with other Einblick users.
Reimagining data science with Python-based operators in Einblick’s visual canvas by Becca Weng
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>>> Python Enhancement Proposals (PEPs): The future of Python is discussed here.
>>> Python Software Foundation
The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers. Learn more