DEPR: Enforce Series(float_with_nan, dtype=inty) #49605
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mroeschke
approved these changes
Nov 10, 2022
mroeschke
reviewed
Nov 10, 2022
asv_bench/benchmarks/groupby.py
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| self.df.groupby("key").transform(method) | ||
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|
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| def time_frame_transform_many_nulls(self, dtype, method): | ||
| if dtype == "int64": |
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Hmm, this may cause the benchmark tracking to look odd with the prior benchmark having some finite time to then completing almost instantly.
Could this benchmark be wholly refactored like:
param_names = ["dtype", "method", "with_nans"]
params = [
["float64", "int64", "Float64", "Int64"],
["cummin", "cummax", "cumsum"],
[True, False]
]
def setup(self, dtype, method, with_nans):
vals = np.random.randint(-10, 10, (N, 5))
if with_nans
if dtype = "int64":
raise NotImplementedError
else:
vals = vals.astype(float, copy=True)
vals[::2, :] = np.nan
vals[::3, :] = np.nan
....
def time_frame_transform(self, dtype, method, with_nans):
Member
|
Thanks @jbrockmendel |
codamuse
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to codamuse/pandas
that referenced
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Nov 12, 2022
* DEPR: Enforce Series(float_with_nan, dtype=inty) * update asv * troubleshoot asv * suggested asv edit
mliu08
pushed a commit
to mliu08/pandas
that referenced
this pull request
Nov 27, 2022
* DEPR: Enforce Series(float_with_nan, dtype=inty) * update asv * troubleshoot asv * suggested asv edit
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.