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Add more summarization functions


  • Difficulty: Easy
  • Skills needed: Python, SQLAlchemy, JavaScript, Svelte, PostgreSQL
  • Length: Medium (~175 hours)

The Problem

The Mathesar Data Explorer enables an action called “Summarize” that let a user view a summary of their data, which is in fact an aggregation of some column(s), grouped by some other column(s). Currently, the only possible aggregation functions are counting or listing.

Feature Description

We want to add more summarization (aggregation) functions to the Mathesar Data Explorer. The functions should either come from the PostgreSQL aggregate functions, or the implementer could create their own. Functions to prioritize are:

  • Summing numeric columns
  • Joining array (list) columns into a single array (list)
  • Merging JSON Object columns
  • Statistical aggregations (Mean, Median, Max, Min)

UX Design Problems

The only real UX issue to solve here is how to present the different options in a way that is understandable to the user. It may be that the current drop-down list needs to be enriched somehow. It’s possible that the implementer could want to do an aggregation that needs some kind of presentation of the output, but that’s doubtful.


  • Determine which summarization functions to add by consulting the documentation and proposing ideas to the mentors.
  • Determine whether any UI/UX concerns will arise from the chosen functions.
  • Implement the back end functions for each summarization function chosen.
  • Add the summarization functions to the Data Explorer UI, handling any UX concerns.

Expected Outcome

We should have at least 3 (preferably more) new summarization functions in the Data Explorer by the end of the internship.

Application Tips

  • Demonstrate proficiency with the required skills.
  • Present some preliminary research into which summarization functions make sense and why.
  • Make sure to demonstrate an understanding of what an aggregation function does, and why they’re useful.
  • Try to provide examples showing why each aggregation function researched might be useful.




Primary Mentor: Brent Moran Secondary Mentor(s): Sean Colsen