7
91%
master: 94%

Ran 14 Feb 2019 02:04AM UTC

Files 108

Run time 10s

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Committed 14 Feb 2019 01:02AM UTC coverage: 91.04%. First build

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

mrocklin

Commit Message

Avoid groupby.agg(callable) in groupby-var

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 2662.7 (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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master: 94%

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Source Files on job 2662.7 (PYTHON=3.6 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='true')

mrocklin / dask / 2662 / 7 91% master: 94%

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Markdown

Textile

RDoc

HTML

Rst

Source Files on job 2662.7 (PYTHON=3.6 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='true')

mrocklin / dask / 2662 / 7
91%
master: 94%

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