7
53%
master: 53%

Ran 14 Feb 2019 04:33PM UTC

Files 108

Run time 121min

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Committed 14 Feb 2019 03:03PM UTC coverage: 91.04% (+0.001%) from 91.039%

Job # PYTHON=3.6 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='true'

Build Type

push

travis-ci

Committed by

web-flow

Commit Message

Avoid groupby.agg(callable) in groupby-var (#4482)

This has two benefits

1.  It's much faster the following benchmark shows a 5x improvement
2.  It doesn't require the pandas-like container to implement
    groupby.agg(callable), which helps cudf

Benchmark
---------

I get five-ish seconds for this on master
And less than one second on this branch

```
from time import time
import dask
df = dask.datasets.timeseries(dtypes={'id': int, 'data': float}).persist()

start = time()
for i in range(3):
    df.groupby('id').data.std().compute()
stop = time()

print(stop - start)`
```

Run Details

17131 of 18817 relevant lines covered (91.04%)

0.91 hits per line

Source Files on job 10779.7 (PYTHON=3.6 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='true')

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Source Files on job 10779.7 (PYTHON=3.6 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='true')

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