pandas 2.0.0 brings some warnings and incompatible changes

[seisman]

pandas 2.0.0 was released on Apr 3, 2023 and it brings some incompatible changes (see https://pandas.pydata.org/docs/whatsnew/v2.0.0.html for the changelog).

Here are the new failures and warnings with pandas 2.0.0 (https://github.com/GenericMappingTools/pygmt/actions/runs/4610492623/jobs/8149020030?pr=2477):

=================================== FAILURES ===================================
______________ [doctest] pygmt.clib.conversion.array_to_datetime _______________
276 
277     Examples
278     --------
279     >>> import datetime
280     >>> # numpy.datetime64 array
281     >>> x = np.array(
282     ...     ["2010-06-01", "2011-06-01T12", "2012-01-01T12:34:56"],
283     ...     dtype="datetime64",
284     ... )
285     >>> array_to_datetime(x)
Differences (unified diff with -expected +actual):
    @@ -1,3 +1,3 @@
     DatetimeIndex(['2010-06-01 00:00:00', '2011-06-01 12:00:00',
                    '2012-01-01 12:34:56'],
    -              dtype='datetime64[ns]', freq=None)
    +              dtype='datetime64[s]', freq=None)

/home/runner/work/pygmt/pygmt/pygmt/clib/conversion.py:285: DocTestFailure
=============================== warnings summary ===============================
pygmt/helpers/decorators.py::pygmt.helpers.decorators.kwargs_to_strings
  <doctest pygmt.helpers.decorators.kwargs_to_strings[13]>:3: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

pygmt/tests/test_x2sys_cross.py: 12 warnings
  /home/runner/work/pygmt/pygmt/pygmt/src/x2sys_cross.py:233: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
    table = pd.read_csv(

[seisman]

pygmt/tests/test_x2sys_cross.py: 12 warnings
  /home/runner/work/pygmt/pygmt/pygmt/src/x2sys_cross.py:233: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
    table = pd.read_csv(

@weiji14 Do you have any idea to fix the pandas 2.0.0 warning in x2sys_cross?

[weiji14]

pygmt/tests/test_x2sys_cross.py: 12 warnings
  /home/runner/work/pygmt/pygmt/pygmt/src/x2sys_cross.py:233: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
    table = pd.read_csv(

@weiji14 Do you have any idea to fix the pandas 2.0.0 warning in x2sys_cross?

Yep, just need to use pd.read_csv(..., date_format="ISO8601"), have started a PR at #2569.