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Why Jupyter is data scientists’ computational notebook of choice

Author

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  • Jeffrey M. Perkel

Abstract

An improved architecture and enthusiastic user base are driving uptake of the open-source web tool.

Suggested Citation

  • Jeffrey M. Perkel, 2018. "Why Jupyter is data scientists’ computational notebook of choice," Nature, Nature, vol. 563(7729), pages 145-146, November.
    • Handle: RePEc:nat:nature:v:563:y:2018:i:7729:d:10.1038_d41586-018-07196-1
    DOI: 10.1038/d41586-018-07196-1
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    Cited by:

    1. C. Sean Burns, 2023. "The Issues with Journal Issues: Let Journals Be Digital Libraries," Publications, MDPI, vol. 11(1), pages 1-7, February.
    2. Cristian D. González-Carrillo & Felipe Restrepo-Calle & Jhon J. Ramírez-Echeverry & Fabio A. González, 2021. "Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    3. Matthias Griebel & Dennis Segebarth & Nikolai Stein & Nina Schukraft & Philip Tovote & Robert Blum & Christoph M. Flath, 2023. "Deep learning-enabled segmentation of ambiguous bioimages with deepflash2," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. David L. Alderson, 2022. "Interactive Computing for Accelerated Learning in Computation and Data Science," INFORMS Transactions on Education, INFORMS, vol. 22(2), pages 130-145, January.
    5. Gregory Giuliani & Elvire Egger & Julie Italiano & Charlotte Poussin & Jean-Philippe Richard & Bruno Chatenoux, 2020. "Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?," Data, MDPI, vol. 5(4), pages 1-25, October.

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