Data library sources that are worth integrating with

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#3 opened on Oct 25, 2022 1 comment 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty: 5/5
Estimated time: Over a week
Newbie friendliness: 20/100

Issue type: Feature
Clarity: Needs clarification
Activity status: Stale
Tech stack: numpy, pandas, python
Domain: data

Research direction

The issue lists possible integrations, including NumPy arrays, pandas, xarray, dask, tiled, WrightTools, h5py, Zarr, functions, web APIs, databases, and networkx, but names no files, tests, or entry points. Start by defining which source or sources are in scope and what integration behavior is required. Done is not specified beyond avoiding overspecialization and supporting useful data sources.

Written by the indexing model from the issue text.

Description

Some of these may be proof of concept level at best while they are under our control, but good to keep many in mind to avoid overspecializing and painting ourselves into a corner:

numpy arrays (or rather dictionaries of numpy arrays)
pandas
xarray
dask
tiled
WrightTools (It's my graduate work, so useful as a test bed for integrations into libraries themselves)
raw h5py
Zarr
functions
web APIs
databases
networkx (graph data rather than array-like)

Dominant language: Python
Stars: 10
Forks: 6
PR merge metrics: No merged PRs in 30d

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