Streaming dataframe visualization
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- data-visualization, stream-processing
Research direction
Read streamz.dataframe.core.Frames and Frame first, then compare the HoloViews/Bokeh and Altair options named in the issue. Done means the chosen approach covers automatic plot-style selection, full updates for Frames, and incremental streaming or removal updates for Frame; no test or file path is named in the issue.
Written by the indexing model from the issue text.
Description
The streamz.dataframe module could use a good plot method. This has two requirements:
- Relatively automatic selection of plot styles
- Support for updating results, either a full update of all data or an incremental update of new data / removal of old data
As far as I can tell there are two options here:
- Holoviews backed by Bokeh cc @philippjfr
- Altair, see https://github.com/altair-viz/altair/issues/435
I believe that both libraries are filling out their capabilities here.
There are two different situations to cover.
- Updating plots, where we get a new dataset every update. In Bokeh-parlance this would presumably trigger a
column_data_source.update(new_df)call. This corresponds to thestreamz.dataframe.core.Framesclass - Streaming plots, where we get a few more rows of the dataset. In Bokeh-parlance this would presumably trigger a
column_data_source.stream(new_df, n)call. This corresponds to thestreamz.dataframe.core.Frameclass.
- Dominant language
- Python
- Stars
- 1.3k
- Forks
- 149
- Avg merge
- 17h 39m
- Merged PRs (30d)
- 1
Contributor guide
First steps
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- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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