Improve groupby performance while keeping API compatibility #16
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devin-petersohn
commented
Jul 5, 2018
modin/dataframe/test/test_groupby.py
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| @@ -207,7 +207,7 @@ def test_large_row_groupby(): | |||
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| ray_df = from_pandas(pandas_df, 2) | |||
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| by = pandas_df['A'].tolist() | |||
| by = [str(i) for i in pandas_df['A'].tolist()] | |||
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This change is necessary because we have column names that are integers, and the result of pandas_df['A'] will be integers. It will follow the codepath where it tries to group by every column.
simon-mo
requested changes
Jul 5, 2018
modin/dataframe/groupby.py
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| # It is expensive to put this multiple times, so let's just put it once | ||
| remote_by = ray.put(self._by) | ||
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| if len(self) > 1: |
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Can you change len(self) to len(self._index_grouped), the same effect but more readable.
modin/dataframe/groupby.py
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| self._group_keys, | ||
| self._squeeze) | ||
| + tuple(part.tolist()), | ||
| num_return_vals=len(self)) |
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Same here: len(self._index_grouped) is more readable
devin-petersohn
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simon-mo
approved these changes
Jul 6, 2018
kunalgosar
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Jul 6, 2018
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There were some completeness regressions:
I ran out of time to completely implement these after the performance updates.
Performance pre-memoization is on-par with pandas. After memoization, it is ~2x faster on 4 cores.