Description
Submitting Author: Name (@rich-iannone)
All current maintainers: (@rich-iannone, @machow)
Package Name: Great Tables
One-Line Description of Package: Make awesome display tables using Python.
Repository Link: https://github.com/posit-dev/great-tables
Version submitted: v0.13.0
EiC: @Batalex
Editor: @Batalex
Reviewer 1: @cjbassin
Reviewer 2: @glemaitre
Archive: TBD
JOSS DOI: TBD
Version accepted: v0.14.0
Date accepted (month/day/year): 01/03/2025
Code of Conduct & Commitment to Maintain Package
- I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
- I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.
Description
The Great Tables package is all about creating tables for the purpose of presentation. You can use
Pandas or Polars DataFrames as inputs, and the Great Tables API allows you to:
- structure the data using column spanners and row groups, and add header and footer information
- format the data with a wide range of powerful formatting methods
- style the table to make it aesthetically pleasing or to highlight important information
- integrate the table display into notebooks, Quarto documents or web pages, and export the table
as HTML or a variety of image formats
Scope
-
Please indicate which category or categories.
Check out our package scope page to learn more about our
scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):- Data retrieval
- Data extraction
- Data processing/munging
- Data deposition
- Data validation and testing
- Data visualization1
- [] Workflow automation
- Citation management and bibliometrics
- Scientific software wrappers
- Database interoperability
Domain Specific
- Geospatial
- Education
Community Partnerships
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- For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
The package can be seen as a data visualization package, but it is perhaps more in the direction of data presentation/publication (i.e., not datavis in the traditional sense). However, tables are important and they are ubiquitous in all sorts of scientific publications.
- Who is the target audience and what are scientific applications of this package?
The target audience is anyone who needs to present data in the tabular format. There is a particular focus on science and engineering applications as many of the formatting methods are geared toward this audience (e.g., scientific notation, significant figures, units notation, chemistry notation, etc.).
- Are there other Python packages that accomplish the same thing? If so, how does yours differ?
There are only a few packages that deal with tabular data presentation. The Pandas styler API is probably the best known of these, but it is limited in its capabilities. A big part of Great Tables is the ability to structure a table to a more traditional table format (one you'd commonly see in journals or reports) instead of interactive tables that are more common in web apps (i.e., displaying hundreds or thousands of rows of data). The formatting capabilities of Great Tables are also much more extensive than Pandas styler or other packages.
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Technical checks
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- uses an OSI approved license.
- contains a README with instructions for installing the development version.
- includes documentation with examples for all functions.
- contains a tutorial with examples of its essential functions and uses.
- has a test suite.
- has continuous integration setup, such as GitHub Actions CircleCI, and/or others.
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JOSS Checks
- The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
- The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
- The package contains a
paper.md
matching JOSS's requirements with a high-level description in the package root or ininst/
. - The package is deposited in a long-term repository with the DOI:
Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.
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Footnotes
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Please fill out a pre-submission inquiry before submitting a data visualization package. ↩
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