docstr: This test will fail for:
      period:
          since period isn't yet implemented in ``select_dtypes``
          and because it will need a custom value converter +
          tick formatter (as was done for x-axis plots)
  
      categorical:
           because it will need a custom value converter +
           tick formatter (also doesn't work for x-axis, as of now)
  
      datetime_mixed_tz:
          because of the way how pandas handles ``Series`` of
          ``datetime`` objects with different timezone,
          generally converting ``datetime`` objects in a tz-aware
          form could help with this problem
docstr_tok: ["This","test","will","fail","for",":","period",":","since","period","is","n't","yet","implemented","in","``","select_dtypes","``","and","because","it","will","need","a","custom","value","converter","+","tick","formatter","(","as","was","done","for","x-axis","plots",")","categorical",":","because","it","will","need","a","custom","value","converter","+","tick","formatter","(","also","does","n't","work","for","x-axis",",","as","of","now",")","datetime_mixed_tz",":","because","of","the","way","how","pandas","handles","``","Series","``","of","``","datetime","``","objects","with","different","timezone",",","generally","converting","``","datetime","``","objects","in","a","tz-aware","form","could","help","with","this","problem"]
code: def test_subplots_timeseries_y_axis_not_supported(self):
      """
      This test will fail for:
          period:
              since period isn't yet implemented in ``select_dtypes``
              and because it will need a custom value converter +
              tick formatter (as was done for x-axis plots)
  
          categorical:
               because it will need a custom value converter +
               tick formatter (also doesn't work for x-axis, as of now)
  
          datetime_mixed_tz:
              because of the way how pandas handles ``Series`` of
              ``datetime`` objects with different timezone,
              generally converting ``datetime`` objects in a tz-aware
              form could help with this problem
      """
      data = {
          "numeric": np.array([1, 2, 5]),
          "period": [
              pd.Period("2017-08-01 00:00:00", freq="H"),
              pd.Period("2017-08-01 02:00", freq="H"),
              pd.Period("2017-08-02 00:00:00", freq="H"),
          ],
          "categorical": pd.Categorical(
              ["c", "b", "a"], categories=["a", "b", "c"], ordered=False
          ),
          "datetime_mixed_tz": [
              pd.to_datetime("2017-08-01 00:00:00", utc=True),
              pd.to_datetime("2017-08-01 02:00:00"),
              pd.to_datetime("2017-08-02 00:00:00"),
          ],
      }
      testdata = DataFrame(data)
      ax_period = testdata.plot(x="numeric", y="period")
      assert (
          ax_period.get_lines()[0].get_data()[1]
          == testdata["period"].values
      ).all()
      ax_categorical = testdata.plot(x="numeric", y="categorical")
      assert (
          ax_categorical.get_lines()[0].get_data()[1]
          == testdata["categorical"].values
      ).all()
      ax_datetime_mixed_tz = testdata.plot(
          x="numeric", y="datetime_mixed_tz"
      )
      assert (
          ax_datetime_mixed_tz.get_lines()[0].get_data()[1]
          == testdata["datetime_mixed_tz"].values
      ).all()
  
imports: import string
  import numpy
  import pytest
  from pandas.compat import is_platform_linux
  from pandas.compat.numpy import np_version_gte1p24
  import pandas
  from pandas import DataFrame, Series, date_range
  import pandas._testing
  from pandas.tests.plotting.common import _check_axes_shape, _check_box_return_type, _check_legend_labels, _check_ticks_props, _check_visible, _flatten_visible
  from pandas.io.formats.printing import pprint_thing
label_desc: Use image node_id 8 for calling the TestDataFramePlotsSubplots obj's underlying member method code with example usage: obj.test_subplots_timeseries_y_axis_not_supported() without return types