Fair warning - mobile users have struggled with the survey. We recommend using a computer.
  • Even if you don't use Jupyter, you can still take this survey. Just indicate this fact in the first question and carry on as best as you can.
  • Thank you. Your participation guides Jupyter's roadmap toward your real-life use cases by quantifiably helping us prioritize the functionality that is important to our userbase. 
  • So that you know what to expect, it's comprised of 20 questions spread across the sections below. As a fair heads up, Question 7  is the biggest one, but it provides critical information.
    • Usage patterns.
    • Data
    • Visualization.
    • Scale.
    • Collaboration.
  • The aggregate survey data itself will be openly shared with the Jupyter community when polling closes in mid January. If you opt to provide your email address for a user interview, it will not be used for Jupyter's promotional purposes and it will not be shared with a 3rd party.
Usage Patterns

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* 1. How frequently do you use Jupyter?

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* 2. How long have you been using Jupyter?

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* 3. What languages do you use in Jupyter? (pick up to 4)

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* 4. What are your primary job roles when you are using Jupyter? (pick up to 2)

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* 5. What are your go-to tools for performing data science, scientific computing, and machine learning on your laptop/ desktop (non-cloud) for data science? (pick up to 3)

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* 6. How do you run and/ or access Jupyter? (pick up to 4)

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* 7. What tasks do you need to perform and what tools do you use to accomplish them?

  How frequently do you perform this task? Has Jupyter met your expectations for this use case? Have alternative tools met your expectations for this use case?
Writing a software package.
Writing software documentation.
Cleaning and preparing data.
Creating content (e.g. blogs, books, education materials).
Finding extensions/ plugins to solve my problems.
Documenting research (e.g. reports, scientific papers).
Run pipelines, workflows, or ETL (extract, transform, load) jobs.
Developing extensions/ plugins to solve my problems.
Visualize data in charts, plots, or dashboards.
Building a machine learning or statistical model.
Writing and running tests for software.
Data

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* 8. What data sources are you primarily working with in your role? (pick up to 3)

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* 9. What data formats are you mostly working with? (pick up to 3)

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* 10. Do you experience these problems with data in Jupyter? (rate from scale of 0-4)

  (0) Not a problem for me. (1) Trivial. (2) Minor. (3) Major. (4) Critical. N/A - skip, don't know.
No grid view for manipulating/ filtering dataframes and arrays.
Poor MVC/ ORM integrations (e.g. Django, Flask).
Can’t see a list of my current variables.
Plaintext or environment variable management of database passwords/ keys/ secrets.
Lost data during failure or restart of kernel/ server.
Data is too big to fit into memory on my machine/ server.

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* 11. What type of analysis are you running? (pick up to 4)

Visualization

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* 12. What tools do you use to create dashboards? (pick up to 3)

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* 13. Do you experience these problems with visualization in Jupyter?

  (0) Not a problem for me. (1) Trivial. (2) Minor. (3) Major. (4) Critical. N/A - skip, don't know.
No built-in UI for creating charts.
Can't publish my charts as web-based dashboards.
Poor/ buggy support for my plotting tool.
Difficulty displaying highly dimensional data (e.g. array of array of arrays, too many rows/ columns to fit on screen).
Lacking templating support (e.g. Jinja2).
Scale

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* 14. How do you scale and schedule your workloads? (pick up to 4)

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* 15. Do you experience these problems with scale in Jupyter?

  (0) Not a problem for me. (1) Trivial. (2) Minor. (3) Major. (4) Critical. N/A - skip, don't know.
Figuring out how to schedule batch execution of notebook-based jobs.
Don’t have the budget for more scalable environment/ cloud services.
Haven’t divided longer notebooks into multiple, modular notebooks.
Not persisting the outputs of a notebook.
Machine learning training jobs take too long.
Can't call code/ modules from other notebooks.
Difficulty managing Spark dependencies (Java).
Collaboration

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* 16. When it comes to working on notebooks in a team setting, with how many other people are you collaborating?

0 25 50+
Clear
i We adjusted the number you entered based on the slider’s scale.

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* 17. What is your reason for sharing a notebook with someone else? (pick up to 3)

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* 18. What is the nature of your collaboration?

  How long have you been working together? How frequently do you work together? How do you divide the work?
Describe the collaboration:

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* 19. Do you have challenges with collaboration in Jupyter?

  (0) Not a problem for me. (1) Trivial. (2) Minor. (3) Major. (4) Critical. N/A - skip, don't know.
Don't know what dependencies (versions of language, packages, extensions) a notebook uses.
Don't know/ have the data a notebook is supposed to use.
Poor support for our version control (git) system.
No built-in way to publish my notebook to a shared location.
Not being able to comment on notebooks.
No "track changes;" can't figure out what changed between notebook checkpoints/ versions.

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* 20. Do you have challenges with the notebook UI?

  (0) Not a problem for me. (1) Trivial. (2) Minor. (3) Major. (4) Critical. N/A - skip.
No progress bar for running long notebooks.
Don't know which cell failed in long notebook.
No modes for editing other Jupyter documents (e.g. MyST, Jupyter Book).
Can't see hidden `.` files in file browser.
No global search.
No marketplace for Extensions (e.g. 5 star ratings, browsable categories).
Poor autocompletion (e.g. LSP, show methods/ attributes).
No native desktop app.
Can't collapse sections of a notebook hierarchically.
You did it - thank you!

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* 21. Open feedback for problems/ pain points you didn't get to share.

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* 22. Optional - Are you interested in giving qualitative feedback on JupyterLab, JupyterHub, and the JupyterLab developer experience? If we have permission to contact you for follow-up questions, please leave your email address below.

0 of 22 answered
 

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