REF: Use np.result_type instead of np.find_common_type #53343
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mroeschke
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cc @jbrockmendel Could I get a review to get the CI back to green? |
| # was only one dtype) | ||
| common_dtype = np.dtype("O") | ||
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| except TypeError: |
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can you give an example of what gets here?
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In [2]: np.result_type(np.dtype(float), np.dtype("M8"))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[2], line 1
----> 1 np.result_type(np.dtype(float), np.dtype("M8"))
File <__array_function__ internals>:200, in result_type(*args, **kwargs)
TypeError: The DType <class 'numpy.dtype[float64]'> could not be promoted by <class 'numpy.dtype[datetime64]'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtype[float64]'>, <class 'numpy.dtype[datetime64]'>)
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AFAICT pretty much anything that would resolve to object before would return a TypeError now
| try: | ||
| common_dtype = np.result_type(*dtypes) | ||
| if common_dtype.kind in "mMSU": | ||
| # NumPy promotion currently (1.25) misbehaves for for times and strings, |
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is there some numpy version at which we can remove this check?
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Not sure cc @seberg any thoughts?
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I have a PR that would fix it, so I hope next release this will happen. That was the PR which get stuck a bit on the discussion of what == should do.
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Owee, I'm MrMeeseeks, Look at me. There seem to be a conflict, please backport manually. Here are approximate instructions:
And apply the correct labels and milestones. Congratulations — you did some good work! Hopefully your backport PR will be tested by the continuous integration and merged soon! Remember to remove the If these instructions are inaccurate, feel free to suggest an improvement. |
…ind_common_type
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.