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JupyterLab 3.0 release survey
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# PyCharm
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# JupyterLab

- 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-December.
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

1. How frequently do you use Jupyter?
- Daily - heavy usage; 3+ hours per day.
- Daily - moderate usage; less than 3 hours per day.
- Weekly.
- Monthly.
- I no longer use Jupyter.
- I have never used Jupyter.

2. How long have you been using Jupyter?
- 2+ years.
- 1-2 years.
- 6-12 months.
- Less than 6 months (welcome =]).
- I don't use Jupyter.

3. What languages do you use in Jupyter? (pick up to 4)
- C (and derivatives)
- Go
- Groovy
- Java
- JavaScript
- Julia
- NodeJS
- Perl
- PHP
- Python
- R
- Ruby
- Rust
- Scala
- Spark SQL
- SQL
- TypeScript
- ❗I wrap/ use bindings for other languages.
- ❗My preferred language is not supported in Jupyter.
- Other (please specify)

4. What are your primary job roles when you are using Jupyter? (pick up to 2)
- Backend engineer.
- Business analyst.
- Data engineer.
- Data scientist.
- Database Admin (DBA).
- DevOps.
- Financial modeler/ analyst.
- Front end/ web development.
- Infrastructure engineer/ cloud architect.
- Scientist/ researcher.
- Student.
- Sysadmin.
- Teacher/ lecturer.
- Tutor/ teaching assistant.
- Other (please specify)

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)
- Atom.
- Emacs.
- IPython.
- Jupyter Notebook - Classic.
- JupyterLab.
- nteract.
- PyCharm.
- RStudio.
- Spyder.
- Sublime Text.
- Vim.
- VS Code.
- Zeppelin.
- Other (please specify).

6. How do you run and/ or access Jupyter? (pick up to 4)
- 🖥️ Run directly on local machine (e.g. laptop, desktop).
- Through a Python virtual environment (e.g. conda, virtualenv).
- Through Docker.
- HPC or on-premise server.
- Cloud server (e.g. AWS EC2).
- JupyterHub.
- BinderHub / MyBinder.
- Cloud service - AWS (e.g. EMR, SageMaker).
- Cloud service - Azure (e.g. Notebooks, ML Studio).
- Cloud service - Databricks.
- Cloud service - Google (e.g. AI Platform, Dataproc).
- Cloud service - IBM (e.g. Watson Studio).
- Google Colab.
- CoCalc.
- Mobile device (e.g. phone, tablet). Comments welcome.
- ❓Don’t know how, I just go to a URL.
- Other (please specify).

7. What tasks do you need to perform and what tools do you use to accomplish them?
- Writing and running tests for software.
- Writing a software package.
- Creating content (e.g. blogs, books, education materials).
- Cleaning and preparing data.
- Run pipelines, workflows, or ETL (extract, transform, load) jobs.
- Developing extensions/ plugins to solve my problems.
- Writing software documentation.
- Finding extensions/ plugins to solve my problems.
- Building a machine learning or statistical model.
- Documenting research (reports, scientific papers)
- Visualize data in charts, plots, or dashboards.
- Other major use cases (please specify).

For each of the items above, provide additional information related to:
- How frequently do you perform this task?
- Never.
- Every few months.
- Monthly.
- Weekly.
- Daily.
- To what degree does Jupyter meet your expectations for this?
- Does not apply.
- No.
- Neutral.
- Yes.
- To what degree do alternative tools meet your expectations for this?
- Does not apply.
- No.
- Neutral.
- Yes.

### Data

8. What data sources are you primarily working with in your role? (pick up to 3)
- 🖥️ My local file system (e.g. files and folder on local machine).
- File system (e.g. HPC, EBS/EFS, JupyterHub volumes).
- Cloud object storage (e.g. buckets, S3, Blob, GS).
- SQL (e.g. PostgreSQL, MySQL).
- SQL - embedded (e.g. SQLite).
- NoSQL - columnar store (e.g. Parquet, Arrow, HDFS, BigQuery).
- NoSQL - document store (e.g. MongoDB, Elasticsearch, DynamoDB).
- Graph database (e.g. Neo4j, TigerGraph).
- Time Series (e.g. InfluxDB).
- Pub/ sub (e.g. Apache Kafka, Druid).
- Key value (e.g. Redis, MemcacheDB).
- Google Sheets.
- ❗Industry or field specific APIs.
- Streaming.
- Other (please specify).

