ENH: fix a bunch of pyarrow duration xfails #50669
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mroeschke
reviewed
Jan 12, 2023
| @@ -653,6 +653,15 @@ def factorize( | |||
| use_na_sentinel: bool = True, | |||
| ) -> tuple[np.ndarray, ExtensionArray]: | |||
| null_encoding = "mask" if use_na_sentinel else "encode" | |||
|
|
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| pa_type = self._data.type | |||
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Similar to #50688 (comment), could you see if going through cast is more performant here?
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import pandas as pd
import pyarrow as pa
parr = pa.array(range(10000), type=pa.duration("s"))
arr = pd.core.arrays.ArrowExtensionArray(parr)
%timeit arr.factorize()
377 µs ± 17.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) # <- astype
335 µs ± 2.92 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) # <- cast
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so ~10% more performant at a similar complexity cost
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if this is a blocker ill change on the affected PRs. i still like this pattern marginally more than the alternative, but mainly i want to get the slow xfails out of my workflow
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I would prefer going through cast (and generally keep ops in pyarrow-land as much as possible). If you could adjust this in #50688 that'd be good.
mroeschke
approved these changes
Jan 13, 2023
|
Thanks @jbrockmendel |
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.