dask: how to groupby, aggregate without losing column used for groupby

Question

How do one get a SQL-style grouped output when grouping following data:

   item   frequency
    A      5
    A      9
    B      2
    B      4
    C      6

df.groupby(by = ["item"]).sum()

results in this:

  item   frequency
    A      14
    B      6
    C      6

In pandas it is achieved by setting as_index=False. But dask doesn't support this argument in groupby. It currently omits item column and returns the series with frequency column.

score 5 · Accepted Answer · answered Feb 11 '18 at 14:53

5

Perhaps call .reset_index afterwards?

answered Feb 11 '18 at 14:53

MRocklin

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Thanks. `reset_index` works. If you have the info, can you also explain how scalable `reset_index` is? – Omley Feb 11 '18 at 15:10
3

It's embarrassingly parallel, so very scalable – MRocklin Feb 11 '18 at 20:21

dask: how to groupby, aggregate without losing column used for groupby

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