9. What data formats are you mostly working with? (pick up to 3)
- Tabular (e.g. csv, spreadsheet, SQL tables, Parquet).
- Images.
- Tensors (e.g. manually handling PyTorch, Tensorflow inputs).
- Nested (e.g. JSON, NoSQL document).
- Hierarchical Data Format (e.g. HDF5 or similar).
- Time series.
- Text.
- Audio.
- Video.
- 3D/ CAD.
- Graph (e.g. nodes, edges).
- Spatial/ geographic (e.g. coordinates, GIS).
- Game/ reinforcement simulation.
- ❗Industry-specific file formats.
- Other (please specify)

10. Do you experience these **problems with data** in Jupyter? (rate from scale of 0-4)
- No grid view for manipulating/ filtering dataframes and arrays.
- 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.
- Poor MVC/ ORM integrations (e.g. Django, Flask).
- Managing database/ source connections and secrets.
- Other (please specify)

For each of the items above, specify:
- Not a problem for me.
- Trivial.
- Minor.
- Major.
- Critical.
- N/A - skip, don't know.

11. What type of analysis are you running? (pick up to 4)
- ❗I am not performing ML/statistical tasks.
- Regression; predict a numeric output.
- Classification; predict a categorical output.
- Generative/ auto-encode; create new data based on existing data.
- Reinforcement learning; actions that maximize a reward.
- Dimensionality reduction (e.g. PCA, K-Nearest Neighbors)
- Feature engineering (e.g. importance, extraction, selection, permutation).
- Natural language processing (NLP).
- Graph data science.
- Outlier detection.
- Other (please specify)


### Visualization

12. What tools does your team use to create dashboards tools? (pick up to 3)
- Dash-Plotly.
- Google Data Studio.
- Grafana
- Kibana.
- Klipfolio.
- Looker.
- R Shiny.
- Spotfire.
- Tableau.
- Voila.
- ❗I don't create dashboards.
- ❗I write my own in HTML & JS.
- Other (please specify).

13. Do you experience these problems with visualization in Jupyter?
- 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 (Jinja2)

For each of the items above, specify:
- Not a problem for me.
- Trivial.
- Minor.
- Major.
- Critical.
- N/A - skip, don't know.

### Scale

14. How do you scale and schedule your workloads? (pick up to 4)
- 🖥️ They run just fine on my local machine.
- ❓I need to scale, but don't know how.
- Server - on premise HPC/ data center.
- Server - cloud (e.g. AWS EC2).
- Cloud ML/ AI (e.g. AWS SageMaker, IBM Wastson Studio).
- Cluster - Spark and/ Hadoop.
- Cluster - Dask.
- Cluster - Kubernetes (or similar e.g. Mesos, Swarm, Slurm).
- Cluster - Jupyter Enterprise Gateway.
- Jupyter BinderHub.
- Quantum (e.g. D-Wave).
- Horovod.
- Kubeflow.
- Elyra.
- Snakemake.
- Papermill.
- CWL, Nextflow, and/ or WDL.
- Apache Airflow.
- Prefect.
- Cloud pipelines (e.g. AWS Batch).
- Cloud queries (e.g. AWS Presto, AWS Athena).
- Other (please specify).

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

For each of the items above, specify:
- Not a problem for me.
- Trivial.
- Minor.
- Major.
- Critical.
- N/A - skip, don't know.

### Collaboration

16. When it comes to working on notebooks in a team setting, with how many other people are you collaborating?
- 0
- 10
- 20
- 30
- 40
- 50+

17. What is your reason for sharing a notebook with someone else? (pick up to 3)
- ❗I am not working with other people.
- Share knowledge.
- Feedback about my writing.
- Feedback about my code.
- Formal code review.
- Integrate my code/ data with their downstream or upstream processes.
- Edit/ contribute some of their own code.
- Edit/ contribute some of their own writing.
- Teach/ tutor them.
- Peer programming.
- Deploy my code/ model/ pipeline/ dashboard.
- Other (please specify)

18. What is the nature of your collaboration?
- Describe the collaboration:
- How long have you been working together?
- I am not collaborating.
- 2+ years.
- 1-2 years.
- 6-12 months.
- Less then 6 months.
- How frequently do you work together?
- I am not collaborating.
- 2+ times per week.
- Weekly.
- A few times a month.
- Monthly.
- Less then monthly.
- How do you divide the work?
- I am not collaborating.
- We work on different projects.
- We work on the same project, but different parts.
- We work on the same part of the same project together.
- Comments about collaboration:

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

For each of the items above, specify:
- Not a problem for me.
- Trivial.
- Minor.
- Major.
- Critical.
- N/A - skip, don't know.

20. Do you have challenges with the notebook UI?
- No progress bar for running long notebooks.
- No marketplace for Extensions (e.g. 5 star ratings, browsable categories).
- No global search.
- Can't collapse sections of a notebook hierarchically.
- Poor autocompletion (e.g. LSP, show methods/ attributes).
- No modes for editing other Jupyter documents (MyST, Jupyter Book).
- Can't see hidden (.) files in file browser.
- Don't know which cell failed in long notebook.

For each of the items above, specify:
- Not a problem for me.
- Trivial.
- Minor.
- Major.
- Critical.
- N/A - skip, don't know.

### You did it - thank you!

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


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.

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