IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v563y2018i7729d10.1038_d41586-018-07196-1.html
   My bibliography  Save this article

Why Jupyter is data scientists’ computational notebook of choice

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/d41586-018-07196-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/d41586-018-07196-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:563:y:2018:i:7729:d:10.1038_d41586-018-07196-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.