REF (string): de-duplicate str_endswith, startswith #59568
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mroeschke
added
Refactor
The question also is to what extent we necessarily want to keep this difference. I don't have a strong opinion on what |
| mask=isna(self._pa_array), | ||
| ) | ||
| else: | ||
| # For empty tuple, pd.StringDtype() returns null for missing values |
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| # For empty tuple, pd.StringDtype() returns null for missing values | |
| # For empty tuple, ArrowDtype(string) returns null for missing values |
I think?
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no, this is the existing comment in the ArrowEA method
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It might be an existing comment, but isn't it wrong? This is in the else path and so the code path for ArrowDtype(string), as far as I understand? (For StringDtype, _object_compat will be set to True?)
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i interpreted it as "StringDtype does this other thing [...] in contrast to what we do here"
But looking at the implementations (and trying it with N=1 example) i think they might actually behave the same?
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Looking at the code, I think that is indeed calculating the same thing. Small equivalent example:
In [7]: arr = pa.array(["a", None, "b"])
In [8]: pa.array(np.zeros(len(arr), dtype=np.bool_), mask=pd.array(arr).isna())
Out[8]:
<pyarrow.lib.BooleanArray object at 0x7fbd98e303a0>
[
false,
null,
false
]
In [9]: pc.if_else(pc.is_null(arr), None, False)
Out[9]:
<pyarrow.lib.BooleanArray object at 0x7fbd98ec1360>
[
false,
null,
false
]
I don't see a way that this can be different. So the question is which one is the most efficient. Timing both, the second seems to be the best option:
In [10]: arr = pa.array(["a", None, "b"]*100_000)
In [18]: %timeit pa.array(np.zeros(len(arr), dtype=np.bool_), mask=arr.is_null().to_numpy(zero_copy_only=False))
711 µs ± 1.96 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
In [20]: %timeit pc.if_else(pc.is_null(arr), None, False)
25.5 µs ± 94.4 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
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great! updated to remove the need for the comment altogether. let's see if the code check job likes it this time
This is my general hope for |
I don't have an opinion on the behavior, but would prefer to keep behavior-changing PRs separate from REF/de-dup PRs. |
jbrockmendel
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i dont understand these complaints |
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lint complaints fixed! |
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| def __init__(self, *args, **kwargs) -> None: | ||
| raise NotImplementedError | ||
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| def _result_converter(self, values, na=None): |
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| def _result_converter(self, values, na=None): | |
| def _convert_bool_result(self, values, na=None): |
Or something that is more specific about it being for bool dtype (in #59616 I was renaming it to _predicate_result_converter)
And then can use a consistent scheme in #59562 for integer conversion
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i have a branch doing this renaming for all the affected methods. for now am keeping the existing naming scheme
pandas/core/arrays/string_arrow.py
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| @@ -278,8 +278,11 @@ def astype(self, dtype, copy: bool = True): | |||
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| # ------------------------------------------------------------------------ | |||
| # String methods interface | |||
| _object_compat = True | |||
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This can be removed now since it was removed from the mixin? (at least for now)
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I think comments have been addressed here |
This adds a _object_compat attribute to govern whether the subclass uses object-dtype semantics in cases where they differ.update_object_compat turned out to be unnecessary.