API: timestamp resolution inference: default to microseconds when possible #62031
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jorisvandenbossche
added
Datetime
| @@ -3770,3 +3769,77 @@ def test_to_datetime_wrapped_datetime64_ps(): | |||
| ["1970-01-01 00:00:01.901901901"], dtype="datetime64[ns]", freq=None | |||
| ) | |||
| tm.assert_index_equal(result, expected) | |||
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| class TestToDatetimeInferUnit: | |||
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I assume this is already tested elsewhere (in various places?), but while developing it just wrote some simple tests with the different cases I encountered.
For example now I see there is pandas/tests/tslibs/test_array_to_datetime.py::TestArrayToDatetimeResolutionInference and pandas/tests/tslibs/test_strptime.py::TestArrayStrptimeResolutionInference that are failing, so can integrate those tests.
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@jbrockmendel would you have time to give this a review? |
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Yes, but its in line behind a few other reviews i owe. |
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Draft PR for #58989
This should already make sure that we consistently use 'us' when converting non-numeric data in
pd.to_datetimeandpd.Timestamp, but if we want to do this, this PR still requires updating lots of tests and docs (and whatsnew) and cleaning up.Currently the changes here will ensure that we use microseconds more consistently when inferring the resolution while creating datetime64 data. Exceptions: if the data don't fit in the range of us (either because out of bounds (use ms or s) or because it has nanoseconds or below (use ns)), or if the input data already has a resolution defined (for Timestamp objects, or numpy datetime64 data).